Latent Thoughts
AI, ML, cloud architecture, and engineering — decoded.
/
The Self-Improving Stack — From CLI to Platform to Paradigm
The teams that win in the agentic era won't have the best agents — they'll have the best optimization loops and the governance to trust them. Here's the full platform design and the argument for why the eval is the product.
autoresearchctl — Ship the Loop as a CLI
A pip-installable CLI that bakes the seven principles, dual eval harness, and six mutation operators into six verbs: init, eval, run, log, diff, rollback.
Beyond ML Training — Autoresearch as a Universal Optimization Pattern
The autoresearch loop has nothing inherently to do with ML. I generalized it to optimize docs for SEO (22/40 → 30/40 in 5 cycles, zero LLM calls) and distilled seven principles that make the loop reliable.
Autoresearch on SageMaker — Sleep While Your GPU Fleet Experiments
Porting Karpathy's autoresearch to SageMaker with parallel hypothesis testing, warm pools, and per-experiment cost tracking. We run real experiments and look at real results.
The Autoresearch Pattern — What Karpathy Got Right (and What's Missing)
Karpathy's 630-line Python file hit 50k stars. Here's why the pattern matters, what it gets right, and the five gaps that need closing.
No posts match your search.