Presumably, inference can be done on TPUs, Nvidia chips, in Anthropic's case, new stuff like Trainium.

Despite the hype that you see on Twitter, the hard tech startup scene is actually incapable of large-scale engineering coordination on the level needed for a nuclear power plant, or even a gas turbine.

If any innovation on fission reactors is going to be successfully commercialized, we will need to see billions of dollars of investment over medium to long time horizons.

Of course, the millstone around the neck of nuclear power is that it's a dual-use technology. There's probably a lot more behind the scenes that's been done to stifle the industry effectively for non-proliferation reasons, but masquerading as cost, regulatory problems, environmental concerns, etc.

There are just too many places, even in California, where I have to limit my trip because of electric range.

I'm surprised no one has done the generator.

That should in theory prevent overly redacted documents for political purposes.

An approach that could be rolled out today would be redacting with human review, but showing what % of redactions the AI would have done, and also showing the prompt given to the AI to perform redactions.

If the objective is to maximize investment by protecting successful results, I don't think our system is doing a very good job.

Votes are the output, not the input. Not that officials seems to listen anyway.

But what Rust has is the best tooling bar none(cargo, build system, compile time checks, ease of first use). The tooling is actually more important than the borrow checker and memory safety in my opinion.

If I clone a Rust repo, it’s actually easier to compile, test, and run the code than any other language. It avoided the fragmentation of JS/TS (npm, yarn, pnpm, bun, deno) and dep hell of Python (which was a nightmare until uv).

If Rust didn’t have the syntax warts (macros), it would be eating the world.

If you give a summary+graph to the model, it can still only attend to the summary for token 1. If it's going to call a tool for a deeper memory, it still only gets the summary when it makes the decision on what to call.

You get the same problem when asking the model to make changes in even medium-sized code bases. It starts from scratch each time, takes forever to read a bunch of files, and sometimes it reads the right stuff, other times it doesn't.

Because my understandings is that, however you get to 100K, the 100,001st token is generated the same way as far as the model is concerned.

There's like a significant loss of model sharpness as context goes over 100K. Sometimes earlier, sometimes later. Even using context windows to their maximum extent today, the models are not always especially nuanced over the long ctx. I compact after 100K tokens.

They are a conceptual overview and don’t seem tied down enough to the actual implementation details of a particular project.

Perhaps this could be the improved.

But taking experimental drugs while you're young is also much higher risk, and you might see people sacrificing their 20s for the sake of their 70s in a way they end up regretting, even if there aren't any side effects.

GitHub is their most trusted “tech” brand by far, and it has their only successful AI product, Co-Pilot.

It’s almost inevitable that GitHub and all its products will be consumed with Microsoft bloat in the next 5 years as more and more products coast off the GitHub brand.

Expect tabloid news in GitHub products soon.