What test-time compute actually means, and why it changed the scaling debate
The idea that a model can get smarter by thinking longer, rather than by being bigger, reshaped how labs spend their money. Here is how it works.
01News
The reasoning-model era has a cost problem
Models that think before they answer are more capable and far more expensive per query. The bill is now a product decision, not an infrastructure detail.
Read the featureField notes: running an AI gateway in front of every model call
A gateway is a single door that every model request walks through. After six months with one in production, here is what it actually bought us.
How to compare LLM evaluation frameworks without getting sold to
Every evaluation framework claims to measure quality. The differences show up in what they let you measure, who can write the test, and what happens when a score drops.
Stop calling it hallucination
The word implies a glitch in an otherwise reliable mind. What the models actually do is guess with confidence, and the remedy depends on saying so.
The state of open-weight licensing, mapped
Every open-weight release comes with terms, and the terms differ more than the marketing suggests. A field guide to the licence families and what each one lets you do.
RAG vs. long context: choose by failure mode, not by benchmark
Retrieval and million-token windows both promise to put your documents in front of the model. They fail differently, and the failure you can tolerate should decide.
02Research
All research
Reading a model card like an auditor
The model card was meant to be a nutrition label. Many have become brochures. A checklist for separating disclosure from marketing.
03Tools
All tools
04Comparisons
All comparisonsRAG vs. long context: choose by failure mode, not by benchmark
Retrieval and million-token windows both promise to put your documents in front of the model. They fail differently, and the failure you can tolerate should decide.
