The legal frameworks around AI training data are still arguing about the wrong layer. The interesting ownership question isn't whether your text was scraped. It's whether the captured shape of how you reason is yours.
Llama 4 is the first time Meta has shipped a mixture-of-experts open-weights model at frontier scale. The release is more interesting for what it implies about Meta's strategy than for its benchmark wins.
Bedrock is built for the enterprise integration story. Most of the AWS-doc treatment assumes that audience. Worth working through what the small-shop or solo-developer version of using it actually looks like.
The framing has become a cliché. The actual mechanics, onboarding, scope of work, performance review, termination, change in interesting ways once you take the metaphor literally.
WWDC 2024 promised the most ambitious consumer AI rollout of the year. Most of what shipped is the easy part. The interesting part, the part that would have actually changed how iPhones feel, is still pending.
MCP is the standard the integration layer of the AI stack converged on faster than anyone expected. Worth knowing what it actually is before you wire one in.
A framework I sketched a couple of years back: capture the priors that shape how someone reasons, ship the result as a portable artifact. Worth revisiting now that the substrate has landed.
OpenAI's o1 and o3-mini are the most public proof yet that scaling test-time compute beats scaling parameters at a category of problems. Worth being clear about which category.
Thirty days of letting an agent actually drive, write the code, run the tests, fix the failures, and then noticing the patterns in what changed. Not the productivity story. The discipline story.
A coding agent shipped by the model maker, built around the terminal rather than the editor. Worth working through what's actually different about that shape, and what it implies for the IDE category.
Three frontier models on the table at the end of February, three different bets about what "frontier" means. The interesting comparison isn't who wins, it's who fits which workload.
Reasoning tokens have to live somewhere. The somewhere is your context window. Worth working through what the budget actually looks like once you stop pretending output is the only thing that costs.
