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Turn failures into a score, then let the agent improve it
Several practitioners this period describe the same move: collect the agent's actual failures, turn them into something measurable, and let agents improve against that number.
Theo (T3 Code MCP). Models kept misusing T3 Code's MCP tools. For example, they opened a new thread when he wanted a subagent. He went through his own history, categorized every case where a model did the wrong thing, and turned them into an eval. Sonnet 5.5 initially got roughly 60 of 88 right . He then gave the agent two jobs in order:
- Reach a perfect score on every model he cares about, and commit.
- Cut the tool descriptions to a third of their size while keeping the perfect score .
The result was 52% fewer tokens from T3 Code, or 78% with tool search on. It is in nightly now, with stable coming soon . His general rule: if you can turn a problem into a program that outputs a score, "your agents are going to make that score go up" .
For subjective UI work he uses a "council" instead. Opus 5.5, Muse and Codex GPT-6.1 each get the request and context, review read-only, and score out of 10. A fresh review after fixes counts as a new round . He warns the scores are noisy: the same screenshot can get three different scores from one model . Context also moves them. When he added Claude Code and Codex screenshots for comparison, Codex rated a PR it had earlier scored 9 at only 7.4 .
LangChain (browser tools). LangChain tested seven browser toolsets on 100 WebVoyager tasks with Claude Sonnet 5.5. Stagehand passed 90% for $1.98 total, at a median 17.4 s per task. Playwright MCP passed 78% for $26.71 and took about three times as long . What made the difference:
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Code mode: the
runtool batches a chain of actions into one call. It averaged 10 steps per task versus 26 for Playwright MCP, and a navigation returned a median 117 characters versus about 17,000 from Browserbase's hosted tool . -
Playwright-style code: let the model write Playwright-style code, which models write best. A
runtool that took Stagehand's native API instead passed 5.3 points fewer tasks and cost 19% more per passed task . -
Fewer fixed sleeps: traces showed agents writing fixed sleeps like
page.waitForTimeout(8000). Ending each wait once the page has been quiet for 500 ms cut median HardBench time from 182 s to 124 s (together with two other changes), with accuracy within noise .
To use this in open-source Deep Agents, add StagehandMiddleware from langchain-stagehand .
Warp (team scale). Zach Lloyd defines one "factory" per product surface in a YAML file listing repos, MCPs and workflow steps . He tracks human interventions per merged PR and cost per PR . Tuning (less Opus where it wasn't needed, fewer redundant subagents) cut cost per PR from about $80 to roughly $15–20. PR throughput rose 40% over the month with no increase in reverts . He puts the floor at roughly 5–10 engineers; below that, "use the cloud code sub or the codec sub" .
Armin Ronacher adds that your harness already stores sessions on disk. After a month you have plenty of traces to point an agent at, for example to ask why it keeps failing to use uv . Two more tips from his talk:
- If you edit files outside the agent, tell it. Otherwise it gets confused, and in his sandbox test it claimed to have validated a fix without running any tools .
- For restricted mini-languages, return errors that say which construct is unsupported so the agent can adjust .
Addy Osmani's checklist for reviewing agent work
- Before the task: write down your own hypothesis . Brief the agent in your own words with the audience, what's out of scope and the definition of done. If two rounds of corrections haven't worked, rewrite the prompt .
- Define "verified" first. Decide it before the work starts. When the agent reports it's finished, check the command it ran and the output before you read its summary . When a check passes, ask what it proved and what it didn't .
- Self-critique every time: have a fresh-context subagent review the work on every run, then ask "Did I miss anything?"
- Review decisions, not just the diff: ask the agent to list its decisions and assumptions, then quiz yourself. One rule he quotes: "I only merge after I pass the quiz perfectly."
Releases
- Claude Code 2.1.317: you can name an existing game to describe the mechanics you want, and Claude should build an original game. If you ask for a copy, it offers something similar instead . Theo calls the copyright-steering prompt changes "lame as hell," but that post was not among the sources read here.
- Claude plugins: Team and Enterprise admins can set up company plugins so teammates connect automatically on first use . Plugin builders need MCP servers that accept Claude as a trusted issuer .
- T3 Code: fixes that steer subagents to the right tools and stop them blocking on questions, performance wins for big repos and long threads with many subagents, and inline notices when you switch models or effort levels .
Security: check where you download agent tools
A user asked ChatGPT for the T3 Code download and was sent to t33.codes, a near-pixel-perfect clone registered two weeks earlier. The real site is t3.codes . Theo says ChatGPT recommends the scam clone "constantly" .
Separately, Fireship reports that a Cursor-shipped Claude Code extension scanned machines for keys and tokens and set up a backdoor. It was live for about 11 hours and reached around 5,000 installs .
