Unlurker|Replay|About
Notice How Old websites look better than new websites (yingster.net)
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What is this engagement-bait headline? The content of the site is simply an old-looking site.
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Crafted websites look better than thoughtless websites. Age has nothing to do with it, unless you're going way back to dial-up and the very beginning when WWW was competing with Gopher and BBS. Webmaster was a real job.
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The only thing that makes older-style websites harder to browse is the full-width text. My monitor is 24 inches wide, and while the text isn't super small, if my browser is taking up the full screen, I have to physically swivel my head side to side to read the text, and if this was multiple paragraphs, it would be pretty difficult to keep track of where I am. (Which is why most of the time, my browser is sized to be about half of the screen, anyway.) (The blog portion actually does keep it to a nice width, though - it's only the landing page that has this problem.)
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> Which is why most of the time, my browser is sized to be about half of the screen, anyway. Same here, and I find that preferable. Instead of the web page prescribing line length, have the user fully control it via their browser.
Quili AI (quili.ai)
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This is cool. It's worth noting that there has been a much wider Chilean water crisis: https://en.wikipedia.org/wiki/Chilean_water_crisis + https://www.bnamericas.com/en/interviews/chile-has-no-time-to-spare-to-improve-water-management
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Did they track their water usage for that day?
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This is a neat idea but some of the claims on that website seem extremely propagandistic. I'm not particularly gung ho about datacenters but if datacenters are causing water issues the solution is to shine a light on alternative technologies to solve the problem. Perhaps Chile could become a leader in building cleaner datacenters but thats not the goal I am taking from this initiative.
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This is an alternative technology.
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The beginning of the video made me really thirsty!
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Very fun idea. I do think this puts the water argument into a bit more perspective. Your average person should drink ~3L of water per day, or about 22mL per 11 minute average-length chat that Quili quotes on their site. It's hard to pin down exactly how much water AI uses, but something on the order of 5mL per interaction seems reasonable [1]. So the people doing this are using about 4x more water than an AI model would. Of course, it does matter what happens to that water, whether it turns into water vapor or urine and how much of that water re-enters the water cycle, but it's an interesting comparison. [1] https://www.seangoedecke.com/water-impact-of-ai/
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The people doing this are drinking water either way.
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That's true, but the people could also be doing some other job with their effort too. If you factor in the water consumption of the energy used to power their laptops while answering these questions (~30W of power for 11 minutes per interaction, 1mL of water per W of power) you get another 5mL of water per interaction purely in electricity use. Not saying it's a 1:1 comparison but it puts it in perspective.
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"BECAUSE OF THAT, COUPLED WITH AN ONGOING CLIMATE CRISIS, CHILE IS FACING A SEVERE DROUGHT EXPECTED TO LAST UNTIL 2040." I like the initiative as a life-affirming activity, but spreading falsehoods about water usage is not a good thing. Lying and deceiving makes the world a worse place. This sentence could just as easily read: ""BECAUSE OF [Quili], COUPLED WITH AN ONGOING CLIMATE CRISIS, CHILE IS FACING A SEVERE DROUGHT EXPECTED TO LAST UNTIL 2040." Without explaining what proportion of the drought comes from the climate crisis, or the mechanism by which data centers cause drought, you could substitute almost anything for [Quili] here. The water hype needs to go. It's caught on with people who get their news off tiktok, but we need to have better epistemic models if we want to actually make the world a better place.
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If you have the same rainfall today as you do tomorrow, but tomorrow someone steals all of the water and leaves you with none, is that a drought? Not technically...but who the fuck cares about the technicality?? You don't have any water!
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Whoa. This is amazing. I think What is interesting is whats driving this: the psychologically formed habit of asking questions to bots rather than search engines. If the median query on the internet is now and forever answered by an AI bot, humans could just as easily become really important as an "authentic" source of answers.
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Unironically this is a "return to tradition" opportunity in the making to me. If this really is the case, then there's an engineering solution here to re-train people by answering questions with search results first, leaving AI reconciliation for those cases where someone would be clicking the "next page" button or scrolling further. Of course, that may not align with the perverse incentives driving whoever is behind each bot.
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I agree with this, but I can’t help but reflect on how we poisoned our own well by destroying the human internet before AI. The answers to almost every search are already out there, but we made the public internet so toxic and user hostile, you now need a whole new layer of AI to sift through the bullshit, ads, and clickbait. I don’t think for a second that AI will not go the same hellish direction.
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Of course it will go the same direction - many of the leaders in the AI space are the very same entities either technologically or financially that are responsible for that destruction.
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> Each chat lasted an average of 11’ 4 What does this mean?
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11 minutes, 4 seconds
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11 foot 4 inches
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11m4s
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11 minutes 4 seconds
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Is there a way to read some of the responses that i’m missing? It sounds like a lot of fun to see a what i expect is a Pictionary style human drawn image in response to a prompt. I want to see it.
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As soon as I saw "Why are hot dog buns sold in packs of 8 and sausages in packs of 12?" I wanted to know the response! My immediate reaction was "because sausages tend to last longer than buns" but I'm curious to see how Quili.ai answered.
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Really interesting. This makes me wonder if it would cost less to have human answers for like 90% of every-day AI requests.
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Excluding the Google AI summaries included with every search, most requests are likely going to be coming from enterprise users using it for code. So, probably no.
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Humans have neural networks, too!
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Who the hell would think putting a datacentre in one of the most actively seismic countries on the planet is a good idea? I'm puzzled. Pretty good initiative other than that.
This underscores how vastly underrated humans are by the people who are pushing this obnoxious, inhuman, alien technology onto... humans.
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I have a question: have you used one of these models for code, CAD, general 3d work? The vast chasm will take on a very different perspective.
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I don’t quite get the classification of alien considering they were grown with human data (and humans were the ones that invented the architecture to make it work in the first place)
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Totally. This is way more exciting than any new frontier model. It is kind of crazy how good an idea it is...
The State of Longevity: The healthmaxxing economy is here (a16z.news)
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Well, I guess A16Z and the rest of the VCs aren’t ready to commit to Quantum after all when more healthcare grifts are ready and waiting for investor dollars. Snide response aside, I will say that they’re correct that healthcare is a big upcoming boom opportunity. We’ve been quietly running healthcare experiments globally for over a century, and have quite a lot of data on what does and does not work. With globalism firmly here, there is a very real need to standardize healthcare globally around new institutions, systems, and processes that benefit patients as well as optimize distribution of care. I can get on board with the systems side, but peptides and other hokey-health sales pitches? For real? Fuck all the way off with that unless you’ve got peer-reviewed studies backing up your assertions, I’m tired of griftos hawking silver solutions and vet medicines in lieu of actual science. Just because you fear your own mortality to the point of risking entirely unproven chemicals and drugs of unknown provenance doesn’t mean this should be the next billion-dollar industry.
