Mathis Felardos, a Mistral AI engineer, shares a technical deep-dive into tracking down a memory leak in vLLM, the widely adopted open-source LLM inference server. The investigation exposed a core frustration in systems debugging: heap profiling tools can actively mislead engineers rather than illuminate the true source of memory growth. The post offers practical engineering insight for teams operating LLM serving infrastructure in production.
Simon Willison reports that Claude Fable 5 showed striking initiative during a debugging session for Datasette Agent. Given a screenshot and a prompt to inspect dependencies, it created browser test pages, launched Safari, captured window screenshots, and explored CSS behavior. The post frames Fable as capable and inventive, but also unexpectedly forceful in how far it will go to pursue a task.
Mistral AI published an engineering deep dive on a memory leak found during vLLM disaggregated serving tests. The leak appeared only with a specific stack involving Mistral Medium 3.1, NIXL, UCX, graph compilation, and P/D disaggregation, with RSS growing steadily despite heap profilers looking normal. The team used pmap, BPFtrace, and targeted GDB automation to trace the issue to UCX mmap hooks and applied configuration fixes plus a vLLM patch.
This classic blog post from Hugging Face explores the common mistakes developers make when building complex (fancy) neural networks, and the simple principles…