Simon Willison reflects on how the once-clear line between careless vibe coding and responsible agentic engineering has begun to blur in his own work, raising sharp questions about trust and accountability in AI-assisted software development.
LocalLLaMA lit up because Xiaomi MiMo dropped an MIT-licensed MoE with 1.02T total parameters, 42B active parameters, and a 1M-token context window. The excitement was real, but so was the hardware reality check: people loved the openness and agentic claims while joking about how many serious GPUs you still need.
A smaller release drew outsized attention on LocalLLaMA because LFM2.5-350M is not trying to be a general-purpose chatbot. Liquid AI is pitching it as a compact model for tool use, structured outputs, and data-heavy edge workflows.
A Reddit thread in r/LocalLLaMA drew 142 upvotes and 29 comments around CoPaw-9B. The discussion focused on its Qwen3.5-based 9B agent positioning, 262,144-token context window, and whether local users would get GGUF or other quantized builds quickly.
A high-signal Hacker News post highlighted StepFun's Step 3.5 Flash launch, describing a 196B-parameter MoE foundation model with about 11B active parameters, 256K context, and vendor-reported coding/agent benchmarks.