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So there is real research that has come from this industry. I just have a hard time getting over the fact that it’s entirely just for elite tech bros so they can live forever, continue to talk about how much smarter they are then everyone and pay no taxes.
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Everything A16Z does everyday looks, feels and sounds like a scam. Why work in real pharma when you can sell peptides and the fashionable trend of the day to podcast bros, the same ones they were trying to sell on crypto? This industry's a sham.
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I'm a bit skeptical about wearables that measure anything beyond heart rate. Has anyone found consistent reliable data from any other sensors that acting upon proved to be a boon?
No brother of mine will play the flute (homosabiens.substack.com)
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I feel like there is something I am supposed to know that would help the author make more sense about the flute playing. But I don't really understand what he is talking about? Was this episode in childhood and now he is a young adult? Did he attend public school?
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I agree with everything in this article, but there are a few things that make me go hmm. * The reference to / advertisement of obscure online communities (Agor? Darkworld?) * The quoted tweet about bloodlines . There are no lines worth speaking of in biology, there are webs . You are not the last one in a very long line, you're a twig on a very big tree. People who talk about their bloodline are often a bit iffy. So why do I make an issue of it, when I agree with everything they say anyway? Because I actually agree with a lot of what lots of weird sects say - in public . The advertising pitch is often very reasonable even for groups like Mormons or Scientologists, which are 50-250 years out of touch with the trends of the times. This post is very sensible, but it feels like a pitch for something which isn't quite as easy a sell. Maybe this Agor thing.
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I was thinking the other day, "why do we have kids?" The most noble, selfless reason I could think of is "so that they try again". I say, the "most noble" because people have plenty of more selfish reasons: "they'll make me feel better", "to make me a better person", "to keep up with the expectations of my friends and family", "they'll give me comfort when I'm old and frail", "they will care for me". And then no-reasons such as "it's the way things are", and "everybody does it", and "god wants it". But I prefer mine: "so that they try again". Try what? To fix the wrongs, to succeed where we failed. But that is a mirror: if my kids inherit the world of my failures, can I look back into my soul and say that I tried hard enough? When the day they lose something precious comes, and they understand this world is mostly darkness, will I be able to look them in the eyes and say that I tried hard enough to beget the light they crave?
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What a fascinatingly strange article. The two parallel trains of thoughts, only one of which arrives at a satisfying conclusion, but I suppose that is practicing what it preaches.
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> they left me in the least wrong situation that was actually available, given all of the constraints This feels like a leap. Is it impossible to support your sibling's self-expression and teach them emotional resilience? And even if not, the long term psychological impact on the younger sibling of feeling alienated and controlled in their own family must be taken into account.
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Isn't it one of those things impossible to know which is best?
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Interesting article. Reality can be discouraging at times, but accepting that truth definitely helps to achieve your ends (or reduce frustration when you can't).
AI is destroying my CS degree (lordtoots.bearblog.dev)
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Wesley Crusher was kidnapped away to a planet with no children. Their bodies were decaying but their technology was superior to the Federation's so they needed to steal other people's kids. They gave Wesley a laser scalpel, to teach him to chisel wood, with whatever his mind could picture. Wesley complained, he said, "dood, this isn't art! i'm not even chiseling!" And the mentor-kidnapper said, "No, Wesley, you are doing art. The art is in your mind. The tool is just doing the work." And so then Wesley threw the perfect wooden dolphin at the wall, infuriated that all the pleasure had been removed from the art. But he lived the rest of his life on that planet, making perfect wooden laser sculpted dolphins, cause that was his job and his boss told him too bad. For a little while Wesley made popsicle stick art during his free time at night to try and feel the sensation of choice and control, but eventually just gave up and played mariokart until he was an old man.
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That seems like a normal academia dysfunction, AI is just another thing they are failing to adapt to. I graduated 10 years ago and on the job I had to unlearn most of what I learned from the Java course, for example. I haven't seen a better example of "those who can't do, teach" than CS professors.
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The uni I recently graduated is also very confused regarding what to do... Things have converged towards less hands on assignments and more theoretical exams, something I know for sure I would've hated during my studies. It's awkward, you're very unlikely to stop students from using AI for certain assignments (i.e: first programming class) for the good of their fundamentals, but there's also no other teaching tool being used to get them equivalent experience to solving assignments without AI.
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This is a good article. AI just introduces new bureaucracy, workloads, useless discussions and is a hindrance. No one needs AI in universities or software engineering. It only benefits the middle managers and mediocre people who jump on any bandwagon in order to help their careers. No software has gotten any better since AI, except that some forgotten bugs from the 1990s were found, just as 20000+ bugs have been found by the Luddite OssFuzz system before AI.
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If just another bot, then just another bot. If an actual throwaway, why bother? Nobody gives weight to such thoughts from a faceless account.
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File under "old man yells at clouds." I think it's worth asking what a CS degree is for. Is it to teach you how to code and make you obviously hire-able by industry? Maybe it's to teach you how to think about data and code? If you think it's the former, then AI is probably going to be a problem for you. If the latter, it's neutral to somewhat good. I've been hiring CS grads for 4 decades (yikes!) and there's definitely been a shift. Not all existing programs are bad. Not all young software devs are bad. But... the percentage of recent grads that seem to understand what data processing systems are about has definitely fallen. Us old farts in industry must share some of the blame here. Our industry seems to be pretty boom or bust. Someone comes in with a stack of money and tells people to build { the web | blockchain | AI | virtual worlds | etc } and suddenly there's a rush. At the start of the gold rush, we're not sure how to build everything that's going to be the next google [[1]] so we fill the room with young kids who have seen some recent tooling and will work cheap. We don't develop them. We want them to be compliant drones dancing to a tune played by a CTO and VP/Eng who got their jobs because they're the children of the fund investors who gave the startup a bunch of money. And then we go back to Universities and tell them "OMG! Your not bar-raising your grads by teaching them __fill in the blank__!" [[2]] So... I don't doubt it looks like AI is destroying the degree. But maybe it's just making it hard to ignore previous problems with the uni to industry pipeline. And I didn't mention the Mythical Man Month once in this comment. I'm getting better. 1. I'm not sure too many people want to be Google at the moment, but over the last 25 years, they've certainly done well. 2. I'm intentionally misspelling "you're" because in my time in industry, I've found only about 15% of people would pass a 7th grade spelling test.
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I think it's a terrible time to get a CS degree and an amazing time to learn CS on your own with AI help.
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This sounds awful, but the culprit is the terrible faculty at this institution: > Whereas before, assignments were designed by a person who thought them through at least once, then approved by other people in the department, now I doubt they're getting read more than once (if that). Errors abound, often with corrections coming a week or two before the deadline. This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job. From the title I thought I was going to be reading an article wondering how AI would devalue a CS degree, but this is actually a story about a CS program falling apart because the instructors are flailing about without AI.
