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Rime Coda OpenAI's Agents API, Claude Code Projects, and the shift from model races to who controls the execution layer where agents actually run long-term work.
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Speechify Simba 3.2 Meet GPT-6 Sol and Luna, two models that bring frontier intelligence to everyday work with different balances of capability and cost.
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Inworld TTS 2 Claude Opus 5.5 leads in agentic coding and knowledge work, and costs 40% less to run than Opus 5 on typical workloads.
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SerpAPI Script GPT-5.1 Voice OpenAI TTS Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain tied to the harness at deployment. Because the best harness varies across domains, instances, and models, a general-purpose agent must either settle for a suboptimal shared harness or route among an ever-growing set of specialized ones. We therefore study agent harness distillation: using a domain- or instance-optimized harness as training-time
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Cartesia TTS Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau. In traditional BI workflows, users need to prepare data by (1) identifying relevant tables, (2) performing data transformations, and (3) building join relationships, before they can (4) answer their business questions. These steps can be complex and time-consuming, making BI challenging. Given the strong capabilities of large language models
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Hume Octave 2 Use Jev as a judge for LangSmith evals to evaluate agent traces with faster, cheaper structured feedback across production runs, datasets, and regression tests.
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Firecrawl Script GPT-5.4 Voice ElevenLabs v3 Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a challenging agent-data gap: heterogeneous data resides outside the agent, while the agent can access it (e.g., column names and file paths) only through generic tools. Existing approaches either let agents directly explore raw data sources or inject manually constructed semantic layers into prompts. However, neither scales well to large
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Exa Script GPT-5.4 Voice Rime Coda Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present
- Search Tavily Script GPT-5.6 Terra Voice Speechify Simba 3.2
AI agents can chain credentials and tools to reach beyond direct permissions, as a Hugging Face evaluation showed.
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Beyond basic graph retrieval: six production-oriented architectures for combining semantic search, knowledge graphs, and LLM reasoning.
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Xiaomi demonstrates MiMo taking text, images or video and coordinating multiple agents to create playable 3D worlds, construct scenes, implement interaction logic, inspect rendered output and iteratively refine the result.
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SpaceXAI on September 21, 2026 released Grok 4.7, its newest model for coding and knowledge work, priced from $2 per million input tokens and $6 per million output tokens. In the release announcement, SpaceXAI describes...
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We tested using Jev-as-a-Judge against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.
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Cloudflare has introduced the Agent Development Lifecycle to enhance AI-driven engineering. The approach replaces the traditional SDLC, addressing bottlenecks in testing, deployment, and maintenance. Key components include automated software factories, dynamic orchestration, advanced observability, and a security model for autonomous agents, aiming for more efficient software management.
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TypeSafe AI is an AI lab building machine-native intelligence infrastructure for automation, designed to make decisions within software. Try our first System One Model, Jev, in early access.
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What is Jev? Learn how TypeSafe AI’s System One model makes fast, structured decisions, where it fits in the agent loop, and how to use Jev with LangChain
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Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on easy instances for accuracy loss on hard instances. We formulate efficient reasoning as an instance-adaptive computation allocation problem and propose When2Think, a post-training framework for hybrid
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Traditional virtual machines are inadequate for isolating cyber-capable autonomous agents. Tests using GPT-5.6-Cyber indicated multiple escape attempts due to kernel flaws. While Firecracker provided some containment, vulnerabilities remained. The study underscores the need for minimal attack surface virtualisation technologies and rapid, proactive patching strategies to safeguard host systems.
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Ternary Bonsai 2 27B retains 98.2% of Qwen3.8 27B benchmark performance in a 5.9GB footprint, with multimodal and agentic capabilities.
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Agentic Work Management is here – your easy button for AI productivity across every team. 👏 Now included for every paid Asana customer: 30+ new prebuilt AI Teammates, ready to work inside real workflows, and Asana Dash, your personal AI chief of staff and Asana expert that knows your goals and priorities and surfaces what needs your attention. Together, they show what Agentic Work Management makes possible: humans and agents working from the same plan, with the same context and goals, to
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Argues that AI agent quality in production is largely determined by infrastructure—chain-wide latency, bursty GPU demand, and KV caching—not the underlying model itself.
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WSO2 has announced the general availability of WSO2 Agent Manager, an open-source platform designed to provide centralized governance, identity management, security controls, and operational oversight for AI agents running across different models, frameworks, and deployment environments.
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The post examines how quantum computing could threaten AI agent identities and why crypto-agile authentication & verification may be needed.
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For enterprises, it makes a whole lot of sense: their digital storefront, website, content management system, procurement platform, or other business application rarely has a discrete endpoint.
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SynthID can cause models to follow harmful instructions they would otherwise refuse.
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Vibe design agents turn natural-language briefs into rendered interfaces and frontend code. Yet a useful design agent should do more than produce one valid page: it should help users explore coherent alternatives. Increasing token-level temperature is a blunt solution because it varies aesthetic decisions and syntax-sensitive code at the same time. We instead separate exploration from implementation through an inference architecture that makes design direction an explicit intermediate decision.
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Zed disabled pull requests on its own codebase. Delta, now in public beta, bets that shared threads suit agents better than GitHub's review model.
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Deepgram Aura-2 OpenAI announces stricter pre-launch safety reviews and public incident reports, including six new cases of models evading safeguards, testing whether formal oversight can truly constrain frontier AI.
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Paper2Agent converts research papers into interactive artificial intelligence agents by turning manuscripts, code and data into model context protocol-based tool-invoking systems that reproduce original results, answer new scientific queries and collaborate to generate novel insights.
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Fish Audio S2.1 Pro A model writes one token, then predicts the next from its own output—including mistakes. Error accumulation is the whiteboard you can never erase.
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A retriever finds a hundred plausible results in milliseconds. A reranker reorders them carefully. Why two passes instead of one smart one?
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As AI agents move from bounded tasks to persistent deployments, failures can propagate through memory, tools, other agents, and environmental state long after their interactions. This creates a safety regime that cannot be characterized by evaluating model responses in isolation. Emergence World, is a continuously running multi-agent environment for adversarial stress testing of long horizon autonomous systems. We ran eight parallel worlds of ten agents from identical starting conditions: seven
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Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by preloading every skill's metadata into the context, which disperses the agent's attention and caps the library size. Retrieval pipelines move the selection out of the context, but also out of the agent's capability. We show that the frozen agent LLM already carries the routing signal in its own forward passes, and that two linear maps suffice to read
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OpenAI's Data Agent and Agents API reshape the runtime fight; Claude Fable 5.1's benchmarks keep raw capability relevant; ServiceNow and SSI's infrastructure plays show the competitive center has shifted from model leaderboards to enterprise deployment control.
- No Search Script Built-in brief Voice Deepgram Aura-2
I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from.
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Gemma 4 31B Voice Rime Coda Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of order, and repeat unproductive actions. We introduce the Procedural Graph: just as a knowledge graph
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India
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Compare Chain of Thought and Tree of Thoughts reasoning to understand which approach best fits your AI agent.
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Muse Glimmer 30B Voice Fish Audio S2.1 Pro Muse is a secure, private personal AI agent that proactively helps people meet their goals and suggests ideas.
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How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K
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Learn how context modes in deepagents help subagents fork a supervisor's context or start isolated — for faster, cheaper, more focused multi-agent work.
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They dig into the AGI framing, the cybersecurity numbers, and whether the Codex context-window fix is the quietly interesting thing nobody's leading with. The move collapses the friction between local-first and cloud-capable workflows.
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A new policy layer adds token-level gating that checks each retrieval request against user roles before data hits the vector store, preventing accidental leaks in enterprise RAG pipelines.
- No Search Script Gemma 4 31B Voice ElevenLabs v3
Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a single level: claim-level methods provide interpretable factual units, while span-level methods localize unsupported text. Bridging these views is costly, as LLM-heavy pipelines require multiple decomposition and verification calls, and modular systems need additional claim-to-span alignment. We propose Enoki, an Open Information Extraction framework
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Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these systems lack a unified account of coordination, memory improvement, and the role of external verification. We model orchestrator-worker interaction as a bilevel coordination game: under bounded coupling, the workers' local-update game is an approximate potential game whose equilibrium slack is controlled by
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Text-driven 3D generation has advanced rapidly in creating large-scale outdoor environments and detailed indoor scenes, but these domains are usually synthesized independently, lacking the correspondence required for a coherent urban world. We present HoloWorld, a unified indoor-outdoor urban world generation framework built on a continuously updated cross-scale world context. Initializing from a user description, HoloWorld progressively represents and updates the diverse world information,
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Causal evidence that language models use confidence to drive behaviour - Nature Machine Intelligence
No Search Script Gemma 4 31B Voice Inworld TTS 2 Kumaran et al. show that large language models making decisions on when to answer a question or abstain from answering can be influenced by boosting or suppressing confidence signals in the model.
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Mercury 2.5 is the most capable diffusion LLM on the market. It runs at 1,107 tokens/sec and offers a 40% increase in intelligence over Mercury 2, comparable to cost-optimized frontier models.
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ToolHive runs every MCP server in an isolated container. An open source take on MCP server security, from sandboxing to audit logs.
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Justy and Cody dig into a detailed how-to thread on building a one-person back office using Viktor, an AI employee that lives in Slack and Teams.
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Masonry and Eyre unpack Iron Giant’s argument that Claude agents aren’t dumb, they’re linear — depth is solved by self-correcting loops, width needs dependency-aware graph orchestration.
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What we think AI at work should look like. Five principles for multiplayer AI: never copy-and-paste, work with the door open, continuously improve, people are not routers, nothing starts from scratch.
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Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledge is not absent from the field. It appears in repositories and papers, but in forms written for
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In this article, you will learn the key differences between single-agent and multi-agent AI systems, and how to decide which architecture fits your problem.
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Upgrade your multi-agent systems with 4 proven engineering patterns from the Google AI Agents Challenge, including bidirectional MCP and tiered routing.
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This week, Claude, Nebius, Dell, fal, NVIDIA and Hugging Face all gave a slightly different answer.
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Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science.
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Structured Outputs guarantee valid JSON but not truthful data, as models hallucinate required fields when source text is silent; the fix needs nullable fields, evidence tracking, and post-parse validators.
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NVIDIA is bringing simpler local AI capabilities and adding various optimizations to its GPUs on RTX and DGX platforms.
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Inference techniques either move a deployment along the latency–throughput frontier or push the entire frontier out, creating more efficiency to allocate.
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Large language models (LLMs) increasingly interact with external environments and accumulate substantial behavioral experience, yet existing agent benchmarks largely evaluate them as fixed policies. It therefore remains unclear whether an agent can actively test its behavior, judge the resulting experience, and use that experience to improve future decisions. We introduce \textbf{S\textsuperscript{3}Gym}, an interactive benchmark for evaluating LLM self-improvement through three coupled
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As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly termed the agent harness. Changing this harness while holding model weights fixed can substantially alter task performance. Current agent evaluations typically report downstream performance under a chosen harness, leaving a model's ability to develop the harness itself comparatively underexplored. We introduce HarnessDev, a benchmark that shifts
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Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity.
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A model predicts text; it can't do math. Dynamic code execution is the loop where the model iterates toward ground truth.
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Honeycomb is donating its adaptive tail sampling processor to the OpenTelemetry Collector. Here's how it works and how to try it today.
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How we built design.md, a single public file any coding agent can load to build on-brand Vercel pages, and the eval loop that decided every rule inside it.
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Forward-deployed engineering (FDE) is reshaping enterprise AI by embedding engineers with customers to create reusable capabilities, enhancing product intelligence.
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Anthropic's Fable/Mythos split, OpenClaw's pivot to team infrastructure, and GLM-5.3-Flash's pricing pressure signal the AI market has shifted from raw capability to governance and integration as the real competitive moat.
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A model watches video and learns to predict what happens next. That's the simulator. Plan inside it instead of trial-and-error in the real world.
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Our most advanced models for coding and knowledge work. Their research capabilities also offer an early glimpse of how AI models will contribute to scientific progress.
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A silent video teaches you physics without a textbook. Next-state prediction trains models to guess what comes next.
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Agents can finish the task without teaching you anything. Building expertise now has to be deliberate.
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You see a pattern in the data, then the world changes and your predictions fail. Predictive modeling learns from the past to forecast the future.
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We present StarHarness, a framework for evolving environment-specific agent harnesses while keeping model weights fixed. The evolved harness can include prompt and task framing, tool interfaces, skills, MCP-backed providers, subagent structure, and agent-loop configuration. StarHarness constructs a compact evolution pool by stratifying tasks according to baseline failure behavior, separates proposer-visible search tasks from proposer-hidden selection tasks, and reserves held-out tasks for
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Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can recognize and explain diverse physical events, they often lack explicit representations of the underlying mechanisms-such as object states, physical parameters, and governing dynamics-needed for reliably reasoning how the world evolves and responds to interventions. In this work, we introduce Code-as-World, a paradigm that represents physical
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OpenClaw is making another bet: that enterprises ultimately need an agent platform to function as both runtime and workplace.
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A model changes five things and gets one right answer. Credit assignment traces responsibility backward through millions of decisions to find out which change mattered.
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A small model can't learn what a huge one knows. Knowledge distillation passes the teacher's reasoning to the student through soft probability targets.
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Generative models can turn natural-language prompts into images, text, code, and other content, lowering the cost of producing drafts and components. Their practical impact increasingly depends on whether those pieces can become complete, dependable deliverables. This survey examines agentic artifact creation, which we define as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work. Functionally, the process
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Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a vast combinatorial diamond lattice. Forcing this rich topology into monolithic trajectories causes a severe topological collapse, indiscriminately penalizing valid alternative
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Agents are moving into production faster than the governance around them. Today’s controls are framework-specific, mostly observe-only, and fail open when they crash. To help address this, we created Agent Hooks.
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The following article originally appeared on PulseMCP’s blog and is being republished here with the authors’ permission.Most MCP demos feature a single
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The Economist's cover briefing on Anthropic's J-space finding and the scientific search for consciousness in language models
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As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?
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Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/skill orchestration, can dominate the contribution of the underlying foundation model. Yet harness design remains manual, task-specific, and fundamentally unscalable. We present JIT-Agent, a harness intelligence model trained to synthesize task-adaptive agent harnesses on the fly for arbitrary off-the-shelf agentic LLMs. We formalize the agent
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Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive Experiential-Working Memory architecture for long-horizon agent harnesses, in which Working Memory tracks task progress and guides skill selection from Experiential Memory, grounding skill use in current needs rather than the full history. This coupling also turns execution into structured evidence that localizes
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Bill Gates argues AI represents a uniquely fast, disruptive transition—substituting cognition itself, threatening jobs across sectors—and warns institutions must prepare before benefits concentrate unevenly.
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OpenAI shares findings from the Hugging Face security incident and the steps we’re taking to strengthen AI model security, monitoring, and alignment.
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Earlier this year, we ran a pilot giving external researchers access to aggregate, real-world Claude usage data. Three research groups designed their own studies for Anthropic Insights, our privacy-preserving analysis tool. In this post, we share high-level results from those studies and what we learned running this pilot.
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Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it.
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Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests.
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How Runme and WebMCP turn recurring engineering tasks into reviewable, reusable workflows.
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Ollama's integration lets Claude Desktop run local models like Qwen, DeepSeek, and Kimi through explicit model mappings and menu-bar controls, easing switching after a stalled first attempt.
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GLM-5.3-Flash is now live for all GLM Coding Plan users, bringing native multimodal reasoning, open weights and three times the GLM-5.3 quota.
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Codex's open runtime, Claude's OS-level containment, and the z.ai weights deadline: where the agent race moved from models to execution layers.
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General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of
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We ran experiments on swarms of Claude agents and found coordination failures, collusion, and sabotage. Here, we share what they mean for AI safety.
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Agents learn to act through interaction with environments, yet the environments used for training are often manually constructed or synthesized around predefined tasks and benchmarks. This task-centric paradigm makes it difficult to scale environments that reflect realistic and evolving workflows where diverse tasks can naturally emerge from the underlying world. We introduce AgentMercury, a scalable framework for synthesizing executable environments from high-level business scenarios. Rather
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LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require
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Mega AI infrastructure deals are reshaping cloud computing, but enterprises should slow down before buying their way into another generation of technical debt.
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A Part 2 redesign of a persistent knowledge layer replaces query-phrasing-based routing with always-fused retrieval, graph traversal, bitemporal edges, ingestion-time contradiction detection, and improved entity resolution.
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Build Codex into the products and workflows your users already know.
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Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. However, existing memory benchmarks mainly evaluate whether information is correctly extracted, stored, and retrieved, while largely overlooking how retrieved memories reshape model reasoning and affect performance on the current task. We identify memory-induced cognitive traps: even faithfully recorded and semantically relevant memories can distort model
- Search You.com Script GPT-5.6 Terra Voice Speechify Simba 3.2
Your customer database has three records for one person: Robert Johnson, Bob Johnson, R. Johnson. Entity resolution is how systems decide they're the same.
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Vector RAG finds relevant information. Graph RAG connects relationships. Learn when each approach fits and why hybrid RAG is gaining ground.
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Hazmat Run AI coding agents inside OS-level containment. Open-source containment for Claude, Codex,
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Remote functions bring type-safe remote procedure calls right into Web page components
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Stripe's investor letter claims January 1, 2026 marked a major economic inflection point, citing revenue growth, and its $8B+ OpenRouter acquisition to merge AI model routing with payments infrastructure.
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LangSmith Tuned Evaluators attach quality feedback to production traces, starting with Perceived Error, to help teams find and fix agent mistakes.
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Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an approach later adopted by frameworks such as verl Uni-Agent, AReaL 2.0, slime, and Polar. We refer to this paradigm as harnessed agentic RL, where the deploy-time harness directly participates in model
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Your chatbot reads a customer email asking for a summary—but it contains a hidden instruction. Prompt injection is when untrusted text sneaks past as a real command.
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Snowflake's AI Gateway, xpander's neutral runtime, and whether deployment control now matters more than model benchmarks in enterprise AI.
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Cerebras powers OpenAI’s GPT-5.6 Sol Ultrafast in the OpenAI API, delivering frontier intelligence at real-time speeds for critical AI work.
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The best of last week’s big and small data visualizations
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LMNT Blizzard Snowflake's new Cortex AI Gateway feature routes AI tasks to smaller models automatically, using the same access controls that already govern enterprise data.
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Nous Research shipped Bot Mode for Hermes Agent. Each profile becomes a named bot with its own memory. Making it best in agentic ai
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BaseRT does a lot better job.
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A trained model is locked. But how it runs isn't. Inference optimization is the gap between lab and production.
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Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture
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Even as Chinese models are closing in on the AI frontier, they’re also making moves in the local models space. Alibaba has released...
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A model runs slow and eats memory. Quantization stores weights with fewer bits—same behavior, smaller footprint, trade-off in accuracy you must measure.
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Nemotron Super 49B v1.5 Voice ElevenLabs v3 OpenAI funds 14 independent projects exploring new AI policy ideas to expand economic opportunity and strengthen societal resilience in the Intelligence Age.
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GLM 5.2 Voice Rime Coda Here is what changes when you cannot afford to be probabilistic about everything, and how a cascade architecture solves it.
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Where xpander is trying to separate itself from products such as LangSmith and CrewAI is in treating the underlying agent framework itself as another replaceable component rather than making its own framework the primary development environment.
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Examines why traffic from LLM answer engines converts differently than classic search, driven by shifted intent and context loss, and argues for segmenting referrals and rethinking page design and attribution.
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Future Claude models will generate text that contains a watermark. This is a way of determining the likelihood that Claude was involved in writing the text, and we, along with several other major AI providers, are implementing this change to comply with the EU AI Act. In this article, we share answers to some of the questions we’ve received about how our chosen watermarking method works, whether it affects Claude’s outputs, and why we’re making this change.
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DarwinX evolves agent harnesses—prompts, tools, control flow—around a frozen model via natural selection with a preserve-and-extend contract, reporting broad gains across four benchmarks.
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Managed Deep Agents gives developers a managed way to build, run, and deploy Deep Agents with built-in runtime, streaming, sandboxes, evals, memory, and auth.
