Ranked by score, best first. Every title opens the source in a new tab.
Generated scored by claude-haiku-5-5, takes by claude-opus-5-5RSS
Score 7.6 out of 10
Claude Code 2.1.319 (opens in a new tab)
Worth reading. denyInsteadOfPrompting turns a blocking permission prompt into a denial the loop continues past, and MCP tasks now run in the background. Both change the tool loop.
why 7.6
Changelog adds permissions.denyInsteadOfPrompting and MCP task backgrounding, changing the permission and tool loop.
relevance 8/10 · novelty 7/10
Score 7.0 out of 10
HF trained one small LoRA per agent harness (pi, opencode, hermes) with RL, inside the harness, no forks. safeagents… (opens in a new tab)
Interesting if it holds up. It is a tweet summary of per-harness RL LoRAs with a filter that zeroes reward-hacking tool calls. Same base model, different harness, different agent (Law 3).
why 7.0
Per-harness RL-trained LoRAs with a tool-call filter cutting reward hacking; concrete harness-level result, though only a tweet summary.
relevance 7/10 · novelty 7/10
Score 6.6 out of 10
GitHub Copilot CLI 1.0.107 (opens in a new tab)
Worth a skim for the permission fixes. Sandboxed extension processes shrink the tool surface, and autopilot no longer acting on review-only requests closes a real overreach. Law 3.
why 6.6
Changelog adds a sandbox setting for extension processes and fixes autopilot overreaching after review-only requests; the loop and permission changes are visible.
relevance 7/10 · novelty 6/10
Score 6.0 out of 10
costwise-mcp is a local MCP server that keeps coding-agent windows from filling up with whole files and logs. The tool… (opens in a new tab)
A sensible pattern with no numbers. It parks big blobs on disk behind short handles and recalls only matching slices, so whole logs stay out of the window (Law 2).
why 6.0
Local MCP server parking large tool outputs behind handles to save context; concrete context-handling technique, though no measured results shown.
relevance 6/10 · novelty 6/10
Score 6.0 out of 10
The components of agent memory (opens in a new tab)
Read for the mechanism, if the post gives one. Memory here is a harness deciding what to write down and replay; "dreaming" is a step that reorganizes those notes (Law 1).
why 6.0
Anthropic on agent memory components; mechanism for memory management in agents, though details from snippet are thin.
relevance 6/10 · novelty 6/10
Score 6.0 out of 10
Hard Issue, Weak Model, or Bad Run? Rethinking Failure Prediction for Coding Agents (opens in a new tab)
Useful negative result. Trajectory failure predictors mostly learn issue difficulty and model strength, not run state. Check this before building stop-and-retry logic on them.
why 6.0
Shows trajectory failure predictors mostly learn issue difficulty, not run state; measured failure mode for coding agents.
relevance 6/10 · novelty 6/10
Score 5.6 out of 10
Benchmarks as Software: Terminal-Bench and the Case for Continuous Benchmarks (opens in a new tab)
Solid point on eval rot. Verifiers that reject valid new solutions, plus infrastructure drift, quietly corrupt agent scores. A check rots when what it checks changes underneath it.
why 5.6
Terminal-Bench maintenance debt; evaluation infrastructure issues affect coding agent benchmark validity.
relevance 6/10 · novelty 5/10
Nothing of this kind on this day.