52 stories
AI can now generate a surprising amount of frontend code from a short description. A developer can ask for a form, a table, a modal, a settings page, or a dashboard view and get something that looks usable almost immediately. It may compile, render, and even arrive with a few tests.
I’ve known Jesse Vincent for more than 20 years, since the days when I was still editing and publishing Perl books and organizing the Perl Conference and he was the chief maintainer of Perl 5 and the project manager for Perl 6.
This post isn’t about OpenAI’s use of AI to solve the Navier-Stokes problem, one of the Millennium Prize problems, at least not directly. I’m not a mathematician; I have a vague understanding of the problem and certainly no understanding of the proof.
The buildings and physical infrastructure for a new hyperscale data-center region are complete. Power is live and redundant, the cooling loops are balanced, the network fabric is up, and tens of thousands of machines are racked, cabled, and reporting healthy.
I’ve been thinking about a gap in the way we discuss graphs in AI. Most of the conversation starts after the graph already exists. We talk about graph neural networks, GraphRAG, graph agents, and graph foundation models.
The following article originally appeared on Elevate and is being reposted here with the author’s permission. A software factory is a repeatable loop around software work. If you’re building a software factory, code good enough to ship still needs human taste and ownership.
The following article originally appeared on Arize’s blog and is being reposted here with the author’s permission. This week the AI community was in uproar about Jev from TypeSafe, not just a new model but a new kind of model: one that classifies, scores, and routes but can’t write a sentence.
Ask most engineers what MCP is and you’ll get the same answer: a way to plug tools into an LLM. Fair enough, as far as it goes. But that description treats MCP like plumbing, and after months building MCP-based integrations for large enterprise platforms, I don’t think plumbing is the right…
The following article originally appeared on Venkatesh Rao’s Substack, Contraptions, and is being republished here with the author’s permission. The sole athletic achievement of my life came in 1993: winning the IIT Bombay freshman 50m freestyle race with a time of 41s.
The early rapid expansion of AI capabilities that focused on frontier models was largely ushered into the world by a few powerful, US-based AI labs. Open-weight models released from labs in China, early on from DeepSeek, and later from Moonshot, Z.ai, and others, have in part disrupted that…
This is the third post in a four-part series about post-training. If you missed them, read part 1 and part 2. The final post, on implementing your own pipeline, will be coming October 7.
When considering the history of AI, Richard Sutton observed that brute force and compute scale has always trumped human expertise, and when you look for it, you can see this “bitter lesson” play out throughout tech history.
The following article originally appeared on Addy Osmani’s blog and is being republished here with the author’s permission. A software factory harnesses loops at scale. You can run the loop with humans in it (light factory), trading judgment and concentration against speed and breakage.
The way we talk about data is changing faster than the way we build it. Every quarter a vendor ships a new approach, coins a new term for it, or quietly adopts a term someone else has been using and redefines it to fit the shape of their product. None of this is malicious. Every […]
Every knowledge program seems to begin with the same request. A senior engineer is leaving in six weeks, and someone asks her to document the process she’s carried for years. She returns a clean flowchart of the happy path. The drawing is accurate and may even be elegant.
My guest on this past week’s Live with Tim O’Reilly was Emmanuel Ameisen, a researcher on Anthropic’s AI interpretability team. I’d heard him give a short talk at Foo Camp on Anthropic’s research into what is going on inside an LLM while it is processing, and I wanted him to reprise the talk and…
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