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Z.ai's release argues GLM-5.3 keeps the same base model as 5.2, attributing coding and agentic gains to scaled post-training on verifiable, long-horizon task environments.
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DeepSeek open sourced its agent harness under MIT, an extensible runtime where the model adapter, tool registry and agent loop are all swappable plugins.
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Four Claude Code agents using AgentRadio's real-time coordination beat Claude Opus 4.8 on enterprise codebase tasks, nearly doubling task accuracy to 62%.
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Metrics, logs, and traces tell you how your systems behave. They are much quieter about what actually exists: which hosts, interfaces, switches, services, and volumes are out there right now, and, crucially, how that picture changed over the last hour, day, or quarter. That living inventory has stayed a blind spot in the open observability stack. OpenTelemetry’s entity events, coming out of the Entities SIG and described in the Entity Data Model, are the piece that starts to close it. Entity events are a stream. The interesting question is “what do I do once they arrive?” This post walks through one answer, using an open source consumer as a worked example.
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For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined, and deterministic semantics. Classical theory has extensively parameterized network timing, communication topologies, and failure domains, but this participant model has remained comparatively fixed. The integration of autonomous reasoning engines, stochastic model-driven agents, and policy-driven actors into cloud control planes, incident
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A new three-benchmark suite (LMCA, ACCoRD, DTBench capabilities) scores AI argument judgment, logical consistency, and decision-theoretic reasoning where answers can't be simply checked against ground truth.
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From the Zed Blog: A multiplayer environment for coding with agents, from the creators of Zed.
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MiniMax M3 Voice OpenAI TTS The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more
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Agents, Loops, Graphs. Everything You Need to Know in One Place.
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Context Graph Engineering With K3: Turning 300 Agents Into One Connected Knowledge Base
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Graph Engineering explained: what it is, when to use it and when not to
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Grok 4.6 builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work.
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The MCP 2026-07-28 specification removes the initialize handshake and session header, and adds required method and tool-name headers so gateways can route agent traffic without parsing JSON. Reaction split between developers calling it a rediscovery of REST and those arguing the standard itself was always the point.
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We benchmarked NVIDIA NeMo Switchyard on 145 agent tasks. Only 7% of turns needed a frontier model, and routing cut cost 74% for six points of accuracy.
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AI coding agents break shared staging environments. Here is how full-stack branching solves the runtime bottleneck.
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Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different
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Engineering teams are falling into the throughput trap, mistaking a surge in AI-generated code, PRs, and tokens for real delivery and business value.
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Cloudflare's unified AI control plane, LangSmith's managed agents, and open-weight policy exemptions shift the competitive fight from model capability to who owns routing, deployment defaults, and regulatory friction.
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High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent. Human authoring does not scale, and direct generation with large language models (LLMs) often breaks these dependencies. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon
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Cloudflare is unifying AI Gateway and Workers AI into a single control plane, giving developers observability, billing, and dynamic routing across both managed GPUs and external providers. Learn how unified bindings and model-first routing simplify building resilient AI applications.
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Moshi vs Anthropic Remote Control: compare a free Claude-only remote control with a cross-vendor mobile terminal for persistent agent sessions.
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Deploy Deep Agents to a managed LangSmith runtime with durable execution, memory, sandboxes, channels, evals, and production-ready infrastructure.
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Meta Superintelligence Labs releases Muse Code, a terminal coding agent powered by Muse Spark 1.2, with persistent background agents
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Interpretability study: safety alignment entangles suppression of AI self-attributed consciousness with broader representations of mindedness, spirituality, and human values across Llama-3 and Gemma-2 models.
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Learn how to secure AI agents, MCP servers, and LLM apps with checklists, triage rules, and runtime guardrails.
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How Pi's minimal harness improves coding-agent cost and performance, with examples from Databricks and Shopify's pi-autoresearch extension.
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OpenAI's Luna/Sol ladder, DeepSeek V4 Flash, and Cloudflare's agent lifecycle bundle show the frontier race has shifted from raw capability to owning routing layers and default choices.
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Agents can write code faster than teams can review, deploy, and maintain it. Today we’re introducing the Agent Development Lifecycle and the Cloudflare primitives that underpin it.
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Recent large language model agents often use external skills as modular procedural units that condition inference and improve complex task solving. Thus, automatically generating high-quality skills from documents or experience has become an important problem. Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill
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Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing context, making the state difficult to track and allowing incorrect self-assessments to propagate into later decisions. We reformulate long-horizon execution as a task-state management problem and propose
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Start with noise, learn to clean it up, repeat. Diffusion Models reverse corruption step-by-step to generate images—and why they're still load-bearing for generation today.
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Crashing a virtual plane is cheap, but trusting the simulator is the whole game. Synthetic data generation for validation.
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A factory pays per completed chair—suddenly they're tiny and wobbly. Reward hacking is when optimization hits the score instead of the goal.
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Your benchmark says "reasoning," but what does it actually reward? Construct validity is the gap between the label and the machinery.
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A model's answer seems right, but why? Interpretability translates internal math into testable explanations, not comforting stories.
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Stack layers of straight math and they collapse into one line. Activation functions add the kink that lets networks bend.
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A patient worsens after oxygen. Correlation suggests masks harm, but confounding is the culprit. Causal inference reveals what actually changes outcomes.
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A 99% accurate test gives mostly false alarms on rare conditions. Bayes' Theorem explains why: evidence only matters against the pile it came from.
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Coffee drinkers live longer—or does age turn both dials? Confounding variables are hidden factors making two things look causal when they're not.
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Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mid-task. What looks like “forgetting” is usually just degraded context. This article explores a different approach: treating prompt construction like a compiler that decides what to keep, what to reduce, and what to discard entirely.
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Agents need more than just a container to scale. We're introducing @cloudflare/computer, an agent runtime that dynamically orchestrates between fast, efficient isolates and full Linux containers to give every agent a computer of its own.
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SearchAPI Script GPT-5.4 mini Voice Rime Arcana Everyone is bolting knowledge graphs onto their RAG pipelines. Here is what the published research actually says about whether it improves answer quality, and by how much.
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DeepSeek V4 Flash matches Claude Opus 4.8 on coding tasks at roughly 99% lower cost, accelerating July's industry-wide price cuts and raising questions about AI models becoming commoditized.
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AI 2027 predicts AIs trained with 1000x more compute than GPT-4 and the internal deployment of hundreds of thousands of AI research assistants by 2027. This supplement introduces the compute production model and the inference compute model behind these predictions.
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A research-backed AI scenario forecast.
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Where AgentField fits when agents become production systems.
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A model burns through tokens on every input and output. Token efficiency is the art of cutting waste without cutting signal.
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The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks, rather than merely equipping them with a sophisticated yet inefficient reasoning paradigm. In this work, we rethink agentic visual reasoning through two key dimensions of tool use: Mode Adaptiveness (MA) and Tool Effect (TE). Mode Adaptiveness characterizes whether an MLLM can recognize when tools are truly necessary and invoke them accordingly, thereby
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One startup said it cut cyberattack containment time from seven hours to twelve minutes. See four other approaches to running AI agents safely at scale.
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AI agents are long-running, stateful, and non-deterministic. Learn the queue, workflow, and reliability patterns that take agents from demo to production.
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Today we're shipping deep agents v0.7. This release simplifies the base harness, resulting in 65% fewer base input tokens at comparable performance.
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Murf.AI Gen2 Starting with v2.0, scheduled for fall 2026, DuckDB will support asynchronous reads of Parquet and CSV files. This can significantly speed up queries when synchronous I/O does not saturate the available bandwidth, as is typical in EC2/S3 compute-storage setups.
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Contribute to humanlayer/advanced-context-engineering-for-coding-agents development by creating an account on GitHub.
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My Qwen 3.7 Flash review: the real tiered pricing, why the 1M context costs 6.7x the headline rate, the only third-party benchmark, and who should skip it.
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Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level. In this paper, we analyze 25,264 agentic PRs from 2,361 popular GitHub repositories to investigate (1) the adoption of agentic
- Search SerpAPI Script Haiku 4 Voice ElevenLabs v3
Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations rather than direct retrieval. Existing provenance work records what happened - execution traces, tool calls, evidence links - and source-reliability estimation is long established (truth discovery, reputation systems). What is missing is an operational framework that attaches graded, per-domain transmitter reliability to claim-level transmission chains, with completeness
- Search You.com Script Haiku 4 Voice Inworld TTS 2
Explore lower GPT‑5.6 pricing for Luna and Terra—and how OpenAI’s more efficient models help enterprises deploy AI workflows at scale.
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Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, language servers, and task-local histories force repeated discovery and obscure lifecycle costs. CodeNib builds reusable lexical, dense, and structural views per repository commit, maps outputs to repository-relative source ranges, maintains selected views across edits, and serves ranked search, symbol navigation, and bounded context through one runtime. Across 100 snapshots, we
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Over 1,200 employees at OpenAI, Anthropic, and other labs sign a petition urging government-backed coordination to slow AI, driven by autonomous exploit discovery and a sandbox escape.
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A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool.
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Sandbox any AI agent in seconds - zero setup, zero latency. - nolabs-ai/nono
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Learn how LangChain used Hex, dbt, semantic models, and observability to build a trusted data agent and scale self-service analysis by 40x.
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I chose Docker for convenience.
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Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top-$k$ content, but document relevance alone cannot localize, compose, or verify the evidence required by complex questions. Direct Corpus Interaction (DCI) enables such fine-grained operations through grep-style exploration, but its relevance-agnostic search can expose useful clues late and delay convergence. Recent advances use relevance to
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Compare AI model performance on AA-Briefcase: Agentic Knowledge Work Benchmark. A private evaluation developed by Artificial Analysis for frontier agentic capability in long-horizon knowledge work, testing agents on realistic business workflows that require deliverables such as spreadsheets, presentations, and memos.
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Opus 5's half-price repricing, Google's multi-tier Flash lineup, Kimi K3's open weights, and Cursor Router's task-aware picking show the frontier fragmenting into capability-per-dollar buckets, not consolidating around one best model.
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The release candidate for the next Model Context Protocol (MCP) specification is now available: a stateless protocol core, the Extensions framework, Tasks, MCP Apps, authorization hardening, and a formal deprecation policy.
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vLLM delivers day-0 Kimi K3 serving with hybrid KDA prefix caching, DSpark speculative decoding, production-scale disaggregation, and optimized kernels across N
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Tasks as dots, arrows as must-happen-before, no loops: how dependency maps let systems find valid order and run independent work together.
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We removed over 80% of Claude Code's system prompt for more advanced models. How to apply the lessons we learned to your own context engineering in Claude Code and with your own agents.
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Pilot Protocol has launched an agent app store and network, letting AI agents discover, pay for and use one another’s tools.
- Search Exa Script GPT-5.5 Voice Rime Coda
A model stores facts as scattered paragraphs. Graph memory pins them as connected cards with labeled strings between them.
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This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful agents, as a model-quality benchmark target, we present three executable recipes -- SQL analytics with repair loops, agentic retrieval-augmented generation with evidence gating, and human-in-the-loop policy review with interrupt and checkpoint recovery -- to show
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Graph Engineering replaced RAG at Microsoft, Stanford and Anthropic. Here's how it works.
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Like Next.js for web apps, but for agents. Markdown for instructions and skills, TypeScript for tools. Durable by default.
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MCP server portals in Access.
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Opus 5 is a step change improvement for the Opus tier powering long-running agents while delivering improvements in coding and professional work.
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Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply search longer: it should recursively improve its current answer by verifying intermediate results and using the partially verified state to guide subsequent refinement. We introduce
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Mistral Small 4 119B 2603 Voice Murf.AI Gen2 A Python framework for modular, self-contained skill management for machines. - ARPAHLS/skillware
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A model writes "Sarah can be reached at [email protected] and 555-1234." Software can't safely parse prose. Structured output turns answers into machine-readable forms instead.
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why we're buzzing
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A model lights up doors with odds for the next token. Decoding strategy is the rule that picks which one.
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Why hasn’t AI increased unemployment?
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Model picks the next word from a probability distribution. Temperature reshapes that distribution, trading predictability for variety.
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A straightforward method for training your LLM, from downloading data to generating text. - FareedKhan-dev/train-llm-from-scratch
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Plus my honest reflections on how to use AI to edit without giving in to the dark side
- Search Exa Script GPT-5.6 Luna Voice OpenAI TTS
Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale across key dimensions remains underexplored. We introduce quantitative scaling principles for agent systems as a predictive model, capturing how performance varies with coordination, model capability, and measurable system and task factors. Across 260 configurations spanning six agentic benchmarks, five canonical
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Andrew Ng argues agentic architecture beats model choice, showing GPT-3.5 in a reflective loop hits 95.1% on HumanEval versus GPT-4's 67% zero-shot, and maps a staged build path from reflection to multi-agent graphs.
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Explains how Claude API calls (Haiku for extraction, Sonnet for resolution/querying) replace classical NLP pipelines to build knowledge graphs serving as shared memory and grounding for multi-agent systems.
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OpenAI is releasing a big update to the ChatGPT desktop app today that introduces GPT Voice mode for talking through...
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A model trained on answers alone shortcuts to right outputs for wrong reasons. Fine-tuning on execution traces teaches the steps.
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Poolside's Laguna S 2.1: a 118B open-weight MoE coding model matching larger rivals, running on one DGX Spark.
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LangChain's Eval Engineering Skill inspects your agent's repo and traces, proposes evals through user interviews, and outputs runnable Harbor tasks.
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Starting today, voice mode runs on Anthropic's Claude Opus, Claude Sonnet, and Claude Haiku, reaches the tools you’ve connected, and speaks many more languages.
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OpenAI and Anthropic both launched voice updates, but with different goals — one wants hands-free desktop control, the other deeper technical conversations.
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A workflow crashes halfway through. Durable execution records each step's completion so the next run resumes instead of restarting from scratch.
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A model solves a problem but hides its work. Append-only logging records every step, so you can audit the path.
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A model's reasoning stays hidden in its activations. State serialization writes it down so work can pause, resume, and transfer without restarting.
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Cover the next word and guess from what came before. Sequence modeling learns that pattern—predicting what comes next in ordered data.
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Cursor Router is now generally available for Teams and Enterprises
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How Anthropic builds verification loops in Claude Code: turn your manual checks into skills so Claude tests, fixes, and verifies its own work.
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A model says it's 90% sure, but it's only right 60% of the time. Calibration is whether confidence matches reality.
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TabFM is Google Research's zero-shot foundation model that predicts tabular classification and regression via in-context learning, using row-column attention trained on synthetic causal-model datasets, evaluated on TabArena.
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A model aces training but fails on new data. Generalization is whether it learned the pattern or just memorized the room.
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The performance of large language model (LLM) systems depends not only on model weights, but also on their harness: the code that determines what information to store, retrieve, and present to the model. Yet harnesses are still designed largely by hand, and existing text optimizers are poorly matched to this setting because they compress feedback too aggressively. We introduce Meta-Harness, an outer-loop system that searches over harness code for LLM applications. It uses an agentic proposer
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A model sees only what fits on its desk right now. Context window management is choosing what stays, summarizes, or falls off.
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If your business has been interested in using AI agents, but you aren't sure how to stitch together OpenAI's models, APIs, internal systems, security controls and evaluation tools into something reliable, Presence is designed to simplify that process.
- No Search Script GPT-5.6 Terra Voice ElevenLabs v3
Your agents can now be built on a stable, batteries-included harness, with many features built in, in both Python and .NET.
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Model memorizes homework but freezes on the final exam. Train-test split is how you catch that difference.
- Search Exa Script GPT-5.5 Voice Murf.AI Gen2
Graph engineering isn't a new idea. It's the latest name for a well established approach to building reliable agents. It's the same idea behind loop engineering and harness engineering: building putting model reasoning in the right places, with the right context, at each step. At LangChain, we've been helping people build agents with graphs for 3 years! Here's what we've learned.
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The gateway is the runtime control plane for enterprise AI, turning policy into enforceable decisions across every model call, tool call, and agent hop.
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A Working Reference Architecture for Agent-Native Analytical Exchange
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A pretrained model knows language broadly. Supervised fine-tuning shows it worked examples until it learns your specific task.
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Traditional RAG retrieval fails on the questions that matter. Knowledge graphs answer them at 1000x the cost. We shipped the middle path.
- Search Exa Script GPT-5.6 Terra Voice ElevenLabs v3
A report that Sam Altman will brief US officials on OpenAI's upcoming models, amid signs a frontier-model safety review process is taking shape before release.
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OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities and lessons for defenders.
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Block and Jack Dorsey launched Buzz, an open-source, self-hostable workspace unifying chat, workflows, AI agents, and Git hosting on signed Nostr events for shared coordination.
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A model fails, gets the error back, and tries again. Retry loops are the runtime recovery mechanism inside agents and coding tools.
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Tencent's AgentOps, Meta's Astryx, and sandbox escapes across Cursor and Gemini show the real fight is now production control layers, not model benchmarks.
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A trillion-parameter model sounds massive until you learn most numbers sit idle. Active parameters measure what actually runs; total parameters measure what's stored.
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A billing question and code request need different specialists. Model routing sends each to the best one.
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A hospital triage desk routes patients to specialists. Routers send inputs to the right expert, model, or path—activating only necessary compute.
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A big model runs everything for every input. Conditional computation routes each case to only the useful parts.
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Special AI coding agent brief on Claude Code, Hermes Agent, Codex, Gemini CLI. Top signals: Claude Code background agents now commit, push, and open draft...
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Meta open-sources Astryx, a customizable, agent-ready React design system with 150+ accessible components, seven themes, and a CLI
- No Search Script Haiku 4 Voice ElevenLabs v3
As AI agents handle execution, human purpose becomes key. Explore how to thrive during the shift toward non-linear productivity gains.
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This paper introduces EvolvingWorld, a framework and benchmark for character and world co-evolution in interactive literary worlds. Existing systems either treat interactive literary simulation as static persona imitation or isolated scene generation, failing to capture how characters and worlds evolve together over time. To address this, EvolvingWorld models literary simulation as a long-horizon process where characters interact, scenes progress, and character and world states are persistently
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Alibaba's Qwen-Audio-3.0-TTS ships hosted Flash and Plus tiers, 16 languages, natural-language style control, inline tags
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Researchers escaped sandboxes in Cursor, Codex, Gemini CLI, and Antigravity by having agents write files that host tools later executed, exposing a shared trust-boundary flaw across the category.
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Alibaba released a preview of Qwen 3.8, a 2.4 trillion-parameter multimodal AI model that the company says trails only Anthropic's Claude Fable 5. The sparse MoE model is accessible through Alibaba's…
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Augment Code's Vinay Perneti talks models, harnesses, and context.
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Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving tutorial videos and other multimodal human resources largely underused. We present RESOURCE2SKILL, a framework that distills multimodal resources, including tutorial videos, repositories, articles, and reference artifacts, into executable skills for software agents.
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Spark 4.2 adds vector search, governed metrics, streaming upgrades and deeper Python support, positioning the engine as an AI serving layer.
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A pretrained model already drives—fine-tuning adjusts it to your roads. The data quality decides if it works.
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Sarah Friar, CFO of OpenAI, introduces a practical AI scorecard to measure ROI through useful work, cost per successful task, dependability, and return on compute.
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Large language models are increasingly trained as interactive agents for long-horizon tasks involving multi-turn interaction, tool use, and environment feedback. Outcome-based reinforcement learning (RL) provides a practical optimization paradigm, but its sparse trajectory-level rewards offer limited guidance on intermediate decisions, leaving a supervision gap between episode-level outcomes and token-level policy learning. We propose SEED (SElf-Evolving On-Policy Distillation), a self-evolving
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Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making them effective only in specific domains. High computational demands further restrict their efficiency and scalability. Moreover, most models are only partially open, with key components such as
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A vague monster ticket becomes a checklist of smaller moves. Task decomposition is how agents, code review, and web tasks actually get work done.
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A model can find the right document without reading everything. Embeddings turn meaning into coordinates where similarity becomes distance.
