Sean Egan

Writing

Writing

Essays

  1. What a language model doesn't see The first drafts I get from language models present in text when people need to see the data, and on hard technical work they stop writing for the reader. I think it's one problem, and I'd like to work on it.
  2. Questions I'm still working on I build almost everything with AI now, and I train machine learning models for weather. Both have changed how I work, and both have left me with questions I haven't settled. Some are practical and some are ethical. I ask them about my own work first, and then about what they mean for other people.
  3. Coding agents without locks How three vendors' coding agents edit the same code concurrently without stepping on each other.
  4. A grounding doctrine for an agent's memory Provenance or it isn't evidence; re-ground before acting; modality survives every hop.
  5. Lint for LLM prose Thirteen rules that took 87 chapters from 61 failing to zero.

Field notes

  1. The tool catalog that was never sent I was told tool definitions were costing 65,811 characters on every call.
  2. 160 places my agent could lose your words One truncated safety instruction started a program.
  3. 93% of the bill was history I assumed tool calls were flooding my agent's context.
  4. Putting the right things in front of the agent, every turn I moved what the model sees each turn into plain code, and tested it blind.
  5. When the prompt files fight each other An agent's standing files compete with each other and with fresh memory for the same space.
  6. A 424-byte question that cost 4.2 million tokens The budget that should have stopped a runaway delegation had been recording zero for eight days.
  7. Five fixes that killed nothing A subagent ran for forty minutes and 117 browser calls, invisible to the agent that started it.
  8. The paper that sounded like a model talking to itself My paper drafts were accurate and read wrong.