Anthropic says Claude writes about 90% of Claude Code's code. This guide walks through their review flow: every PR passes seven layers, human approvals scale with risk from zero to two, and the goal is at most one human per change with no rise in reverts.
REA is an MIT-licensed MCP server that lets Claude Code, Codex, Cursor, and other agents read native binaries through Ghidra, Hopper, or IDA, and take apart Electron, .NET, APK, and web targets directly. What separates it from single-engine bridges like GhidraMCP is that every result carries an evidence ID, a coverage status, and its limitations. One npx rea-agents setup installs it, and JavaScript analysis needs no disassembler at all.
To cut the input tokens resent every turn, agent platforms often truncate, clear, or summarize old tool results, and skill instructions, write receipts, and error messages get cut along with them. The Complexity Trap shows observation masking can halve cost, yet Gemini 2.5 Flash with thinking still dropped from 40.4% to 36.4% solve rate. Measure savings against full history and fixes against the current setup, and judge by total cost per successful task, not shorter per-turn input.
Claude's docs say tool-selection accuracy degrades past 30–50 tools, and both Claude and OpenAI now load tools on demand via tool search. But finding a tool isn't finishing the task: on ToolRet's 43k-tool benchmark, no retriever put the complete required toolset in its top 10 more than 35% of the time. This post splits the problem into five failure points, maps seven approaches onto them, and lays out an evaluation.
Matt Pocock posted 18 'mistakes you might be making' on X with no explanations. Grouped against his public skills and talks, they fall into five: verification, letting go, context, alignment, thinking. Six map directly onto /retro's check categories, so you can try one session tonight.
A chat model is rewarded for a single response people prefer; an agent has to be rewarded for whether a multi-step task actually finished, which needs different data and environments.
Reasoning is the intermediate tokens between input and output; RL fine-tuning beats imitating human solutions because of a reliable verifier, and sampling several times then taking the most frequent answer pushes accuracy further.
AlphaGeometry2 relies on a symbolic engine and AlphaProof on Lean, for a combined 28 points in 2024; in 2025 Gemini Deep Think scored 35 points in natural language, with verification done by human graders.
Laskin's view is that engineers spend 80% of their time understanding systems rather than writing code, and that how far RL goes depends on clean rewards, a problem he considers as hard as superintelligence itself.
A VLA adds an action module to a vision-language model; letting that module's gradients flow back harms language understanding, so knowledge insulation blocks the gradient, while π0.5 co-trains on web data and data from many robots to gain generalization.
Alexandr Wang says Meta listed 100+ behaviors a personal agent needs, built an eval for each, and set two thresholds per row: a launch-blocking floor and a bar for 'good'. They shipped after the first all-green checkpoint and a hands-on check that it was clearly better; they also ran new-user testing. The method is reusable, but post-launch safety reports show all-green is not the same as safe.
Open-ended evolution changes agent code and workflows; archives preserve branches, while held-out tests assess generalization.
Program candidates need requirement-based counterexamples; speed matters only after correctness checks pass.
Retrieval, trained KV representations, and cache reuse address different bottlenecks; speed does not guarantee freshness.
On 2026-10-07 Spotify grouped Confidence, Portal, Xirp and its Backstage plugins under one brand, Spotify Technology. Its bet: once AI makes code cheap, judgment becomes the bottleneck and shared organizational context becomes the asset. Revenue is undisclosed; Confidence has public pricing and a free plan; Xirp is a macOS-only beta.
Useful training feedback must distinguish candidates; filtering, relative advantages, and sampling determine the learning signal.
Candidate search selects solutions; retrieval fills knowledge gaps. Both require specifications and checkable evidence.
Human task duration, artifact quality, and citation support measure different things; evaluate both execution and deliverables.
Diversity, verification, task selection, and efficiency are distinct bottlenecks; experiments need holdouts and falsifiable hypotheses.
An RL system samples with an inference engine, assigns rewards through verification, recomputes logprobs and gradients in a trainer, then synchronizes new weights back to inference. Walk through these five stages in an invented example before examining caching, preserved histories, and asynchronous updates.