Apple study: LLMs not consistently Bayesian in belief updates

A Reddit user built a custom ComfyUI node called "durable" to stop overnight runs dying from OOM errors or random crashes that left an empty queue. The node is a few MB to download.

Two r/ClaudeAI posters report passing Anthropic's Claude certification exams: one scored 781/1000 on CCAF after roughly two weeks of prep, calling the questions confusing; another says they hold the Architect (Professional and Foundations) and Developer Foundations certs.

SelfCompact lets the model itself decide when to summarize its context via a compaction tool plus a lightweight rubric, needing no fine-tuning. Across six benchmarks and seven models it beat a no-summarization baseline by up to 18.1 points on math and 5-9 points on agentic search at 30-70% lower per-question cost.

Apple researchers present Minions, a distributed virtual GPU cloud that pools idle GPUs across macOS devices to run large-scale ML inference without buying new hardware. The system adds fault tolerance and flexible task execution for teams facing constrained centralized GPU capacity.

graphsignal-top ranks GPU kernels by time, live, with readable names, inside the open-source Graphsignal profiler. It wraps vllm serve or sglang serve via graphsignal-run and serves measurements as JSON so an agent can read them in a loop.

Apple researchers tested LLMs against human annotators on MQM and Error Span Annotation across 70 language pairs plus WMT23 and WMT25 data. LLM agreement beat human-human agreement on some tasks but varied widely by scheme, language pair, and domain, staying unreliable in most settings.