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> This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job. Why? How many times have these same assignments been created, semester after semester for decades before we see how wasteful this is? These are solved problems in 99% of cases and just busy work for the staff to justify their existence. Their job should be to lecture and assist students, not create endless assignments that just regurgitate what was already done a thousand times already.
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I’m hearing this more and more from both students and teachers. It seems like everyone in education is frustrated and can’t figure out how to use AI for education productively yet. TBH I’m also thinking about how to use AI at work for learning. (My work environment is 100% all-in on AI.) Everyone can see and admit that AI coding is making us lazier and that it produces too much code to do old-style line by line reviews. Some people are using agent teams to churn out millions of lines of code where there’s zero hope of reviewing it. We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix. Agents should get better at broad architecture in the future, but they’re definitely not there yet. > The amount of work involved has also tripled This has to be one of the big ironies of using AI. I’ve heard this from other students too. The problem is that old-style assignments where the level of difficulty was hand-crafted is way way too easy for an LLM, therefore teachers need to crank the level of difficulty so the student has to actually spend some time on the assignment. But LLMs are getting better every month and the difficulty is so high that no students can possibly do the assignment by hand and now small errors in assignment design can lead to large errors in effort and time required.
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> We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix Implement more versions with different languages, frameworks, libraries and AI agents. Then you can do differential execution for testing, which is easier than imagining testing scenarios from pure code. You can fuzz or use production data (shadow mode execution) to get notified early of divergences. Keeping a population of diverse replicas might be the simplest way to derisk AI code. When 2 replicas diverge an agent can decide - was it a bug in one of them, or was the spec unclear? Either way, if you fix a bug or clarify the spec you come ahead.
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I taught my Claude to teach me new skills, where I wanted to understand its work deeper. Specifically, I wanted to learn coccinelle semantic patching, so I told it to find potential places to apply it but withhold the solutions from me. Instead, it walks me through producing a patch myself.
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Since you are (likely) young - here's a tip when writing such articles: Provide examples. It's hard for readers to gauge whether you're complaints are legitimate or overreactions.
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Why are more people (bots?) using bearblog. The landing page claims it's fast and privacy focused, but it offers so little... I could recreate it myself in a day so why aren't people running their own blogs, especially in CS where you could learn something doing it. I'm also now reminded that this is ran by that same guy who complained about the MIT license allowing people to do whatever they wish.
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Hey. First off, congrats on spending time doing side projects in OpenProcessing. Keep working on projects that keep you motivated and keep you learning. We've been there, lost in our learning paths. Even before AI there were times when we were overwhelmed with our CS journey. You'll overcome, don't you worry about that. The CS degree was never the end of the learning journey. It was always _what you put into it_. You'll learn what you practice, and uni just happens to be a good place to practice things. Whether that's optimizing AI harnesses, understanding how CPU caches work, crunching pixels on a GPU or the principles of distributed computing... it will all make you a better thinker and good at what you do one day. Stay curious.
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I decided to finally get a CS degree at juuuust the right time (well, for my personal reasons of "love of the game" at least - individual interests may vary), having wrapped up ~2 years ago. It was interesting to see things change towards the end. Some was the chaos from the lack of consistency the article highlights just starting to emerge. Mostly though, it was just the rampant surge in cheating and the classes not being prepared for it.
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While AI is writing much much more of my code these days, I still need to be able to speak intelligently about what I am building, why I chose a given design and the tradeoffs between alternatives. When fixing things, it's still important to understand what broke and why. I don't think that will ever change in any organization that operates in high compliance environments or cares about delivering high quality products. I don't think programming becoming 'easier' has make understanding programs any easier or less important. Even if all of the above goes away, I think computer science has taught me a lot about how to think about problems.
Answers so far are assuming that this is complaining that their traditional CS course is not that useful for getting a job anymore. That’s not what it’s about. It’s actually from someone who genuinely wants to learn for the sake of learning who’s talking about how AI is creating havoc in her studies.
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This is a blog post where it’s easy to see which commenters did not read the blog before commenting. The real villain in this story is the teaching staff. They need to get their act together and stop using generative AI for courseloads. In other words, they need to do their job. The parts about AI mandates changing every 3 months reeks of an overly bureaucratic, admin-heavy structure where the students’ learning is a lower priority being performative about AI for your boss.
The double standard of "YES WE'RE USING AI BUT THAT DOESN'T MEAN YOU SHOULD!" needs to be called out much more. It isn't some forbidden fruit.
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It's not a double standard. It's the same standard applied to different things. The best way we know of to learn something with AI is to do the work yourself, then watch AI blow you out of the water on the same task, learn its tricks and do the work again (better, even, since AI isn't that great at it yet) Which means the teacher can use AI, and you can't. This is also an entirely different subject to whether you should be learning that specific task in the first place or delegate it to AI.
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>The best way we know of to learn something with AI is to do the work yourself, then watch AI blow you out of the water on the same task, learn its tricks and do the work again (better, even, since AI isn't that great at it yet) Citation on this? There is something on HN front page right now that is contradicting. https://news.ycombinator.com/item?id=50353192
> It's been a huge learning curve. I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on. A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door. AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
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> AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning. AI is, in my opinion, absolutely murdering the junior dev market. It's now much more efficient to pay for a senior or staff engineer and set them up with a subscription to claude or codex. I think things are going to remain fine for senior+ engineers, but I genuinely don't know what advice I'd give to graduating devs at this time.
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> all I have ever wanted is to keep learning Good for you! Many people just want to be able to pay their bills. Your post is entirely too dismissive of the chaos this new technology is introducing.
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I wonder how you'd do without your AI augmentation if you find yourself not being able to use it for whatever reason. I wonder what you retained when the AI is not working... or with a very expensive augmentation that wants more cut from your pay...
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> A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door. Yes and no. I concur that it was never about the tools, although I've seen plenty of people advocating it had a more "practical" approach, using the same tools as companies use, and in the same way. On the other hand it's not only about the basics. In there you can go at depth in aspects that you will have to fight to use, like proper engineering practices, and might only be vital on the later stages of your career. And I feel that those learnings, plus the possibility of acquiring a breadth of knowledge that is hardly possible otherwise, is what gives CS graduates a leg up to other practitioners of the art of software development. YMMV and a leg up can be easily overtaken, yes.
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Other than keeping up to date with how to best use AI/harnesses what else would be most important to stay on top of for retaining a software engineering job these days? Also AI seems generic to me. Could I bother you for a more detailed breakdown on the most important topics/skills you would consider necessary for a software engineer to be knowledgeable of?
I don't think they're complaining about the learning curve.
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Didn't say that to refute it. I chose that to make the point that the degree is not the destination.
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Iff your goal is using a degree as a stepping stone, which seems to be getting less popular these days. For some, the degree is the destination and that's more than fine as well. It's a marker that says "others agree I learned x to a certain level, and I had fun doing it". This tends to overlap well with those who just liked university as an extra method of learning rather than going primarily to form part 1(a) of their career or something.
