brown

If LLMs live up to their potential, then they should be able to rewrite language runtimes to eventually be as fast or faster than systems languages. "Sufficiently intelligent compiler" and whatnot

It’s true that many providers need a custom solution for their unique workflows, and the one-size-fits-all EHR is often a myth. The problem is that many EHRs try to solve this with customizations, which can be expensive and still feel like a compromise.

On the other hand, when a team tries to build their own tools, they quickly realize they have to build a ton of compliance and interop code they never wanted to touch in the first place. That’s why open source platforms that handle the core infrastructure, like Medplum, HAPI, or OpenEMR, can be such a good starting point. They get the team 90% of the way there, so they can focus on what really matters: building a great UI/UX for their users.

I don’t think providers truly want to go back to pen and paper, but they are looking for a better way. They can see the promise of what the solution could be, but they just haven't experienced it yet.

Disclaimer: I work for Medplum.

> given AI seems like a winner takes all type market

Unfortunately for OpenAI and Softbank, it seems like AI will not be "winner take all", and may actually be quite commoditized. It's as easy as choosing a different model in a dropdown in Cursor or whatever your tool of choice.

RDAP replaces WHOIS, offering a more technologically advanced way to discover the domain is protected by privacy services.

Learning assembly was profound for me, not because I've used it (I haven't in 30 years of coding), but because it completed the picture - from transistors to logic gates to CPU architecture to high-level programming. That moment when you understand how it all fits together is worth the effort, even if you never write assembly professionally.

Love Postgres, but major version upgrades remain the one genuine pain point. One misstep and you're in backup-restoration hell. Solid guide.

There are two interesting "safety nets" at play here: the classic "Nobody ever got fired for choosing IBM" principle (where ubiquity creates an implicit guarantee of continuity), and a less visible but equally powerful one where certain open source tools become such fundamental infrastructure that they're essentially "too critical to fail." Think curl, gpg, or apt - most users never directly interact with these, but they're so deeply embedded in the internet's fabric that the ecosystem ensures their maintenance. One heuristic I've found helpful is looking at major corporate adoption patterns - it can be a decent signal for identifying which tools fall into these categories.

js13k is a game jam competition. Make a game that fits in a 13 kilobyte zip.

"The thirteenth, anniversary edition of the online js13kGames competition starts… NOW! Build a Web game on a given theme within the next month and fit it into a 13 kilobyte zip package to win lots of cool prizes, eternal fame, and respect!"

“Training costs scale with the number of researchers, inference costs scale with the number of users”.

This is interesting, but I think I disagree? I'm most excited about a future where personalized models are continuously training on my own private data.

My favorite URL oddity has to be "id.me" for U.S. Citizen identity services.

Seems a bit odd to use a Montenegro domain, doesn't it?