Reproducible Evaluation of AI Coding Models

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

<h2> Your Code Reviews Are a Bottleneck </h2> <p>Every engineering team has the same problem: PRs sit in review queues for hours (or days). Senior engineers spend 30% of their

<!-- 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>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p>A patch touches zero test files. No assertion changes, no new skips, no fixture edits. The suite goes green on the first run.</p> <p>Then someone opens the job duration graph

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,

<p>Anthropic shipped Opus 4.7 on April 16, 2026. Same $5/$25 pricing as 4.6. Same 1M context. Same 128K output ceiling. Different model entirely when you put it in an agentic