OPAL: a reactive optimizer that watches your training and tunes it for you
vincesitzmann · x · 2026-10-22
Discussion of OPAL, a reactive training optimizer that watches training signals and makes optimization decisions on the fly — aimed at compute-starved PhD students or small-dataset industrial use cases. Key insight from Xingjian: agent FLOPS and training FLOPS aren't fungible (Claude Code tokens can't train your model on four 5090s), so buying a few bucks of OPAL tokens is worth it in practice.
More from coding & agent
- SF Systems meetup: agents-only version control Tally, and Postgres rewritten in Rust for 900x faster analytics — ShadajL · 2026-10-22
- dsh-prompt-layer plugin injects your rules into DSH's system prompt, overriding AGENTS.md — teortaxesTex · 2026-10-22
- LLM writing still garbage in 2026, dev shows the blunt prompt he uses — arthurcolle · 2026-10-22
- "Patch my PHP vulnerabilities" — Claude also refactors 37 files and breaks the app — petrusenko_max · 2026-10-22
- He hooked a cheap receipt printer to Claude for proactive daily printouts — beglen · 2026-10-22
- Nous Research ships official Hermes Agent plugin for Meshy AI 3D generation via MCP — NousResearch · 2026-10-22