A collection of 14 posts about agents.
I built Daytripper and RepoFinder entirely with OpenAI Codex, then turned my Claude Code tooling loose on the results. The QA and design findings are numbered in the git history, one regression test cites the report that found the bug, and both products came out better than either agent would have produced alone.
RepoFinder turns a project and a goal into a short list of open source tools that genuinely fit, then lets you interrogate every pick. Built entirely with OpenAI Codex on the Responses API: gpt-5.6-luna for extraction, gpt-5.6-terra for ranking and chat, strict JSON Schema outputs, a labeled fallback, a remote MCP tool, and 15 checked-in lessons on how Codex actually works.
I learn AI coding agents by shipping with them. Daytripper is the first app I built to learn OpenAI Codex: a travel planner built in one overnight session, deliberately zero LLM calls at runtime, with tests covering all 1,296 possible preference combinations, deployed to Cloudflare Workers. Here is what the build taught me about how Codex actually works in August 2026.
I'm tired of my Mac Mini powering down or dropping my agent while I'm away from home. Here's the exact pmset and LaunchDaemon setup that made it self-healing, including the bootstrap error that almost made me give up.
We shipped RepoRadar in four hours at the AI Tinkerers SF Generative UI Global Hackathon: a radar that ranks trending GitHub repos, then an agent deploys a working micro-app for any of them at its own subdomain. Here's what we built, how it works, and how to grab it.
Cloudflare and Stripe shipped an agent provisioning protocol last week that lets autonomous agents create accounts, register domains, and deploy production code. I work at Cloudflare. Here's why this is the announcement I keep coming back to.
There's a new agent product announcement every other day. Nate B Jones gave me a way to triage them in 30 seconds. I'm using it.
Cloudflare ran Agents Week last week. I work there, and even I needed a second pass to absorb the scope. Four announcements actually change how I'd architect a personal agent stack today.
Matthew Berman has spent 2.54 billion tokens learning what OpenClaw is actually for. The list of 21 use cases is interesting. The decision underneath is more interesting.
Matthew Berman's framing in his March OpenClaw tutorial reframed how I think about my agent. Hope isn't a script I run. She's someone I onboarded.
Alex Finn's case for running OpenClaw on local hardware is right. His specific model recommendation isn't, if you're on the base Mac Mini I'm running. Here's what fits and what to use instead.
Everyone's arguing about which model is smartest. The real game is being played across three layers - and the most interesting one is the one nobody's talking about.
Most people use AI as a chatbot. I run autonomous agent fleets that build, trade, research, and create while I sleep. Here's why the multi-agent approach changes everything.
Everyone debates which model is smarter. The real constraint that determines what AI agents can actually do? The context window.