My LLM drift tracker flagged four regressions this week. All four were wrong.

<blockquote> <p>A controlled study finds that data distribution, not just volume, dictates LLM capability. Benchmark-aligned training inflates scores but creates narrow, brittle

<p>Your AI system isn't going to crash. It's going to drift.</p> <p>A recommendation engine making 1.4 model calls instead of 1. A retrieval pipeline fetching 5 chunks instead of

<h2> The most dangerous failure is a clean run </h2> <p>The most common mistake teams make when moving a LangGraph pipeline to production is assuming that a successful exit means

<p>332 items. That's how many AI releases, papers, repos, and tool launches we ingested last week into the tracker I've been building.</p> <p>I sat down this morning to review the

<h1> Drift Detection for LLM Routing: Catching Silent Model Degradation </h1> <p>It's 2am and I am staring at a routing layer I spent weeks tuning, running a thought experiment

<!-- SC_OFF --><div class="md"><p>Projects are <a href="https://github.com/snap-research/locomo/issues/31">still submitting new scores on LoCoMo as of March 2026.</a> but the

<p><em>Archival copy of record, committed before this post: <a href="https://github.com/Tania-coder/SEISMOGRAPH/blob/main/docs/reports/2026-09-22-weather-report-02.md"

<p><em>What we shipped on 2026-07-16</em></p> <p>We caught a "Sonnet-canary leak" today that serves as a stark reminder of how dangerous misnomers are in an LLM pipeline. We had