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🔥 TOP SIGNAL
The highest-alpha workflow today is an agent maintenance queue, not another chat-to-PR demo. Boris Cherny says Claude Tag runs from a Slack channel with daily routines across iOS, Android, Desktop, web, CLI, and Agent SDK: a simulator crash fuzzer, duplicate-abstraction unifier, dead-code remover, and “abstraction police.” Over a few weeks, those routines opened 388 PRs; 180 were merged after Claude Code Review plus human review, and failures were fed back into routine tuning.
⚡ TRY THIS
Front-load alignment, but batch the human I/O. swyx modified
/align-meto ask questions in batches rather than round-by-round, looking 2–10 steps ahead; he says it works “INCREDIBLY” for design exploration. Theo says Matt Pocock’sgrill-meskill helps align agents with his intent; in one long session, question 27 exposed the real goal and cut scope by about 90%. Run a batched pre-build interview, then hand the resulting scope to the coding agent. Review the alignment artifact first: Theo says the output can be slop enough to override hisunslopskill.Make subagents a fallback, not the default path. Unifi models a subagent as an explicit function call carrying a prompt, model, and reasoning budget; its main agent maps code over rows and waterfalls through cheaper APIs first, invoking the subagent only after those options are exhausted. The economic reason is concrete: 1,000 calls at one cent each costs $10 against a $20 base plan. Build the interface with an explicit model and budget, try deterministic or cheaper routes first, and escalate only on failure.
Add a real planning pass before expensive execution. Connor Heggie says a robust first step moved the needle: pause, brainstorm solution paths and pitfalls, ask clarifying questions, and scout high-recall versus high-precision trajectories before running the full task. Pair that with trace review: Unifi says its 90–95% cost reduction came partly from reducing mass subagents, removing contradictions between system and skill prompts, and eliminating tool calls whose results were not used.
📡 WHAT SHIPPED
Agents on Rails benchmark. The first report ran 8 models against 21 atomic Rails tasks, with three runs per task covering a bug report, security finding, and feature request. Claude Opus 5 led at 92% solved (58/63); Kimi delivered almost the same accuracy for a little over half the cost. GPT-5.6 Luna was cheapest and fastest at 73% solved, $0.90 for all 63 runs, and a 3.3-minute median; GPT-5.6 Sol was the best combined result at 84%, $0.52, and 5 minutes per run. Treat this as a Rails-specific routing snapshot, not a universal leaderboard.
DeepSeek Harness v0.1 entered Developer Preview under the MIT license. Built on Cordis, it treats models, tools, skills, sessions, sandboxes, filesystems, loops, orchestration, and UI as replaceable plugins; the deepseek-ai/deepseek-harness repo is open. A separate post by @eliebakouch claims roughly 20% of the harness’s commits and PRs come from Codex worktrees—an interesting adoption signal to verify, not a benchmark.
Cursor Builds now prepare ready-to-use development environments continuously in the background, with Cursor saying cloud agents start 3× faster and builds add no extra cost. Failed builds never go live; agents continue from the last successful build while the new one is debugged. Cursor says Faire, Headway, and Descript have seen starts fall from minutes to seconds and are increasingly trusting cloud agents with end-to-end tasks.
Grok 4.6 got a useful plan-following test. DHH gave Grok 4.6 Fable’s existing Rust-rewrite plan; with “a couple of nudges,” it repeated the work in 1 hour 24 minutes using 8.6M tokens at about $55—roughly one-tenth of Fable’s implementation cost. The important caveat is that Grok did not plan the project from scratch, so this is evidence about execution and cost, not autonomous end-to-end planning.
Gemini 3.7 Flash is available in the API, AI Studio, Antigravity, and more; Google’s announcement says it is 50% cheaper than 3.6 Flash through year-end and gained intelligence in roughly three weeks. Google DeepMind claims gains in debugging and issue resolution, web layouts with fewer prompts, and real-world business workflows. Those are vendor claims; the Rails report above is the more useful independent comparison signal for coding-agent routing.
AgentCookie is a small open-source fix for a recurring cloud-agent failure mode: it syncs Chrome cookies from a Mac to Grok Bot in the cloud using Tailscale so the agent does not get logged out. Repo: github.com/mvanhorn/agentcookie.
🎬 GO DEEPER
- Harrison Chase — “When to Build Your Own Agent Harness”. Start with a general harness for fast time-to-value, then customize as the task moves out of the model’s training distribution; keep model-native tools for subtasks such as file editing. The eval section is the useful implementation detail: Harbor packages a Dockerfile-defined sandbox, golden solution, tests, and
instruction.md, while experiments track accuracy, latency, and tokens.
- Connor Heggie — “How Unify cut its AI agent costs 95% in two weeks”. Watch the cost-control segment for the move from mass subagents to a smarter main agent, trace-bucketed prompt optimization, contradictory-instruction cleanup, unused-tool-call reduction, and the upfront planning pass.
- Study DeepSeek Harness for plugin boundaries. Its v0.1 design makes the harness—not just the model—the interchangeable unit: swap models, tools, sessions, sandboxes, filesystems, loops, orchestration, and UI independently.
Editorial take: The durable edge is the control loop: narrow routines produce reviewable PRs, planning and cheap-first routing suppress waste, and harness-level evals decide what can safely run unattended.