AI Agents, Growth & Startups

Detailed writeups on multi-agent orchestration, AI engineering patterns, and what actually works in production.

Optimizing Skills

I spent two weeks benchmarking agent skills on Steel. The surprising result wasn't that prompts matter. It was that variance is expensive, browsing makes it obvious, and the biggest unlock came from redesigning the CLI so the environment carried more of the load.

AI-Native Dev Teams Start With Structure, Not Models

If you want an AI-native dev team, don't start with autonomous coding demos. Start with requirement quality, design systems, task schemas, centralized memory, and explicit validation. AI amplifies structure. It also amplifies chaos.

The Bubble and the Long Game

The printing press took 60 years to become economically sustainable. LLMs are on a similar diffusion path—broad adoption, shallow integration. The winners won't have model access—they'll have the complements: context, workflow, trust.

Claude Code with Multiple Accounts on One Machine

The clean way to use Claude Code with both your normal account and z.ai is one neutral config and two simple entry points.

The Post-Copyright Era of Software

Copyright does not disappear in the AI era, but it stops functioning as a meaningful scarcity mechanism for software. As reimplementation gets cheap, the real moats shift to trust, governance, provenance, maintenance, and operational legitimacy.

Explore once, script forever: turning web runs into scripts

Give the agent a Steel CLI and SKILL.md contract, force a snapshot/click/fill loop, then turn the successful run into a rerunnable bash script.

The Hidden Language of Search

There's a hidden layer between human questions and search results. AI tools translate messy prompts into precise queries - and you can see the evidence in Google Search Console.

What Makes a Great Coding Agent

The best coding agents aren't about smarter models. They're about harness design: minimal core, extension hooks, radical transparency, real sandboxing, session forking, and headless RPC.

Designing CLI Tools for AI Agents

AI agents are now power users of your CLI tools. If you want them to succeed, you need structured output, deterministic exit codes, explicit sessions, and recovery primitives. Here's the complete checklist.

From Bash Script to AI-Native Go CLI in One Session

A single AI session turned `scribe.sh` into `scriby`: a Go CLI with deterministic output, runtime bootstrap, and cross-platform releases.

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