dcre

Do you think can they work for 5 minutes without guidance? Because that's something Ed said would not and could never happen, and the people who said it would were dupes and idiots.

I'm going with the pathologically incurious guy who is wrong in essentially every detail.

Everyone is free to make their own judgment about who is offering a genuine analysis that clarifies reality rather than obscuring it.

See edit. Tens of thousands of lines of borderline gibberish for the gullible.

It's a response to this: https://shumer.dev/something-big-is-happening

The post is silly, but I do not expect Zitron's commentary to be particularly illuminating as he is a charlatan himself. I could point to many examples, but here is a blog post I wrote about one case of him trying very hard to not understand a simple and familiar situation: https://crespo.business/posts/cost-of-inference/.

I can’t find any news stories mentioning a $500B target for Anthropic. You might be thinking of OpenAI’s October raise.

https://www.cnbc.com/2025/10/02/openai-share-sale-500-billio...

Not a serious test, but I tried M2.5 briefly in OpenCode on a very simple task equivalent to the last commit or two here[0] and it was really, really bad. This is a 250 line self-contained standalone script and what it does is very simple. M2.5 would have required far more detailed prompting to get me the result Opus 4.6 can do with the vaguest hints.

[0]: https://github.com/oxidecomputer/console/pull/3070/commits

More likely 99.9% of users never press ctrl+o to see the thinking, so they don't consider it important enough to make a setting out of.

Valve is primarily a software company with zero marginal cost to a game sale.

It doesn’t strictly exclude Europe, we have a few employees in Europe. But as the other reply says, they work slightly odd hours.

People often describe the models as averaging their training data, but even for base models predicting the most likely next token this is imprecise and even misleading, because what is most likely is conditional on the input as well as what has been generated so far. So a strange input will produce a strange output — hardly an average or a reversion to the mean.

On top of that, the models people use have been heavily shaped by reinforcement learning, which rewards something quite different from the most likely next token. So I don’t think it’s clarifying to say “the model is basically a complex representation of the average of its training data.”

The average thing points to the real phenomenon of underspecified inputs leading to generic outputs, but modern agentic coding tools don’t have this problem the way the chat UIs did because they can take arbitrary input from the codebase.

But that’s not an argument, that’s just assertion, and it’s directly contradicted by all the more rigorous attempts to do the same thing through benchmarks (public and private).

Just because there is someone who could understand a given system, that doesn’t mean there is anyone who actually does. I take the point to be that existing software systems are not understood by anyone most of the time.

That’s what I said: it’s technically true but misleading.

“Shrinking since the election”, while technically true, is misleading because the election is when bsky experienced a massive spike in usage that was well over double the average before the election. Usage has been gradually decaying since then to a steady level much higher than it was before the election.

If you zoom out to a few years you can see the same pattern over and over at different scales — big exodus event from Twitter followed by flattening out at level that is lower than the spike but higher than the steady state before the spike. At this point it would make sense to say this is just how Bluesky grows.

https://bsky.jazco.dev/stats

Besides that, the entire point of this project is to increase the barrier to entry for potential contributors (while ideally giving good new people a way in). So I really don’t think they’re worried about this problem.

Yes, but GPT-5.2 and Codex were widely considered slower than Opus before that. They still feel very slow, at least on high. I should give medium a try more often.

We have investigated. Millions of people are investigating all the time and finding that the coding capacity has improved dramatically over that time. A variety of very different benchmarks say the same. This one random guy’s stupid prompt says otherwise. Come on.

That is not what No True Scotsman is. I’m pointing out a bad argument with weak evidence.

Human-written compilers have bugs too! It takes decades of use to iron them out, and we’re introducing new ones all the time.

Yeah, this is a bullshit article. There is no such degradation, and it’s absurd to say so on the basis of a single problem which the author describes as technically impossible. It is a very contrived under-specified prompt.

And their “explanation” blaming the training data is just a guess on their part, one that I suspect is wrong. There is no argument given that that’s the actual cause of the observed phenomenon. It’s a just-so story: something that sounds like it could explain it but there’s no evidence it actually does.

My evidence is that RL is more relevant is that that’s what every single researcher and frontier lab employee I’ve heard speak about LLMs in the past year has said. I have never once heard any of them mention new sources of pretraining data, except maybe synthetic data they generate and verify themselves, which contradicts the author’s story because it’s not shitty code grabbed off the internet.

I think this is underrating the role of intuition in working effectively with deterministic but very complex software systems like operating systems and compilers. Determinism is a red herring.

This is dead wrong: essentially the entirety of the huge gains in coding performance in the past year have come from RL, not from new sources of training data.

I echo the other commenters that proprietary code isn’t any better, plus it doesn’t matter because when you use LLMs to work on proprietary code, it has the code right there.

Fine article but a very important fact comes in at the end — the author has a human personal assistant. It doesn't fundamentally change anything they wrote, but it shows how far out of the ordinary this person is. They were a Thiel Fellow in 2020 and graduated from Phillips Exeter, roughly the most elite high school in the US.

The use of the name Codex and the focus on diffs and worktrees suggests this is still more dev-focused than Cowork.

Because they are too slow and not smart enough.

Hard to think of a worse name. Maybe Moistbot?

Sort of. It’s not necessarily a single call. In the general case it would be spinning up a long-running agent with various kinds of configuration — prompts, but also coding environment and which tools are available to it — like subagents in Claude Code.

It's a ridiculous argument. Complete gibberish. They're all just API calls. There's no incentive to be biased toward approving bad code.

Oh! Well that makes sense.

TS + Deno + dax is my favorite scripting environment. (Bun has a similar $ function built in.) For parsing CLI args, I like the builders from Cliffy (https://cliffy.io/) or Commander.js because you get typed options objects and beautiful help output for free.

If you want to script in Rust, xshell (https://docs.rs/xshell/latest/xshell/) is explicitly inspired by dax.