He’s replying on this twitter thread - perhaps someone with an account can ask there and link his comment here?

https://xcancel.com/RonanFarrow/status/2041127882429206532#m

Here is the actual link, not a link to some weird third-party site that can't be trusted.

https://x.com/RonanFarrow/status/2041127882429206532

Whereas X can be trusted?

Yes? It's the data source, not a third-party. How is this even a question?

X restricts what you can view without logging in. Many folks don't want to log in to X, for obvious reasons. Posting an xcancel link is kinda like folks posting various `archive` URLs to bypass paywalls, work around overloaded servers, etc. That's an extremely common practice here that usually goes without comment.

What is an "obvious reason" one might not want to log into X? I can't think of any rational reason.

You can't think of even one reason?

https://www.theguardian.com/technology/2026/feb/12/elon-musk...

There's pedantic, and then there's needlessly pedantic.

xcancel is a valid workaround for X links on Hacker News and is sufficient for original attribution.

FYI xcancel is just a mirror that allows reading replies without needing an account.

> You may want to provide proof online that you are who you say you are

Unfortunately it probably doesn't even matter here on HN considering how brigaded down this story is predictably getting.

But yeah, it was a fantastic piece.

Fair request, here you go: https://x.com/RonanFarrow/status/2041203911697068112

It wasn't getting "brigaded down" - it set off a software penalty called the flamewar detector. I turned that off as soon as I saw it.

Thank you for keeping HN sane :-)

Thank you for this, very much appreciate the thoughtful response.

The piece captures some of the anxieties within OpenAI right now about their competitive position. This obviously ebbs and flows but of late there has been much focus on Anthropic's relative position. We of course mention the allegations of "circular deals" and concerns about partners taking on debt.

Thank you. Yes, I saw that. The company's always been surrounded by endless talk about insane hype, speculative bubbles, and financial engineering. I wasn't asking so much about that.

I was asking more about your informed view on how OpenAI's technology, products, and roadmap are perceived, particularly by customers and partners, in comparison to those of competitors.

If you have an opinion about that, everyone here would love to hear about it.

My guess is that the answer to your question, fantastic question, is that nobody knows. I remember having the same thoughts when Covid was first “arriving” if you will: we wanted people in the know to throw us a nugget of information, and they just didn’t know.

As it turns out, and what I’m kind of going with for this LLM shit, is that it’ll play out exactly how you think it will. The companies are all too big to fail, with billionaire backers who would rather commit fraud than lose money.

How would fraud help here? Don't they just need scale of lots of customers paying a little bit? How do you fraud your way into that?

they don't need customers, when the customers ere each others companies for example the deals openAI nvidia oracle made

That's not fraud, and it's not sustainable. They aren't going to just keep doing that. It only makes sense if an AI company wants to pay for GPUs with stock, and - more importantly - the GPU company agrees to sell in exchange for stock.

s/fraud/corrupt, illegal $something.

If you're picking on my vocabulary, that's fair. Fraud wasn't the point, I think you're smart enough to realize that.

I appreciate the implication that either you're right or I'm stupid, but maybe you should write the comment you meant to write.

Trading shares for GPUs is not corrupt either.

Ronan Farrow's expertise is investigations into elite amorality, not evaluating technical products. Why are you asking this question?

Much of the article and general palace intrigue is predicated on the idea that OpenAI has a singularly revolutionary product. If it later turns out to be a commodity, or OpenAI is simply outcompeted nonetheless, then the idea that Sam Altman's personal shortcomings are something to stress about would seem quaint. Just another hubristic tech billionaire acting in bad faith doesn't really pry attention the same way as someone "owning your future".

I didn't asking him to evaluate them. I asked him how customer and partners perceive them.

He's had so many conversations that he likely has a sense of how perceptions of the company and its offerings have changed.

I'm curious.

at this point even googles ai search results are better than gpt - obv. this is not for full programs but if you know what youre doing and just want a snippet, thats all you need.

Wild how different experience people can have. Both Google's models and Anthrophic's hallucinate a lot for me, even when I try the expensive plans and with web searches, for some reason, and none of them come close to the accuracy and hallucination-free responses of ChatGPT Pro, which to me still is SOTA and has been since it was made available. But people keep having opposite experiences apparently, I just can't make sense of it.

Kagi (assistant.kagi.com) with Kimi K2.5 (their current default) has worked great for me in scenarios where the search result data is more important than the model.