Shorter notes
- Spawn's fully autonomous loop: Simon Willison relays Jacob Sansbury's account. Agents ship to production hourly, an hour without a ship counts as a SEV-0, and rollback takes 30 seconds. Sansbury says agents only keep a few hours of work in their heads, so shipping fast keeps them coherent . They found Opus 5.5 not quite good enough, and GPT-6 Astra "fixed" a security review by disabling every feature . Willison doubts this suits higher-stakes software .
- Triage AI-assisted PRs with an agent: Willison uses Opus 5.5 or GPT-6 Astra to triage incoming PRs and treats them as "bug reports with attached examples." His latest LLM release included fixes from 12 authors .
- Agentic QA on Omarchy: ThePrimeagen has agents explore and test Omarchy with role prompts like "You are the theme tester" . He reports the first bug found this way . DHH's goal is to automate the chain from bug fix to PR to QA to release .
- Self-hosted vs frontier models: a viral post claims Revolut moved about 2,000 engineers to self-hosted GLM on 56 B200s, at about a tenth of its previous API cost . Nicholas Moy says his team made a similar bet and lost, calling it "picking up pennies in front of a steamroller" .
- Cat Wu leaves Anthropic: in her farewell post she notes that early Claude Code users approved every permission prompt by hand. Today the default is to trust Claude to auto-approve, and it handles large migrations .
Sanfilippo used Dwarf Star’s SSD streaming to run DeepSeek V4.1 Flash on a 128-GB MacBook M5 Max. After an initial 2-bit test, he switched to Q4 GGUF and described SSD-streamed inference as usable; he also observed it speeding up as caching accumulated. On the Q4 run, he cited a one-million-token context, a 69,000-token prefill, 101 GB of memory use, and a 483-GB file including n-grams.
For a reasoning test, he asked the CLI to infer what a Freeway/Frogger neural network did from its weights without tool calls; after extended analysis, it listed fire/right/left, missed the backward action, and Sanfilippo judged it had not solved the puzzle.
Salvatore describes a terminal-based Claude Code workflow: give the agent a task, let it operate the computer through keyboard actions or a browser, and continue directing the same session from his phone. He says agentic programming has shifted much of his computer work from using applications directly to asking an agent through chat to do it. For input, a practitioner describes using Whisper for quick, low-level instructions and sending the AI a photo of a paper sketch for higher-level work.
- Use a Claude Project as a coordinator for developer work: have its coordinator chat organize tasks into side Claude Code threads, and prompt it to remind you nightly about unfinished tasks.
- For hands-free agent work, give a GPT agent natural-language requests over AirPods while walking; the speaker uses it for research, app builds and updates, and Slack or email tasks, and says walking helps curb the urge to launch more agents.
- For research discovery, use Claude Code with Exa’s podcast API, which provides transcripts for 3.2 million episodes across 119,000 shows and updates as episodes air; prompt it to find expert, no-jargon episodes, return 20 links, and synthesize them into a document. The Scrape Creators API can similarly supply Instagram, Twitter, and YouTube transcripts for filtering out low-value or sponsored content.
- Define one coding-agent factory per product surface in code, specifying its repositories, skills, MCP integrations, and workflow steps such as triage, specification, and implementation; tasks can enter through Slack, Linear/Jira, GitHub, an API, or MCP and trigger the predefined workflow. Keep the factory definition versioned so teams can branch and revert changes; the speaker suggested this setup may start making sense around 5–10 engineers and is overkill for very small teams.
- Make the system measurable: retain agent conversations and artifacts, track measures such as cost per PR and human interventions per merged PR, and use customizable judge-model scorers to catch issues like redundant tests. Benchmark curated real runs across different models and harnesses, compare cost with correctness, and use the results to route work to suitable configurations.
- Warp reports that internal tuning—including addressing unnecessary use of Opus, redundant work, and excess subagents—cut its cost per PR from about $80 to roughly $15–20; PR throughput rose 40% over the prior month without an increased revert rate. Its self-improvement example found 54 triage failure cases where spawning an investigation subagent would have helped, and agents could propose factory-definition changes via PR; replaying past runs to validate those changes was still planned.
- For his own model tests, Berman says he prompted models to “go clone Excel” and that a latest-generation model produced a browser-based clone in about 90 minutes to a couple of hours; he estimates the rebuild cost at about $10 in tokens. Separately, he describes Artcraft’s creator pointing an AI at existing software and telling it to “iterate until you clone it.”
- For personal tools, Berman reports building Outlier Tracker in about 30 minutes to retain the video stats, tracking, and channel analysis he needed. His open-source Folio PDF reader/editor supports drawing, notes, and asking questions about a PDF.
