Built a training stability monitor that detects instability before your loss cur

<!-- SC_OFF --><div class="md"><p>Anyone actively training models want to try a stability monitor on a real run? Trying to get real world validation outside my own benchmarks.</p>

<!-- SC_OFF --><div class="md"><p>We just did a big revamp of <strong>WeightsLab</strong> and wanted to share it here.<br /> If you’ve ever spent hours debugging a training run

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1s7p0au/tracer_learntodefer_for_llm_classification_with/"> <img alt="TRACER: Learn-to-Defer for LLM

<!-- SC_OFF --><div class="md"><p>I've been applying the Fiedler value (second-smallest eigenvalue of the weight graph Laplacian) combined with Scheffer critical slowing down

<!-- SC_OFF --><div class="md"><p>Disclosure: I built LabelSets (labelsets.ai). Sharing the technical approach behind how we score dataset quality.</p> <p>THE PROBLEM</p> <p>Most

<!-- SC_OFF --><div class="md"><p>Been heads down building graphify since April 5th. The idea: instead of stuffing your entire codebase into context every session, build a

<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2e0d5/built_an_opensource_llm_router_for_consumer_gpus/"> <img alt="Built an open-source LLM router for