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Flavio Copes1 Hot0 comments7 min read
What changes when an agent improves its own harness?
Use Google Research's Harness² to understand the pieces around an AI model, how recursive refinement works, and how to evaluate changes on untouched tasks.
Source: https://flaviocopes.com/agent-harness-components. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
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Philipp Schmid1 Hot7 min
Why Agents Are Soon Only Going to Have 1 Tool
Instead of triggering tools one at a time across turns, agents are collapsing their tool catalog into a single exec(code) tool—chaining, filtering, and awaiting actions inside an isolated sandbox.
Medium4 Hot10 min
Your PDFs in a chat window is not a RAG system
Your PDFs in a chat window is not a RAG system The gap between one people trust and one they quietly stop using is entirely in the retrieval step. Somebody in your company has already tried the cheap …
Medium1 Hot10 min
Stop Asking ChatGPT to Write Your Code. Ask It This Instead
Stop Asking ChatGPT to Write Your Code. Ask It This Instead Learning to code is frustrating. Nobody warns you how frustrating. You watch the tutorials. You read the docs. You follow along with every …
Medium4 Hot12 min
The Missing Cent: A Beginner’s Guide to Harness Engineering.
The Missing Cent: A Beginner’s Guide to Harness Engineering. AI agents can write code, but can you trust it? Part 1 of my Harness Engineering From Scratch series. This is Part 1 of my series …
XDA Developers1 Hot16 min
My RTX Spark laptop still doesn't have Copilot+ features, so a local LLM on the same machine built its own from one prompt
It called it "Spark Copilot", and it works similarly to the real thing.
Daily Dose of Data Science | Avi Chawla | Substack88 Hot7 min
RAG vs. Vectorless RAG, clearly explained!
...explained visually (with tradeoffs in production).
mlflow1 Hot9 min
Governing Coding-Agent Sprawl with MLflow AI Gateway
A demonstration of how to use MLflow AI Gateway to manage coding agent sprawl.
Flavio Copes1 Hot6 min
Write agent instructions that explain the decision
Turn a brittle AGENTS.md into useful project guidance. Explain the reason behind a rule, keep hard checks in code, and make approval boundaries explicit.
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