# ai-agents

@daniel_mac8 shares an open-source Elixir implementation: create a Linear issue and move it to 'in progress,' and Symphony picks it up in a dedicated Codex workspace. Codex even writes status updates back. The author argues this is software development moving up an abstraction layer.

Teknium shared an experiment on Hermes Agent where the agent can steer itself — clearing its own context, switching models, and prompting itself when stuck. A short tweet, but it points at a big shift in how agent control works.

Simon Willison published his full workshop handout from NICAR's data journalism conference — a three-hour guide to using coding agents like Codex CLI and Claude Code for data exploration, visualization, and analysis.

The original author argues that Vera CPU reveals Nvidia's hand: they're not just selling GPUs anymore—they're building the entire AI agent stack, from compute to deployment.

Dan McAteer announced ACE is now open source and self-hostable. Hosted service remains available, with major improvements planned.

Simon Willison finally defines Agentic Engineering after 11 hands-on chapters: using coding agents to help build software. The interesting part is why he needed the patterns first before the simple definition felt earned.

Simon Willison's Agentic Engineering Patterns, Chapter 3: AI should help us ship better code, not worse. Technical debt cleanup costs near zero now, architecture decisions can be validated with prototypes instead of guesses, and quality compounds over time.

Simon Willison's First Run the Tests pattern is four words with three effects: the agent learns the test command, gauges codebase size, and shifts into a test-maintenance mindset. Tiny instruction, large behavioral nudge.

Simon Willison shared his agentic engineering playbook at the Pragmatic Summit — five tokens to start TDD, Showboat for manual verification, reverse-engineering six frameworks into a standard, and why bad code is a choice you make.

Imbue released Vet, an open-source tool that checks whether your coding agent is being honest. It reviews conversation logs and code changes, catching agents that claim tests passed when they never ran them. Runs locally, zero telemetry, CI-ready.

Andrej Karpathy argues the IDE era isn't over — it's evolving. The basic unit of programming has shifted from 'one file' to 'one agent,' and soon we'll be forking entire agent organizations.

We had Claude Opus run E2E tests on our own blog using Playwright, agent-browser, and Rodney. The surprise? The tool mattered way less than the prompt.

Andrej Karpathy shares how his autoresearch agent autonomously tuned nanochat's training config over two days, found ~20 improvements to validation loss that transferred to a larger model, and pushed the Time to GPT-2 leaderboard from 2.02h to 1.80h — about 11% better.

Simon Willison's new Agentic Engineering anti-pattern hits hard: do not submit AI-generated code you have not personally verified. That is not saving time; it is stealing reviewer time. The post pairs principles with a terraform destroy horror story.

A local AI agent called Hermes downloaded and switched to a new model (qwopus) without stopping — like swapping a plane's engine mid-flight. Teknium from Nous Research saw it and said 'submit this to a hackathon.'

Simon Willison introduces Agentic Manual Testing: let AI agents manually operate code and UI like humans do, catching bugs that automated tests miss. With Playwright, Rodney, and Showboat, the 'tests pass but it's broken' nightmare becomes a thing of the past.

Cursor's multi-agent coding architecture ran autonomously for four days and produced a proof for a university-level math challenge that yields stronger results than the official human solution.

Since Opus 4.6 dropped, developers are going through a fundamental shift — from being the ones who execute, to being the ones who verify. Your hands leave the keyboard, but your brain works harder than ever.

Oaktree's Howard Marks went from 'Is AI a bubble?' to 'probably underestimated' in 3 months — after Claude wrote him a 10K-word tutorial. Level 3 agents = multi-trillion dollar labor replacement. His advice: don't go all-in, but don't sit this out.

Karpathy shared a Cursor data chart showing the evolution from Tab completion to Agents. Too conservative means leaving leverage on the table. Too aggressive means creating more chaos than useful work. His advice: the 80/20 rule.