Topic
Prompt Engineering
17 episodes
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Use My No AI Slop Skill to Remove 20 AI Slop Patterns
Jessica and Cathy examine Peter Yang’s open-source /no-ai-slop skill, asking whether removing recognizable AI writing patterns can preserve human voice or merely create a new style filter. They focus on the article’s 25/50/25 editing process, the limits of heuristic detection, and the practical boundary between AI assistance and human judgment.
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Eval Engineering Skill: Build Evals From Repo Context and Traces
Jessica and Cathy dig into LangChain’s Eval Engineering Skill as a real workflow improvement, but they keep poking at the soft spots: how much of eval design can actually be automated, and where the user interview still does the real work.
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Model Behavior: Week of July 20, 2026
We think this week made the same point from a few different angles: the fight is moving from raw model bragging rights to who controls the agent stack in production. We keep circling the same uncomfortable truth, which is that the boring control layer is starting to decide who actually wins.
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Overview: Task Decomposition
We finally slow down on task decomposition, the quiet trick underneath agents, code review workflows, web tasks, and a lot of the stuff we keep arguing about. We use one mental model, a messy project board becoming manageable tickets, and build from intuition to mechanism to where it still matters now.
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Harness Handbook: Making Evolving Agent Harnesses Readable, Navigable, and Editable
The hosts discuss the research paper 'Harness Handbook: Making Evolving Agent Harnesses Readable, Navigable, and Editable' and its implications for AI agent development.
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Skillware AI Agent Skill Framework
Skillware is a Python framework that lets you equip agents with deterministic, modular skills, cutting out raw tool‑call boilerplate and letting you swap brains without touching the skill logic.
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Better tools made Copilot code review worse. Here's how we actually improved it.
Pippa and Tyler dig into GitHub’s post on why giving Copilot code review better tools actually regressed its performance—and how rewriting tool instructions for a reviewer’s workflow flipped the regression into a 20% cost win without losing review quality.
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Overview: In Context Learning
We finally slow down and explain in-context learning, the thing we keep leaning on whenever prompts, agents, examples, and adaptation come up. We make the core idea concrete: the model is learning from the temporary packet you hand it, without changing itself permanently.
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Overview: Token Economics
We finally slow down on Token Economics: why tokens are the meter for cost, speed, memory, and product decisions in language models. We keep using the tiny-slip postage analogy until the whole thing clicks, from tokenization to context windows to real API bills.
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Building Agents for Teams: Turning conversations into outcomes Microsoft 365 Developer Blog
The Microsoft Teams dev blog argues agents should live *in* chats, channels, and meetings—where work happens—so teams turn conversations into outcomes in real time. They preview a new monthly series and a Teams SDK that hands devs authentication, routing, and MCP/A2A plumbing so you can ship a task agent in hours. We dissect whether ‘collaborative agent’ is the right abstraction, how MCP and A2A are now the default integration layer, and who this actually helps.
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LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does MachineLearningMastery
Fern and Lintel dig into a comparison of RAGAS, DeepEval, and Promptfoo, landing on the article’s real argument: eval frameworks are less about novel metrics than about where evaluation fits in your workflow, and the dangerous part is trusting LLM-as-a-judge without auditing its biases. They like the article’s practical split between RAG scoring, CI gates, and prompt red-teaming, but push on where the examples are a little too toy-like and where teams can overread framework choice as the main problem instead of test-set design and human calibration.
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Overview: Prompt Engineering
We’re finally doing the overdue deep dive on prompt engineering, the weirdly practical skill of getting language models to do the thing you actually meant. We keep coming back to it because the difference between a flimsy prompt and a good one is often the difference between nonsense and a usable product.
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Large language models often prioritize Western moral values, overlooking other cultures
A research paper finds LLMs tend to mirror Western moral priorities when asked to roleplay citizens of 48 countries, and two hosts discuss what this actually means for users, products, and culture.
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Improving Agents is a Data Mining Problem
Laura and Harper dig into Vivek Trivedy's claim that improving agents is fundamentally a data-mining problem, unpacking what that means for continual learning, harness engineering, and who should actually care.
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How to Run Open Source AI Models
Sid Saladi argues that frontier AI vendors (Claude, GPT) bundle model, compute, access, and application into one proprietary stack—trapping users in unpredictable pricing and competitive capture. The counter: open-weight models like GLM-5.2, DeepSeek V4, Qwen, and Kimi are now frontier-adjacent in capability (GLM-5.2 beats GPT-5.5 on coding benchmarks, matches Opus 4.8 on others) and cost roughly one-sixth as much. The real problem isn't model quality anymore; it's that companies like Tesla, Uber, and Meta are hemorrhaging money on metered AI because they can't decouple the stack. The guide walks four layers—model, compute, access, harness—and shows how to own each one deliberately instead of letting a vendor own all four by default.
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Tuning the harness, not the model: a Nemotron 3 Ultra playbook
A LangChain/NVIDIA case study claims harness tuning alone can push Nemotron 3 Ultra to 0.86 on Deep Agents at ~$4.48/run vs $43.48 for Opus 4.8, with parity latency. The hosts parse the real mechanism (matched scaffolding vs post-training), test limits (where harness hits a ceiling), and weigh who actually benefits. They surface concrete repos (langchain-ai/deepagents, langchain-ai/deepagentsjs) and a vendor profile workflow, then poke at the article’s reliance on Deep Agents and the cost math. Final take: a plausible win for teams already deep into harness work, not a universal unlock, with the hosts pushing back on ‘ten-x cheaper’ framing and under-specified benchmark footnotes.
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Measuring the Gap Between Human and LLM Research Ideas
Cooper and Miles dig into a study that literally measures how much LLMs' research ideas diverge from humans' by reconstructing literature contexts and running comparative idea generation. They walk through the two-axis 'research-taste' taxonomy, the paper's finding that model outputs skew toward synthesis and bridge-building at the expense of broader human distributions, and what it implies for AI-scientist stacks. Ends up bullish on this line of work for aligning LLM ideation tools.