The AI Daily Brief: Artificial Intelligence News and Analysis

Autoresearch, Agent Loops and the Future of Work

1378 snips
Mar 9, 2026
A new system runs overnight experiments to tweak models automatically while a human defines success. The conversation unpacks how iterative agent loops, inspired by a simple coding pattern, externalize memory and keep what works. Examples span software, sales, and finance as roles shift toward designing strategies and evaluators. Listeners are challenged to try automating repeatable tasks with clear ‘better’ signals.
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AI Agents Running Overnight ML Research

  • Andre Karpathy's AutoResearch hands the full ML experiment loop to an AI agent that edits train.py and runs fixed five-minute training runs.
  • The human writes program.md (a research strategy doc) and the agent keeps only commits that improve validation BPB, iterating dozens to hundreds of times overnight.

Agent Loop As A Universal Primitive

  • The loop pattern abstracts to four steps: human writes strategy, agent runs experiments, a clear metric decides what stays, and repeat many times.
  • Craig Hewitt and others say the architecture and mindset matter more than the specific code or model.

Company Brain File Prevents Agent Amnesia

  • Vadim from Vugola built company-wide loops where every agent reads and appends to a shared learnings.md to avoid pure amnesia.
  • This turned isolated agent runs into a network that accumulates knowledge and runs tens of thousands of experiments.
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