HN user
No one here actually cares about Cycle Double Cover Conjecture. I can demonstrate this by pointing out that the only time this conjecture was ever mentioned on the website was 14 years ago in a submission[1] that linked to a (now retracted) proof paper. That story received exactly zero upvotes. No one cared enough to upvote it and no one cared enough to ever mention this conjecture again.
Languages designers will have to make a choice whether to continue to design for humans or for big slop machines. The design goals are not compatible. This is obvious. I don't understand how anyone can miss such an obvious point.
Another obvious point is that an industry that runs on code slop will stagnate in terms of language an human tooling design.
Which frame inspires a more productive research program?
This question depends on how you define research productivity. There is close to two hundred AI papers published every weekday. Most of them are about GenAI. Most don't seem to be all thay good. The progress in actual model improvement had mostly stalled. If you interact with the latest "raw" models they display all of the issues we've seen in GPT-3.5, just at a smaller rate. The "amazing gamechanger breakthroughs" I read about on social media every week do not seem to lead anywhere. It's all kind of boring, really.
The new "hotness" in AI is clearly building more and more elaborate harnesses. This is not at all the direction AI boosters have predicted couple years ago.
Personally, I think the "stochastic parrot" mental model is far more useful for science, because it primes people for proper testing, skepticism and researching alternatives. If you want useful AI, you want people working on it being skeptical, not credulous.
Anecdotal, but I'm convinced it screws up sleep quality.
It absolutely does.
I'd just accepted for the longest time that waking up groggy with a slight headache and tired was the norm until I put a CO2 monitor in my room. With the door closed, it climbed up to 1500ppm in under an hour.
Same experience here. Opening windows just a bit totally changed my sleep quality.
"Valve will not be making and providing their own e-ink display for the Steam Machine"
Too bad. The picture in the articles looks awesome. Like a device from some alternate reality. Neither retro nor the standard flat-panel LCD.
I don't want to mod a pre-build $1,049 device. I want it to be good our of the box and I'd rather pay more to get more. (If it was a $3K top-of-midrange machine, I would buy it in a second.)
The article has solid observations, but I would correct one important thing. It's not AI confidence, it's AI psychosis.
A lot of people I know are forced to use AI at work. They universally tell me that their coworkers generate awful PRs with bugs, nonsensical code and fake unit tests. But they also universally tell me that they are different, have special workflows and prompts that create good code.
The psychology behind this is obvious. Hype and the literal threat of being fired forces everyone to develop coping mechanisms. Bragging about your own adaptability is one of those mechanisms. Unfortunately, the scale and intensity of this and the fact that the mechanism is clearly weaponized for marketing means we're living in an increasingly deranged society.
For most developers OOP seems to be just a handful of memes they are vaguely aware of and a kind of generally irritating sense of things not quite working in Java. No amount of writing, discussions, examples and history will change this.
That's not the only CVE in question. The issue also involves CVE-2025-6965.
Nonsensical corporate posturing.
"Microsoft will contribute expertise, resources, and AI technologies to help responsibly identify and fix vulnerabilities"
As a reminder, Microsoft runs NPM and GitHub. Microsoft has access to the best AI models and massive data centers. Despite that, their own products are rapidly getting worse at security and their services are central hubs through which various exploits are propagated. They are not making things better, they are actively and rapidly making things worse.
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For a great example of how Microsoft deals with security issues within their own Open-Source projects, I recommend reading this GitHub thread:
https://github.com/dotnet/efcore/issues/38257
EF core currently distributes a version of SQLite that has a severe vulnerability. The issue was discovered over a year ago. It was fixed by SQLite within one week. EF core didn't mark their driver as vulnerable until a user recently reported it, got bounced around and argued with developers. The current stable version of .NET core will only get a fix in roughly two months.
What you're describing is people who fail to follow the most basic principles of academic research. (Check existing academic literature, mention and give credit to prior work.) This would be fine if these people didn't claim to be doing scientific research, didn't boast their academic credentials, didn't publish their finding as original work and didn't demand credit for their work in academia. Of course, they do all of these things. They benefit from a system they're actively denigrating (and in some ways degrading).
To put it more simply, people with academic credentials should not demand acknowledgement of their current intellectual work while denigrating and ridiculing the importance of very similar work done in the past.
It's an interesting project, but the discussion on HN looks weird. It gets brought up every few weeks[1] and everyone just spams comments with messages about how "fast" it is.
DuckDB is fast for some specific workloads. If you use it for most other things, it is at least an order of magnitude slower than SQLite. It also has some limitations in terms of what SQL it will currently run (e.g. I immediately ran into an issue with recursive queries). That will probably get better with time.
[1] If you search HN for "sqlite" and "duckdb" you get 4,310 hits and 2,398 hits respectively. That's a very heavy skew, considering SQLite is everywhere and had been around for a quarter century, while DuckDB effectively appeared on the scene two years ago.
The fact that people here are looking at these numbers and saying "this is fine" is absolutely bonkers.
Basically, it's a company that's not sustainable for two separate reasons. The first one is that they have an extremely high overhead. SG&A of 55% is really bad. The seconds reason is that their R&D costs are truly astronomical. They could probably cut those costs to some extent, but they're not going to cut them to nothing. They're already losing ground to Anthropic even with this much R&D.
To put it differently, even if OpenAI cut its R&D and inference costs by half, they would still be leaking money like a sieve.
You interpretation of my comment is either very biased or just made in bad faith.
The claim made by Open AI has two necessary components. The first one is that the conjecture had been disproven. (This is what had been verified by "real human mathematicians".) The second necessary part of their claim is that the work to disprove the conjecture was done mostly by their AI model rather than by people employed by Open AI.
Funny thing is that even the explainer on OpenAI's own website points out the issue:
"This result does not show us all the times AI has claimed to have a proof of something and been wrong."