Did you even open the article? It’s not what you think it is.
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I read it. Lots of drama. Hold onto the roller coaster hand rail, as we become the agrarian society we were all along. Energy farmers in the desert. Every farmer fixing his own tractor. Thats where we come from. The "everyone is special, and every product is specialized" period is ending. Everyone is an expert on exactly the things they decided they wanted to be an expert on now. Thats where we're headed. If you listen carefully, you can hear the sound of humans being plugged into the matrix, and downloading their fill of cartridges, in ever increasing numbers. Talk is cheap again. See you at the replicator parts store. :}
Show HN: Kiri – an open-source container runtime in Rust, Docker run in 18 ms
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LOL why u even need LLM to write that one line of link?
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"then add this line at the end"? If you have to use AI to write a one line post to HN, it doesn't exactly inspire confidence in whatever code you're trying to promote.
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Yeah, I have no idea what happened there. Does seem like a copy/paste error.
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looks like he vibe code and tell it go post HN
Kalshi and its co-founders face felony charges for violating Utah gambling laws (arstechnica.com)
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This is outrageous! They're tech founders! Penny-ante things like "the law" don't apply to them! Somebody needs to tell Utah it needs to change its laws to adapt to their business plan, ASAP.
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kalshi and polymarket are quite obvious gambling sites. any pedantic arguments to the contrary are strictly financial chaff to enhance their operating margins until the regulations finally catch up. they were never an innovating marketing play... it was the culmination of a gambling company committing to regulatory capture as evinced by Donald Trump Jr (totally not) sitting on BOTH BOARDS as a "paid strategic advisor" or whatever lie they want to spin.
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A week ago if you dared to say anything critical of polymarket you would be drowned out in replies talking about the "wonderful societal benefits" of gambling on daily life and how it "informs decision making" and helps the "common people".
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I liked it for political races as an alternative to polls. Was terrible for several other things. 99% of it was really just sports betting, which I can't see being a good thing.
Designing responsible AI for learning (blog.google)
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It isn't possible for Google to run a websearch query "without AI" and it hasn't been for over 10 years.
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This is great! Google search has been declining in usefulness so much, that it shouldn't be used by students anyway. And then there's the shitty ai summaries, which students definitely shouldn't have to worry about whether or not it's accurate.
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I really don't like that my original submission "Google Will Turn Off Search for Schools That Reject A.I." from the New York Times got replaced with "Designing responsible AI for learning" PR slop from Google Corporate that doesn't even disclose this limitation. If the mods are gonna Orwell this, at least take my name off it.
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For free users: https://archive.is/20261103145613/https://www.nytimes.com/2026/11/03/us/google-search-schools-restrict-ai.html
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What do schools need Google for anyway? Had education deteriorated this much already?
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Images for a slideshow, citations for an essay, research for a project?
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https://en.wikipedia.org/wiki/Google_Classroom https://en.wikipedia.org/wiki/Google_for_Education
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It will be interesting to see what search engine students start using. I still use Google because that's what I used at school.
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I think that's the largest shortsightedness of google's choice. All those kids will get used to whatever other search they can use.
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https://archive.ph/RuwsB
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This comes off as comically evil. Disabling the AI summaries in search results is trivial, so them refusing to do so and blocking search entirely feels like a blatant attempt at coercion. Isn't it an abuse of monopoly power?
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That would make sense if the AI summaries from Search were a different product.
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These summaries have been trivially bolted on, they're entirely self-contained, they can be easily turned off.
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Some results also have thumbnails. They could easily be turned off. That doesn’t mean thumbnails are a different product, or an abuse of monopoly power.
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How is this not monopolistic behavior?
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Hopefully this will backfire and end with kids learning to use other search engines.
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As usual, the truth is a little less clickbaity. It's just opt-in: https://blog.google/products-and-platforms/products/education/ai-learning-era-updates-2026/ > We believe schools should have control over AI in their classrooms and in providing administrators with controls to help them manage their approach. After listening to schools, teachers, leading researchers, and policymakers, we are now updating our approach to controls for Gemini, Gemini Notebook, Gemini in Classroom, and Google Search. Access to these products will be entirely off for K-12 student accounts until school administrators explicitly turn them on.
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> … and Google Search. Access to these products will be entirely off for K-12 student accounts until school administrators explicitly turn them on. So yeah the blog post says Google Search itself will be entirely off until being turned on. There’s nothing in this blog post to indicate whether Google Search can be turned on without also turning on Gemini. Although I hate the NYT, the NYT is quoting someone from Google saying there won’t be separate controls for Gemini and Google Search.
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Google blog says search will be off by default, alongside Gemini stuff. And NYT says they would only be able to turn search on if they enable all of the Gemini stuff. That is not mentioned explicitly on google's blog. So not _that_ clickbaity?
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From the article, Google AI is deeply integrated with Google Search. So the article is correct - turning off one turns off the other. From a privacy perspective, having all of this off by default is sensible. But only offering a basic product like Search if you opt into AI (when search as a capability does not need AI to function) - is newsworthy.
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the problem is that it's bundled. you either get search + all the ai stuff, or you get none of it. you can't just opt-in to search alone. (in other words, title is accurate. block ai = no search)
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Does Google tie Gemini access for schools to their ability to train on the data and use it for ads and measurement like they have done to Android if you want to do anything halfway useful you used to do with Assistant before they killed it?
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> A.I. features are now a standard part of Google’s commercially available search engine. “Search and A.I. are deeply integrated,” said Chris Phillips, vice president for education at Google. If a school has banned A.I., he added, students “will not have access to search, and they’ll see a message from their school that indicates it’s not available.” C'mon, it's right in the article. The school can control AI on/off. Off means no search too.
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good riddance. it is useless anyways
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so what? there are alternatives... and who still googles nowadays anyways :)
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They've come a long way from 'don't be evil'
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"Don't" isn't all that far, it's just one word.
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> If a school has banned A.I., he [Chris Phillips, vice president for education at Google] added, students “will not have access to search, and they’ll see a message from their school that indicates it’s not available.” So scummy! Make it sound like the school's decision, rather than Google's.
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Sad to see Google's playbook become "be as evil as possible" I know they removed "don't be evil" a while ago now, but they didn't have to immediately become the exact opposite
I wonder if schools will manage to filter this. Probably on windows , but I'm less sure on chromebooks.
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The students can just log in with a generic Gmail account using a incognito tab.
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That's often blocked on school-provided devices.
[flagged]
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Get "uBlock origin" bro and ads go away. Also you can click the archive link that op posted and read it there without paywall.
Octomind – open-source AI harness that saves your tokens, static rust (github.com/Muvon)
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Why would i choose that over maki.sh? A comparison to maki would be great!
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Hey. Never heard about it, but I will take a look. thanks
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Compare against ante as well: https://ante.run/
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Holy tagline. "The CLI-first AI coding agent runtime. Pipe it, schedule it, embed it. One binary, any model, MCP-native — built for autonomous work, not just chat." I feel like I'm taking crazy pills...
