Lars Grammel

@lars.grammel.cloud

Working on software factories.

thoughts on measuring the effectiveness of a software factory there are 2 kinds of metrics: existing, standard metrics that should improve as the software factory takes on more and more work, and metrics specific to the software factory

Why is a dedicated software factory UI needed? once you set up the initial pieces like sandboxing, several agents, and basic orchestration, you want to run a lot of work in parallel while ensuring the processes are followed (and supporting human-in-the-loop supervision and manual work)

what is the evidence that pre-built agent harnesses such as claude code or codex are superior (in terms of outcomes)? e.g. compared to using an open source harness like pi or opencode? or compared to creating a custom harness for a specific task/project?

i think open source community PRs do not make sense any longer (issues are still very valuable though). why? because the cost/benefit ratio has shifted, especially as we move towards software factories for open source packages.

Use local docker sandboxes with AI SDK on Mac You can run tools against the sandbox, manipulate it directly, or use the AI SDK harness package to run e.g. Codex or Claude in the sandbox

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Make your own Mac app launcher. I published a small base project that handles the boring parts: global shortcut, launcher UI, app scanning, and launching. Fork it, then use Codex/Claude/Cursor to add custom shortcuts, prioritization, scripts, workflows, and design. github.com/lgrammel/app...

GitHub - lgrammel/app-launcher: Hackable app launcher base for MacOS

Hackable app launcher base for MacOS. Contribute to lgrammel/app-launcher development by creating an account on GitHub.

github.com

After the last Cursor update, I got fed up with their constant changes and increasing push towards their own models. I removed Cursor and switched to Codex plus VSCodium.

Added step performance stats to AI SDK 7 canary: - step time - llm response time - time to first token - tool execution times - tokens per second (several variants)

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i created a llama.cpp provider for vercel ai sdk that runs directly in the Node process (no separate server required, it uses llama.cpp bindings) the llama.cpp provider supports reasoning, tool calling, image inputs, prompt caching (for a single conversation)

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We revamped our (previously experimental) telemetry in AI SDK: - no OTel dependency in AI SDK (if you want OTel you can register our OTel integration, genai standard comaptible) - automatic emitting to the node diagnostics channel - telemetry integrations can be more custom and powerful

Humans have been writing software for about 75 years. AIs will write software for hundreds and thousands of years. Eventually everything - hardware, OS, libraries, APIS, services, applications - will be designed to facilitate AI software development.