Are you one of those naive people that still take these coding benchmarks seriously?

If you randomly sample letters from the alphabet and those letters make up actual words, then actual sentences. Did you think about it? Probably not

> you randomly sample letters from the alphabet and those letters make up actual words, then actual sentences

That sounds like a decently apt description of how I (a human) communicate. The only thing is that I suppose you implied a uniform distribution, while my sampling approach is significantly more complicated and path-dependent.

But yes, to the extent that I have some introspective visibility into my cognitive processes, it does seem like I'm asking myself "which of the possible next letters/words I could choose would be appropriate grammatically, fit with my previous words, and help advance my goals" and then I sample from these with some non-zero temperature, to avoid being too boring/predictable.

It's not sampling randomly though.

If "randomly sampling from a trained distribution" can't produce useful, meaningful output, then deterministic computation is even more suspect. After all, it's a strict subset. You're sampling with temperature zero from a handcrafted distribution.

(this post directionality ok, but there's many a devil in the details)

How do we know we're not doing that based on our memories and reaction to external stimuli though?

You should read the article you posted before you write a comment. Hint: check P_F=0 in tables 2, 3 and 4.

"Factored" is doing a lot of lifting here and is borderline deceptive. Plenty of researchers have long ago pointed out that this won't scale, see M Mosca for reference.

I'm aware; I don't think gate model machines have demonstrated much potential of scaling in practice any time soon so this is more of a lark to show how unimpressive the current Shor's attempts have been

To me, Github has always seemed well positioned to be a one-stop solution for software development: code, CI/CD, documentation, ticket tracking, project management etc. Could anyone explain where they failed? I keep hearing that Github is terrible

It always starts out good enough, but the reason they pursue horizontal integration is that it ensures that you won't be able to get out even if (when) you eventually want to. You'll be as glued as a fly to flypaper.

That's the reason you hear the complaints: they're from people who no longer want to be using this product but have no choice.

Because Microsoft doesn't need to innovate or even provide good service to keep the flies glued, they do what they've been doing: focus all their resources on making the glue stickier rather than focusing on making people want to stay even if they had an option to leave.

We use GH and are investing more in the platform features.

Codespaces specifically is quite good for agent heavy teams. Launch a full stack runtime for PRs that are agent owned.

    >  keep hearing that Github is terrible

I do not doubt people are having issues and I'm sure there have been outages and problems, but none that have affected my work for weeks.

GH is many things to many teams and my sense is that some parts of it are currently less stable than others. But the overall package is still quite good and delivers a lot of value, IMO.

There is a bit of an echo chamber effect with GH to some degree.

We use GitHub actions and we have more build failures from actions than we do any other source.

They got acquired by Microsoft.

If this happens to software development, this will happen to most mental jobs.

CRUD

It's always something that already exists but requires 100x the code.

(Geopolitical) prediction markets almost always tend to overreact. This is expected from retail, so I'm not sure about the signal.

So why not just always bet against their reaction? Over time you should make money then, right?

Oh, like the LLM OS?

Sure. Reading a book is a much more difficult and ultimately, productive, task than writing a book.

This is just the creator of Claude Code overselling Claude Code