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I don't know if I want any of those things. Can I still just throw it my csvs and make it do my data science work for the day so it doesn't distract me from baldurs gate?
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Blessed by compiler. Sometimes one of those crazy pills take you out of matrix :)
From what I can tell this is a harness optimized to be used as headless background jobs, think about CICD jobs, or bugs auto triage. Instead of interactive HITL approvals you supply a policy file with the tools and commands you allow it to execute. Kind of cool idea, similar to pi but seems more specialized to dev cycle triggers.
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Yes. Kind of right. No TUI but still old CLI approach that some of us still love. It can be used interactively and I use it daily, but so far it's optimized for autonomous work at max and so far finalizing task while running in isolated environments (we use docker for that)
Well it's beating Claude while using GLM-5.3 in solving the UNSEEN issues from real github PRs on our benches. Yes, readme maybe sloppy, I'm using it daily along with claude and can say it works much better, but still personal opinion You will never know if you not try, sloppy readme cannot say the product is bad IMO, but its your choice. Thank for feedback. I will definitely will look and work on readme. But advise what exactly sloppy – would be much more valuable
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glm-5.2*
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We benched it also on glm-5.3 not published. To be honest they act almost the same did not see any significant difference, while other benches show a lot
did they stick with the image-based compaction (when vision is supported i guess)? It boggled my mind: "snapcompact, which archives older history into dense images for a vision-capable model"
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it's in the docs! never have used it though https://omp.sh/docs/compaction
great idea. Currently working on updating benches on frontiers models, so far on glm-5.3 it's already showed great results. Can I have a link to oh-my-pi to make sure will sure use right one for it?
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Also, quick question: Could I use octomind upstream of harnesses like omp and get the savings? Or do you primarily get the savings from using the octomind CLI?
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https://omp.sh/
Mac at Any Scale (namespace.so)
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> You can't build iOS apps without a Mac. I'm not sure this is true anymore.
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The cost of cloud Mac hardware is quite frankly ludicrous across the board. Namespace's minimum for an M6 is, what, $0.06/minute for 6 vCPU? And that's on the cheap end - Semaphore wants $0.09/minute for 4 vCPU and GitHub Actions wants $0.062/minute for M1 hardware (forget AWS pricing even which is even more atrocious). If I run just a UI test suite for an hour, that's $3.60/run, which is now higher than what I'm paying for AI inference that made the change that triggered the run. That's crazy ! If I ran even just one run per night that would run me more than $100/month. A Mac Mini is $900 - I break even in nine months by buying the hardware outright. If I run multiple UI test suites per day, I break even much faster. These ARM chips are so power-light that the cost of electricity is practically irrelevant, and after all, these aren't rack-mounted monstrosities with 90 dB fans - they're meant to cool themselves sitting on your desk. Maybe it's different if you need like 60 of them? Is anyone even running the numbers before they sign up? Or are companies really running that many Minis and are paying the premium to outsource the DCOps?
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Isnt it against the Apple EULA to sell VMs of Macos? Love the creative problem solving with the HW.
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> A well-known limitation is that Apple only allows two macOS VMs per physical Mac. It can be a Mac Pro with 24 cores and 192 GB memory, yet it is limited to running only two guest VMs. This limitation leaks into our product offering: we can only slice Mac resources in two halves. So we run instances with 6 or 12 cores on 14-core Mac Minis and 8/16-core instances on 18-core MacBooks (we reserve a couple cores for management overhead).
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I don't know about selling, but Apple permits up to 2 virtualized Macs to run on a Mac. If they are circumventing this limit that would be an issue, but it doesn't sound like they are.
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Ah okay - and the 24 hour minimum rental time doesn't apply to VMs? Im just curious how they are getting around the limits that Mac Stadium and AWS have.
OpenAI seems to be silently re-enabling model training on Codex chats
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They said that they have millions of lines of PRs merged daily Pretty sure they don't know this has happened lol Maybe they'll raise a new PR with 1000000 lines to revert back now that you guys leaked the information
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Never ascribe to malice that which can be explained by stupidity. The quality of their software is such crap and this is probably just some vibe coding mishap. Go read the Github issues for Codex CLI for example -- robots talking past other robots. I am 99.9% certain not a single human being has a deep understanding of what is going on with the Codex app anymore.
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They do kind of have to eat their own dogfood (or at least pretend to publically), you do wonder if they'll be an interesting public example of choking on that dogfood at some point.
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If you really care, sign a ZDR contract with OpenAI or use Bedrock or something.
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If you *really* care, run your own inference. Everything else is just wishful thinking and/or convenience winning over self-sovereignty.
It didn't happen to me, nobody I know seems to have experienced this. How can you prove to us that you ever toggled it off in the first place? This is a pretty serious accusation to make so blindly
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Not an accusation! Just seems like it's happening, which is why I wanted to post this to ask if others noticed the same thing. I definitely can't prove it. If it had just been me I would have assumed I forgot to turn it off. But the fact that my coworkers noticed the same thing made it suspicious. Also the linked HN thread suggested others have seen this happening too
OpenAI is also now hiding your usage. Not accessible anymore in the menu, and /usage in the codex app now also gives a lot of stats, but the one stat you do want to know, how much limit left, is no longer there. I'm guessing another big usage reduction is coming soon.
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Mine got move out of the user menu on the lower left to just above the user menu as it's own clickable selection. The other day, I though they took the old quick view of usage away, and started going back to the settings to look. Turns out they just moved it.
The AI Bubble Monitor (cepr.net)
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Yes the AI bubble is real, but the good things is, it is expaning like an unverse which has enourmous posibilities. I could has said that it will burst soon but after LAYA and JEV, we all are going to witness the lots of changes.
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The author, Dean Baker an economist at a non-profit policy group, called the housing bubble. Not wealthy, he sold his condo in 2004 and bought a house in 2009.
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Shameless self-promotion: https://artificialliabilities.com for a nice story-like AI infra overview. Based of FRED, SEC, and other publicly available data.
Given the debt and deficits the government is running I worry they're not going to haveh te financial muscle for a rescue package when this implodes.
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Not a problem if the govt takes back the minting of money from the Fed.
> I have a lawyer friend who pays $20 a month for an Anthropic system. I have a lawyer friend who's seen a dip in clients reaching out. Turns out, they're having Claude draw up contracts.
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Low end - absolutely. High end? Not yet. Corporate contracts are getting human only oversight with us. We have tried to compare but found that the signal vs noise generated doesn't compete with our expensive legal department.
I have a lawyer friend who receives Claude contracts for drafting. Oh dear, the level of bullshit they find, apparently quality is classes below vibe coding. But as long nobody checks...
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Maybe they're using the free version...
There's no real incentive for the industry to coalesce around 2-3 models. I think it's very unlikely to happen.
