Editorial
Notes on an ecosystem that moves faster than the press releases.
Long-form coverage of open source AI, written for readers who already know the basics and want the part the announcement omitted.
Kimi K3 changes the definition of 'open'
A 2.8-trillion-parameter model with public weights that almost nobody can run. What the largest open weight ever released actually means for people who run AI locally.
July 18, 20269 min readFrontier · Kimi K3 · AnalysisChoosing a GGUF quantization without lying to yourself
Q4, Q5, Q8 and the rest of the GGUF zoo, with a practical decision rule that holds up across hardware classes.
May 15, 202610 min readQuantization · llama.cpp · GuideApple Silicon or NVIDIA for local LLMs in 2026
Unified memory, raw VRAM, and the workloads where each wins. A practical comparison that goes beyond benchmark snippets.
May 14, 202612 min readHardware · Comparison · Apple SiliconWhy openSUSE is a serious option for running AI locally
Rolling releases, immutable variants, and an honest line between community Linux and paid enterprise. A practical look at when openSUSE earns its place in an AI stack.
May 13, 20269 min readopenSUSE · Linux · InfrastructureThe state of open weights in May 2026
Five frontier-class releases in the last thirty days, three of them from Chinese labs. A short tour of where the field actually is.
May 12, 20269 min readModels · Ecosystem · AnalysisWhich local inference engine should you actually use
Ollama, llama.cpp, LM Studio and vLLM solve different problems. A practical map of when to reach for which, and why it matters more than benchmark numbers.
May 5, 202611 min readTools · Inference · Guide