LLM.
Reads like AI..
"It reads like AI" has become open source's way of losing an argument without having one. A request to maintainers: judge the comment, not your guess about who typed it.
What a token actually costs
Prices per token keep falling. Bills keep rising. How do you read inference pricing? Which margins are real, which are accounting, and where prices go next.
Bananas beat Paris
A reader's comment on the last embedding post sent me back to check EmbeddingGemma on two backends, llama.cpp on the GPU and FastFlowLM on the NPU, against the reference model. Both were wrong, in different ways.
Nobody can tell. Everybody thinks they can.
Notes on how readers decide a piece of writing is human, why they get it wrong, and what to do about it.
FastFlowLM: Systemd memlock Limits.
Fix mmap(err=-11) in FastFlowLM by overriding systemd's 8 MB DefaultLimitMEMLOCK. Learn why PAM limits fail for user services.
Too Long, Didn't Embed
A field guide to the quiet ways local embedding models fall over, and how to catch them before the bad vectors reach your index.
OpenLLMetry vs. OpenInference: The Observability Stack Your LLM App Needs
Your app looks fine on the surface, but users say it's failing. Traditional logs can't explain why. Enter OpenLLMetry and OpenInference—tools that bring clarity to AI observability by capturing rich, standardized telemetry from your LLM stack.
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