Topic
10 articles tagged “AI Strategy”.
Dan Lorenc of Chainguard says we're staring at the largest technology modernization effort ever, and nobody realizes how big or how fast it's going to be. I think he's right, and most of the organizations we work with aren't ready for it.
Eight models, one prompt, one screenshot: rebuild the Superhuman UI in SvelteKit. Four free models, four paid ones, and the cost per attempt ranging from nothing to $1.18.
Claude, ChatGPT and Grok all went offline at roughly the same time. If AI sits at the core of your product, availability is a design problem, not a vendor problem.
We split building a product into eleven jobs and tested free and paid models against each one. For most of them, the free model is good enough. Here is where paying is worth it, and where it never is.
This week's AI Briefing: an Altman, Musk exchange about SpaceX being worth more than the planet turned into the more interesting question underneath it, can you actually run AI inference in orbit, and does the maths close? Not yet, and here's why.
Most LLM outages are not caused by the model. They are caused by the pipeline around the model. The retries, the routing, the fallbacks, the rate limits, the observability. A concrete playbook for hitting three-nines on an LLM-powered product.
Two days at AWS Summit in Washington, DC, big, well attended, informative, and underwhelming. In the rush to bolt an LLM onto everything, a lot of vendors forgot to do anything novel. Notes on what impressed, what didn't, and why it's still the data that decides the outcome.
Every week there's a new headline about AI replacing software engineers. The data tells a different story, and the real gap it's exposing isn't in code production, it's in engineering leadership.
Every product now has an 'AI' badge, but slapping a label on a fridge doesn't make it intelligent. Three critical questions separate leaders who understand AI from everyone else buying stickers.
Anthropic's acquisition of Bun isn't just another dev tool purchase, it's a signal that AI companies are done relying on external infrastructure. When you own the model, the dev tools, and the runtime, you control the entire coding experience.
