🔥 TOP SIGNAL

The local coding-agent baseline moved up, but the default configuration is actively bad. Simon Willison ran Qwen 3.8 27B as a 17GB local model and, through Pi, got it to answer a multi-file auth question and write and test pi_jsonl_to_md.py from a prompt. simonwillison The catch is xhigh: a simple SVG consumed 22,276 reasoning tokens and took 21 minutes, versus 137 seconds with reasoning off; Willison’s recommendation is low or no reasoning first, with the full 262,144-token context. simonwillison The remaining gap is speed, not basic capability: he reports 15–30 tokens per second locally and says performance is what keeps it from daily-driver status. simonwillison

⚡ TRY THIS

• Tune Qwen before you evaluate it, then give it a thin harness. In LM Studio, load the full 262,144-token context and start at low or no reasoning; only turn reasoning up when a task actually needs it. simonwillison For Pi, Simon’s working pattern is an OpenAI-compatible provider in ~/.pi/agent/models.json—replace the endpoint with your own LM Studio host:

  {
    "providers": {
      "spark": {
        "baseUrl": "https://YOUR-LM-STUDIO-ENDPOINT/v1",
        "api": "openai-responses",
        "apiKey": "dummy",
        "models": [{"id": "qwen3.8-27b", "reasoning": true}]
      }
    }
  }

  Run pi --provider spark --model qwen3.8-27b in the repo. Willison’s useful smoke tests were how does auth work? followed by Write Python code to convert this jsonl to markdown; the agent inspected multiple files, then built and tested the utility. simonwillison

• Make 1M context an opt-in long-session mode. At the top level of ~/.codex/config.toml, before any section headers, use:

  model = "gpt-5.6-sol"
  model_context_window = 1000000
  model_auto_compact_token_limit = 900000

  Restart Codex and start a new session. For a one-off CLI test: codex -m gpt-5.6-sol -c model_context_window=1000000 -c model_auto_compact_token_limit=900000. The Codex maintainer’s warning is worth keeping: the smaller default was tuned for performance and cost, so treat 1M as an escape hatch for unusually long code, tool-output, or history-heavy sessions. @thsottiaux

• Use AGENTS.md as the lightweight instruction layer. Armin Ronacher says he removed most CLAUDE.md files, then found that explicitly telling Claude Code to read AGENTS.md worked well enough when he returned to debug a regression. Put the repo’s durable rules in AGENTS.md and make “Read AGENTS.md before changing anything” the first instruction in a Claude Code session. @mitsuhiko

• Put a control plane in front of agent fan-out and external writes. Kent C. Dodds’s Kody package fingerprints run errors and triages them without spawning a thousand agents, then creates Cursor cloud agents to fix the affected packages. Copy the pattern: deduplicate by error fingerprint, dispatch one repair per unique failure, and queue or rate-limit writes. @kodykoala +1 DHH’s Omabot filed 128 legitimate QA issues in about a minute and still tripped GitHub’s spam protection; his separate rule is that increasingly automated development ends with a human merge decision. @dhh +2

📡 WHAT SHIPPED

• Qwen 3.8 27B — Apache-2 licensed, 27B, and vision-capable. Simon tested the 17GB Q4 build on an M5 Max MacBook Pro and an NVIDIA DGX Spark; its benchmark lead over Qwen 3.6 27B and closed-weight Qwen 3.7-Plus is self-reported, with independent benchmarks still pending. simonwillison

• GPT-5.6 Sol 1M in Codex — the 1M context option, previously limited to API-key usage, now works through ChatGPT accounts too. The documented model window is 1,050,000 tokens, but the maintainer repeats that the current default was tuned deliberately for performance and cost. @thsottiaux +1

• Kody issue triage — Kent C. Dodds shipped a package that subscribes to error events and creates a Cursor cloud agent to fix errors in the affected packages automatically; the companion description emphasizes fingerprinting and bounded triage rather than unbounded agent spawning. @kentcdodds +1

• Coming in Omarchy: voice-driven OS changes. DHH says the next version will integrate Voxtype with the default agent so users can speak requests for widgets, panels, and apps. This is an announcement, not a demonstrated release or benchmark. @dhh

🎬 GO DEEPER

• Bilawal Sidhu on OpenClaw → Codex — Start with Sidhu’s migration story: six persona agents on an M1 Max and WhatsApp gave way to Codex as a connected daily driver that he can control remotely from the ChatGPT app without tunneling; he still uses Claude for many coding tasks. Riley Brown

Continue into the browser-as-shared-canvas workflow: Sidhu triggers YouTube A/B-test monitoring from his phone, while detailed Google Docs comments become Codex’s review input and he implements the final fixes himself. Riley Brown

• Study Simon’s pi_jsonl_to_md.py. It is a small, inspectable artifact of the local-agent loop above: Qwen received a single conversion request, wrote the Python utility, tested it, and the resulting tool was used to publish the transcript. simonwillison

Editorial take: The durable edge today is controlled delegation: tune model behavior, keep context explicit, deduplicate before fan-out, and make every external write or merge pass through a human-controlled boundary. simonwillison +2