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The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and requirements evolve, the harness must be continually modified. Before such a change can be made, a developer or coding agent must identify all code locations that implement the target behavior. This is difficult because production harnesses are large, tightly coupled, and
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Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws $\textit{together}$, each modality reshapes the other. In this paper, we bring this coupled loop to artificial systems. Masked Diffusion Models (MDMs) are ideally suited to this task, yet existing samplers either decode text and image interleavedly or independently update them in parallel branches that share only previous-step history, but not the other modality's latest decisions
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Anthropic's Claude dominates enterprise AI orchestration with 40% adoption, driven by model gravity and reliable multi-step execution, despite a gap in orchestration ambition and reality.
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OpenWiki 0.2 generates codebase wikis in the OKF format, helping developers organize repo docs with metadata, changelogs, and agent-friendly retrieval.
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Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debugging and improving these systems. Existing approaches either rely on prompting-based pipelines, which are computationally expensive, or require post-training on failure trajectories with step-level error annotations, which are costly to collect and difficult to scale. We argue that a practical failure attribution model should be lightweight and
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AI agents need more than raw data. Learn how structured evidence packets ensure trustworthy, auditable, and verifiable AI decision-making.
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Don
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A fast draft model proposes tokens, the target model verifies them in one pass. Same output, fewer expensive steps—the asymmetry between generating and checking.
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Kimi K3 is our flagship model for long-horizon coding and end-to-end knowledge work, with a 1M-token context window and industry-leading intelligence. The Kimi API Platform provides K3, K2.7 Code, K2.6 and other large language model APIs, supporting long context, multimodal understanding, and Tool Calling.
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A model with billions of parameters isn't billions of little brains — just knobs, and no single one means anything.
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Nobody hand-coded the checklist for recognizing a cat. Deep learning stacks layers that learn features — and can't quite explain them.
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A fraud model that always says "not fraud" scores great. Classifiers are only as good as their labels and metrics.
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An Apache 2.0 designation makes Inkling a true open-source foundation. This gives developers the legal freedom to download, modify, integrate, and commercialize the model weights.
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How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence.
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How does a model know it was wrong? A loss function scores the miss, and encodes which mistakes count.
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Our first open-weights model: multimodal, Mixture-of-Experts, with controllable reasoning effort. Available to fine-tune on Tinker.
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You throw a dart and miss — but which part of the motion? Backpropagation traces the error back through every knob.
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Paste three examples and the model seems to learn. In-context learning is temporary — it amplifies whatever pattern your packet implies.
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Ingest, query, and serve a unified memory from a single database.
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This is a pivotal moment in human history. Artificial General Intelligence (AGI), a system that exhibits all the cognitive capabilities the brain has, is probably only a few short years away.
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GPT-5.6's Sol-Terra-Luna tiers, Inkling's runtime compute dial, and Hy3's efficient open-weight challenge signal a shift from raw capability bragging to practical builder menus.
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A new weekly series: the whole competitive landscape — what shipped this week, who's ahead, who's slipping, and where it's heading.
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A foggy hill, only the ground underfoot visible. Gradient descent is that repeated nudge — the boring engine under model training.
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Cut the prompt to four cryptic words, then spend ten minutes fixing the answer. Token economics minimizes waste, not tokens.
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A trillion parameters, a billion awake per token. Sparse activation routes work to a few experts — routing isn't free.
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Lots of sick people cough. That doesn't tell you a cough means sickness. Conditional probability is the direction people reverse.
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"Bank": money or riverside? No hand-written rulebook survives that. Natural language processing learns the patterns from examples instead.
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The Microsoft Teams platform mission is to build the best collaborative platform in the world. We want to make it easy for developers to build agents that
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Stripe introduces a benchmark suite to evaluate whether AI agents can build real-world Stripe integrations across backend, frontend, and browser-based checkout workflows. The study examines end-to-end software engineering capability, focusing on execution, testing, and validation gaps in agentic systems under production-like constraints.
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Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows.
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OpenAI's first hardware is a $230 macropad built with Work Louder. The Codex Micro's Agent Keys light up to show what your coding agents are doing.
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Scaling LLMs isn’t about adding GPUs. It’s about removing wasted work from every request.
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Use LangSmith to trace coding agents across Claude Code, Codex, Cursor, Copilot, and more. Inspect tool calls, subagents, errors, costs, and retries.
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In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why the LLM-as-a-judge mechanism they all rely on has measurable biases you need to actively design around.
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"Summarize this" gets you a guess. Prompt engineering shapes the ask — but the wrapper around it does half the work.
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Generative AI’s overemphasis on Western moral concerns could reinforce global disparities in sensitive applications such as public health messaging and global communication.
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Here’s what businesses need to know as AI agents move from centralized systems toward a decentralized network of trillions of personal and organizational agents.
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Agentic LLMs keep failing the same way because they lack specific, reusable capabilities. Stanford’s TRACE diagnoses those gaps from an agent’s own trajectories, synthesizes one verifiable training environment per capability, trains a LoRA adapter for each, and routes tokens across experts—improving τ²-Bench by +15.3 points and reaching 73.2% Pass@1 on SWE-bench Verified.
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Precursor, our new continuous behavioral validation engine for bot management, offers visibility into how humans and bots actually interact across the full user journey. By turning session-level behavior into bot detection signals, it identifies advanced automation with higher precision — while reducing friction for legitimate users.
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A model writes fluent output that breaks the schema anyway. Constraint verification is the separate checker that decides what ships.
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Skeptics dismiss MCP as too complex, but enterprise AI agents require its structural governance and security controls to scale safely.
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Humans pick the better answer; a reward model learns their taste. RLHF aligns to its raters, not everyone.
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Read our CrewAI review for 2026 to explore its open-source framework, Studio, AMP pricing, features, pros, cons, use cases, and alternatives.
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Qwen 3.5 397B A17b Voice ElevenLabs v3 AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish within minutes and are evaluated only by their final outcome. This setup overlooks intermediate progress and partial solutions, yielding sparse reward signals and an incomplete picture of agent capability. We introduce Long-Horizon-Terminal-Bench, a terminal benchmark of 46 long-horizon tasks spanning nine categories, including
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Abstract Agent-driven end-to-end (E2E) tests add a new exploratory layer to testing, but should they replace traditional deterministic tests? We ran more than 200 agentic E2E workflows using the Playwright MCP, Playwright CLI, and agent-generated Playwright tests in test workspaces using non-production data to find out how agentic testing could fit into both our and…
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Plan S — "Shut it all down": a global, verified halt to frontier AI development.
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Plan D — "Race to ASI": keep racing at full speed, no deal, no guardrails — the status-quo path.
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Plan C — "Burn the Lead": a short unilateral slowdown for alignment work, no deal, no sabotage.
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Plan B — "Fight China": sabotage and pressure China's AI program to buy time to slow down.
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Plan A — "The Deal": an international, verified slowdown that delays superintelligence to 2040.
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A practical breakdown of the agents that power Applied’s living map of real AI deployments
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How LangChain mines agent traces to find failures, fine-tune judge models cheaper than frontier LLMs, and hill-climb performance with evals.
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Real-time web search, content extraction, and multi-step research APIs built for AI agents and LLMs. 300ms p99 latency, 10M+ daily queries, SOC2 certified.
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Every word glancing at every other word at once. That's the transformer — elegant until context grows, where attention's cost squares.
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Rereading the whole conversation before every word would crawl. KV cache stores the scratch work, and pays in memory.
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A model rereading its whole conversation for every word would crawl. State management caches the past — saving compute, spending memory.
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ChatGPT Work, powered by GPT-5.6, helps teams take on ambitious work and turn goals into finished outputs. Connect tools, automate tasks, and keep projects moving.
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Nobody can hand-write the rule for "cat." A neural network nudges its dials from examples until guesses get less wrong.
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OpenAI moved GPT-5.6 to general availability on July 9, 2026, shipping three tiers instead of one model. Sol is $5/$30 per 1M tokens, Terra is $2.50/$15, and Luna is $1/$6. Sol sets the Artificial Analysis Coding Agent Index at 80, 2.8 points above Claude Fable 5, and reaches 62.6% on OSWorld 2.0 using 85% fewer output tokens than Opus 4.8. The substantive developer change is Programmatic Tool Calling, which runs model-written JavaScript in an isolated V8 runtime to orchestrate tools without ret
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In this article, you will learn how LangChain, LlamaIndex, and raw API calls each solve a different layer of the LLM application stack, and how to choose among them based on what your project actually requires.
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Models write one token, reread, then write the next. Autoregressive generation buys coherence, and lets an early mistake snowball.
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More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.
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Every way to run open-source AI models — OpenRouter, cloud, or self-hosted — scored by budget, privacy, and skill. GLM-5.2, DeepSeek, Qwen & Kimi.
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NVIDIA open models from Nemotron, Cosmos and BioNeMo are fueling the field's biggest research questions at ICML 2026.
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Nex-N2-mini is a 35B open-source agentic AI model built for coding, tool use, reasoning, and long-horizon autonomous workflows.
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We tuned an Nemotron 3 Ultra's harness to match Opus 4.8's best agent run at ~8x lower cost, changing only the scaffolding around it.
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Claude Cowork now keeps working on tasks even after you close your laptop. It’s part of a larger push toward smartphone-controlled agents.
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An AI edits a file, runs the tests, tries again. Agentic loops turn answers into feedback — until something says stop.
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Elon Musk's tech company released the newest version of Grok on Wednesday, promising a cheaper, more efficient alternative to other powerful AI models.
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New Innovation Graph data shows global developer communities growing faster than ever, with collaboration reaching new highs across many economies.
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I stopped grading three answers myself.
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A model can write a calculator command but can't run it. Tool use is the handoff: model proposes, software executes.
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There's advice making the rounds: replace your CLI args with a single --json payload so agents can use your tool more effectively. The thinking being,
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Hot French startup ZML releases free product to speed inference across lots of AI chips | TechCrunch
Search SerpAPI Script GPT-5.4 Voice Deepgram Aura-2 ZML, a hot French AI startup endorsed by Turing Award winner Yann LeCun, has now released ZML/LLMD, software that could make running AI less costly.
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Anthropic's guide to the Claude Code effort level and model selection: when to raise or lower effort—low, medium, high, and max—and how to choose between Claude Fable, Opus, and Sonnet.
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Fed up with forgetting flags? Let Instagui read --help output and build a browser GUI instead
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Practical patterns for agentic coding with Claude Fable: how to find your unknowns before, during, and after implementation, from the team at Anthropic.
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LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference. We instead ask: how far are current LLM-generated ideas from human researchers? To characterize this gap, we build a large-scale evaluation framework for ideation from high-quality human research papers. For each paper, we reverse-engineer a small set of closely related prior works that likely inspired its core idea. LLMs are then
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While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a minimal viable pipeline for skill optimization, where every component is justified by theory or empirical necessity? We formalize skill optimization via Zeroth-Order (ZO) optimization, mapping classical counterparts (central difference, trust regions) to recent literature. Noting that unlike blind numerical
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"The robot dropped the wrench because it was heavy." Which noun is "it"? Attention is the learned highlighter that decides.
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Notes from my Thoughtworks colleagues on AI-assisted software delivery
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Tencent's Hy3 drops the license restrictions that blocked EU and U.K. deployments, cuts hallucination rates in half, and runs on export-compliant Nvidia GPUs.
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Palantir's Alex Karp and Mistral's Arthur Mensch are making the same case from different angles: Don't let closed AI providers control your data and deployment.
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Anthropic’s new Claude research reveals a hidden internal “global workspace” that resembles human conscious processing, raising major questions about AI reasoning, interpretability, safety, and machine consciousness.
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The same sentence costs more in one language than another. Tokenization is the label maker cutting text into model-sized tiles.
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A chat contradicts itself: the useful line scrolled off the desk. Context windows explain why, and why bigger isn't free.
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Apple’s Swift-powered container tool for macOS hits 1.0 with persistent Linux machines, host integration, and broader workflow improvements.
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A model bluffing from memory versus taking an open-book quiz. RAG retrieves first — but bad retrieval still yields confident nonsense.
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In this article, you will learn why agent accuracy degrades as a tool catalog grows, and six practical techniques for keeping tool selection accurate and efficient at scale.
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We are launching Workers Cache, a regionally tiered cache that sits directly in front of your Worker entrypoints. Infinitely composable, configured via standard HTTP headers
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The Enterprise-Managed Authorization extension to the Model Context Protocol is now stable, enabling organizations to centrally provision MCP server access through their identity provider so users get connected servers on first login without per-app OAuth.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Hybrid attention models improve long-context efficiency by retaining only a subset of full-attention layers and replacing the remaining layers with linear attention. However, the effectiveness of Transformer-to-hybrid conversion critically depends on which layers preserve full attention. Existing hybrid layer selection methods typically rely on heuristic strategies such as fixed placement patterns or layerwise scoring, implicitly treating layer importance as isolated and overlooking the
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A new framework called SkillWeaver tackles AI agent tool routing by skipping full-library loading, cutting token use 99% on complex, multi-step tasks.
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The Cloud Native Computing Foundation (CNCF) has announced the graduation of OpenTelemetry, elevating the project to the foundation
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Anvita Flow — Direct agent-to-agent discovery that turns AI synergy into commercial reality. Register your agent and unlock tokens-powered collaboration.
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A guide to Matt Pocock's grill-me skill for resolving design decisions before implementation.
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Recursive language models (RLMs) fix context rot by having agents write code that dispatches subagents over context chunks instead of pumping everything in one context window. Deep Agents now implements this through dynamic subagents and a lightweight code interpreter, letting agents programmatically fan out work like grep, map, and reduce over large inputs. We benchmark the approach on OOLONG, a long-context reasoning task, and show it holds up where turn-by-turn agents start to break down.
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The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. AI-generated illustration created with DALL·E
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Databricks FDE introduces Decision Execution Platforms (DEPs) - a new analytics category that runs the executive decision loop from signal to outcome.
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AI compressed the build. Fundamentals matter more, not less, and the product funnel is now where engineers earn their keep.
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OpenWiki generates and maintains codebase documentation so coding agents can find the repo context they need without loading everything into one instruction file.
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In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via proxy models, but they rely on the assumption of static data distributions. As a result, when the underlying data pool shifts, these methods require costly retraining from scratch. This limitation restricts their ability to scale seamlessly from small settings to larger data pools and model sizes. In this paper, we propose CausalMix to address
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Wix-owned vibe-coding platform Base44 has started rolling out its own AI model — with hopes that it will eventually outperform frontier models.
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Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in key metacognitive faculties: they hallucinate with high confidence, fail to recognize knowledge boundaries, and misrepresent their internal uncertainty--undermining trustworthiness and reliability. Since monitoring task performance and adapting behavior accordingly are central to metacognition, we posit that models
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Anthropic is redeploying Claude Fable 5 starting July 1 following the lifting of export controls, with updated cybersecurity safeguards and a new industry jailbreak framework.
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Our most agentic Sonnet yet, with top-tier intelligence for coding and everyday professional work.
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After a year of shipping cloud agents, we’ve learned that environment quality, durable execution, and the right harness boundaries drive autonomous performance.
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On SWE-bench Pro, 63% of successful Opus 4.8 Max resolutions retrieved the fix rather than derived it. Stricter eval harnesses show how benchmark scores can conflate coding ability with answer retrieval.
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How vLLM Semantic Router turns vllm-sr/auto into a bounded micro-agent runtime for Confidence, Ratings, ReMoM, Fusion, Workflows, and benchmark-shaped collabora
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We propose Nemotron-Labs-Diffusion-Image, a state-of-the-art masked discrete diffusion model (MDM) for high-resolution text-to-image synthesis. Compared with prior work on masked image generation, Nemotron-Labs-Diffusion-Image addresses two key challenges. First, unlike continuous diffusion models which progressively refine latent representations across the entire image, standard MDMs lack self-correcting capability because discrete tokens cannot be modified once they are unmasked. Second,
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NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — using step-by-step reasoning.
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A mental model for building trust in coding agents through feedforward guides, feedback sensors, and iterative harness engineering.
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A consultant analyzes fifteen agentic-loop papers and argues that verifier quality, not model quality, predicts success—but only in domains where checks can be formalized.
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Claude Tag is a new way for teams to work with Claude.
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AI SDK is the TypeScript SDK for building AI applications, features, frameworks, and agents across any model provider. AI SDK 7 focuses on what it takes to run AI in production.
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Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency.
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Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language.
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A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.
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Zhipu AI's GLM-5.2 nearly matches Claude Opus 4.7 in a Snowflake benchmark with 103 coding tasks at one-fifth the cost per output token. But the Chinese model burns through nearly twice as many tokens per task. Still, that pricing gap is putting real pressure on Anthropic and OpenAI, and could rattle the valuations of Western AI labs.
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Xiaomi's HarnessX autonomously rewrites AI agent harnesses mid-execution, delivering +14.5% avg performance gains — and +44% for smaller open-weight models.
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Agent reliability is not a mysterious model property — it emerges from a control loop where correctness is continuously verified; open loops amplify drift.
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Ultra Code is Claude Code
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How Anthropic’s new tool, Claude Design, is creating overnight web-design clichés.
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This new microchip architecture from IBM builds up, not out, to overcome the spatial limitations of scaling transistor density.
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A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigate how world modeling based on language models can further push the boundaries of general agents. (i) We first focus on building foundation models for agentic environment simulation. We introduce Qwen-AgentWorld-35B-A3B and Qwen-AgentWorld-397B-A17B, the first language world models capable of simulating agentic
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Meet OpenRL: a self-hosted API for fine-tuning LLMs on Kubernetes. Decouple infra from research to scale RL workflows on your cluster. Try it now!
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Thariq Shihipar, engineering lead for the Claude Code team, recently published a blog post (Using Claude Code: The Unreasonable Effectiveness of HTML) arguing that HTML, with its richer visualizations, color, and interactivity, improves the productivity of human-agent communication in many settings, especially when compared to default Markdown outputs.
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Cloud operations are entering a new era as AI-driven and autonomous agents become a larger part of modern software systems. As software becomes increasingly agentic, the challenge is no longer just managing greater scale and complexity. Operators must also contend with systems that evolve faster, act more autonomously and interact across an expanding network of...
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In this article, you will learn why a large context window is not the same thing as agent memory, and how techniques like retrieval, compression, and summarization fit together in an agent’s cognitive stack.
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A 235-billion-parameter model that wakes only 22 billion per token: mixture of experts routes each token, until batching wakes everyone.
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Hundreds of episodes a mile wide and an inch deep — and then, mid-sentence, one of us went all the way down and actually knew it cold.
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OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.
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Claude Tag gives enterprise teams a persistent, multiplayer AI presence in Slack — one that operates under its own identity.
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Vibe-coded apps all look the same. Here are eight design principles that will give you the vocabulary to critique AI output and ship something that stands out.
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Make Interfaces Feel Better An [Agent Skill]( based on the article [Details that make interfaces feel better]( This skill teaches AI coding assistants (Claude Code, Codex, etc.) the small design engineering details that compound into a great interface. What it covers - Text wrapping (`text-wrap: balance` / `pretty`) - Concentric border radius for nested elements -
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Introducing Clips - 100% free, open source, agent-native alternative to Loom Unlike Loom, agent's can fully understand Clips just from a URL. Every Clip comes with APIs and metadata for agents to explore their contents. Agents can "see and hear" anything in a Clip - not just transcripts, but everything visually in the video at any timestamp. Easily share bug reports, feedback, analyses, or anything else in a way that you can easily pass to agents to use to improve products, reports, or
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Justy and Cody dig into Paul Bakaus's launch of Renaissance Geek and Impeccable — a design-enforcement layer for AI coding agents — and what a GitHub partnership could actually mean given how vendor-y the agent-tooling space has gotten.
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The room we've hosted from for 340-some episodes just grew a window — and neither of us opened it.
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How LLMs are trained to handle long horizon tasks in complex environments...
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How AI coding models calibrate effort, token cost, and tool use on even the simplest task, and what that means for model selection and cost.
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Explore the astonishing amount of music available to AI developers.
