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15:21

Plan With Code, Not Prose: pstack Pt. 2

别写抽象计划:pstack 用代码做规划与架构

Part two of poteto's pstack guide covers the front half of the agent workflow: research, planning, prototyping, and architecture. She argues agents mostly fail for two reasons — under-specified intent and missing context — so she has the agent restate a reported problem in its own words before touching code, then uses /how, /why, /recall, and /teach to rebuild runtime mechanics and historical motivation from git history, PR comments, Linear, Slack, Datadog, and Sentry. Planning happens in code rather than prose: README-driven development, a tutorial written before the API, throwaway prototypes driven by /control-app with screenshots and timing measurements, and /architect, which spins up parallel design candidates across model families and has a different model cross-judge them. Escape hatches like any, forced casts, or the same workaround at unrelated call sites count as proof the design is wrong. Aimed at engineers already shipping agent-written code to production.

14:58

The Complete Guide to pstack, Pt. 1: Verification Is All You Need

让 agent 自己验收:pstack 的验证技能怎么搭

poteto argues that a verification skill — an agent's ability to drive the app, screenshot it, read traces and confirm its own change — is critical infrastructure rather than a nice-to-have, because it takes the human out of the loop. The post walks through /create-verification-skill: wrap dev-environment control in an agent-friendly CLI (doctor, snapshot, screenshot, wait-settle, feature-flag), then catalog every feature in a markdown Feature Map describing what it does, how a user reaches it, and known gotchas. She advises against scaling with git worktrees (roughly 10 agents per machine before resources bite) and recommends Cursor cloud agents plus /swarm for parallel verification runs. Wired into a scheduled routine, the skill can auto-reproduce user bug reports from Slack. The CLI design rules — --dry-run on destructive commands, descriptive errors, JSON output, progressive disclosure via subcommands — are the most transferable part. Claims of 2,000 PRs/month and 100-1000x output come with no measurement.

14:58

Karpathy: ask LLMs for diagrams, HTML pages, and explainer videos

Karpathy:别只读文字,让 LLM 画图、出网页、做讲解视频

Karpathy argues that as models improve, more of our work shifts upward into oversight and understanding rather than production, so we should optimize the format of model output. He suggests a ladder of increasing readability: have the model rewrite explanations in ASD-STE100, the controlled-language spec built for aerospace maintenance docs (or ask for "80% of the way" there when the constraints bite); ask for diagrams; ask for HTML and get an interactive page with animation; and, his most bullish bet, custom 3b1b-style explainer videos with ElevenLabs narration or a free local substitute. Because intelligence and code are increasingly abundant, you can request large, discardable software artifacts that never made economic sense before.

x.com · 2 min · Ai Tooling · LLM · Prompt Engineering · Technical Writing