Vendor stacks, protocols, certification paths, and field notes from delivery.
Teams tune the embedding model and the vector database, then wonder why answers are still wrong. Both are the least important parts of the stack. Retrieval is a two-stage ranking problem, and information retrieval solved the shap…
Most teams reach for fine-tuning when they should reach for retrieval. The cost is not just dollars - it is months of confused engineers trying to figure out why the model started lying again.
Most teams still treat Anthropic as 'the Claude API.' That framing misses the platform that has grown around it: managed agents, a universal integration protocol adopted by every major AI vendor, a governance layer with 28 securi…
Anthropic stopped shipping a chatbot and started shipping an ecosystem. From Artifacts to Cowork to Design, the through-line is the same: move the model out of the chat box and into the work. For marketing teams, that change is n…
Open surgery beat percutaneous nephrolithotomy on small kidney stones, 93% to 87%. It beat it on large stones too, 73% to 69%. Pooled across both, it lost, 78% to 83%. The arithmetic is correct in all three statements, and no amo…
A fraud model can score perfect recall offline and block nothing in production, because its training join looked a few hours into the future. Feature stores exist to enforce one contract: a training row may only see what the serv…
The most consequential AI release of late 2024 was not a model. It was a protocol. The story of how a single open standard ended the N-times-M integration nightmare is the story of every standard that ever mattered.
In 2019 a masked language model recalled facts almost as well as a relation extractor with an oracle entity linker, and people asked whether knowledge graphs were finished. They were not, but the reason is narrower than their adv…