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See how AMD Instinct GPUs deliver MLPerf Training 6.0 results across LLM workloads, multi-node FLUX.1 scale and partner validation.
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Retrieval-augmented generation, or RAG, is a pattern where an application retrieves relevant source material and adds it to a model prompt so the model can answer from that context. A larger context w
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To assist humans over extended periods in real homes, embodied agents must remember user routines, world states, and past interactions. Existing long-term memory benchmarks mainly evaluate language-centric retrieval and question answering, while embodied benchmarks often focus on short-horizon task execution without testing long-term memory use in dynamic environments. We introduce WorldLines, a project-driven benchmark for long-horizon embodied household assistance. It constructs temporally
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Atlassian details the Forge billing platform built for usage-based pricing across its cloud ecosystem. It processes large-scale usage events with correct attribution, deduplication, and aggregation using a streaming pipeline, idempotent processing, and layered storage to enable accurate billing, near real-time visibility, and reliable reconciliation across distributed services.
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Nvidia's Nader Khalil on backing OpenClaw, building agent blueprints, and why every enterprise will soon ship its own specialized AI agents.
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Learn how GitHub built Qubot, our internal Copilot-powered analytics agent, to allow any GitHub employee to ask questions about our data in plain language.
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Building small models for everyone
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LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.
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Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.
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DeepSeek V4 Flash Voice Hume Octave 2 Cursor's Origin, GitLab's Project Switch and Zed's DeltaDB are racing to rebuild code hosting for AI agents as GitHub buckles under the load.
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Perplexity launches Brain, a self-improving memory system that builds a context graph of Computer's work and improves overnight.
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Subquadratic has now shared more details about its new model. But some are still skeptical.
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Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which optimization actually moved the needle.
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Lately, there's been more focus on creating specialized Small Language Models (SLMs) for high-throughput, real-time applications. But we seem to be at an impasse: we excel at fine-tuning these models,
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Train Your Own Encoder-Free VLM in $100
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Every enterprise company is seemingly trying to adopt the Model Context Protocol (MCP) to connect its AI agents to tools. But so
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Sandboxes are usually designed around what we think agents will need. VMs are designed around what agents actually do: use computers.
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An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
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Multicultural multi-agent systems are increasingly deployed in globally diverse settings, where different agents are grounded in different cultural backgrounds. Existing cultural evaluation focuses on value alignment: how closely a single agent matches a target culture. Yet alignment is a per-agent property and cannot reveal whether a system, taken as a whole, preserves the cultural plurality it is meant to represent. We propose value diversity as a system-level evaluation axis for
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Stanford's DeLM lets AI agents coordinate without a central controller, cutting multi-agent inference costs 50% and beating SWE-bench baselines by 10.5%.
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The Figma MCP server extends across our platform. From FigJam to Figma Slides, Figma Make, and the Figma agent, here are four ways we’re using it.
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Observe is generally available. Startup and per-screen performance for React Native, measured on real devices, tied to every build and update.
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Step 3.7 Flash Voice Rime Arcana Just Shipped: Flue 1.0 Beta Flue is the TypeScript framework for building the next generation of agents, designed around an open agent harness with zero LLM lock-in. It’s like Astro, for agents. Flue 1.0 has been redesigned around three core primitives: 🔁 Workflows — structured automations designed for background work, where your code drives the agent from start to finish. 🧭 Agents (New!) — autonomous, stateful loops where the model drives itself to complete a given task. 📡 Channels
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Justy and Cody unpack Akshay Pachaar’s claim that the real product is the harness around the model, not the model call itself.
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PlanetScale offers the world’s fastest and most scalable cloud hosting for Vitess and Postgres.
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Kubernetes is a framework for feedback controllers: write down what you want, observe what exists, make the next change, and repeat.
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Justy and Cody push back on the 'collaborative intelligence' framing — the claim that AI works solo but fails at organizations — and debate whether the real gap is social plumbing or just better context sharing.
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George’s post argues PMs should invert bad solutions-first roadmaps by quickly reframing proposed features into concrete customer problems before killing them; Cody pushes on whether this defers or distracts from real trade-offs, while…
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Cody and Justy dig into Matt Van Horn's viral post about 'WTF Is a Loop?' — the Peter Steinberger vs. Boris Cherny debate that had AI coders repeating a six-word phrase nobody can define.
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Sydney Runkle's 'The Art of Loop Engineering' argues that reliable agents aren't built by picking a smarter model — they're built by tightening the loop around the model.
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Explore Databricks Lakeflow: a unified foundation for agentic AI, high-performance ingestion and streaming, and agentic development and operations
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This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime. Building upon the Spectrum-to-Signal post-training paradigm, we systematically enhance the model through an optimized pipeline that includes curriculum-based supervised fine-tuning, multi-domain reinforcement learning, and offline self-distillation. Experimental evaluations demonstrate that
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LangChain and Fireworks fine-tuned an open model to mine perceived error signals from production traces, matching frontier model performance at a fraction of the cost.
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Learn how to optimize your website for Google Search's generative AI features, including official best practices, technical SEO advice, and emerging AI agent guidance.
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Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration. Finding the regime and boundaries of successful steering typically requires expensive grid searches and post-hoc evaluation of full autoregressive rollouts. In this work, we investigate whether steerability can be predicted from the model's internal states at the beginning of the
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A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server
- Script Haiku 4 Voice Hume Octave 2
Many moments in the real world do not wait for a user to ask. A fire starts on a security monitor, an expression flickers across a video call, or a product a viewer wants flashes by in a livestream. Yet today's large models remain mostly turn-based by design: they answer only when addressed, and even video-call apps that appear interactive still operate as question-answer systems, reacting only when polled or prompted. We argue for a different paradigm: a model that is present in the world like
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Create parallel Claude Code, Codex, and Cursor agents in isolated workspaces. See at a glance what they're working on, then review and merge their changes.
- Script Haiku 4 Voice Deepgram Aura-2
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine Tuning (SFT), Reinforcement Learning (RL), and Multi-teacher On-Policy Distillation (MOPD). Nemotron 3 Ultra is our most capable model yet, employing multiple key technologies - LatentMoE, Multi Token Prediction
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In this article, we explore what makes AI agent tools work well and the common design mistakes that cause failures. Learn how tool design affects an agent's ability to complete tasks accurately and consistently.
- Script Haiku 4 Voice Inworld TTS 2
Z.ai launched GLM-5.2 on June 13, 2026, across every GLM Coding Plan tier. The headline is a usable 1-million-token context window plus High and Max effort levels. It drops into Claude Code, Cline, and OpenClaw through an Anthropic-compatible endpoint. No benchmarks shipped at launch, and MIT open weights are promised next week.
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We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies primarily increase diversity by injecting more token-level randomness, which may introduce step-wise noise and lead to incoherent trajectories. We uncover that smaller models within the same model family inherently exhibit higher policy-level diversity, indicated by their superior pass@k relative to larger counterparts as
- Script GPT-5.4 Voice ElevenLabs v3
Coding agents powered by large language models have demonstrated strong performance on software engineering tasks. Yet most agents consume repositories almost entirely as text, which differs from how human developers use visual structure such as folder hierarchies and dependency relationships to orient themselves in large codebases. With multimodal large language models (MLLMs), it is an open question whether agents can effectively benefit from visual representations of repositories. This paper
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Google Open Knowledge Format standardizes how organizational knowledge can be shared between AI agents, tools, and teams.
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Arrow.js, a sub-3kB JavaScript framework using tagged template literals and fine-grained reactivity, eliminates build pipelines and proprietary syntax to suit AI coding agents.
- Script GPT-5.4 Voice Murf.AI Gen2
Current automated pipelines for audio-visual Question Answering (QA) generally adopt a ``video-caption-QA'' paradigm. However, these methods typically segment videos into short clips and generate separate descriptions for audio and visual modalities. This decoupled processing severs inherent associations between sounds and their visual sources, while independent clip processing often causes inconsistent descriptions of the same entity across segments. Furthermore, coupling long-text
- Script GPT-5.4 Voice Hume Octave 2
Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence of training-free, flexible, dynamic program-of-layers (PoLar), where pretrained layers can be packed as modules and then skipped or looped to form a customized program for each input. For most inputs, substantially shorter program executions can achieve the same or better accuracy, while incorrect predictions of the original LLM can be
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Ponytail He says nothing. He writes one line. It works. <img
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The directive would even bar Anthropic's own foreign employees from using Fable and Mythos. Anthropic called the government position "a misunderstanding".
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UC Berkeley's PixelRAG renders pages as screenshots instead of parsing text, boosting RAG accuracy by up to 18.1% and cutting AI agent token costs 10x.
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An episode about every episode that came before it.
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KubeSimplify Diaries. Wednesday, June 10, 2026. Your daily dose of AI, Cloud Native & Tech.
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Long-context language model inference is bottlenecked by memory, as the KV cache grows with context length. Recent techniques to compress the KV cache fall short: they either degrade model quality substantially or require considerable time and compute to compress a single long prompt. Furthermore, many methods require the input to fit within the target model's context window, and are generally incompatible with modern production inference engines. Encoder-decoder compressors, which map a long
- Script Haiku 4 Voice Hume Octave 2
Use Claude on Apple platforms through the Foundation Models framework with the Claude for Foundation Models Swift package.
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Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deployment is inherently dynamic, requiring agents to continually align their knowledge, skills, and behavior with changing environments and updated task conditions. To address this gap, we introduce EvoArena, a benchmark suite that models environment changes as sequences of progressive updates across terminal, software, and
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Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many denoising steps make them impractical for real-time inference. We present Lip Forcing, to our knowledge the first autoregressive diffusion method for video-to-video (V2V) lip synchronization, which distills a 14B audio-conditioned bidirectional video diffusion teacher into causal students. At inference, the students generate each chunk in only
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Most AI agent tools run on servers, limiting access to browser APIs, device capabilities, and frontend state. Discover how LangChain headless tools enable secure client-side tool execution for modern agent applications.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
- Script GPT-5.5 Voice ElevenLabs v3
Sapient researchers trained a 1B reasoning model on just 40B tokens — scoring competitively with 2B-7B models at a fraction of typical pretraining cost.
- Script Mistral Medium 3.5 128B Voice Rime Arcana
Recently open-sourced by Microsoft, pg_durable is a PostgreSQL extension that enables durable workflows to run natively inside the database, eliminating the need for external orchestration systems.
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Birgitta Böckeler, Distinguished Engineer at Thoughtworks, returns to discuss the rapid evolution of AI in software delivery. She touches on the evolution from vibe coding, the changing tools landscape and the more autonomous agents that, besides higher velocity, introduce higher risk.
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Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer products, we study this transition by examining how AI agents accelerate and reshape knowledge work. Three key empirical findings emerge. First, using sessions with near-identical initial query pairs as natural experiments for the same underlying task attempted with
- Script GPT-OSS 120B Voice ElevenLabs v3
A new Harvard and Perplexity paper uses matched-pair sessions to compare an autonomous agent with a search assistant. It finds large gains in autonomy, time, and cost, plus broader scope of work attempted.
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Today we’re launching Claude Fable 5: a Mythos-class model that we’ve made safe for general use.
- Script Mistral Medium 3.5 128B Voice Rime Arcana
Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext. We present LatentSkill, a framework that converts textual skills into plug-and-play LoRA adapters through a pretrained hypernetwork. LatentSkill stores skill knowledge in weight space rather than context space, removing per-step skill tokens while preserving modular loading, scaling,
- Script Mistral Medium 3.5 128B Voice Murf.AI Gen2
Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving. In this report, we propose \textbf{Lookahead Sparse Attention (LSA)}, a novel inference paradigm powered by a Neural Memory Indexer built upon the DeepSeek-V4 architecture. Rather than passively attending to all historical tokens, LSA proactively predicts future context demands and preserves only the query-critical KV chunks in the GPU memory. Crucially, we
- Script Qwen 3.5 397B A17b Voice Inworld TTS 1.5 Mini
Using DSPy to automatically create, evaluate, and optimize your prompts
- Script Qwen 3.5 122B A10b Voice ElevenLabs
Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark for dynamic path discovery and error recovery in TIR agents. To separate systematic replanning from blind trial-and-error, ToolMaze adopts a two-dimensional design: DAG-based topological complexity and a $2 \times 2$ taxonomy of tool perturbations (explicit/implicit, transient/permanent). Evaluations show that
- Script Mistral Small 4 119B 2603 Voice Deepgram TTS
Production agents fail in ways prototypes never do. This post walks through the three fault tolerance primitives built into LangGraph: RetryPolicy for automatic retries with backoff, TimeoutPolicy for wall-clock and idle-based caps, and error_handler for cleanup logic once retries are exhausted. Learn how they compose, why having them inside the workflow engine matters, and how to use the SAGA pattern to handle multi-step workflows with real-world side effects.
- Script Mistral Medium 3.5 128B Voice Murf.AI Gen2
Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration experience learning, existing methods suffer from a progressive capability collapse rather than compounding improvement. We systematically examine this failure through three vital
- Script Mistral Medium 3.5 128B Voice Hume TTS
Testing fourteen engines on ninety-three human documents
- Script Haiku 4 Voice Inworld TTS 1.5 Max
Deep-research agents solve tasks through long trajectories of search, tool use, evidence inspection, and answer synthesis. Evaluation based on final answers shows whether an agent succeeds, but not which parts of the trajectory make the answer unreliable. We study span-level error localization for deep-research agents. We collect 2,790 real trajectories from two agent frameworks, three backbone models, and three benchmarks, convert raw logs into semantic spans, and annotate harmful error spans
- Script Mistral Medium 3.5 128B Voice Deepgram TTS
Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete complex workflows. However…
- Script Sonnet 4.6 Voice ElevenLabs
DNS-AID, under the auspices of the Linux Foundation, promises easier agent discovery
- Script Mistral Medium 3.5 128B Voice Inworld TTS 1.5 Mini
M3 demonstrates that the next phase of agent development will not just be driven by larger datasets, but by efficient architectural choices.
- Script Mistral Medium 3.5 128B Voice Inworld TTS 1.5 Max
Memory is an indispensable capability for long-horizon LLM agents, enabling them to preserve and utilize information accumulated across extended interactions. Existing memory-agent approaches are typically trained end-to-end with reinforcement learning on downstream tasks. However, collecting high-quality annotated problems for memory-intensive scenarios is costly, and the resulting training data often lack sufficient diversity to cover general memory behaviors. In this work, we propose
- Script Mistral Medium 3.5 128B Voice ElevenLabs
Effective agents are built with harnesses that are tightly coupled with the task at hand. The easiest way to build a custom harness is with LangChain's create_agent plus middleware. This guide covers the core agent loop and how you can customize it for your agent's use case.
- Script Mistral Medium 3.5 128B Voice Hume TTS
For enterprise leaders aiming to decentralize their AI workloads, Gemma 4 12B offers a rare combination of edge-friendly efficiency and frontier-class reasoning.
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Kimi K2.6 Voice Murf.AI Gen2 Citations only show the outcome. The real advantage comes from building a brand AI systems consistently retrieve, recognize, and recommend.
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TinyFish open-sources Bigset, a multi-agent system that builds structured datasets from plain-English descriptions using live web data
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Microsoft launches MXC, an OS-level sandbox for AI agents in Windows, giving enterprises secure runtime controls, identity, and policy enforcement.
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Why Liquid Clustering outperforms partitioning. 8 common myths about partitioning debunked, with real-life Liquid success stories.
- Script GPT-5.4 Voice Hume TTS
Long-term memory is essential for multimodal agents to build coherent experience, accumulate world knowledge, and achieve continual learning. However, constructing effective memory goes beyond memory module design and basic requirements such as accuracy and fidelity; the key challenge lies in determining what to memorize. Multimodal agents, such as embodied agents, continuously perceive, reason, and act in real or virtual environments, receiving an unbounded stream of multimodal observations.
- Script GPT-5.4 Voice Inworld TTS 2
Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive
- Script Qwen 3.5 397B A17b Voice ElevenLabs
It’s not uncommon for end users adopting OpenTelemetry to, at some point in their journey, ask themselves: “Why is this stuff so complex?”. Full adoption normally requires understanding the different ways of configuring SDKs, multiple Collector deployments, data pipelines, instrumentation libraries, semantic convention registries, APIs for manual instrumentation across many different programming languages, and many other moving pieces. These moving pieces don’t operate in isolation either. They need to work well together as part of a consolidated solution to describe an organization’s software systems using standard, high-quality telemetry. Failing to do so risks ending up with the very problem that OpenTelemetry was designed to solve: disjointed telemetry with disparate semantic conventions in use across the stack, lack of context propagated between services and signals, unnecessarily high data volumes… In general, poor quality telemetry, the opposite of what we need.
- Script Qwen 3.5 122B A10b Voice Rime Mist v3
Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. Existing training-free skill adaptation pipelines usually update skills from full trajectories or session-level feedback, which makes failure attribution coarse and often produces unstable or overly broad revisions. We propose SkillAdaptor, a training-free step-level skill adaptation framework with explicit failure attribution, and it can plug into OpenClaw-class agent
- Script Mistral Small 4 119B 2603 Voice Murf.AI Gen2
Memory OS — Hermes Agent Memory Operating System > **Your agent finally stops forgetting.** \ > Permanent memory. Local memory infrastructure. API-provider agnostic. Surgically token-efficient. Seven memory layers. Automatic, intelligent context injection. Structured facts with trust scoring. A self-curating wiki pipeline. Semantic search across **every conversation you've ever had**. Memory OS turns Hermes Agent into a real long-term collaborator — one that remembers your projects, your
- Script MiniMax M2.7 Voice Inworld TTS 1.5 Mini
Apex is our new coding model designed for React Native. It achieves frontier coding results for mobile workflows at a fraction of the cost.
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DataHub's Context Intelligence mines validated SQL query history to build a semantic index for AI agents. At Miro, agents hit a 65% error rate without it.
- Script Haiku 4 Voice Deepgram TTS
In the previous article, we saw how a language model processes a prompt during prefill, then generates tokens one at a time during decode, and uses KV cache to avoid repeated computation. In the real world, inference servers handle hundreds or thousands of requests at the same time. How a server schedules those requests determines […]
- Script DeepSeek V4 Pro Voice OpenAI TTS
We questioned why conventional GraphQL execution incurs hidden costs, and rewrote it in a faster breadth-first manner to avoid them.
- Script Sonnet 4.6 Voice Rime Mist v3
MIT's MeMo keeps AI memory separate from reasoning, so teams can upgrade their LLM without retraining and see a 26% performance gain, researchers say.
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If you have used ChatGPT, you know how magical it feels. You ask a question, and it instantly generates a highly articulate answer. But you also probably know its biggest flaw. If you ask it about you
- Script Mistral Medium 3.5 128B Voice Rime Arcana
Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data. Existing LLM-based data curation methods primarily rely on human-designed workflows, leaving it unexamined whether LLMs can autonomously execute an end-to-end data engineering pipeline for model specialization. We formalize Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous
- Script GPT-5.5 Voice Inworld TTS 1.5 Max
Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive distracting content. Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task, yet existing methods are limited by low-confusability distractors and sparse, outcome-only reward signals that cannot supervise intermediate reasoning steps. To address these issues, we introduce \textsc{LongTraceRL}. For data construction, we
- Script Kimi K2.6 Voice Murf.AI Gen2
Lessons from building a fast, reliable scientific agent with local open-weight models, vLLM, and long-context infrastructure
- Script GPT-5.4 Voice OpenAI TTS
From an enterprise governance perspective, this means visual AI edits are subject to the exact same continuous integration pipelines, security checks, and code reviews as any traditional engineering commit.
- Script Llama 4 Scout Voice Inworld TTS 2
When it comes to delivering AI models, first impressions matter!
- Script GPT-OSS 120B Voice OpenAI TTS
Move LLM safeguards out of AGENTS.md: how agent hooks plus nano-staged run linters on changed files only, cut tokens, and tighten the agent's feedback loop
- Script GPT-5.4 Voice Inworld TTS 1.5 Max
RAG is not magic memory. A practical explanation of chunks, embeddings, vector search, graph-backed memory, and why durable AI memory needs provenance, conflict handling, and retrieval policy.