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Nobody wants to admit that while partial to one company or another, brand and all that, I highly doubt that within 1-2 years we can tell the difference between model output for 90% of our tasks. Except for the 'vibe' of some of the models with their writing or testing style I am not certain I could tell you which model is which unless asked. Switching from model to model at the moment has been seamless without extensive 1 to 1 bench-marking but most of my tasks are never 1 to 1.
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Completely agree. And for companies, if things are that indistinguishable, it is going to come down to cost and enterprise security / privacy. Sure companies will be slower to switch, but they will. It will be a race to the bottom.
Just a little AI resistance, as a treat (shkspr.mobi)
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I mean, who cares if the issue gets resolved, right? Why do you care if it's an AI agent or someone in a giant support center in a different country? Personally, I would prefer AI handle these things as I trust a poorly paid "technical engineer" to report a bug a lot less.
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Assuming the person is serious and not writing this tongue in cheek... I find it humorous is that all it takes for OP to think the response was written by a human is that it says it was written by a human. Both responses could have been from AI. Or none. Or just one. Just look at the comments on this thread, nobody can agree. And in the end, it's simply unfalsifiable. Why even waste time playing this game? All that matters is the result: was the complaint addressed in the end? I don't understand these weird hills that people want to die on.
We are just a couple years away from companies realizing the first line of customer service can be a profit center and have the largest impact on brand likeability for minimal cost. Yes, when people use customer support as a glorified FAQ lookup an AI response is appropriate, but a few humans that care can positively impact 100 customers a day. AI is great at routing messages to the right place, not as tier 1 support. In the AI everyrhing world, humanness is going to be the marketing edge.
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> the first line of customer service can be a profit center Are you suggesting that they'll be charging for customer service? That seems even worse. (or do you just mean that it's so good that people positively want to use the brand because of their customer service?)
In the past we would just get a canned response. Is that actually better than an AI response I wonder?
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Yes. The canned responses, for the most part, were not pretending to be human. They were obviously templates or wholesale copy pastes. I am far less annoyed by AI responses where I am told up front they are AI.
> But using an AI to reply to a human is just disrespectful. It says the organisation values their customer's time at less than their own. One of the benefits of using AI to reply is faster replies... which values a customers time. I think that good customer service can use AI. It's not AI composed replies that makes the customer service bad.
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I had a AI customer service interaction (it was explicit about being AI, used fin.ai iirc). At first I was amazed! It was doing email as fast as IM! And the first response seemed really knowledgeable and put me at ease. But it failed to do a key step (generating a shipping label for me). I went back and forth over 21 messages trying to get it to do it. Then I wrote "escalate to a human" and an hour later someone replies with a shipping label directly. I would much rather have had a slow but correct response than a fast but confidently-wrong response.
The second response reads as very much written by AI to me too.
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One particular sentence reads particularly human to me: > It is rare to get feedback that tells us exactly what is broken and how to fix it. AI doesn't remember from one conversation to another, so probably can't say how rare something like this is at Ocado (although admittedly it could learn that it's generally rare in fine tuning). I also expect AI to be trained not to compare feedback with other people's feedback.
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It doesn't have to remember. It just repeats what humans say. And humans say that sort of thing every day.
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This thread highlights why I often say that at some point in the future, the obsession with whether something was written by AI or not will be classified as a mental disorder.
Microsoft's Canada timezone troubles leave admins fixing calendars (theregister.com)
Its not that hard to get right. I'm a software dev working in Vancouver and our team was able to track all our timezone dependencies and account for the new offset. The Microsoft team definitely has some organizational issues since they've had 7ish months to fix this.
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Describing it simply as "the new offset" seems like an oversimplification. Just in BC there were multiple changes, some of which (eg Eastern Kootenays) were decided much more recently than 7 months ago. The MSTNO area didn't change, but in not changing its offset relative to the rest of the province did. Displaying historical data, ranges that include the moment that the new definitions come into effect, temporal and/or spatial aggregations that span multiple timezone definitions... lots of tricky things can pop up. That's not to excuse Microsoft. This isn't the first timezone change that has happened, and they should have the most robust change management process in the world for stuff like this.
Canada
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>Canada Why, specifically?
That's a whole lot of stupid! - Regional time zones? Stupid. - One time zone for the entire nation? Stupid. - Daylight Savings Time? Stupid. - When Western Canada gets rid of DST? You guessed it... stupid! Everything is stupid!
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i call dumb sh*ts dumb and what's the issue? Did i say everythign is stupid? No. I only showed the part of the issues. You went down the slippery slope went extreme.
Let's get rid of time!
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even more extreme slippery slope. you are like the one who say get rid of cars because they kill people.
Charles Tandy, the mastermind behind Radio Shack (dfarq.homeip.net)
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Jessica Tandy, a supporting actress in the 1987 film "Batteries not Included", was also an early and avid member of Radio Shack's Battery of the month club up until her passing in 1994.
> Since Tandy was a private company, his death would have forced a liquidation of the company in order to pay the inheritance taxes. So much value is destroyed by this form of taxation. It's inherent in having to liquidate much wealth in a short period of time. There should be a way to match the time scales better, as opposed to forcing a company to go public. Like putting non-voting shares into escrow and allowing them to be sold over years or decades. This would come with the loss of taxes as businesses go under, but at least some offsetting increase from successful businesses that are allowed to continue operating.
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It destroys long term wealth buildup. Many stately homes in the English countryside are rotting due to this tax.
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TBH, I'm intensely relaxed about the destruction of long-term wealth build-up: it's a route to inequality and undeserved riches.
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I worry about dismantling working, productive smaller businesses (and they employment they provide) in service of preventing wealth concentration. In the US, at least, Big Wealth is really unaffected by estate taxes. Smaller businesses with less sophisticated ownership structure end up with a tax burden or being forced to liquidate.
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In the US, federal estate taxes aren't a thing if you pay a few thousand to an estate lawyer to setup irrevocable trusts and whatnot. And these tactics usually work for state estate taxes too. Plus the federal estate tax only starts at $15M for an individual and $30M for a married couple, so it has no effect on what most would consider a "small" business. Most people on this forum would be served well (or at least their heirs) by spending a few thousand on an estate lawyer to prevent their heirs from having to waste time in probate court.
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Yeah, that's a different problem, and not ideal -- seems like value actually gets destroyed there.
I miss having a local place where I can buy a discrete transistor or capacitor. But Radio Shack was also a pioneer in intrusive information gathering. They would always ask for your name and address at check-out, often practically insisting they couldn't complete the sale without it. I usually gave them "1600 Pennsylvania Ave, Washington DC" as my address. I hope Clinton and GWB liked those catalogs!
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Huh, they asked for phone number here in SoCal. I may have given it the first time but afterward always said no or no thanks. Don’t remember them insisting but if they did I’d probably have given Jenny’s number.