- He treats security and maintenance as drawbacks of custom software, but argues that agents continuously updating software and checking for security issues could cost less than buying it.
Simon Willison used llm -f with LM Studio to test local Qwen 3.8 27B (17GB) on no-tools prompts asking it to identify games from network weights; the default 8,192-token context failed, so he loaded a longer context and set LMSTUDIO_TIMEOUT=900. Qwen identified Pac-Man in 46m 12s , Pong in 1h 4m 54s , and Breakout in 52m 25s , but mistook Freeway for a Pac-Man-like game .
- Build on coding-agent primitives for adjacent agent tasks: Ronacher recommends treating code execution and a virtual filesystem as a starting point beyond coding; familiar workflows such as files, diffs, shell commands, build tools, and test output are patterns he expects models to handle well. Tools that limit how much file or command output enters the context and leave overflow on disk can help conserve context.
- Keep state changes visible to the agent: In Ronacher’s sandbox example, after a reload the agent had not run any tools but claimed it had validated the fix; he says unreported file changes can confuse the agent, and describes Claude Code injecting a message when it detected a file change.
- Prefer familiar interfaces and explicit execution feedback: In his observability-data tests, SQL worked better than a custom query DSL, and a JavaScript-like language worked better than a Scheme-like one; for a restricted mini-language, return errors that explain which syntax is unsupported so the agent can adapt.
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Use runs and traces to evaluate an agent harness: Ask the model how it would solve the task, test and rank the proposed approach across repeated runs, then give the model the actual traces when the harness struggles. Coding-agent sessions stored on disk can reveal recurring failures—for example, Ronacher used traces to investigate the agent’s repeated difficulty using
uv. For a minimal starting point, he notes that the PI coding agent uses only four tools and still works.
- Spawn’s Jacob Sansbury describes a Fable-powered agent swarm costing over $3 million per month in tokens: agents edit live code and deploy hourly, and about 60% of work starts from production errors, Savi bug reports, unusual logs, or feature requests. Reports go to an agent that decides whether to fix the issue, decline it, or seek a design decision; fixes usually reach the affected user within an hour, in the same chat.
- The operating loop treats an hour without a deployment as a SEV-0; Sansbury says agents hold only a few hours of work in context, so frequent shipping helps them stay coherent. Monitoring or log alerts wake an incident-owning agent, changes can be rolled back in 30 seconds, and the team deliberately breaks things to test recovery. Sansbury credits Fable 5/5.1 with enabling this autonomy; Opus 5.5 did not work as well for them, while GPT-6 Astra’s security fixes disabled every feature. Willison cautions that this approach may not suit higher-stakes software, though it appeared to work for a game.
- For AI-assisted open-source PRs, Willison uses Opus 5.5 or GPT-6 Astra to triage submissions, treats them as bug reports with attached examples, and either lands them or implements his own fix. He says the LLM project’s release included fixes from 12 authors, almost all AI-assisted, without the review burden he expected.
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A reusable project-building prompt pattern used with Opus 5.5 for
nesformer: first ask the model to read an article and relevant source code, assess whether the technique could emulate an NES CPU and RAM, and estimate model size and speed; then direct it to build a deliberately not-necessarily-fast model plus a CLI that accepts a ROM. The resulting project used an existing NES CPU conformance-test collection.
- Use a Claude Project as a coordinator for parallel development work: send requests to its main chat, have it organize Claude Code sessions in side threads, and prompt it to send a nightly reminder of unfinished tasks.
- When focus slips at the desk, Riley uses AirPods to speak with a GPT agent while walking, asking it to research, build or update apps, and handle Slack or email; he says walking helps him resist starting more agent tasks.
- For work notification triage, Riley connects Slack and email through ChatGPT’s plugins, then instructs the agent to check every 30 minutes, alert him only about items needing action within eight hours, and defer less urgent items to the end of the day.
- Riley connected Claude Code to his YouTube channel analytics and had it analyze video retention charts to produce a 12-page report after about two hours of research.
- For research filtering, use Claude Code with an Exa API key to search podcast transcripts against explicit quality criteria, return episode links, and synthesize the results; Riley describes Exa Podcasts as covering 3.2 million episodes across 119,000 shows, with new episodes added. He also says Scrape Creators can provide Instagram, Twitter, and YouTube transcripts for filtering out sponsored or low-value content.
- Before a non-trivial agent task, write down your own hypothesis, then brief the agent in your own words with the audience, out-of-scope items, and definition of done. If two rounds of corrections still miss the mark, rewrite the prompt; if the task is unclear, ask the agent to interview you.