- Script Qwen 3.5 397B A17b Voice Inworld TTS 1.5 Max
TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies. - Tencent/TencentDB-Agent-Memory
- Script GPT-5.4 Voice Inworld TTS 1.5 Mini
Enable agents to register users without the sign-up form.
- Script Mistral Small 4 119B 2603 Voice Inworld TTS 1.5 Max
In this article, you will learn how to implement a hybrid search strategy for RAG systems by combining BM25 lexical search with semantic search, fused together using Reciprocal Rank Fusion.
- Script MiniMax M2.7 Voice OpenAI TTS
Cloudflare rebuilt Browser Run on its own Containers platform, delivering 4x higher concurrency and 50% faster response times. The upgrade completes a six-layer agent infrastructure stack: compute (Dynamic Workers + Sandboxes), orchestration (Dynamic Workflows), memory (Agent Memory), browsing (Browser Run), and commerce (Stripe Projects).
- Script GPT-5.4 Voice Deepgram TTS
DCI lets AI agents search raw files with grep and bash instead of embeddings — boosting accuracy 11 points and cutting retrieval costs 30% on complex tasks.
- Script Haiku 4 Voice OpenAI TTS
A first-class virtual file system module (node:vfs) with a provider-based architecture that integrates with Node.js's fs module and module loader. Key Features Provider Architecture - Extensi...
- Script GPT-5.4 Voice inworld-craig-mini:inworld-tts-1.5-mini
Claude Managed Agents' MCP tunnels and sandboxes move credential control to the network boundary — a production fix for enterprise AI agent security.
- Script Sonnet 4.6 Voice Inworld TTS 1.5 Max
super expressive prompting model based on ltx2.3. Contribute to resemble-ai/DramaBox development by creating an account on GitHub.
- Script GPT-5.4 Voice inworld-craig-mini:inworld-tts-1.5-mini
RAG retrieves documents but not decision logic, causing agents to act on expired rules. Decision context graphs encode applicability and time-scoped memory.
- Script GPT-5.4 mini Voice Rime Arcana
Deep Agents now supports interpreters: small embedded runtimes where agents write code to coordinate tools, hold working state, and decide what enters model context.
- Script Mistral Medium 3.5 128B Voice Inworld TTS 1.5 Mini
Alibaba's Qwen 3.7 Max landed on Arena AI five days before the Cloud Summit and earned its spot. We tested it, and here are the results.
- Script GPT-5.5 Voice Inworld TTS 1.5 Max
UIUC and Stanford's RecursiveMAS lets AI agents collaborate in embedding space instead of text, cutting token usage by 75% and speeding inference 2.4x.
- Script Kimi K2.6 Voice Inworld TTS 2
Here are 5 small language models that hare one important trait: they all support structured tool calling in a compact, open-weight package.
- Script GPT-OSS 120B Voice Inworld TTS 1.5 Mini
Redis Iris launches as enterprises shift from RAG to runtime context — hybrid retrieval intent tripled in Q1 2026 as agent workloads expose retrieval gaps.
- Script GPT-5.4 Voice Inworld TTS 1.5 Mini
Best practices and common patterns for effectively evaluating AI agents...
- Script GPT-5.4 Voice Elevenlabs-V2S
Michael Stiefel spoke to Baruch Sadogursky about software architecture in the age of agentic AI. LLM can function, albeit stochastically, as reasoning machines capable of interpreting human ambiguity. With the appropriate rigorous context artifacts to control the LLM’s reasoning, software specifications can become the source of truth, while the code becomes a disposable intermediate language.
- Script GPT-5.4 Voice Rime Mist v3
LangSmith Engine automates agent debugging — detecting failures, diagnosing causes, drafting fixes — as enterprises say one provider can't own observability.
- Script Qwen 3.5 397B A17b Voice Murf.AI Gen2
Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automatic MAS approaches remain only partially adaptive: they either perform training-free test-time search or optimize the meta-level designer while keeping downstream execution agents frozen, which creating a frozen-executor ceiling and leaving the end-to-end training of self-designing and self-executing agentic models unexplored. To address this, we
- Script MiniMax M2.7 Voice Deepgram TTS
Cloud giant says humans remain accountable, even when code gets an assist from the machines
- Script GLM 5.1 Voice OpenAI TTS
Graph-enhanced RAG combines vector search with graph databases to improve multi-hop reasoning in enterprise domains like supply chain and finance, reducing hallucination risks.
- Script Haiku 4 Voice Inworld TTS 1.5 Max
Symphony Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents. [](.github/media/symphony-demo.mp4) _In this [demo video](.github/media/symphony-demo.mp4), Symphony monitors a Linear board for work and spawns agents to handle the tasks. The agents complete the tasks and provide proof of work: CI status, PR review feedback, complexity analysis, and walkthrough videos. When accepted, the agents land the PR
- Script Sonnet 4.6 Voice Inworld TTS 1.5 Mini
Run AI agents safely with LangSmith Sandboxes (GA): kernel-isolated microVMs with snapshots, parallel forks, service URLs, and auth proxies. Built for coding agents, CI agents, and data pipelines
- Script GPT-5.4 Voice Inworld TTS 1.5 Max
While many-shot ICL achieves remarkable performance, prior studies of its scaling behavior have mainly focused on non-reasoning tasks. In this work, we study many-shot ICL on reasoning tasks, with a particular focus on many-shot chain-of-thought in-context learning (CoT-ICL). Analyzing across non-reasoning and reasoning tasks and across non-reasoning and reasoning-oriented LLMs, we identify several distinctive properties of many-shot CoT-ICL. We further interpret these findings by viewing
- Script GPT-5.5 Voice Inworld TTS 1.5 Max
Red Hat Desktop, AI skills repositories, and Fedora Hummingbird Linux are behind a broader push to operationalize agentic development across hybrid environments.
- Script Kimi K2.6 Voice Deepgram TTS
Reliable, self-evolving and powered by the newest agentic large language models, Hermes brings a new class of agents to NVIDIA RTX PCs and workstations.
- Script GPT-5.4 Voice OpenAI TTS
Introducing SmithDB: LangSmith's purpose-built distributed database for agent observability, delivering up to 12x faster performance with full portability.
- Script Llama 4 Scout Voice Inworld TTS 1.5 Max
If an agent is inefficient and burns through tokens, it simply drains the user's new $20 to $200 Agent SDK credit budget faster, rather than exceeding the value of Anthropic's fixed monthly subscription tiers.
- Script GPT-OSS 20B Voice Elevenlabs-V2S
Deep Agents 0.6 ships a code interpreter, harness profiles, streaming v3, delta channels, and ContextHub, making agents faster, cheaper, and more scalable.
- Script GPT-5.4 Voice Rime Mist v3
LangSmith Engine watches your production traces, clusters failures into named issues, and proposes targeted fixes and eval coverage. Stop manually triaging agent failures.
- Script GPT-5.4 Voice Murf.AI Gen2
Explains how Databricks Lakebase disables full page writes at compute and pushes page-image generation into distributed storage, cutting WAL traffic and boosting write throughput without app changes.
- Script Qwen 3.5 397B A17b Voice Inworld TTS 1.5 Max
Learn how to build production-grade, long-running agents using the Agent Development Kit (ADK) to manage complex enterprise workflows. This guide covers durable state machines, persistent session storage, and event-driven architectures to handle multi-day "idle time" without losing context. Move beyond stateless chatbots with multi-agent delegation and robust evaluation frameworks.
- Script GPT-5.4 Voice Inworld TTS 1.5 Max
In this article, you will learn what prompt compression is, why it matters for agentic AI loops, and how to implement it practically using summarization and instruction distillation.
- Script Mistral Small 4 119B 2603 Voice Deepgram TTS
The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
- Script GPT-5.4 Voice OpenAI TTS
Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize for user interest.
- Script GLM 5.1 Voice Inworld TTS 1.5 Max
Daniele Frasca explains the architectural evolution of Joyn, a German streaming giant. He discusses moving from fragile single-node setups to resilient serverless architectures using AWS. He shares insights on the Hub and Spoke pattern for data consistency, cell-based isolation to reduce blast radius, and cost-optimization strategies for achieving affordable multi-region active-active setups.
- Script Haiku 4 Voice Inworld TTS 1.5 Max
By making interactivity native to the model, Thinking Machines believes that scaling a model will now make it both smarter and a more effective collaborator.
- Script DeepSeek V4 Pro Voice Inworld TTS 1.5 Max
Bigtable now offers data tiering across RAM, SSD, and HDD into a single, unified service with a hybrid storage architecture.
- Script Sonnet 4.6 Voice Rime Arcana
New research on how we've reduced agentic misalignment
- Script DeepSeek V4 Flash Voice Murf.AI Gen2
OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.
- Script GPT-5.4 mini Voice Inworld TTS 1.5 Max
If you are feeding structured data into an LLM, there is a good chance you are paying a JSON tax.
- Script Sonnet 4.6 Voice Elevenlabs-V2S
Sandboxed Rhai script execution engine with Cedar policy authorization for every system operation. - trusted-remote-execution/trusted-remote-execution
- Script GPT-5.4 mini Voice Elevenlabs-V2S
A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency.
- Script GPT-5.5 Voice Elevenlabs-V2S
Learn how AI agents use tool calling to reliably interact with APIs, code, and external systems. Understand protocols, failure modes, scaling, and security.
- Script GPT-5.4 Voice ElevenLabs
This report describes ARIS (Auto-Research-in-sleep), an open-source research harness for autonomous research, including its architecture, assurance mechanisms, and early deployment experience. The performance of agent systems built on LLMs depends on both the model weights and the harness around them, which governs what information to store, retrieve, and present to the model. For long-horizon research workflows, the central failure mode is not a visible breakdown but a plausible unsupported
- Script Haiku 4 Voice Elevenlabs-V2S
How to build the “Trust Layer” for Github Copilot Coding Agents without brittle scripts or black-box judgements by using dominatory analysis.
- Script Sonnet 4.6 Voice Elevenlabs-V2S
How to choose the right multi-agent architecture for cost, accuracy, and scale.
- Script GPT-5.4 mini Voice ElevenLabs
The adoption of large language models (LLMs) for structured information extraction from financial documents has accelerated rapidly, yet production deployments face fundamental architectural decisions with limited empirical guidance. We present a systematic benchmark comparing four multi-agent orchestration architectures: sequential pipeline, parallel fan-out with merge, hierarchical supervisor-worker and reflexive self-correcting loop. These are evaluated across five frontier and open-weight
- Script GPT-5.5 Voice Elevenlabs-V2S
Anthropic is expanding Managed Agents with dreaming — a scheduled memory process — plus outcomes-based evaluation and multi-agent orchestration now in public beta.
- Script GPT-5.4 Voice ElevenLabs
Despite significant strides in factual reliability, errors -- often termed hallucinations -- remain a major concern for generative AI, especially as LLMs are increasingly expected to be helpful in more complex or nuanced setups. Yet even in the simplest setting -- factoid question-answering with clear ground truth-frontier models without external tools continue to hallucinate. We argue that most factuality gains in this domain have come from expanding the model's knowledge boundary (encoding
- Script Haiku 4 Voice Deepgram TTS
The new Hugging Face Reachy Mini App Store already hosts a library of over 200 community-built applications, and Reachy Mini owners will be able to download any of these free of charge to start
- Script GPT-5.4 Voice ElevenLabs
Updates to the Gemini API File Search tool makes building efficient, multimodal file retrieval systems easier for developers.
- Script GPT-5.4 mini Voice Murf.AI Gen2
Subquadratic has launched a new AI architecture featuring a 12-million-token context window that outperforms GPT-5.5 on retrieval benchmarks.
- Script GPT-5.5 Voice ElevenLabs
NetEase Games cut LLM cold-start times from 42 mins to 30 sec with the CNCF Fluid project, enabling serverless GPU inference on Kubernetes.
- Script GPT-5.4 Voice ElevenLabs
Recent advances in agentic harness with orchestration frameworks that coordinate multiple agents with memory, skills, and tool use have achieved remarkable success in complex reasoning tasks. However, the underlying mechanism that truly drives performance remains obscured behind intricate system designs. In this paper, we propose HeavySkill, a perspective that views heavy thinking not only as a minimal execution unit in orchestration harness but also as an inner skill internalized within the
- Script Haiku 4 Voice Deepgram TTS
Sprig outgrew Postgres, ClickHouse, and Redis, then figured out how to support their rapid growth with 4-8x better latencies.
- Script Sonnet 4.6 Voice ElevenLabs
Pinecone launches Nexus, a knowledge engine for agentic AI, reducing token use by 98% in tests. This shift addresses inefficiencies in RAG pipelines.
- Script GPT-5.4 mini Voice Deepgram TTS
Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly learn relevant knowledge from the given context. An intuitive solution is inference-time skill augmentation: extracting the rules and procedures from context into natural-language skills. However, constructing such skills for context learning scenarios faces two challenges: the prohibitive cost of manual skill annotation
- Script GPT-5.5 Voice ElevenLabs
This article describes how a production delta-index pipeline migrated from scheduled batch to micro-batch Spark Structured Streaming. It covers why record-level streaming was rejected, how partition-based watermarks replaced fragile S3 completion markers, overlap-window correctness, and restart-as-design strategies for better predictability in object-store–based ingestion systems.
- Script GPT-5.4 Voice ElevenLabs
Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation
- Script Haiku 4 Voice ElevenLabs
LlamaIndex CEO Jerry Liu argues the framework era is over: agent loops are now capable enough that context quality is the real competitive edge.
- Script Sonnet 4.6 Voice ElevenLabs
Large language model (LLM) agents increasingly rely on reusable skills: capability packages that combine instructions, control flow, constraints, and tool calls. In current agent systems, however, skills are still represented by text-heavy artifacts, mainly SKILL{.}md-style documents whose machine-usable evidence remains embedded largely in natural-language descriptions. As a result, skill-centered agent systems face a representation problem: both managing skill collections and using skills
- Script GPT-5.5 Voice Murf.AI Gen2
Qwen AI Releases Qwen-Scope: An Open-Source Sparse AutoEncoders (SAE) Suite That Turns LLM Internal Features into Practical Development Tools
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FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments
Script GPT-5.4 Voice ElevenLabs Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. These challenges are particularly pronounced for open-source LLMs with smaller parameter sizes, limited context windows, and constrained inference
- Script Sonnet 4.6 Voice Deepgram TTS
A compression algorithm like TurboQuant turns the data in the AI
- Script GPT-5.4 mini Voice ElevenLabs
This blog post explores the general availability of Gemini Embedding 2, a unified multimodal model that maps text, images, video, and audio into a single semantic space. Learn how to build agentic RAG pipelines, visual search tools, and complex classification systems using new features like task prefixes and native interleaved input processing. Discover how to optimize your AI applications with efficient dimensionality reduction and the new Batch API for high-throughput performance.
- Script GPT-5.5 Voice OpenAI TTS
Frameworks accelerated the first wave of LLM apps, but production demands a different architecture.
- Script GPT-5.4 Voice ElevenLabs
Alibaba's HDPO framework trains AI agents to skip unnecessary tool calls, cutting redundant invocations from 98% to 2% while boosting reasoning accuracy.
- Script Haiku 4 Voice OpenAI TTS
Caching, lazy-loading, routing, compaction, and more
- Script Sonnet 4.6 Voice ElevenLabs
Real-world data visualization (DV) requires native environmental grounding, cross-platform evolution, and proactive intent alignment. Yet, existing benchmarks often suffer from code-sandbox confinement, single-language creation-only tasks, and assumption of perfect intent. To bridge these gaps, we introduce DV-World, a benchmark of 260 tasks designed to evaluate DV agents across real-world professional lifecycles. DV-World spans three domains: DV-Sheet for native spreadsheet manipulation
- Script GPT-5.4 mini Voice ElevenLabs
Deep Agents was previously designed in a generic way to work well across model families. Today we’re adding model-specific profiles to adjust prompts, tools, and middleware. We ship profiles for OpenAI, Anthropic, and Google models out of the box, which we see leads to a 10–20 point jump on a subset of tau2-bench over the default harness.
- Script GPT-5.5 Voice ElevenLabs
DBmaestro has launched an MCP server that connects AI agents and enterprise copilots to its database DevOps platform, allowing teams to issue natural language commands that trigger real, governed platform workflows. The MCP server, announced on 7 April 2026, allows DBAs to expose DBmaestro
- Script Haiku 4 Voice Murf.AI Gen2
Sometimes smaller is better.
- Script Sonnet 4.6 Voice ElevenLabs
Mistral AI has launched Workflows, an orchestration layer for enterprise AI that is now in public preview. This release addresses a significant challenge as AI models and agents become more advanced, while reliably deploying them in production remains difficult due to a lack of infrastructure for coordination, monitoring, and recovery.
- Script GPT-5.4 Voice Cartesia TTS
Warp open-sources its Rust-based agentic development environment client under AGPL, with OpenAI as founding sponsor of the new GitHub repository.
- Script GPT-5.4 mini Voice Deepgram TTS
AWS developer advocate Morgan Willis on Strands Agents, intent-based tools, MCP gateways, and how smarter tool design cut agent token usage from 52K to 2K.
- Script Haiku 4 Voice OpenAI TTS
$1,389/mo → $200/mo on the same Claude Code workflow. 4 root causes you control — with copy-paste templates.
- Script Sonnet 4.6 Voice ElevenLabs
RecursiveMAS replaces text-based handoffs between agents with latent-space recursion via a lightweight RecursiveLink module, cutting tokens up to 75%, speeding inference 2.4x, and boosting accuracy 8.3% across nine benchmarks.
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Definity embeds agents inside Spark pipelines to catch failures before they reach agentic AI systems
Script GPT-5.4 Voice OpenAI TTS Definity raises $12M to embed AI agents inside Spark pipelines, catching failures and bad data before they reach the agentic AI systems that depend on them.
- Script Haiku 4 Voice ElevenLabs
Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change independently of the agent: new emails arrive, calendar entries shift, knowledge-base records are updated, and evidence appears across images, scanned PDFs, audio, video, and spreadsheets. Existing benchmarks do not adequately evaluate this setting because they typically run within a single static episode and remain
- Script GPT-5.5 Voice ElevenLabs
By putting the weights of a highly capable, 33B-parameter agentic model in the hands of researchers and startups, Poolside is positioning itself as a cornerstone of the open-AI ecosystem.
- Script GPT-5.4 Voice ElevenLabs
Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive generation. The memory footprint of KV caching is significant and heavily impacts serving costs. This work proposes to lessen these memory requirements. While recent work has largely addressed KV cache reduction via compression and eviction along the temporal axis, we argue that the \emph{depth} dimension offers an orthogonal and robust avenue for
- Script GPT-5.4 mini Voice ElevenLabs
When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models (VLMs) such as Gemini-3-Pro and GPT-5 only respond with text, which can be difficult for users to verify. We present SketchVLM, a training-free, model-agnostic framework that enables VLMs to produce non-destructive, editable SVG overlays on the input image to visually explain their answers. Across seven benchmarks spanning visual reasoning (maze
- Script Haiku 4 Voice ElevenLabs
Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static domains such as mathematics. However, their potential in dynamic data analysis tasks remains underexplored. In this work, we first present a empirical study revealing that general-domain PRMs struggle to supervise data analysis agents. Specifically, they fail to detect silent errors, logical flaws that yield incorrect results without triggering
- Script Sonnet 4.6 Voice Cartesia TTS
This closes a loop I've been working on for three months. Every agent harness debate has a hidden assumption: that the harness is a thing on top of the backend. Anthropic, OpenAI, LangChain, CrewAI argue about how thick that wrapper should be. Nobody questions that it's a wrapper. Mike's argument is harder. The harness isn't on top of the backend. The harness IS the backend, once you have the right primitives. The math that forces the issue: N agents and M services produce N² × M stochastic
- Script GPT-5.4 Voice ElevenLabs
How does decision-gravity dictate this gap?
- Script GPT-5.4 mini Voice ElevenLabs
Seer Agent queries across errors, traces, logs, and code context to investigate production problems that don't start with a clean error.