"I believe if the level and type of human expertise that is represented on this note had been assembled to find a counterexample to this conjecture a month ago, and those people put in similar amounts of time working on it than they did to reading and thinking about Chat GPT’s solution, the mathematicians would have found a counterexample."
[1] https://cdn.openai.com/pdf/74c24085-19b0-4534-9c90-465b8e29a...
"Open AI claims that its model disproven an Erdős conjecture, therefore my crappy way of arguing about software quality is valid."
I really don't know how I'm supposed to reply to stuff like this.
"It’s easy to forget, but for most of 2025, the idea that AI-generated code was slop and might always be slop was not only a reasonable position to hold, it was the default, mainstream position.
That question was answered decisively last November."
It's easy to forget that people said this exact thing about every model after GPT 3.5. This is a standard trick the industry uses to invalidate negative experience with LLMs. 'You are prompting it wrong' becomes 'you are using Gemini, but you should use Clade' which then becomes 'well, all of your criticism is now irrelevant, because everything is fixed in this new version'.
This "discussion" about capabilities is set up to be asymmetrical and basically non-falsifiable.
This "how to adapt" prop-slop is getting tiresome. So much of it is obviously intended to project and demoralize rather than provoke thought or give legitimate advice. The trick of this kind of writing is to skip arguing that something rather questionable will happen and go straight to giving advice about how everyone should adapt to the new, totally inevitable reality. This isn't even a particularly sophisticated method of manipulation.
AI seems to be ruining every single major thing that drove economic growth for the past 4 decades. PCs, the Web, software in general, high-capacity servers, Raspberry Pis and so on. The next thing to be affected will probably be smartphones. All of these things are foundations of profitable businesses right now and we are destroying them on the mere promise to get to some idiotic utopia in the future.
The premise of the article is faulty. "Nerds" became "cool" as technology became more important, so sociopaths in leadership positions stated to pretend to be nerds. It's as simple as that.
I think a far more important question is why we no longer have more reasonable public figures. Who are the modern equivalents of Isaac Asimov or Richard Feynman?
What this shows me (again) is that the whole system where vulnerabilities need to be constantly discovered, reported, analyzed, then patched, then the new version distributed to every singe user - again and again - is quite obviously unsustainable. The industry must come up with some alternative system for dealing with bugs and security issues. Currently the industry prefers to play dumb and turn its own failures into a profit (rent seeking) opportunity.
If none of that is happening in your workplace or life in general - genuinely good for you.
The majority of software engineers I know work for companies that have very similar things happening inside of them.
First, it's not made up. I've heard numerous stories of this sort (maybe a bit less extreme) from people I have known for many years working for large and mid-sized companies. Second, large companies have entire teams for monitoring social media and they absolutely will try to zero in on you and fire you if you speak too much about their crappy internal processes. This goes double for anything in S&P 500.
You can post any number of snarky booster comments, but at the end of the day they are the opposite of insightful. They are an obfuscation.
What I'm seeing is that the whole security model built around endless code re-evaluation and continuous (usually online) updates is collapsing in a spectacular fashion. This is not "good for red teams" or "good for security AI". This is not good for anyone except malicious actors.
I rarely do these, but here is my prediction: doing more of the same but faster is not going to work. No matter how much AI compute people will throw at security scans and patching, the number of security incidents and the overall instability will keep going up until the underlying security model is fundamentally changed.
I think it should be quite obvious that perceptrons are far from the smallest units that are capable of learning. They store many bytes of information, require a non-local update process, need numeric (i.e. symbolic) inputs and involve relatively complex computations. You can go much simpler. For example:
https://medium.com/@VictorBanev/the-simplest-learning-machin...
This is a description of a 5-line algorithm that learns and stores approximate probability of an event using just 1 byte of persistent memory.
Would you prefer the harsh unpopular truth of Erich Schmidt, or a sweet lie of Wozniak?
What Erich Schmidt is doing is not about describing hard reality. He is trying to make a particular version of the future come true by painting it as inevitable. It's literally a propaganda technique.
Here is a quote that is far more representative of his overall message:
“When someone offers you a seat on the rocketship, you do not ask which seat, you just get on.”
The article we're commenting on lists several examples of the dynamic and it aligns with my personal experience offline and online. There are also stats like these:
https://on.substack.com/p/the-substack-ai-report
"Publishers 45 and over were more likely to use AI than those under 45."
I can, of course, dig up more supporting data, but that is not as important to me as making sense of what I'm actually seeing.
https://www.pewresearch.org/science/2025/09/17/views-of-ais-...
"Younger Americans are generally more likely than older Americans to think the increased use of AI will worsen human abilities."
GenAI is the first technology that I've ever seen that is actively rejected by young adults and fervently pushed by people over 55.
It seems Eric Schmids of the world think they (in their 70s) have more say about the future of these students than the students themselves. That is very unlikely.
Clearly, the future is LLM-generated patches that get instantly vibecoded and installed on all machines without any human review. In fact, this is such a good idea that it should be illegal and impossible to run your computer without being connected to such a system. There are no other alternatives. /sarcasm
there’s zero chance any AI lab would train a model for such a ridiculous task.
A lot of people here stated that this is a ridiculous metric, but no one seems to remember that it was introduced in the initial GPT report ("Sparks of Artificial General Intelligence: Early experiments with GPT-4" [1]) by Microsoft about 3 years ago. Shortly after that it was parroted by a network of booster accounts and became a thing every clueless AI hype peddler does to "test" models.
100% marketing, 0% science.
Ed Zitron[1] has a lot of articles and podcast episodes on these deals. The nice thing about it is that he occasionally revisits the old announcements to check what happened with them. Apparently a lot of these deals just evaporate after prolonged contact with reality.