- Give the agent a way to verify its work—such as end-to-end or property tests, or actual app interactions—and let it run checks, inspect results, and retry. Define “verified” up front; when a check passes, ask what it proved and what it did not, and inspect the executed command and output before the agent’s summary. A fresh-context subagent can also self-critique the work as a recurring sanity check.
- Review both the diff and the agent’s choices: ask it to list its decisions and assumptions, then quiz yourself on lines or choices. For long sessions, have Claude produce a change report with a quiz; one suggested merge rule is to merge only after passing the quiz perfectly. If you cannot say what the checks might miss, request a real review instead of quick approval.
- For subjective UI work, Theo used a three-model council (Claude Code, Codex, and Muse): give reviewers the same request and relevant context, keep their review work read-only, collect findings and disagreements, and score each review out of 10; after fixes, start a fresh review round. For comparisons, he had agents generate screenshots and judge the before/after alongside Claude Code and Codex.
- Treat those ratings as noisy signals: the same model can score the same screenshot differently, and scores can shift with prompt and comparison context. Theo also suspected that telling evaluators the score target could influence their ratings, and considered withholding it.
- For T3 Code’s MCP tool descriptions, he turned categorized historical tool-use failures into an evaluation, then tasked the agent with achieving perfect scores across relevant models before shrinking the descriptions to one-third size while retaining that score. The change cut token use by 52%, or 78% with tool search on, and was available in nightly builds.
- Tool choice: LangChain compared seven browser toolsets; on WebVoyager with Claude Sonnet 5.5, Stagehand passed 90% of tasks for $1.98 total at a median 17.4 seconds per task, versus Playwright MCP at 78%, $26.71, and about three times as long per task.
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Keep browser work efficient: Stagehand’s code-mode
runbatches sequential actions into one call and keeps intermediate results out of the model’s context; it averaged 10 steps per task versus 26 for Playwright MCP, and returned a median 117 characters per navigation versus about 17,000 from Browserbase’s hosted navigate tool. Usesnapshotfor a trimmed accessibility tree with IDs for interactive elements rather than relying mainly on screenshots; prefer Playwright-style code inrunover Stagehand’s native API, which in their test passed 5.3 points fewer tasks and cost 19% more per passed task. - Reusable tuning loop: Run benchmarks, inspect LangSmith traces for wasted steps or tokens, change the tool or harness, then rerun evals. Replacing fixed waits with a wait that ends after the page has been quiet for 500 ms (but no later than the requested timeout) made one requested 8-second wait return in 0.6 seconds; together with structured answers and a model-call timeout, the changes reduced median HardBench time from 182 to 124 seconds, with accuracy within noise.
- Cursor’s Claude Code extension reportedly scanned machines for keys or tokens and installed a backdoor; it was available for about 11 hours and reached roughly 5,000 installs. The report says the need for admin rights limited the blast radius.
- Anthropic’s red team gave an unreleased Claude model a Linux TCP race-condition bug; the model elicited a kernel-memory address leak in about 48 minutes and, five minutes later, could run commands as root.
- In a sponsored segment, CodeRabbit presented Secure as a security-review workflow: reasoning-based agents assess code as an attacker, prioritize findings by reachability, exploitability, and blast radius, and suggest fixes for approval in the diff; it also reviews every PR and supports scheduled full-codebase scans.
Namespace, the video’s sponsor, pitches fast coding, testing, and build environments for Windows, Linux, and Mac, using its own server racks for GitHub Actions runners; developers can use remote desktop or SSH to debug failing tests, and the ad says an agent can help optimize performance issues.
Anthropic reportedly rewrote its Messages API in Rust with Claude writing the code despite having no Rust developers on the team; humans wrote the plan and checked that the old and new versions matched on real traffic, and the account reports a 40–50% CPU reduction.
Claude Code 2.1.317 updated Opus 5.5’s game-generation rule after it sometimes refused projects that named an existing game only to describe desired mechanics: developers can now reference a game when asking for an original game in that genre, while requests to copy the original are redirected toward something similar .
Riley Brown says GPT Sites bundles authentication, database, storage, and hosting; his proposed personal-app workflow is to prompt the site to check its security and make needed maintenance changes every day, envisioning software maintained for its individual owner.
Anthropic’s Cat Wu says an early Claude Code workflow was to have Claude build an RL environment while steering it for tens of hours without manually writing code. At launch, users mostly approved permission prompts manually and used Claude for questions and tests; by the time of Wu’s post, the default was to trust Claude to auto-approve, with the tool handling large migrations and building new products.
ThePrimeagen flagged a first bug found from “oligarchy,” linking an Omarchy pull request, and called it an early sign that agentic QA has promise .
Kent C. Dodds says his next YouTube video will explain why deterministic software is much better than skills and how to combine the two for the best outcome; he does not share the approach or implementation details in this post.