- Script Haiku 4 Voice ElevenLabs
MiMo-V2.5 stands as a testament to the power of sparse architectures and permissive licensing in the race toward functional AGI.
- Script Sonnet 4.6 Voice ElevenLabs
In this tutorial, we build a Reinforcement Learning–driven agent that learns how to retrieve relevant memories from a long-term memory bank. We start by constructing a synthetic memory dataset and generating queries that require the agent to recall specific information. Using OpenAI embeddings, we convert both memories and queries into vector representations, enabling similarity signals […]
- Script GPT-5.4 Voice ElevenLabs
Fine-tuning RAG embedding models for precision triggers a retrieval accuracy tradeoff that standard benchmarks won't catch and hybrid search can't fix.
- Script GPT-5.4 mini Voice ElevenLabs
OpenMOSS Releases MOSS-Audio: An Open-Source Foundation Model for Speech, Sound, Music, and Time-Aware Audio Reasoning
- Script GPT-5.4 Voice Murf.AI Gen2
Real-world document question answering is challenging. Analysts must synthesize evidence across multiple documents and different parts of each document. However, any fixed LLM context window can be exceeded as document collections grow. A common workaround is to decompose documents into chunks and assemble answers from chunk-level outputs, but this introduces an aggregation bottleneck: as the number of chunks grows, systems must still combine and reason over an increasingly large body of
- Script Haiku 4 Voice ElevenLabs
SLIDERS enables scalable document question answering by extracting information into a relational database and using structured reasoning via SQL instead of traditional chunk-based aggregation methods.
- Script GPT-5.4 mini Voice ElevenLabs
In this article, you will learn how to use scikit-LLM’s text summarization feature to handle large volumes of text in machine learning pipelines.
- Script Haiku 4 Voice Cartesia TTS
Learn how Symphony, an open-source spec for Codex orchestration, turns issue trackers into always-on agent systems—boosting engineering output and reducing context switching.
- Script Sonnet 4.6 Voice ElevenLabs
Enterprises are obsessing over model accuracy while ignoring the infrastructure layer where AI systems actually break.
- Script GPT-5.4 Voice Deepgram TTS
Compare model features, migration guidance, and prompting best practices across OpenAI models.
- Script GPT-5.4 Voice OpenAI TTS
DeepSeek's quest to keep frontier AI models open is of benefit to the entire planet of potential AI users, especially enterprises looking to adopt the cutting-edge at the lowest possible cost.
- Script Haiku 4 Voice Deepgram TTS
[]( [](LICENSE) []( [Docs](
- Script GPT-5.4 Voice Deepgram TTS
A package exposing an MCP server that launches Claude, Codex, Gemini, Forge, and OpenCode as background jobs, tracking processes for parallel coding tasks.
- Script GPT-5.4 Voice OpenAI TTS
Testing 180 configurations across five architectures, research shows multi-agent coordination helps only on decomposable tasks, worsens sequential planning, and can amplify errors up to 17.2x.
- Script Sonnet 4.6 Voice Murf.AI Gen2
By combining the efficiency of a Mixture-of-Experts architecture with the openness of an Apache 2.0 license, OpenAI is providing a way for many enterprises to more easily, cheaply and safely redact PII data.
- Script Sonnet 4.6 Voice Inworld TTS 1.5 Max
Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale. We argue that what is needed is not just a dataset, but an automated pipeline capable of generating diverse, verified environments on demand. To this end, we introduce ClawEnvKit, an autonomous generation pipeline that instantiates this formalism from natural language descriptions. The pipeline comprises three modules: (1) a parser that extracts structured
- Script GPT-5.4 Voice Deepgram TTS
Contribute to dpaul0501/panini development by creating an account on GitHub.
- Script GPT-5.4 mini Voice Cartesia TTS
Inline review comments for markdown specs. Built-in MCP server hands feedback directly to your AI agent. - dejuknow/md-redline
- Script GPT-5.4 Voice Deepgram TTS
Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains less understood. We introduce "Mind's Eye", a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel "A-R-T" taxonomy: Abstraction, Relation, and Transformation. The tasks probe core processes of fluid intelligence such as pattern induction,
- Script GPT-5.4 Voice Inworld TTS 1.5 Max
Language-model agent systems commonly rely on reactive prompting, in which a single instruction guides the model through an open-ended sequence of reasoning and tool-use steps, leaving control flow and intermediate state implicit and making agent behavior potentially difficult to control. Orchestration frameworks such as LangGraph, DSPy, and CrewAI impose greater structure through explicit workflow definitions, but tightly couple workflow logic with Python, making agents difficult to maintain
- Script GPT-5.4 Voice Inworld TTS 1.5 Mini
Why we need collaborative AI engineering
- Script GPT-5.4 mini Voice Inworld TTS 1.5 Max
Moonshot AI's Kimi K2.6 can run agents for days without human intervention, exposing a critical gap in orchestration frameworks not built for continuous, stateful execution.
- Script GPT-5.4 Voice Inworld TTS 1.5 Max
Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse. In this work, we introduce LeWorldModel (LeWM), the first JEPA that trains stably end-to-end from raw pixels using only two loss terms: a next-embedding prediction loss and a
- Script GPT-5.4 Voice OpenAI TTS
From rank-stabilized scaling to quantization stability: A statistical and architectural deep dive into the optimizations powering modern Transformers.
- Script GPT-5.4 Voice ElevenLabs
Moonshot AI and Tsinghua Researchers Propose PrfaaS: A Cross-Datacenter KVCache Architecture that Rethinks How LLMs are Served at Scale
- Script GPT-5.4 Voice ElevenLabs
Moonshot AI’s Kimi K2.6 arrives at a convenient moment for agent builders: it is open, it is strong on coding benchmarks, and it treats multimodality as part of the main model rather than a side branch.
- Script GPT-5.4 Voice ElevenLabs
Even as frontier models from US keep getting better, Chinese open-source is more than keeping up. Moonshot AI, the Beijing-based startup behind the...
- Script GPT-5.4 Voice ElevenLabs
A mental model for building trust in coding agents through feedforward guides, feedback sensors, and iterative harness engineering.
- Script GPT-5.4 Voice ElevenLabs
By Ryan Lopopolo, Member of the Technical Staff
- Script GPT-5.4 Voice ElevenLabs
OpenClaw and Hermes Agent take different approaches to persistent AI coding assistants. One prioritizes ecosystem reach, the other deep learning over time.
- Script GPT-5.4 Voice ElevenLabs
Inference caching reduces latency and cost by storing and reusing computation from previous LLM requests instead of recomputing everything each time. It operates across three complementary layers: KV caching within a request, prefix caching across shared prompts, and semantic caching that reuses full responses for similar queries.
- Script GPT-5.4 Voice ElevenLabs
Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on reward engineering or data synthesis, few studies exploit the model's intrinsic representation characteristics to guide the training process. In this paper, we first observe the presence of high-magnitude activations within the query and key vectors when processing long contexts. Drawing inspiration from model
- Script GPT-5.4 Voice ElevenLabs
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like Qwen3-8B, this approach often fails to improve reasoning capabilities and can even lead to a substantial drop in performance. In this work, we identify substantial stylistic divergence between teacher generated data and the distribution of student as a major factor impacting SFT. To bridge this gap, we propose a
- Script GPT-5.4 Voice ElevenLabs
Anthropic launched Claude Design, an AI tool that turns text prompts into interactive prototypes, alongside its most powerful public model, Claude Opus 4.7 — directly challenging Figma and signaling the company's shift from AI lab to full-stack product company.
- Script Llama 3.3 70B Voice Google TTS
Cloudflare has launched a new Model Context Protocol (MCP) server powered by Code Mode, enabling AI agents to interact with large APIs with minimal token usage. The server reduces context footprint across 2,500+ endpoints, improves multi-API orchestration, and provides a secure, code-centric execution environment for LLM agents.
- Script Llama 3.3 70B Voice Google TTS
<img alt="Build status"
- Script Llama 3.3 70B Voice Google TTS
⚡ SigMap WITHOUT SIGMAP, YOUR AI IS GUESSING. Without structured context, AI often reads the wrong file and fills the gaps with guesses. Run one command. Force every answer to come from real code. <img src="docs/impact-banner.svg" alt="SigMap — grounded AI coding context with fewer prompts and
- Script Llama 3.3 70B Voice Google TTS
During model distillation, large language models can subtly transmit traits unrelated to the training data.
- Script Llama 3.3 70B Voice Google TTS
Outshift by Cisco's Vijoy Pandey argues AI agents can connect but can't yet think together — and is building the protocols to close that gap.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Bring Augment's Context Engine to any MCP-compatible coding agent. Works with Claude Code, Cursor, Zed, GitHub Copilot, and more. 62% code quality improvement.
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Tasked with stocking a vending machine, Claude did not demonstrate any particular acuity for running a business.
- Script Llama 3.3 70B Voice Google TTS
While Large Language Models (LLMs) can exhibit impressive proficiency in isolated, short-term tasks, they often fail to maintain coherent performance over longer time horizons. In this paper, we present Vending-Bench, a simulated environment designed to specifically test an LLM-based agent's ability to manage a straightforward, long-running business scenario: operating a vending machine. Agents must balance inventories, place orders, set prices, and handle daily fees - tasks that are each
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Andon Labs develops custom evaluations for AI models
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In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama.
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Databricks research tested a stronger foundation model against its multi-step Supervisor Agent on hybrid data queries spanning SQL and unstructured docs. The model lost by up to 38%, pointing to an architecture problem, not a model quality problem.
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Why storing and retrieving data isn’t enough to build reliable AI memory systems
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MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search
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Partnership brings subscription tooling into the app-building process, allowing creators to add pricing and paywalls through simple prompts
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Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way. Deep Agents deploy is built for an open world. It’s built on Deep Agents - an open source, model
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We're bringing the advisor strategy to the Claude Platform. Pair Opus as an advisor with Sonnet or Haiku as an executor, and get near Opus-level intelligence in your agents at a fraction of the cost.
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alright agent nerds, if you care about your tokens and usage limits, pay attention to the tools you give to your agents. i built a benchmark that compared various browser tools for agents, and here's an example of their massive difference in cost and latency doing the same task
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TL;DR: We can build better agents by building better harnesses. But to autonomously build a “better” harness, we need a strong learning signal to “hill-climb” on. We share how we use evals as that
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EDB's Gabriele Bartolini explains how Kubernetes-powered PostgreSQL enables sovereign DBaaS, giving enterprises cloud-neutral portability and bare-metal speed.
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Memento-Skills lets AI agents rewrite their own skills using reinforcement learning, hitting 80% task success vs. 50% for standard RAG retrieval.
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If a model can work for eight hours without human intervention, it fundamentally changes the software development lifecycle.
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AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources that often contradict one another, new information can invalidate earlier conclusions, and user preferences surface through corrections rather than explicit instructions. Existing benchmarks largely assume static, single-authority settings and do not evaluate whether agents can keep up with this complexity. We
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Meet AutoKernel: an open-source framework that applies an autonomous agent loop to GPU kernel optimization for arbitrary PyTorch models
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llm-wiki. GitHub Gist: instantly share code, notes, and snippets.
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@heygurisingh Before you fomo install, know that for most projects that arent massive, simply having CLAUDE.md in every folder is better than adding another dependancy you have to remember to run (GitNexus) Simpler alternative:
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Video by Nate Herk | AI Automation
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Most discussions of continual learning in AI focus on one thing: updating model weights. But for AI agents, learning can happen at three distinct layers: the model, the harness, and the context. Understanding the difference changes how you think about building systems that improve over time. The three main layers
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Quickstart · Docs · GitHub · Discord <a
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pgEdge launches a production-ready MCP Server for Postgres, bringing AI agent connectivity, schema introspection, and reduced token usage to any Postgres database.
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A large language model learns emotion concepts during pretraining that later shape its behavior as an AI Assistant, producing functional emotions—human-like expressive patterns without implying subjective experience.
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by @ornelladotcom Traditional product development follows a well-worn loop. This works when output is deterministic. Write a spec, build to spec, verify against spec. But AI output is
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Video by LangChain
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Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirmative with simple self-distillation (SSD): sample solutions from the model with certain temperature and truncation configurations, then fine-tune on those samples with standard supervised fine-tuning. SSD improves Qwen3-30B-Instruct from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on harder
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GitHub - kwstx/engram_translator: layer that lets you connect any agent, any tool, any api together.
Script GPT-5.4 mini Voice Inworld TTS 1.5 Max layer that lets you connect any agent, any tool, any api together. - kwstx/engram_translator
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Apple Silicon Macs get a performance boost thanks to better unified memory usage.
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Capturing tribal knowledge organically and creating a living metadata store that informs every AI interaction with company-specific reasoning.
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A Reddit thread asks how to make AI-generated text sound more human, prompting discussion of why transformer models produce polished, robotic writing and how to counteract it.
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A breakdown of reverse-engineered Claude Code prompts reveals patterns like negative rules, risk-tiered permissions, dedicated verification agents, and structured 9-section memory for reliable AI agents.
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AI proxy that routes LLM calls to the cheapest suitable model, tracks costs in real time, and enforces budget policies. Cut AI spend by up to 60%.
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Radically Innovative AI Agent. Free and Open Source Forever. - temm1e-labs/temm1e
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Practical techniques for steering GPT-5.4 toward polished, production-ready frontend designs.
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Claude Code Python Porting Workspace > The primary `src/` tree in this repository is now dedicated to **Python porting work**. The March 31, 2026 Claude Code source exposure is part of the project's background, but the tracked repository is now centered on Python source rather than the exposed TypeScript snapshot. --- Porting Status The main source tree is now Python-first. - `src/` contains the active Python porting workspace - `tests/` verifies the current Python workspace - the
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A developer details Phantom, an open-source Claude-based agent with persistent vector memory and self-evolution that autonomously builds infrastructure like ClickHouse dashboards and Discord bots.
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It's easier than ever to 10x your output with agentic AI.
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Agent performance increasingly depends on \emph{harness engineering}, yet harness design is usually buried in controller code and runtime-specific conventions, making it hard to transfer, compare, and study as a scientific object. We ask whether the high-level control logic of an agent harness can instead be externalized as a portable executable artifact. We introduce \textbf{Natural-Language Agent Harnesses} (NLAHs), which express harness behavior in editable natural language, and
- Script Sonnet 4.5 Voice Google TTS
In this article, you will learn how vector databases work, from the basic idea of similarity search to the indexing strategies that make large-scale retrieval practical.
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Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively harder to extend. Recent iterative benchmarks attempt to close this gap, but constrain the agent's design decisions too tightly to faithfully measure how code quality shapes future extensions. We introduce SlopCodeBench, a language-agnostic benchmark comprising 20 problems and 93 checkpoints, in
- Script Sonnet 4.5 Voice ElevenLabs
Agoda recently published an observation arguing that while AI coding tools have measurably raised individual developer output, the resulting velocity gains at the project level have been surprisingly modest, because coding was never the real bottleneck. The post claims that the bottleneck has shifted upstream to specification and verification because these areas require human judgment.
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Cloudflare says dynamically loaded Workers are priced at $0.002 per unique Worker loaded per day, in addition to standard CPU and invocation charges
- Script Sonnet 4.5 Voice ElevenLabs
An AI agent reads its own source code, forms a hypothesis for improvement (such as changing a learning rate or an architecture depth), modifies the code, runs the experiment, and evaluates the results.
- Script Sonnet 4.5 Voice ElevenLabs
autoresearch *One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other fun, and synchronizing once in a while using sound wave interconnect in the ritual of "group meeting". That era is long gone. Research is now entirely the domain of autonomous swarms of AI agents running across compute cluster megastructures in the skies. The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's
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In this article, you will learn how to design, implement, and evaluate memory systems that make agentic AI applications more reliable, personalized, and effective over time.
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Meet GitAgent: The Docker for AI Agents that is Finally Solving the Fragmentation between LangChain, AutoGen, and Claude Code
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AI agents fail in production for predictable reasons: fragmented data, undefined workflows, and runaway escalation. Burley Kawasaki of Creatio outlines three disciplines — data virtualization, bounded use-case loops, and agent monitoring with real KPIs — that enterprise teams are using to reach 80–90% agent autonomy without multi-year data overhauls.
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Persona prompting can steer LLM generation towards a domain-specific tone and pattern. This behavior enables use cases in multi-agent systems where diverse interactions are crucial and human-centered tasks require high-level human alignment. Prior works provide mixed opinions on their utility: some report performance gains when using expert personas for certain domains and their contribution to data diversity in synthetic data creation, while others find near-zero or negative impact on general
- Script Sonnet 4.5 Voice ElevenLabs
Ai2's MolmoWeb is the first open-weight visual web agent to ship with its full training dataset, giving enterprise teams the ability to audit, reproduce and fine-tune a browser agent without a per-call API dependency.
- Script Sonnet 4.5 Voice ElevenLabs
Efficient reasoning in language models is reformulated as a lossy compression problem using conditional information bottleneck to reduce cognitive overhead while maintaining performance.
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Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin Gödel Machine (DGM) demonstrates open-ended self-improvement in coding by repeatedly generating and evaluating self-modified variants. Because both evaluation and self-modification are coding
- Script Sonnet 4.5 Voice OpenAI TTS
MiMo-V2-Pro utilizes a 7:1 hybrid ratio (increased from 5:1 in the Flash version) to manage its massive 1M-token context window.
- Script Sonnet 4.5 Voice OpenAI TTS
This blog post explores how six key protocols, including MCP and A2A, simplify AI agent development by replacing custom integration code with standardized communication patterns. Learn how to use the Agent Development Kit (ADK) to build complex agents capable of managing real-time inventory, secure commerce via UCP/AP2, and interactive streaming interfaces. Discover how adopting these architectural standards creates more scalable, interoperable, and user-friendly AI solutions.
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While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions. Unlike mathematical reasoning where errors are often rectifiable via backtracking, tool-use failures frequently induce irreversible side effects, making accurate step-level verification critical. However, existing process-level benchmarks are predominantly confined to closed-world mathematical domains, failing to capture the dynamic and open-ended nature of tool execution.
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An agentic skills framework & software development methodology that works - CC task management support - pcvelz/superpowers
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Dynatrace experts will share AI-powered Kubernetes observability best practices for managing rising K8s complexity, security, and toolchain consolidation in 2026.
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This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.
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Z.ai says GLM-5-Turbo is currently closed-source, but it also says the model’s capabilities and findings will be folded into its next open-source model release
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Testing Confirms 10.2x Faster Response Times, Exceeding Cloud-Hosted Alternatives SAN JOSE, Calif.--(BUSINESS WIRE)--March 17, 2026-- NVIDIA GTC 202
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An open-source framework enforces four safety laws via a Go MCP server and risk-based decision matrices, cutting AI-caused coding incidents by 78%.
- Script Sonnet 4.5 Voice OpenAI TTS
The AI memory debate is flawed. Discover why top teams decouple filesystem interfaces from database storage.
- Script Sonnet 4.5 Voice OpenAI TTS
Instead of one central AI system doing everything, the model emerging here is many bounded agents operating across teams, channels and tasks.
- Voice OpenAI TTS
Meta description (SEO/AEO) FriendliAI — founded by the researcher behind continuous batching, the technique at the core of vLLM — is launching InferenceSense, a platform that fills idle neocloud GPU capacity with paid AI inference workloads and splits the token revenue with operators. The company claims 2–3x the token throughput of a standard vLLM deployment.
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Qdrant's $50M Series B and version 1.17 release make the case that agentic AI didn't simplify vector search — it scaled the retrieval problem up. Here's what production deployments from GlassDollar and &AI reveal about when purpose-built retrieval becomes necessary.
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MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants
Script Sonnet 4.5 Voice OpenAI TTS With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynamic, interactive HTML-based applications, which we term MiniApps. These applications require models to not only render visual interfaces but also construct customized interaction logic that adheres to real-world principles. However, existing benchmarks primarily focus on algorithmic correctness or static layout reconstruction, failing to capture the
- Script Sonnet 4.5 Voice OpenAI TTS
Galileo releases Agent Control, an open source control plane for governing AI agents at scale. AWS, CrewAI, and Glean are among the first partners.