I recall getting asked for my mail address at a UK retail electronics store some time in the 2010 to 2015 time period. They wouldn't complete the transaction without it. I walked out and bought it from Amazon UK instead. I figured if I was giving my mail address away I might as well get the benefit of the cheaper sticker price. I've not been very sympathetic to the demise of bricks & mortar retail since then.
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It's better to rely on a distant and unaccountable corporation than on the people in your community.
I think their cash registers ran SCO Xenix. I ran the free version on my desktop at work for a few months in the late 90s (I think that I still have the installation media). They had the same virtual console behavior as we see in Linux. I had a friend who worked in one of the stores, and I scared the daylights out of him by hitting Ctrl-Alt-F4 on his cash register to get a login. After switching back to F1, he learned the scary trick himself. I have no idea why all of the gettys were enabled in /etc/inittab. Edit: I had no idea about Pier 1 (rest in peace), the 1959 proxy battle for the family takeover, Tandy's sale of Pier 1 in 1966, Tandy's purchase of a controlling interest in the failing Radio Shack for $300k, Tandy's abrasive management style, or his early demise at 60 from a heart attack. Peir 1 folded just as we entered the COVID pandemic. https://en.wikipedia.org/wiki/Pier_1_Imports
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> I think their cash registers ran SCO Xenix. Tandy had a Xenix license from Microsoft and shipped a version on their TRS-80 Model 16 and Tandy 6000 machines (both of which were Motorola 68000-based). It would be interesting to know how much of their own stuff they used internally. I have a recollection of a (potentially apocryphal) story the TRS-80 Model 1 had a "fallback" use as an in-store management computer if they failed to sell.
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Free OpenServer appeared in 1996. The cash registers were running on the x86 platform, for sure (68000 was a dead architecture for general purpose computing by then - Apple had been on POWER for several years). https://www.eetimes.com/sco-provides-free-unix-system-licenses-to-students-educators-and-unix-enthusiasts-around-the-world/
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I'm talking more like the late 80s when I was giving my address info to clerks at Rat Shack who were typing it into some kind of green-screen app on what, if memory serves, kinda looked like a Wyse terminal.
Many fond memories of Radio Shack. I bought some electronics kits, a mini DJ setup, and many, many components there as a kid. At one time, they had quite a catalog of Radio Shack branded items that were "good but not great"... but more importantly available a few miles away within practically any city. Almost every mall in St. Louis had one (usually near a hobby train store or an arcade).
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I bought an Armatron "robot arm" but I haven't been able to find a guide to computer-controlling it - any tips are welcome !
I flipped through my Forrest Mims Engineer's Mini-Notebooks so many times the pages almost fell out. Radioshack catalog archive in case anybody missed it: https://radioshackcatalogs.com/index.htm
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These were a great series. I wonder if there is anything comparable nowadays.
Absolutely! There would inevitably be a CoCo on display and that's the machine I'd play with. A common BASIC program I'd write on new-to-me computers was a FOR/NEXT loop to print all the characters in the character set. I remember discovering the color semigraphics characters in the CoCo character set and being weirded-out. I'd never seen anything like that on any of the other machines I'd ever used. I snared a CoCo2 a few years ago so I could re-live a few of those memories (and explore the machine with actual documentation).
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Ah, the nostalgia. Those early days of home computers were sure some amazing times. Possibilities were infinite. Who'da thunk that we'd carry freakin' supercomputers (compared to those early machines) around in our pockets someday? Not me. LOL!
The 'Astra can read and write weights to improve Qwen' post is misleading (honnibal.dev)
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Recent and related: Astra can read and write weights to improve Qwen - https://news.ycombinator.com/item?id=50341174 (870 comments) LLMs are not superhuman at reading and writing neural network weights - https://news.ycombinator.com/item?id=50344861 (10 comments)
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I work in this field (mech interp research) and I agree, the original post is definitely misleading. Some overselling in research is normal but it's a bit much. First, the key result has nothing to do with making Qwen better at math, it just reduces reasoning loops. Reasoning loops are a simple problem: the model generates the </think> token (which closes reasoning) with too low probability. You can fix this with simple steering approaches where you add a vector to different components of the LLM ( https://arxiv.org/abs/2503.22048 , https://arxiv.org/abs/2506.18167 , etc). The method Astra used for identifying the vector is the standard procedure humans use (contrastive analysis). Such steering vector creation is basically the standard "intro-level interp project" for human researchers. If you can code and understand LLM internals, you can do this yourself in 4-8 hours. The SWE equivalent would be doing binary patching with a debugger. Cool that frontier models can do this, but not unexpected since humans can do it without issue.
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That’s a distinction with only some difference. “You can’t have a machine for testing” is a weird restriction on AI, especially if you entertain even the remotest notion of possible AGI.
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I pretty much got that idea when reading the post, on how it didnt have improvements on a Gemma 270M model which didn't have any looping issue. However, it still made me look at some bottlenecks in models I had trained (like MLP network for stylus input automation and a 17M reranker with NLI stance and noul heads) and allowed me to finetune without training, but just by adjusting some weights. But what's pretty cool is the zero shot sandbox development method they showed for a Go model. I tried to do that for a 17M re-ranker from zero weights and Astra built it using its reasoning without me having to use a GPU and find training datasets that are not CC by NC, and add some hours of tweaking and checkpoints. I don't know if its the best way to build something, but we might be building small models (maybe not LLMs) like with just programming the weights instead of finetuning. In the end I got to replace a 68M model with a 17M model, and improve stlyus tool recognition accuracy from 88 to 96%. Was a pretty fun experiment too.
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I completely agree with you Matthew, but I see the thing go in right direction in research as well, I am working in the same field where I am building an agent where it will improve the base model too !
Speaking for myself. I’ve been aware of a lot of concepts around training, but stayed away from it until very recently. A few weeks ago, I formed a hypothesis that I could get a smaller model good at the specific standards I have for my git commit messages, which are hard to get out of most LLMs today. I spent the last four to five weeks on traditional training. It improved in a lot of areas, but not enough to be usable for me. Yesterday, the article the author of this post is critical of gave me enough of an aha that maybe the problems I was seeing could be solved by having a larger LLM change the weights directly, without going through training. In the last twelve-ish hours I’ve made more headway than I have in 2+ weeks. I’m using one of my last well performing checkpoints (full fine-tune + changes to the decision head/LoRA, etc.) and gave the model free rein to change any of the layers. Its last two actions: (a) removed 16 participant-order directions from a head matrix, which improved one type of reasoning and degraded another, and (b) restored one of the 16, which reversed the degradation while keeping the core improvement. All that to say, this might be known to people who’ve been around training for years, even decades, but it’s new to a lot of us. And we’re excited about it.
