Content History
Interactive explanations
Agentic Engineering Patterns > When we lose track of how code written by our agents works we take on cognitive debt. For a lot of things this doesn't matter: if the code fetches some data from a database and outputs it as JSON the implementation details are likely simple enough that we don't need to care. We can try out the new feature and make a very solid guess at how it works, then glance over the code to be sure. Often though the details really do matter. If the core of our application becomes a black box that we don't fully understand we can no longer confidently reason about it, which makes planning new features harder and eventually slows our progress in the same way that accumulated technical debt does. How do we pay down cognitive debt? By improving our understanding of how the code works. One of my favorite ways to do that is by building interactive explanations. Understanding word clouds In An AI agent coding skeptic tries AI agent coding, in excessive detail ...
MLX is an astoundingly great piece of software which helped make Mac hardware credible as a platform for running LLMs I'm surprised Apple didn't move heaven and earth to keep Awni, can't wait to see what what he does next
Today is my last day at Apple. Building MLX with our amazing team and community has been an absolute pleasure. It's still early days for AI on Apple silicon. Apple makes the best consumer hardware on the planet. There's so much potential for it to be the leading platform for
Just deleted this tweet because it implied Google had been banning long-term existing Google accounts for using Antigravity accounts in unsupported ways, but that doesn't appear to have been the case
Unicode Explorer using binary search over fetch() HTTP range requests
Unicode Explorer using binary search over fetch() HTTP range requests Here's a little prototype I built this morning from my phone as an experiment in HTTP range requests, and a general example of using LLMs to satisfy curiosity. I've been collecting HTTP range tricks for a while now, and I decided it would be fun to build something with them myself that used binary search against a large file to do something useful. So I brainstormed with Claude. The challenge was coming up with a use case for binary search where the data could be naturally sorted in a way that would benefit from binary search. One of Claude's suggestions was looking up information about unicode codepoints, which means searching through many MBs of metadata. I had Claude write me a spec to feed to Claude Code - visible here - then kicked off an asynchronous research project with Claude Code for web against my simonw/research repo to turn that into working code. Here's the resulting report and code. One intere...
I didn't think we would get Gemini 3.1 Flash Image before we got Gemini 3.1 Flash!
Say hello to Nano Banana 2, our best image generation and editing model! 🍌 You can access Nano Banana 2 through AI Studio and the Gemini API under the name Gemini 3.1 Flash Image. We are also introducing new resolutions (lower cost) and tools like Image Search!