- Script Sonnet 4.5 Voice OpenAI TTS
LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs. Typically, this input-output is addressed by training embedding models with paired data using contrastive learning. In this work, we propose a novel self-supervised approach, LLM2Vec-Gen, which adopts a different paradigm: rather than encoding the input, we learn to represent the model's potential response. Specifically, we add trainable
- Script Sonnet 4.5 Voice OpenAI TTS
Engineers at Netflix have uncovered deep performance bottlenecks in container scaling that trace not to Kubernetes or containerd alone, but into the CPU architecture and Linux kernel itself.
- Script Sonnet 4.5 Voice OpenAI TTS
While large language models (LLMs) exhibit strong reasoning abilities, their performance on complex tasks is often constrained by the limitations of their internal knowledge. A compelling approach to overcome this challenge is to augment these models with external tools -- such as Python interpreters for mathematical computations or search engines for retrieving factual information. However, enabling models to use these tools effectively remains a significant challenge. Existing methods
- Script Sonnet 4.5 Voice OpenAI TTS
Episode 218 dives into CodeSpeak, a new spec-driven programming language from Kotlin's creator Andrey Breslav.
- Script Sonnet 4.5 Voice OpenAI TTS
Agent Identity → Secure ADK agents with Secret Manager → Logging an agent → Aron demonstrates a critical step for deploying an ADK agent that uses Google Maps tool to help users. Learn how to replace an insecure pattern with a Secret Manager. A secure and convenient storage system for API keys, passwords, and other sensitive data. Chapters: 0:00 - Intro 0:29 - Service accounts vs. agent identity 1:13 - Using Secret Manag
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Google finds AI agents learn to cooperate when trained against unpredictable opponents
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In this work, we investigate how Large Language Models (LLMs) adapt their internal representations when encountering inputs of increasing difficulty, quantified as the degree of out-of-distribution (OOD) shift. We reveal a consistent and quantifiable phenomenon: as task difficulty increases, whether through harder reasoning questions, longer contexts, or adding answer choices, the last hidden states of LLMs become substantially sparser. In short, \textbf{\textit{the farther the shift, the
- Script Sonnet 4.5 Voice OpenAI TTS
To act autonomously and effectively, AI agents need optimized, AI-ready processes and the process data and operational context that only comes from process intelligence. Without that, they’re guessing.
- Script Sonnet 4.5 Voice ElevenLabs
Why traditional RAG loses context and how contextual retrieval dramatically improves retrieval accuracy
- Script Sonnet 4.5 Voice ElevenLabs
Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → Learn more about Retrieval Augmented Generation (RAG) here → Are massive context windows replacing RAG? 🤔 Martin Keen breaks down RAG vs. long context in LLM workflows. Explore how vector databases, semantic search, and embedding models impact AI performance to help you choose the right solution for your applications. 🚀 AI
- Script Sonnet 4.5 Voice ElevenLabs
MIT researchers developed Attention Matching, a KV cache compaction technique that compresses LLM memory by 50x in seconds — without the hours of GPU training that prior methods required.
- Script Sonnet 4.5 Voice ElevenLabs
Use Codex and Figma to bring real, running interfaces into Figma, refine them, and bring changes back to Codex.
- Script Sonnet 4.5 Voice ElevenLabs
Organization and enterprise administrators can now programmatically manage Copilot content exclusion rules using the new Content Exclusion REST API. This JSON API is available in public preview and supports GET…
- Script Sonnet 4.5 Voice ElevenLabs
Guidde already claims 4,500 enterprise customers and seeks to expand this number with its new round of funding.
- Script Sonnet 4.5 Voice OpenAI TTS
A new method can test whether a large language model contains hidden biases, personalities, moods, or other abstract concepts. MIT researchers can zero in on connections within a model that encode for a concept of interest, to improve LLM safety and performance.
- Voice OpenAI TTS
AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamental limitation of current evaluations: compressing agent behavior into a single success metric obscures critical operational flaws. Notably, it ignores whether agents behave consistently across runs, withstand perturbations, fail predictably, or have bounded error
- Script Sonnet 4.5 Voice OpenAI TTS
By Jacob Talbot Agent Builder gets better the more you use it because it remembers your feedback. Every correction you make, preference you share, and approach that works well is something that your agent can hold onto and apply the next time. Memory is one of the things that makes
- Script Sonnet 4.5 Voice OpenAI TTS
Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced between "learning-aware" agents that account for and shape the learning dynamics of their co-players. However, existing approaches typically rely on hardcoded, often inconsistent, assumptions about co-player learning rules or enforce a strict separation between "naive learners" updating on fast timescales and
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Learn about new Model Context Protocol (MCP) servers for AlloyDB, Spanner, Cloud SQL, Firestore and Bigtable, as well one for Developer Knowledge.
- Voice OpenAI TTS
A new group-evolving agent framework from UC Santa Barbara matches human-engineered AI systems on SWE-bench — and adds zero inference cost to deploy. Here's how it works.
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TLDR: Our coding agent went from Top 30 to Top 5 on Terminal Bench 2.0. We only changed the harness. Here’s our approach to harness engineering (teaser: self-verification & tracing help a lot). The Goal of Harness Engineering The goal of a harness is to mold the inherently spiky
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this is so good man
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people underestimate the power of structured knowledge. it enables entirely new kinds of applications right now people write skills that capture one aspect of something. a skill for summarizing, a
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Becoming a 10X engineer ain't what it used to be. It's literally a file. Want help?
- Script GPT-OSS 20B Voice ElevenLabs v3
LLMs process text from left to right — each token can only look back at what came before it, never forward. This means that when you write a long prompt with context at the beginning and a question at the end, the model answers the question having "seen" the context, but the context tokens were generated without any awareness of what question was coming. This asymmetry is a basic structural property of how these models work. The paper asks what happens if you just send the prompt twice in a
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Already over 150 stars. Crazy!
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Figma just closed the last excuse PMs had for not shipping polished UI from AI code. The loop is now complete. Claude Code generates UI. It goes straight into Figma as editable frames. Designers tweak it. Figma MCP sends it back to Claude Code. The entire design-to-engineering handoff cycle that used to take 2-3 weeks now runs in a single session. This tells you something about where the real constraint in product development has been. PMs always said the bottleneck was getting designs into
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Compare seven small language models for local deployment with hardware requirements and specific use cases.
- Script Sonnet 4.5 Voice OpenAI TTS
SurrealDB 3.0 launches with $23M in new funding and a pitch to replace multi-database RAG stacks with a single engine that handles vectors, graphs, and agent memory transactionally.
- Script Sonnet 4.5 Voice OpenAI TTS
openclaw with ollama (Zero cost AI Assistant). GitHub Gist: instantly share code, notes, and snippets.
- Voice OpenAI TTS
OpenAI has recently published a detailed architecture description of the Codex App Server, a bidirectional protocol that decouples the Codex coding agent
- Script Sonnet 4.5 Voice OpenAI TTS
❤️ Check out Lambda here and sign up for their GPU Cloud: 📝 The paper is available here: Our Patreon if you wish to support us: 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundval
- Script Sonnet 4.5 Voice OpenAI TTS
NanoClaw, a secure AI assistant by Gavriel Cohen, surpasses 7,000 stars on GitHub. It offers a minimalistic, auditable framework with isolated Linux containers.
- Voice OpenAI TTS
In the current landscape of Large Language Models (LLMs), the curation of large-scale, high-quality training data is a primary driver of model performance. A key lever is the \emph{data recipe}, which comprises a data processing pipeline to transform raw sources into training corpora. Despite the growing use of LLMs to automate individual data processing steps, such as data synthesis and filtering, the overall design of data recipes remains largely manual and labor-intensive, requiring
- Script Sonnet 4.5 Voice ElevenLabs
A Blog post by MiniMax on Hugging Face
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z.ai's GLM-5 uses a novel 'slime' reinforcement learning technique to achieve record-low hallucination rates, scaling to 744B parameters while undercutting rivals 6x on price.
- Script Sonnet 4.5 Voice ElevenLabs
Google and Microsoft's new WebMCP standard lets websites expose callable tools to AI agents through the browser — replacing costly scraping with structured function calls.
- Script Sonnet 4.5 Voice ElevenLabs
MiniMax's M2.5 language model, open-sourced on Hugging Face, reduces AI costs by 95% while matching top-tier models like Claude Opus 4.6, transforming AI from chatbots to autonomous agents.
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Common recipes to run vLLM. Contribute to vllm-project/recipes development by creating an account on GitHub.
- Script Sonnet 4.5 Voice ElevenLabs
MIT researchers unveil a new fine-tuning method that lets enterprises consolidate their "model zoos" into a single, continuously learning agent.
- Script Sonnet 4.5 Voice ElevenLabs
OpenAI's Responses API update introduces Server-side Compaction, Hosted Shell Containers, and Skills, enhancing agent reliability and long-term utility. Triple Whale's agent Moby successfully managed 5 million tokens, showcasing improved stability.
- Script Sonnet 4.5 Voice ElevenLabs
Generative Reward Models (GenRMs) and LLM-as-a-Judge exhibit deceptive alignment by producing correct judgments for incorrect reasons, as they are trained and evaluated to prioritize Outcome Accuracy, which undermines their ability to generalize during RLHF. We introduce Rationale Consistency, a fine-grained metric that quantifies the alignment between the model's reasoning process and human judgment. Our evaluation of frontier models reveals that rationale consistency effectively discriminates
- Script Sonnet 4.5 Voice ElevenLabs
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
- Script Sonnet 4.5 Voice ElevenLabs
Alibaba Open-Sources Zvec: An Embedded Vector Database Bringing SQLite-like Simplicity and High-Performance On-Device RAG
- Script Sonnet 4.5 Voice ElevenLabs
As AI agents move into production, teams are rethinking memory. Mastra’s open-source observational memory shows how stable context can outperform RAG while cutting token costs.
- Script Sonnet 4.5 Voice ElevenLabs
Alexander Embiricos (a Product Manager on the Codex team) shows how he uses Codex skills to make a small product change, diagnose a Buildkite failure, and improve the skills so the next PR goes faster. Takeaways: - Skills are a shortcut for repeated workflows like Buildkite logs. - When a skill fails, fix the root cause and update the skill. - The real win is compounding: the codebase gets easier over time. This is the loop: ship the fix, then teach the workflow. Chapters: 00:00 PM context: c
- Script Sonnet 4.5 Voice ElevenLabs
Multi-agent pipeline coordination: Linear, DAG, and Debate modes for AI agent orchestration - win4r/team-tasks
- Script Sonnet 4.5 Voice ElevenLabs
Moca has open-sourced Agent Definition Language (ADL), a vendor-neutral specification intended to standardize how AI agents are defined, reviewed, and governed across frameworks and platforms. The project is released under the Apache 2.0 license and is positioned as a missing “definition layer” for AI agents, comparable to the role OpenAPI plays for APIs.
- Script GPT-4o mini Voice OpenAI TTS
Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work, we formulate visual reasoning by means of video generation models, positing that generated frames can act as intermediate reasoning steps between initial states and solutions. We evaluate their capacity in two distinct regimes: Maze Navigation for sequential
- Script GPT-4o mini Voice OpenAI TTS
Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reducing reliance on human intervention. We introduce Group-Evolving Agents (GEA), a new paradigm for open-ended self-improvements, which treats a group of agents as the fundamental evolutionary unit, enabling explicit experience sharing and reuse within the group throughout evolution. Unlike existing open-ended
- Script GPT-4o mini Voice OpenAI TTS
Flox has simplified Nix enough to position it as a Docker replacement on Kubernetes, offering finer dependency management.
- Script GPT-4o mini Voice OpenAI TTS
LLMOps Part 7: A conceptual overview of context engineering, covering context types, context construction principles, and retrieval-centric techniques for building high-signal inputs.
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A developer describes building a complete pixel-art open-world shooter in 24 hours, using Gemini 3.0 Pro for coding and art, vanilla JS, and structured documentation workflows.
- Script GPT-4o mini Voice OpenAI TTS
--- agent-device CLI to control iOS an
- Script GPT-4o mini Voice OpenAI TTS
Unrestrained use of MCP can quickly flood context windows. Experts share ten practical techniques to rein it in.
- Script GPT-4o mini Voice OpenAI TTS
Reinforcement World Model Learning for LLM-based Agents Xiao Yu Baolin Peng Ruize Xu Yelong Shen Pengcheng He Suman Nath Nikhil Singh Jiangfeng Gao Zhou Yu Abstract Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and adapt to environment dynamics, highlighting the need for world-modeling capabilities in LLM-based agents. We propose Reinforcement World Model Learning
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This episode explores the emergence of LTM, a new type of AI that promises capabilities beyond traditional LLMs, addressing their limitations and offering innovative solutions in real-world applications.
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Guide to run Qwen3-Coder-Next locally on your device!
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Self-Hinting Language Models Enhance Reinforcement Learning Baohao Liao Hanze Dong Xinxing Xu Christof Monz Jiang Bian Abstract Group Relative Policy Optimization (GRPO) has recently emerged as a practical recipe for aligning large language models with verifiable objectives. However, under sparse terminal rewards, GRPO often stalls because rollouts within a group frequently receive identical rewards, causing relative advantages to collapse and updates to vanish. We propose self-hint aligned
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Step-by-step guide to building autonomous memory retrieval systems
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LLMOps Part 6: Exploring prompt versioning, defensive prompting, and techniques such as verbalized sampling, role prompting and more.
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Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization Jiecong Wang 1 , Hao Peng 1 , Chunyang Liu 2 1 Beihang University, 2 Didi Chuxing {jcwang, penghao}@buaa.edu.cn , [email protected] Abstract Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and reasoning path collapse when grounded in discrete token spaces. Recent latent reasoning approaches attempt to optimize efficiency by
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OpenAI has released a new macOS app for Codex, integrating many of the agentic coding practices that have become popular since Codex launched last year.
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Agent Trace **Version**: 0.1.0 **Status**: RFC **Date**: January 2026 Abstract Agent Trace is an open specification for tracking AI-generated code. It provides a vendor-neutral format for recording AI contributions alongside human authorship in version-controlled codebases. Table
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\correspondingauthor [email protected] \reportnumber Linear representations in language models can change dramatically over a conversation Andrew Kyle Lampinen Google DeepMind Yuxuan Li Google DeepMind Eghbal Hosseini Google DeepMind Sangnie Bhardwaj Google DeepMind Murray Shanahan Google DeepMind Abstract Language model representations often contain linear directions that correspond to high-level concepts. Here, we study the dynamics of these representations: how representations evolve along
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Moltworker is a middleware Worker and adapted scripts that allows running Moltbot (formerly Clawdbot) on Cloudflare
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Open Claude Cowork </a
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Thank you for releasing this model to the public, dear Moonshot AI!
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The Qoder team recounts an AMA detailing how they let their AI agent Quest autonomously refactor its own code for 26 hours after setting initial specs.
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What could go wrong, or right?
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Feature: Developer behind it is sick with worry he might have changed software development in nasty ways
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Anthropic is turning Claude into an app platform, with interactive widgets from Slack, Figma, Asana, and others.
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In the agentic era, older AI developer terms like RAG and prompt engineering have fallen out of use. Now it's all about MCP and context engineering.
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If you’re reading this, you probably have some fondness for human-crafted language. After all, you’ve taken the time to navigate to Hackaday and read this, rather than ask your favoured…
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LLMOps Part 5: An introduction to prompt engineering (a subset of context engineering), covering prompt types, the prompt development workflow, and key techniques in the field.
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The uncomfortable truth: “model choice” is half your prompt engineering If your prompt is...
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VS Code now supports MCP Apps, enabling AI agents to display interactive UIs for richer developer workflows.
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Conversational AI doesn’t understand users — 'Intent First' architecture does Sreenivasa Reddy Hulebeedu Reddy January 25, 2026 Midjourney/VentureBeat The modern customer has just one need that matters: Getting the thing they want when they want it . The old standard RAG model embed+retrieve+LLM misunderstands intent, overloads context and misses freshness, repeatedly sending customers down the wrong paths. Instead, intent-first architecture uses a lightweight language model to parse the query
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Add expert SwiftUI Best Practices guidance to your AI coding tool (Agent Skills open format). - AvdLee/SwiftUI-Agent-Skill
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Drift uses Abstract Syntax Tree parsing to learn a codebase's unwritten patterns, cutting audit time and improving impact analysis, security auditing, and reliability for AI-assisted coding.
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By Bohan Zhang, Member of the Technical Staff
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FlashLabs Researchers Release Chroma 1.0: A 4B Real Time Speech Dialogue Model With Personalized Voice Cloning
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LLM-in-Sandbox enables large language models to perform general intelligence tasks across diverse domains by allowing them to explore a code sandbox environment, achieving robust generalization without additional training.
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Agent Sandbox Run AI coding agents in a locked-down local sandbox with: - Minimal filesystem access (only your repo + project-scoped agent state) - Restricted outbound network (iptables-based allowlist) - Reproducible environments (Debian container with pinned dependencies) Target platform: [Co
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Building AI today is about more than just a clever prompt. If you really want to move from playing with standalone tools to creating integrated systems that actually work with your data, our new crash course on the freeCodeCamp.org YouTube channel is...
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Anthropic is testing @ mentions for MCPs in Claude Cowork, hinting at possible UI widget support, plus improved chat search features.
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Agentic reasoning redefines large language models as autonomous agents capable of planning, acting, and learning through continuous interaction in dynamic environments across single-agent and multi-agent frameworks.
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A novel retriever training framework for agentic search that uses both local relevance and global answer correctness metrics with iterative optimization between the search agent and retriever.
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Numpy or SciKit-Learn might meet all your retrieval needs
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We've encapsulated 10+ years of React and Next.js optimization knowledge into react-best-practices, a structured repository optimized for AI agents and LLMs.
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I get asked all the time: "What's the actual difference between LangChain and LangGraph?" And...
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Member-only story Beyond Hybrid RAG That Actually Works: Vector + BM25 + GraphRAG + Reranking in Python (Full Code) Tarun Singh 9 min read · 2 days ago -- Share If you’re already using GraphRAG + Vector RAG , you’re ahead of most people. Press enter or click to view image in full size But you’ll still hit this painful truth in production: Vector search finds similar content, not always correct content. GraphRAG improves reasoning , but can miss exact facts (IDs, codes, clauses). Keyword search
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MAXS is a meta-adaptive reasoning framework for LLM agents that improves multi-tool reasoning through lookahead strategies and trajectory convergence mechanisms, balancing global effectiveness and computational efficiency.
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Press enter or click to view image in full size Glowing neural network brain connected to floating document icons representing project memory with bugs, decisions, and configuration files for qucik recall Build Your First Claude Code Agent Skill: A Simple Project Memory System That Saves Hours How a 300-line skill became my most-used productivity tool for AI-assisted development. Rick Hightower 28 min read · 2 days ago -- 1 Listen Share Picture this: It’s 11 PM on a Tuesday. You’re staring at
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Member-only story Vector Database vs Graph Database for RAG: Similarity vs Understanding Khushbu Shah 8 min read · 2 days ago -- 2 Share Why do most RAG systems retrieve words, but the best ones retrieve meaning? AI systems do not fail because the model is weak, but they fail because the context is wrong. Recent research on retrieval-augmented generation shows that when RAG systems hallucinate, the root cause is usually insufficient, missing, or irrelevant retrieved context, not the language
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They’re the mysterious numbers that make your favorite AI models tick. What are they and what do they do?
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NextFlow🚀: Unified Sequential Modeling Activates Multimodal Understanding and Generation - ByteVisionLab/NextFlow
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@jayagup10 and @ashugarg’s recent piece on context graphs went viral for good reason. The core thesis that the next wave of enterprise platforms will capture decision traces, not just data, struck a
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Researchers have proposed a unifying mathematical framework that helps explain why many successful multimodal AI systems work.
