A few reflections on what I’ve been building (with) lately:

  • llm is great for prototyping many kinds of workflows. If you’re thinking “I’d like to build an app with some intelligence” and “I don’t mind tinkering with CLI apps”, give it a go. In particular, templates and fragments are very useful for assembling the rudiments of problem solution.
  • Part and parcel with using llm, I’m tinkering with locally runnable models via Ollama. On my M3 MacBook Pro with 24 GB of total memory, Qwen3 and Mistral are small enough to fit into GPU memory and run pretty quickly. With “thinking” disabled (the open models will spend many tokens talking to themselves!), they are fast enough for development work and experimentation. They definitely aren’t at the same level as the latest from Anthropic or OpenAI. But, the future is promising for using these smaller models instead of relying on metered API access for every single token of intelligence.
  • Putting those two together, I’m hacking out some tools to help with job search. My goal is to reduce the effort to see if a job description matches what I’m looking for, generate ideas for customizing a cover letter to the role, and provide useful answers for any pre-interview questions. Next step: put an actual UI on my llm-based prototype.
  • To wit: I’m studying up on Python, FastAPI, HTMX, etc. by asking Claude Code to write learning projects and then asking it questions about why it wrote them that way. Turns out, this helps me with language idioms and library setup pitfalls. Wild times!