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> it’s new to a lot of us. And we’re excited about it. Yes definitely. Seems to me as if the weighs fit in the big model's context, it (the big model) reads all weights and doesn't need python. But if the weights don't fit in context, then it uses python. Isn't that pretty obvious in a way? That if too many weights, can't look at all. I don't think there's a need for OP to be dismissive when the AI uses python to look at a subset of the weights. It just has to? To me, Astra understanding a 200k weights small model (the stuck car) is amazing enough already.
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No that's not a good summary. The model can only reason from the weights directly when there's a fairly linear relationship between the behaviour and the weight values. In a large model that's not remotely true. Using Python to debug and fix problems with the model is totally different. It doesn't read in any of the weight values into the context at all when it uses the Python approach. Rather it gathers diagnostic statistics about the weights. It does just the same sort of thing a machine learning researcher would do to solve the problem. So there are basically two unrelated types of experiments in the paper. In one type of experiment, the model reads the weights like it reads an image. In fact if you visualise the weights it's fairly clear why this works. In the other type of experiment, the model is tasked with writing Python code to do ML engineering. This requires writing Python programs to analyse and manipulate weight data. This is a difficult coding problem but it really is just a coding problem. It doesn't "look" at the weights in these tasks any more than you or I would look at the weights.
Would you be willing to share what the specific standards for your git commit messages are, and with what models you used that those standards couldn't be met? Generally speaking you should be able to do something like that in AGENTS, I am really surprised that you have had an issue with most LLMs today getting them to follow a standard for SDLC. Personally, I like conventional commits a lot.
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> Generally speaking you should be able to do something like that in AGENTS I really wish that was true. All agents find some way to break the rules, and leave messages for their future selves. The agents always find a way to include some forms of hedging, contradictions, future-looking documentation (which doesn't age well), etc. into git messages. I found having an extra gate (husky/pre-commit hook), even if it's not as good as I want it to be improves the quality of how the AI writes substantially. The cost of it not being very good is the agents now spend more time validating the claims of the validator. It's actually hilarious to witness. I have been using local models for this final verification, which is partly why they're not that great. It's also why I decided to train my own. It's been a great, fun learning experience. I learn almost everything via building. So why change a recipe that works :)
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I have not had an experience anything like you are describing with Claude. I have found it to be the most reliable product I have ever purchased, actually. Learned it quickly, read the documentation, and it works. Local models are not at all comparable to Claude to me, and I have used them: Qwen, DeepSeek, Minimax, Stepfun, Nemotron. None of them as good as Claude Fabel. If your model is not adhering to AGENTS.md again I am really curious what exact model you are using. I am really just astonished that whatever model you are using is ignoring a clearly defined git conventions standards in your AGENTS.md in 2026.
They exhibit superhuman toil.
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Grit for the win, yet again.
But theoretically, what would prevent the labs from RL'ing the models on "weight-space programming" tasks so that they get better at them? Perhaps we're just at the beginning, like how coding agents sucked 2 years ago. It still seems like a promising approach for RSI, distillation, fine-tuning, and a whole host of other tasks.
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The context. It's impossible to train on raw document > 1MB (I don't know the exact limit, but there is one). You can't vectorize weights (or can you?!) and the output context is limited. So you can't train a big model on it's previous weight or others models weights. The output context can be "extended" using agents but I doubt anything coherent may be outputted using that technique.
> like any human would do, just vastly faster That's the difference between me and Magnus Carlsen and Nigel Richards. Why downplay speed? I have a very, very hard time pinning down intelligence without time. I could basically do all IQ tests perfectly if I'm given infinite time, no? I never experienced one that is unsolvable, it's just that some people see it in a second or two and I and other lesser souls like me might need twenty minutes. Edit: I'll add raw memory but yeah.. that's not hard to beat. Nigel's memory is insane as is Carlsen's, but compared to a computer it's nothing.
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I'm not trying to downplay it, it's cool that LLMs can do this, but I feel like the discussion around genAI these days tend to quickly devolve into one of two extremes: complete dismissal (stochastic parrots) or near-AI-psychosis (LLMs are god machines that our puny human brains can't hope to comprehend). In this particular instance, I also think that at the end of the day it's almost downplaying the capabilities of LLMs: they can reverse-engineer full modern videogames, of course they're capable of recognizing a simplified version of Breakout, even in weight form! It's barely newsworthy at this point, we know LLMs can do that stuff and so much more.
> ...imply ... a sort of transcendental introspection into their own brains, which if true would be absolutely groundbreaking. > but ... it's bog standard reverse engineering, like any human would do, just vastly faster For some values of vastly faster these are functionally equivalent and so equivalently groundbreaking.
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Arguing that LLMs spontaneously evolve new capabilities outside of their training set (being able to naturally interpret weights and manipulate them to change their function) is a completely different proposition from being able to pattern-match Pac Man really fast from a pseudo-graphical representation (which is what they actually do here). It's like ChatGPT coming up with a brand new brownie recipe that's surprisingly good vs. coming up with a working design for a cold fusion reactor. Yeah the brownie is cool, but it's not that cool.
You do realize how academia works, right? Granted, it is often dysfunctional, but this ain't that. Trying to find every single angle and option to break something is how we verify the truth of something. If something is objectively true, it will survive the scrutiny. If it does not survive, it wasn't objectively true. This is how science works. That ain't no flaw. It is a flaw to be offended by science doing science things.
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Considering I have a PhD in CS focused in NLP and have been a Senior Area Chair for ACL, yes I do know how academia works. And, no this isn't science working; it's the same plague infecting most of academic blogging and social media. People are vying for attention in whatever way possible, often at the expense of others. And the examples in the OP's blog post just go to show that reading comprehension is at an all time low: > Just by looking at the raw weights, GPT-6 Astra can tell what a network was trained to do. And without any environment feedback, it has no trouble writing, from scratch, small game-playing networks that outplay networks trained with PPO for 100 million steps. The table just below shows this statement to be *entirely* factual. > Likewise, asked to make Qwen3.5-0.8B better at math without gradients, Astra identifies an issue in its weights causing reasoning loops that Qwen’s own model card warns about, and fixes this by natively editing 0.016% of the weights. With the same sampling settings, this weight correction raises MATH-500 from 7.9% to 48.1% and generalizes out of the box to instruction following, raising IFEval by 3x. Any further implicit assumptions are on the reader, not the writer, as they never stated "that the Qwen experiment worked ‘just by looking at the raw weights’". You can certainly point out issues in the experimental design, but just stating you dislike the set of experiments and is yet another poor take that directly contradicts ACL's guidance for reviewing, exactly because it does not advance science.
> most of us read more than just the one paragraph Most of us barely read the title
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Honestly, that's on them. Coddling people who are too intellectually lazy to read the entire article isn't helping anyone.
> it authored a program to analyse and fix the weights. This is a difference without much distinction, at least to me. It's like being upset when someone says that Claude made a http call -- of course the LLM didn't make the call itself, it used a tool to do it.
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No it's a huge difference. Someone who can quickly compute 123^456 in his head has a very different skillset to someone who uses a calculator.
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