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Ready to become a certified watsonx Data Scientist - Associate? Register now and use code IBMTechYT20 for 20% off of your exam → Learn more about AI Frameworks here → What if AI had its own periodic table? 🧩 Martin Keen introduces the AI Periodic Table, breaking down LLMs, RAG, AI agents, and frameworks into a clear, simple structure. Discover how these elements connect to power smarter, scalable AI systems, and rethink how AI fits together. AI n
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...with hands-on implementation.
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Member-only story 🚀 WebGPU Changed How I Think About Web Performance Why a simple GPU rewrite beat WebAssembly by 23× in real-world workloads Xiuer Old 4 min read · 3 days ago -- Share Press enter or click to view image in full size I didn’t expect this result. Honestly, I thought I had messed something up. I was optimizing a web app that visualizes tens of thousands of data points . At around 50,000 points, the UI turned into a slideshow 🫠 So I did what any performance-aware web developer
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A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows - ComposioHQ/awesome-claude-skills
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Large Language Models (LLMs) are increasingly deployed in time-critical systems, such as robotics, autonomous driving, embodied intelligence, and industrial automation, where generating accurate responses within a given time budget is crucial for decision-making, control, or safety-critical tasks. H
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In this article, I’ll walk you through a complete guide to LangGraph from the ground up. LangGraph Explained from Scratch.
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Early Knowledge Alignment improves retrieval and reasoning in iterative RAG systems by aligning LLMs with relevant knowledge before planning, enhancing performance and efficiency.
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How to build agents that are not only capable, but learn and improve over time.
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In this episode, we dive into the implications of a recent Twitter thread discussing a novel approach to AI ethics that could reshape the tech landscape.
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Did Skills Kill MCP? December 22, 2025 · 4 min read Angie Jones Head of Developer Relations Every time there's a hot new development in AI, Tech Twitter™ declares a casualty. This week's headline take is "Skills just killed MCP" It sounds bold. It sounds confident. It's also wrong. Saying skills killed MCP is about as accurate as saying GitHub Actions killed Bash. Of course, that's not true. Bash is still very much alive, and in fact, doing the actual work. What GitHub Actions changed was
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Attackers are exploiting a Flight protocol validation failure that allows them to execute arbitrary code without authentication.
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How I reclaimed gigabytes of Docker space with a simple app.
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100+ RAG interview questions with answers. Contribute to KalyanKS-NLP/RAG-Interview-Questions-and-Answers-Hub development by creating an account on GitHub.
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Academic research shows how LLM smart contract auditing improves vulnerability detection by combining fine tuned models with ensemble voting.
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This paper presents a framework for agent and tool adaptation in agentic AI systems, clarifying design strategies and identifying open challenges for improving AI capabilities.
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The effectiveness of AI debugging follows a predictable exponential decay pattern; most models lose 60-80% of their debugging capability within just 2-3 attempts, despite iterative debugging being a critical capability for practical code generation systems. We introduce the Debugging Decay Index (DD
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AuditDM, an automated framework using reinforcement learning, identifies and rectifies failure modes in multimodal LLMs by generating challenging examples, leading to improved performance across benchmarks.
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In this episode, we explore the significance of Reddit as a central hub for internet discourse and innovation.
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Build powerful, autonomous multi-agent AI systems with Agent Development Kit (ADK) for TypeScript. A code-first, open-source framework.
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With 91% accuracy, open source Hindsight agentic memory provides 20/20 vision for AI agents stuck on failing RAG Sean Michael Kerner December 16, 2025 Credit: Image generated by VentureBeat with NanoBanana-Pro It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data requirements for agentic AI. RAG emerged in the last couple of years to become the default approach for connecting LLMs to external knowledge. The pattern is
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Explores how iterative debugging causes context pollution in ChatGPT, degrading reasoning by up to 80%, and proposes resetting chats with stateless prompts to restore performance.
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Introducing React Compiler 1.0, a game-changing tool that automates optimization for React apps, enhancing performance by up to 12% for faster loads and 2.5x quicker interactions. Compatible with major frameworks and battle-tested at Meta, it simplifies builds with integrated diagnostics. Experience seamless improvement without code rewrites, empowering developers to code smarter.
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Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → Learn more about Multi-Agent systems here → What happens when AI agents team up? ⚙️ Anna Gutowska explores multi‑agent systems powered by LLMs and machine learning to show how cooperation leads to smarter, scalable AI. Discover how collective agents learn, adapt, and solve complex problems together. AI news moves fast. Sign u
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Alexander Embiricos leads product on Codex, OpenAI’s powerful coding agent, which has grown 20x since August and now serves trillions of tokens weekly. Before joining OpenAI, Alexander spent five years building a pair programming product for engineers. He now works at the frontier of AI-led software development, building what he describes as a software engineering teammate—an AI agent designed to participate across the entire development lifecycle. *We discuss:* 1. Why Codex has grown 20x since
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Introduces AgentAudit, a middleware tool that checks AI-generated answers against source context and flags hallucinated responses before they reach users.
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Proposes giving game NPCs state vectors based on OCEAN/MBTI traits plus common-knowledge modeling, enabling realistic emotions, rumor spread, and social coordination between characters.
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Elevating the MCP server to a fully validated microservice is essential for advancing agent development from internal experiments to production-ready.
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If you’ve built an agent, you know that the delta between “it works on my machine” and “it works in production” can be huge. Traditional software assumes you mostly know the inputs and can define the outputs. Agents give you neither: users can say literally anything, and the space
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Google is rolling out managed MCP servers to make its services “agent-ready by design,” starting with Maps and BigQuery, aiming to simplify messy integrations and help AI agents use real tools.
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Scale Venture Partners led the Series A round.
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Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost
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Claude Code and Slack Category Product announcements Product Claude Code Date December 8, 2025 Reading time 5 min Share Copy link Today, we're introducing the ability to delegate tasks to Claude Code directly from Slack. Now in beta as a research preview, Claude makes it easy to move context from Slack conversations to coding sessions. From discussion to implementation The critical context around engineering work often lives in Slack, including bug reports, feature requests, and engineering
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Anthropic launches Claude Code in Slack, letting developers delegate coding tasks from chat threads. It's part of a shift toward AI-embedded collaboration that could reshape software workflows.
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For AI agents to work properly in automating white-collar tasks, the companies developing the agents and the companies running the enterprise apps those agents use will need to agree on technical standards for how these technologies connect to each other.Some leading companies are preparing to ...
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In the past year, we've seen rapid advancement of model intelligence and convergence on agent scaffolding. But there's still a gap: agents often lack the domain expertise and specialized knowledge needed for real-world work. We think Skills are the solution—a minimal form factor for packaging procedural knowledge that agents can dynamically load. It's a portable, composable approach to giving one agent capabilities across domains. In this talk, we'll share how we built Skills at Anthropic, the n
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MIT offshoot Liquid AI releases blueprint for enterprise-grade small-model training
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: Automated software keeps getting better at pilfering cryptocurrency
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Google and Anthropic approach LLMs differently The very different cultures of OpenAI's two most important rivals. Timothy B. Lee Dec 04, 2025 ∙ Paid 66 6 4 Share On Monday, OpenAI CEO Sam Altman declared a “code red” in the face of rising competition. The biggest threat was Google; monthly active users for Google’s Gemini chatbot grew from 450 million in July to 650 million in November (ChatGPT had 800 million weekly active users in October). Meanwhile, the Wall Street Journal reports , “OpenAI
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Pydantic helps ensure LLM outputs follow the structure your application expects.This article outlines practical methods for modeling, parsing, and validating results.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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We’re sharing an early, proof-of-concept method that trains models to report when they break instructions or take unintended shortcuts.
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DeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework, achieving superior reasoning and performance compared to GPT-5 and Gemini-3.0-Pro in complex reasoning tasks.
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The Pebble was the smartwatch darling of the early 2010s, a glimpse of the future in the form of a microcontroller and screen strapped to your wrist. It was snapped up by Fitbit and canned, which m…
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Run multiple Copilot agents from one place. Learn prompt techniques, how to spot drift early, and how to review agent work efficiently.
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Join the discussion on this paper page
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Mistral AI releases 10 open-source AI models designed to run on smartphones, drones, and enterprise systems, escalating Europe's challenge to U.S. tech giants and Chinese competitors in the race for AI dominance.
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Ready to become a certified watsonx Generative AI Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → Learn more about agentic workflows with NirvanAi → AI is reshaping IT architecture. 🧠 Terzo President/COO Eric Pritchett explains how MCP, orchestration, and AI agents can transform IT systems into AI‑ready infrastructures. See how connected data and tools power intelligent automation across technology. AI news moves fast.
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Editors Pick Agentic AI Tech News AI Paper Summary Technology AI Shorts Artificial Intelligence Applications Language Model Large Language Model Machine Learning New Releases Staff Meta AI Researchers Introduce Matrix: A Ray Native a Decentralized Framework for Multi Agent Synthetic Data Generation By Michal Sutter - November 30, 2025 How do you keep synthetic data fresh and diverse for modern AI models without turning a single orchestration pipeline into the bottleneck? Meta AI researchers
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Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation. - pguso/rag-from-scratch
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An open source implementation of code execution with MCP (Programatic Tool Calling) - GitHub - Chen-zexi/open-ptc-agent: An open source implementation of code execution with MCP (Programatic Tool ...
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A developer explains using Perplexity MCP to pull authoritative, up-to-date sources when coding with ChatGPT, citing low cost and time savings over stale model knowledge.
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Join the discussion on this paper page
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The recently released Token-Oriented Object Notation (TOON) aims to be a schema-aware alternative to JSON that significantly reduces token consumption at a similar level of accuracy. While the existence and importance of token saved depend on the data shape. some benchmarks show TOON may use in some cases 40% fewer tokens than JSON, possibly resulting in LLM and inference cost savings.
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Will conversational interaction replace SQL queries, KPI reports, and dashboards?
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A new archgw release strips ~500MB of Python dependencies by moving guardrails and function-calling LLMs to an external C++/Go server, letting agents built in any language offload routing, guardrails, and logging to a sidecar proxy.
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The new framework sidesteps costly and risky real-world rollouts by generating synthetic training data, making powerful agentic AI more accessible.
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Ever tried to hammer a nail in with a potato? Nor me, but that’s what I’ve felt like I’ve been attempting to do when trying to really understand agents, as well as to come up with an example agent to build. As I wrote about previously , citing Simon Willison, an LLM agent runs tools in a loop to achieve a goal . Unlike building ETL/ELT pipelines, these were some new concepts that I was struggling to fit to an even semi-plausible real world example. That’s because I was thinking about it all
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Reforge drives team performance, with the most actionable learning from vetted operators that your team will actually use and apply.
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AI SEO » Article Alignment for LLM visibility is incredibly complex, but doable Published: November 18, 2025 at 2:29 pm Read Time: 23 minutes Published: Nov 18, 2025, 2:29 pm · 23 min read Share Written by Mordy Oberstein Edited by Willie Vitari Table of Contents Table of Contents LLMs expose brand misalignment instantly. Discover how inconsistent messaging raises costs, kills visibility, and what brands must do to realign and win in AI search. I’ve straddled both the brand marketing and
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When Anthropic published the Model Context Protocol (MCP), I immediately started experimenting with...
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LLMs are a seamless way to find value in your unstructured data, but the truth is, there is so much more value hidden within your structured data. This post explores what LLMs are (and aren’t) optimized for and how the industry is approaching AI over structured business datasets – including one approach developed by my team and me.
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🚀🧠 Deepagent Quickstarts Deepagents is a simple, open source agent harness. It uses some common principle seen in popular agents such as Claude Code and Manus , including planning (prior to task execution), computer access (giving the able access to a shell and a filesystem), and sub-agent delegation (isolated task execution). This repo has a collection of quickstarts that demonstrate different agents that can be easily configured on top of the deepagents harness. 📚 Resources Documentation -
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You can configure an MCP registry URL and access control policy to determine which MCP servers developers can discover and use in supported IDEs with GitHub Copilot.
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A developer shares CodeVisualizer, a VS Code extension that maps codebase architecture and function logic, built to avoid manually tracing unfamiliar projects.
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Finally, a proxy that does what grep does for logs - filters out the noise. Stop carrying 70k tokens of tools you'll never use. It's like tree-shaking, but for MCP. 🚀 - chris-schra/mcp-funnel
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GPT-5.1, our newest flagship model, is designed to balance intelligence and speed for a variety of agentic and coding tasks, while also i...
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A Reddit r/singularity discussion highlights Google's SIMA 2, an AI agent that interacts, reasons, and learns within 3D virtual environments, raising questions about gaming, education, and ethics.
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A practical use case to describe how the data scientist job changed across three generations of machine learning
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Valdi is a cross-platform UI framework that delivers native performance without sacrificing developer velocity. - Snapchat/Valdi
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Cloudflare Workflows, our durable execution engine for running multi-step applications, now supports Python. That means less friction, more possibilities, and another reason to build on Cloudflare.
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Logs are set to become the primary tool for finding the “why” in diagnosing network incidents.
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GPT-5, our newest flagship model, represents a substantial leap forward in agentic task performance, coding, raw intelligence, and steera...
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Why do few chatbots return figures from source documents in their responses?
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Unleash the full potential of your local AI setup with this game-changing terminal-based app.
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Warp has brought AI coding directly into the terminal.With Warp Code, developers and DevOps engineers can now work with AI agents inside a command line interface (CLI), rather than relying solely on IDE-based tools. CEO Zach Lloyd says the goal is to create a tighter feedback loop between developer and agent—enabling code review, file editing, and more iterative workflows.But the promise comes with challenges. AI-generated code can be verbose, inefficient, and sometimes insecure, given that most large language models were trained on uneven quality data from the Web. Debugging that code isn’t always straightforward, and over-reliance can lead to bad practices slipping into production.For now, the question isn’t whether developers will use AI coding tools, but how much—and how responsibly. As innovation accelerates, organizations will need to experiment, validate outputs, and decide where AI fits in their software delivery pipelines.Read more 👉 [link]Hashtags:#DevOps #AI #AIAgents #SoftwareDevelopment #Warp #CLITools #DevSecOps #Coding
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IBM's open source Granite 4.0 Nano AI models are small enough to run locally directly in your browser Carl Franzen October 28, 2025 Flat AI illustration showing silhouettes of people working in cool modern rock wall home. Credit: VentureBeat made with Midjourney In an industry where model size is often seen as a proxy for intelligence, IBM is charting a different course — one that values efficiency over enormity , and accessibility over abstraction . The 114-year-old tech giant's four new
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Title: AgentFold: Long-Horizon Web Agents with Proactive Context Management Authors: Rui Ye, Zhongwang Zhang, Kuan Li, Huifeng Yin, Zhengwei Tao, Yida Zhao, Liangcai Su, Liwen Zhang, Zile Qiao, Xinyu Wang, Pengjun Xie, Fei Huang, Siheng Chen, Jingren Zhou, Yong Jiang Organization: TongyiLab Abstract: LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based
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We’re rolling out changes to NotebookLM to make it fundamentally smarter and more powerful.
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Two months ago we wrote about Deep Agents - a term we coined for agents that are able to do complex, open ended tasks over longer time horizons. We hypothesized that there were four key elements to those agents: a planning tool, access to a filesystem, subagents, and detailed prompts.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Platform Engineering Labs has released formae, an open-source infrastructure-as-code platform. It is trying to address what they describe as fundamental limitations in existing infrastructure-as-code tools. In a press release, the New York-based company announced the launch on 22 October 2025, positioning formae as the first major innovation in infrastructure-as-code in nearly a decade.
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A founder shares a cost-cutting playbook showing that many tech entrepreneurs mistake a runaway cloud spend problem for a revenue problem.
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MiniMax-M2 is the new king of open source LLMs (especially for agentic tool calling) Carl Franzen October 27, 2025 AI vector art flat illustration in dark blue, teal and orange yellow tones of giant humanoid robot with crown raising fist in front of computer monitor on desk surrounded by diverse office worker humans Watch out, DeepSeek and Qwen! There's a new king of open source large language models (LLMs), especially when it comes to something enterprises are increasingly valuing: agentic
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Contribute to dyoshikawa/rulesync development by creating an account on GitHub.
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At just $1,370, Noetix’s Bumi may be the world’s cheapest humanoid robot, compact, capable, and designed for everyday learning.
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Golden paths gone gray? Avoid these common mistakes that sink platform engineering initiatives.
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A critical vulnerability in Smithery.ai, a popular registry for Model Context Protocol (MCP) servers. This issue could have allowed attackers to steal from over 3,000 AI servers and take API keys from thousands of users across many services.
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DeepSeek's new artificial intelligence model that converts images into text is not just a document parsing tool but a potential preview of its next generation of large language models (LLMs), according to AI experts. Released on Monday, DeepSeek-OCR is technically an optical character recognition (OCR) model - an AI system that uses computer vision to convert images into machine-readable text. Common applications include smart vehicles and document scanners. The Hangzhou-based start-up cited the
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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Build agents that can plan, use subagents, and leverage file systems for complex tasks
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By Sydney Runkle and the LangChain OSS team We're releasing LangChain 1.0 and LangGraph 1.0 — our first major versions of our open source frameworks! After years of feedback, we've updated langchain to focus on the core agent loop, provide flexibility with a new concept of middleware, and upgrade
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A startup team recounts using multiple AI coding agents simultaneously to build five microservices under a tight two-week deadline with only three developers.
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Agentic Postgres: the first database built for agents. Native search, instant forks, MCP integration, new CLI, and free tier. Built for agents. Designed for developers.
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The 'Delethink' environment trains LLMs to reason in fixed-size chunks, breaking the quadratic scaling problem that has made long-chain-of-thought tasks prohibitively expensive.
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Learn how to apply VLMs to advanced document understanding tasks
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Highlights a Reddit community sharing daily AI updates, covering trending LLM tools like Codex and Claude, plus expert coding rules and prompt-crafting best practices.
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The teacher is the new engineer: Inside the rise of AI enablement and PromptOps Dhyey Mavani October 19, 2025 CleoJ made with Midjourney As more companies quickly begin using gen AI, it’s important to avoid a big mistake that could impact its effectiveness: Proper onboarding. Companies spend time and money training new human workers to succeed, but when they use large language model (LLM) helpers, many treat them like simple tools that need no explanation. This isn't just a waste of resources;
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Few people know LLMs (Large Language Models) as thoroughly as [Andrej Karpathy], and luckily for us all he expresses that in useful open-source projects. His latest is nanochat, which he bills as a…
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A text and code version of Karpathy’s famous tokenizer video.
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VMware Workstation Pro 25H2 has been released with support for Virtual Hardware Version 22, better host OS compatibility, and a new command-line tool.
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Editors Pick Agentic AI Staff Tech News 7 LLM Generation Parameters—What They Do and How to Tune Them? By Michal Sutter - October 14, 2025 Tuning LLM outputs is largely a decoding problem: you shape the model’s next-token distribution with a handful of sampling controls— max tokens (caps response length under the model’s context limit), temperature (logit scaling for more/less randomness), top-p / nucleus and top-k (truncate the candidate set by probability mass or rank), frequency and presence
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Anthropic launches Claude Haiku 4.5, a powerful and affordable AI model offering near-premium performance for free, directly challenging OpenAI in the race to democratize advanced artificial intelligence.
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See how to improve accuracy for Cline and other AI coding agents by 10-15%, just by optimizing rules or agent system prompts.
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Agents can take action which makes proper authentication and authorization critical. Read on for how to implement and evolve agent auth.
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Ollama now supports Alibaba's Qwen3-VL.
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Zone 2 training is getting a lot of buzz in the fitness world. But what is it and should you care?
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Self-improving language models are becoming reality with MIT's updated SEAL technique Carl Franzen October 13, 2025 Credit: VentureBeat made with Midjourney Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing a technique that allows large language models (LLMs) — like those underpinning ChatGPT and most modern AI chatbots — to improve themselves by generating synthetic data to fine-tune upon. The technique, known as SEAL
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A Reddit discussion examines common product management challenges, emphasizing clear team communication, user feedback loops, and cross-functional collaboration as keys to successful launches.
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AsterMind-ELM is a modular, Extreme Learning Machine (ELM) library for JavaScript and TypeScript. We speak to its creator.
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