Topic
Context Window Management
12 episodes
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Pi, Minimal and Performant | EARENDIL
Tyler and Pippa dig into Pi, the minimal coding harness from Earendil, and the Databricks benchmark that claims simple harnesses beat bloated ones on real-world tasks. Tyler's skeptical about how much of this is genuine insight versus flattering a tool that happens to match a particular workload; Pippa thinks the cost-per-task framing is the actual product story and the Shopify autoresearch numbers are hard to dismiss.
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Model Behavior: Week of August 3, 2026
We spent this week arguing that the frontier race has quietly turned into a battle for the default, not the smartest model, and we used the price war and agent stacks as our receipts. We’re both a little uneasy about how much power is moving into routing layers and “lifecycle” platforms while everyone pretends it’s just about cheaper tokens.
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Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler | Towards Data Science
Fern and Lintel dig into the article's claim that coding agents need smarter context pruning, not bigger context windows. They break down the mechanics of context compilation, how it slashes prompt bloat, and whether this shift changes the real product story for code AI. Along the way, they tease each other's optimism and skepticism, call back to their running jokes about infrastructure, and reflect on the broader context engineering debate.
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The new rules of context engineering for Claude 5 generation models | Claude by Anthropic
Anthropic's post on context engineering for Claude 5 models reveals a surprising finding: they removed over 80% of Claude Code's system prompt with no measurable loss in performance. The core insight is that newer models need fewer explicit constraints and benefit more from clean interfaces, progressive disclosure, and letting the model use judgment rather than following hard rules. The shift reflects a broader pattern: as models get stronger, the infrastructure around them gets simpler.
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Overview: Graph based Memory Representation
We finally slow down and explain graph-based memory representation, the thing we keep gesturing at whenever agent memory, receipts, and relationship-aware retrieval come up. We use one corkboard mental model to make nodes, edges, traversal, and the real trade-offs feel less mystical.
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AREX: Towards a Recursively Self Improving Agent for Deep Research
Pippa and Tyler dig into AREX, a recursively self-improving deep research agent from BAAI that alternates between an inner search loop and an outer constraint-verification loop — and discuss whether that architecture is genuinely novel or a smarter repackaging of ideas the field already had.
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Towards a Science of Scaling Agent Systems
Onyx and Echo examine “Towards a Science of Scaling Agent Systems,” a controlled study of when multi-agent architectures help, when coordination becomes a liability, and why task structure matters more than simply adding agents.
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Overview: Context Window Management
We finally slow down and explain Context Window Management from the ground up, because we keep hand-waving it whenever agents, memory, cost, and long tasks come up. The whole thing is the fixed-desk problem: what stays on the desk, what gets compressed, and what falls off.
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The Microsoft Agent Framework Harness is now released | Microsoft Agent Framework
Microsoft Agent Framework has released a stable, batteries-included agent harness for Python and .NET, packaging planning, memory, tool loops, approvals, context compaction, and telemetry behind a configurable agent wrapper.
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Building Governed Agents: A Framework for Cost, Control, and Compliance
Vince and Ava examine LangSmith’s framework for governed agents, focusing on the LLM gateway as a runtime control plane for model choice, cost, permissions, evidence, and continuous improvement.
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Long Horizon Terminal Bench: Testing the Limits of Agents on Long Horizon Terminal Tasks with Dense Reward Based Grading
Laura and Harper dig into Long-Horizon-Terminal-Bench, a new benchmark exposing the gap between short-task agent demos and real multi-hour workflows. They break down the dense-reward grading system, the staggering token costs (9.9M per task), and why current models are failing at sustained execution despite high step-level competence.
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How to Use RLMs in Deep Agents
Exploring Recursive Language Models (RLMs) and their implementation in Deep Agents for handling long contexts efficiently.