I.e. what I used to use Google for and when I don't want an AI to overly summarize / editorialize result data.

oh thats probably because im a cheap-skate and just use the free garbo models. im sure the pro version is quite good.

UPDATE: Well-regarded people on HN are saying OpenAI's most recent GPT-5x codex model is better than Claude 5x for certain coding tasks:

https://news.ycombinator.com/item?id=47707494

If you were in charge of the deciding what should be done with Sam Altman, what would you choose?

I mean, its a fair question, though it does make some wonder how extreme the answers could be, so I could see why you're being downvoted.

The problem is sometimes on paper everything people like Sam Altman do is legal, despite it harming so many. We've literally had a major RAM producer pull off the consumer RAM market. I feel like Sam Altman should be investigated and heavily scrutinized. He kind of is the biggest bubble in the AI bubble, we're letting him fester too far into it too, and these circular deals have seemingly somewhat stopped for now, but it might only get worse.

Totally. Lying about others can be so harmful. But lying to hostiles in order to protect? Acceptable.

I guess my question was more, if the article author was the judge of fate or morality, what should happen?

As to AI and Sam, I think it’s too early to tell what effects will be. So we should adopt non judgement, build good ourselves and see what unfolds.

Many of us prefer OpenAI's Codex, because we think it's a better product.

No comment on the CEO: I just find the product superior in everything but UI/UX and conversation. It's better at quality code.

Who is “us”? It does seem that some scientists prefer Codex for its math capabilities but when it comes to general frontend and backend construction, Claude Code is just as good and possibly made better with its extensive Skills library.

Both codex and Claude code fail when it comes to extremely sophisticated programming for distributed systems

Us = me and say /r/codex or wherever Codex users are. I've tried both, liked both, but in my projects one clearly produces better results, more maintainable code and does a better job of debugging and refactoring.

That's interesting, I actively use both and usually find it to be a toss up which one performs better at a given task. I generally find Claude to be better with complex tool calls and Codex to be better at reviewing code, but otherwise don't see a significant difference.

If you want to find an advocate for Codex that can give a pretty good answer as to why they think it's better, go ask Eric Provencher. He develops https://repoprompt.com/. He spends a lot of time thinking in this space and prefers Codex over Claude, though I haven't checked recently to see if he still has that opinion. He's pretty reachable on Discord if you poke around a bit.

Quite irrelevant what factions think. This or that model may be superior for these and those use cases today, and things will flip next week.

Also. RLHF mean that models spit out according to certain human preference, so it depends what set of humans and in what mood they've been when providing the feedback.

On the contrary, I very much care about what the other factions think because I want to know if things have already flipped and the easiest way to do so is just ask someone who's been using the tool. Of course the correct thing to do is to set up some simple evals, but there is a subjective aspect to these tools that I think hearing boots on the ground anecdata helps with.

Any difference in performance on mobile development?

For that I'm not so sure. I tried both early 2025 and was disappointed in their ability to deal with a TCA based app (iOS) and Jetpack compose stuff on Android, but I assume Opus 4.6 and GPT 5.4 are much better.

Haven't done it in a while, but I've done some tasks with both Codex and Claude to compare. In all cases I asked both to put their analysis and plans for implementation into a .md file. Then I asked the other agent to analyze said file for comparison.

In general, Claude was impressed by what Codex produced and noted the parts where it (i.e. Claude) had missed something vs. Codex "thinking of it".

From a "daily driver" perspective I still use Claude all the time as it has plan mode, which means I can guarantee that it won't break out and just do stuff without me wanting it to. With Codex I have to always specify "Don't implement/change, just tell me" and even then it sometimes "breaks out" and just does stuff. Not usually when I start out and just ask it to plan. But after we've started implementation and I review, a simple question of "Why did you do X?" will turn into a huge refactoring instead of just answering my question.

To be fair, that's what most devs do too (at least at first), when you ask them "Why did you do X" questions. They just assume that you are trying to formulate a "Do Y instead of X" as a question, when really you just don't understand their reasoning but there really might be a good reason for doing X. But I guess LLMs aren't sure of themselves, so any questioning of their reasoning obliterates their ego and just turns them into submissive code monkeys (or rather: exposes them as such) vs. being software engineers that do things for actual reasons (whether you agree with them or not).

Codex has plan mode too - /plan

yea Im not in this "us" you speak of.

