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🔥 TOP SIGNAL
Theo’s T3 Code performance postmortem is a useful boundary for agentic debugging: a vague Codex request produced a confident diagnosis and a 10,000+ line PR that changed nothing. The breakthrough was to stop asking for a fix and have the agent build a console-driven toggle harness; testing the hypotheses isolated an infinite sidebar opacity animation. The human supplied the hypotheses and validation—the agents were valuable as fast codebase searchers and diagnostic-tool builders, not autonomous diagnosticians.
⚡ TRY THIS
Turn bug reports into experiments. Isolate the target in a one-tab browser and use Task Manager: Theo notes that browser tooling is weak for CSS/compositor work, while DevTools changes performance characteristics. Ask the agent to build a console-pasteable toggle for suspected effects, apply all, reset, then flip one feature at a time; separate transitions from animations before editing. Theo got the GPU process down to 3% or less with the toggles applied, back above 25% after reset, and eventually traced the worst offender to the sidebar terminal icon’s pulse.
Put a conductor over the fleet. Kent C. Dodds’ pattern is one supervisor spawning isolated Cursor Cloud Agents, using the Kody Koala MCP for handoffs, creating a PR when a worker stalls, receiving completion messages, and sending a Discord summary at the end. Make each worker’s environment, handoff state, and completion message explicit; the control plane is more valuable than another giant prompt.
Use near-free models as sidecars. Theo says Luna became effectively free after an 80% cost reduction and is using it for T3 Code title generation; he wants it on every prompt for descriptions, feedback, and statuses. Route low-risk metadata and auxiliary outputs there, but measure whether the extra calls improve the workflow before letting cheap inference become unbounded background work.
Normalize shared skills with a compatibility shim. DHH reports that Claude Code still does not natively scan
~/.agents/skills; his workaround is a symlink, despite the Claude docs acknowledging the pattern. Keep skills in one canonical tree, symlink where needed, and add a fresh-machine discovery check to your agent setup.
📡 WHAT SHIPPED
LLM 0.32: Simon Willison’s major CLI/library release sends reasoning traces to stderr (
-R/--hide-reasoningsuppresses them), adds the GPT-5.6 family with GPT-5.6 Luna as the default, and supports server-side Code Interpreter, WebSearch, WebFetch, CodeExecution, and MCP tools. The newllm openai endpointcommand can run one-off prompts against any OpenAI-compatible endpoint—including a local LM Studio model—without logging them. It also adds typedstream_events()for mixed reasoning/text/tool outputs and tool-chain pause/resume from stored history: primitives worth copying into any coding-agent loop that needs human gates and durable state.OpenWiki 0.3: A full codebase-wiki prompt rewrite reports a 28.57% relative success increase (35% → 45% at
n=2), 14% fewer tokens, and 26% fewer tool calls per successful task. Install withnpm install -g openwiki@0.3.0; treat the numbers as an early, self-reported signal and rerun the eval on your own repositories.Model routers are becoming a coding-agent layer. Not Diamond Code announced routing across gateways and harnesses, including Claude Code, claiming 20–65% lower cost without a quality hit. Mckay Wrigley sees the larger opportunity in blending “jagged” models into smoother behavior, describes router engineering as a third layer after model and harness engineering, and says DeepSeek V4 Flash was cheap enough to offload roughly a half-dozen tasks from his Fable 5 workflow. The cost claim is vendor-reported; the actionable test is per-task routing and blending, not headline token price.
Resilience and fallback primitives: LangChain says Deep Agents, LangGraph, and LangChain can retry interrupted work, follow a safe recovery path, resume from saved state, and fall back to alternate models. Its Gateway announcement adds fallback rules across models and hosts when a provider fails or rate-limits. This is the right production direction: recovery should be part of the agent runtime, not a human restarting a dead run.
🎬 GO DEEPER
- Video — Theo’s T3 Code performance postmortem. Focus on the diagnostic-harness segment: the agent becomes useful when the engineer turns competing theories into measurable toggles, then the video explains why infinite compositor animations and layered effects kept the page busy.
Repo — OpenWiki. Study the prompt rewrite as an example of improving an agent by changing its codebase-understanding instructions rather than swapping models; reproduce the success, token, and tool-call measurements before trusting the tiny
n=2sample.Agent framework — llm-coding-agent. LLM 0.32’s lower-level work was driven by Datasette Agent and llm-coding-agent; inspect the combination of model/tool mixing, structured streaming, human approval, and resume-from-history rather than treating an agent as a single prompt wrapper.
Editorial take: The frontier is shifting from prompt quality to control-plane quality: humans design the measurement, agents build the probes and patches, and supervisors carry state between workers.