Peter Steinberger 🦞

The usefulness of Amazon’s 6-page memo has been nuked by AI-generated text (employees sit in meetings suspecting text is AI and tune out). Suspicious memos tagged as AI;DR (AI, didn’t read).

Tool calls are the hidden bottleneck killing the AI coding speed. Models are getting faster, but tests and builds still take time, making the agent spend more time waiting for tool calls to end than coding. @steipete's Crabbox is one interesting way to speed up your agent. To make your agents faster, ask yourself: 1) Where is the time going? Measure which tool calls are slowing your agents down. 2) Can I run fewer checks? Select relevant tests instead of always running the entire suite. 3) Can I parallelize? Run independent tests concurrently. 4) Am I hitting CPU/RAM limits? Offload expensive work to other machines with tools like Crabbox. 5) Can I use faster tools? Try Oxlint instead of ESLint, or build your own. We built a Swift linter that speeds things up by 20x. We walk through these approaches, what we've tried, and the tradeoffs in our new episode. 00:00 The hidden bottleneck in coding-agent speed 01:27 Where agents spend their time 06:28 Running fewer tests without losing coverage 15:40 Parallelizing verification 17:43 Offloading tests with Crabbox 23:56 Faster tools: Oxlint and custom linters 29:30 Which optimizations are worth trying?

it’s clear we need to retire the term vibe coding because the models now produce better architecture & way more coherent code than an average software engineer. perhaps even better than top software engineers too because unlike a human, ai has a much much larger context window. this ain’t vibe coding at all. it’s precision engineering.