Of course you're not one of "us" if you're one of "them".

Not a scientist and use codex for anything complex.

I enjoy using CC more and use it for non coding tasks primarily, but for anything complex (honestly most of what I do is not that complex), I feel like I am trading future toil for a dopamine hit.

I'm in that camp -- I have the max-tier subscription to pretty much all the services, and for now Codex seems to win. Primarily because 1) long horizon development tasks are much more reliable with codex, and 2) OpenAI is far more generous with the token limits.

Gemini seems to be the worst of the three, and some open-weight models are not too bad (like Kimi k2.5). Cursor is still pretty good, and copilot just really really sucks.

I've found claude startlingly good at debugging race conditions and other multithreading issues though.

My rule of thumb is that its good for anything "broad", and weaker for anything "deep". Broad tasks are tasks which require working knowledge of lots of random stuff. Its bad at deep work - like implementing a complex, novel algorithm.

LLMs aren't able to achieve 100% correctness of every line of code. But luckily, 100% correctness is not required for debugging. So its better at that sort of thing. Its also (comparatively) good at reading lots and lots of code. Better than I am - I get bogged down in details and I exhaust quickly.

An example of broad work is something like: "Compile this C# code to webassembly, then run it from this go program. Write a set of benchmarks of the result, and compare it to the C# code running natively, and this python implementation. Make a chart of the data add it to this latex code." Each of the steps is simple if you have expertise in the languages and tools. But a lot of work otherwise. But for me to do that, I'd need to figure out C# webassembly compilation and go wasm libraries. I'd need to find a good charting library. And so on.

I think its decent at debugging because debugging requires reading a lot of code. And there's lots of weird tools and approaches you can use to debug something. And its not mission critical that every approach works. Debugging plays to the strengths of LLMs.

As a scientist (computational physicist, so plenty of math, but also plenty of code, from Python PoCs to explicit SIMD and GPU code, mostly various subsets of C/C++), I can confirm - Codex is qualitatively better for my usecases than Claude. I keep retesting them (not on benchmarks, I simply use both in parallel for my work and see what happens) after every version update and ever since 5.2 Codex seems further and further ahead. The token limits are also far more generous (and it matters, I found it fairly easy to hit the 5h limit on max tier Claude), but mostly it's about quality - the probability that the model will give me something useful I can iterate on as opposed to discard immediately is much higher with Codex.

For the few times I've used both models side by side on more typical tasks (not so much web stuff, which I don't do much of, but more conventional Python scripts, CLI utilities in C, some OpenGL), they seem much more evenly matched. I haven't found a case where Claude would be markedly superior since Codex 5.2 came out, but I'm sure there are plenty. In my view, benchmarks are completely irrelevant at this point, just use models side by side on representative bits of your real work and stick with what works best for you. My software engineer friends often react with disbelief when I say I much prefer Codex, but in my experience it is not a close comparison.

>As a scientist (computational physicist,

Is there one that you prefer for, i dunno, physics?

I've tried both against similar and haven't found it such a clear cut difference. I still find neither are able to fully implement a complex algorithm I worked on in the past correctly with the same inputs. Not sharing exactly the benchmark I'm using but think about something for improving performance of N^2 operations that are common in physics and you can probably guess the train of thought.

I can roughly guess the train of thought and I am a bit surprised that Claude is failing you.

That said, I am puzzled at the algorithms that Claude & GPT "get" and ones that they do not.

(former physicist here. would love to know the kind of things you're working on. email on my profile)

I've had reasonable success using GPT for both neighbor list and Barnes-Hut implementations (also quad/oct-trees more generally), both of which fit your description, haven't tried Ewald summation or PME / P3M. However, when I say "reasonable success", I don't mean "single shot this algo with a minimal prompt", only that the model can produce working and decently optimized implementations with fairly precise guidance from an experienced user (or a reference paper sometimes) much faster than I would write them by hand. I expect a good PME implementation from scratch would make for a pretty decent benchmark.

Think another level of complexity of algorithm, different expansion bases plus a mix of input sources. Also not trying to one-shot it.

Have you tried the latest (3.1 pro) Gemini? In my experience, it's notably better for a similar type of problems than Opus 4.6. However, I don't really use OpenAI products to compare.

I actually haven't - I tried Gemini 3.0 Pro in Antigravity and was disappointed enough that I didn't pay much attention to the 3.1 release, it was notably worse than Opus and GPT at the time, and much more prone to "think" in circles or veer off into irrelevant tangents even with fairly precise instruction. I'll give 3.1 a try tomorrow, see what happens.

Claude Code, Codex, and Cursor are old news. If you're having problems, it's because you're not using the latest hotness: Cludge. Everyone is using it now - don't get left behind.

Cludge has been left behind by Clanker, that’s the new hotness. 45B valuation!

ive heard that poob has it for you!

As some other people mentioned, using both/multiple is the way to go if it's within your means.

I've been working on a wide range of relatively projects and I find that the latest GPT-5.2+ models seem to be generally better coders than Opus 4.6, however the latter tends to be better at big picture thinking, structuring, and communicating so I tend to iterate through Opus 4.6 max -> GPT-5.2 xhigh -> GPT-5.3-Codex xhigh -> GPT-5.4 xhigh. I've found GPT-5.3-Codex is the most detail oriented, but not necessarily the best coder. One interesting thing is for my high-stakes project, I have one coder lane but use all the models do independent review and they tend to catch different subsets of implementation bugs. I also notice huge behavioral changes based on changing AGENTS.md.

In terms of the apps, while Claude Code was ahead for a long while, I'd say Codex has largely caught up in terms of ergonomics, and in some things, like the way it let's you inline or append steering, I like it better now (or where it's far, far, ahead - the compaction is night and day better in Codex).

(These observations are based on about 10-20B/mo combined cached tokens, human-in-the-loop, so heavy usage and most code I no longer eyeball, but not dark factory/slop cannon levels. I haven't found (or built) a multi-agent control plane I really like yet.)

This is the way. Eg. IME Gemini is really damn good at sql.

Codex won me over with one simple thing. Reliability. It crashed less, had less load shedding and its configuration is well designed.

I do regular evaluation of both codex and Claude (though not to statistical significance) and I’m of the opinion there is more in group variance on outcome performance than between them.

Many paying customers say that Anthropic degraded the capability of Opus and Claude Code in the last months and the outcomes are worse. There are even discussions on HN about this.

Last one is from yesterday: https://news.ycombinator.com/item?id=47660925

I’m one of those ‘us’, Claude’s outputs require significant review and iteration effort (to put it bluntly they get destroyed by gpt and Gemini). I’m basically using sonnet to do code search and write up since it is a better (more human-like) writer than gpt and faster and more reliable than gemini, but that’s about it.

I have been using Codex AND Claude side by side for the same project*, with the same prompts.

Codex has been consistently better on almost every level.

* (an open source framework for 2D games in Godot 4.6 GDScript, mostly using AI to review existing code)

Yeah, there are dozens of you. Dozens!

I also find Codex much more generous in terms of what you get with a Pro ($20/mo) subscription. I use it pretty much non-stop and I have yet to hit a limit. Weekly reset is much better as well.

Shill talk

Does this work for people? To me having a "better product" would be completely irrelevant if the use cases are evil.

I prefer GLM 5.1 and MiniMax 2.7. With a better harness like Forge Code, I have better results for way less money than by using GPT and Opus.

GPT/claude/gemini is pretty interchangeable at this point.

Absolutely not the case. They're complementary.

i find myself being more productive with codex/copilot on coding tasks, but claude does seem to be better at planning

Usage limits are more generous and GPT 5.4 is a good model, but yes, UI/UX lags behind Claude Code. Currently I'm especially missing /rewind with code restoration and proper support for plugin marketplaces

Here’s a reality check.

There are two types of vaccine be coders. Those who review the code generated and those who don’t.

Either because they don’t understand code at all, or because they don’t have time and don’t care.

Code quality is only one factor. Naive vibe coders, who don’t code otherwise, rate performance based on output alone.

It's worth noting Codex has 2x more stories than Claude https://hn.algolia.com/?query=codex

But by page 5, those stories have around 50-60 karma, while claude page five is still 500+

(i found your comment surprising based on my daily hn reading recollection - i mostly read top N daily and feel i only occassionally see codex stories).

Yeah we moved to Claude a few months ago, mostly because the devs kept using it anyway. Altman stuff is interesting but at the end of the day you just go with whatever tool works

Personally, I prefer Claude for coding, but I still prefer ChatGPT for hashing out ideas for my projects (which tend to be game designs). So I use both.

I’m assuming this is all sarcasm.