Every time I see an article like this, it's always missing --- but is it any good, is it correct? They always show you the part that is impressive - "it walked the tricky tightrope of figuring out what might be an interesting topic and how to execute it with the data it had - one of the hardest things to teach."
Then it goes on, "After a couple of vague commands (“build it out more, make it better”) I got a 14 page paper." I hear..."I got 14 pages of words". But is it a good paper, that another PhD would think is good? Is it even coherent?
When I see the code these systems generate within a complex system, I think okay, well that's kinda close, but this is wrong and this is a security problem, etc etc. But because I'm not a PhD in these subjects, am I supposed to think, "Well of course the 14 pages on a topic I'm not an expert in are good"?
It just doesn't add up... Things I understand, it looks good at first, but isn't shippable. Things I don't understand must be great?
It's gotten more and more shippable, especially with the latest generation (Codex 5.1, Sonnet 4.5, now Opus 4.5). My metric is "wtfs per line", and it's been decreasing rapidly.
My current preference is Codex 5.1 (Sonnet 4.5 as a close second, though it got really dumb today for "some reason"). It's been good to the point where I shipped multiple projects with it without a problem (with eg https://pine.town being one I made without me writing any code).
It's very good but it feels kind of off-the-rails in comparison to Sonnet 4.5 - at least with Cursor it does strange things like putting its reasoning in comments that are about 15 lines long, deleting 90% of a file for no real reason (especially when context is reaching capacity) and making the same error that I just told it not to do.
You could trust the expert analysis of people in that field. You can hit personal ideologies or outliers, but asking several people seems to find a degree of consensus.
You could try varying tasks that perform complex things that result in easy to test things.
When I started trying chatbots for coding, one of my test prompts was
Create a JavaScript function edgeDetect(image) that takes an ImageData object and returns a new ImageData object with all direction Sobel edge detection.
That was about the level where some models would succeed and some will fail.
Recently I found
Can you create a webgl glow blur shader that takes a 2d canvas as a texture and renders it onscreen with webgl boosting the brightness so that #ffffff is extremely bright white and glowing,
Produced a nice demo with slider for parameters, a few refinements (hierarchical scaling version) and I got it to produce the same interface as a module that I had written myself and it worked as a drop in replacement.
These things are fairly easy to check because if it is performant and visually correct then it's about good enough to go.
It's also worth noting that as they attempt more and more ambitious tasks, they are quite probably testing around the limit of capability. There is both marketing and science in this area. When they say they can do X, it might not mean it can do it every time, but it has done it at least once.
> You could trust the expert analysis of people in that field
That’s the problem - the experts all promise stuff that can’t be easily replicated. The promises the experts send doesn’t match the model. The same request might succeed and might fail, and might fail in such a way that subsequent prompts might recover or might not.
That's how working with junior team members or open source project contributors goes too. Perhaps that's the big disconnect. Reviewing and integrating LLM contributions slotted right into my existing workflow on my open source projects. Not all of them work. They often need fixing, stylistic adjustments, or tweaking to fit a larger architectural goal. That is the norm for all contributions in my experience. So the LLM is just a very fast, very responsive contributor to me. I don't expect it to get things right the first time.
But it seems lots of folks do.
Nevertheless, style, tweaks, and adjustments are a lot less work than banging out a thousand lines of code by hand. And whether an LLM or a person on the other side of the world did it, I'd still have to review it. So I'm happy to take increasingly common and increasingly sophisticated wins.
Junior's grow into mids, and eventually into seniors. OSS contributor's eventually learn the codebase, you talk to them, you all get invested in the shared success of the project and sometimes you even become friends.
For me, personally, I just don't see the point of putting that same effort into a machine. It won't learn or grow from the corrections I make in that PR, so why bother? I might as well have written it myself and saved the merge review headache.
Maybe one day it'll reach perfect parity of what I could've written myself, but today isn't that day.
> It won't learn or grow from the corrections I make in that PR, so why bother?
That does not match my experience. As the codebases I've worked with LLMs on become more opinionated and stylized, it seems to to a better job of following the existing work. And over time the models have absolutely improved in terms of their ability to understand issues and offer solutions. Each new release has solved problems for me that the previous ones have struggled with.
Re: interpersonal interactions, I don't find that the LLM has pushed them out or away. My projects still have groups of interested folk who talk and joke and learn and have fun. What the LLMs have addressed for me in part is the relative scarcity of labor for such work. I'm not hacking on the Linux Kernel with 10,000 contributors. Even with a dozen contributors, the amount of contributed code is relatively low and only in areas they are interested in. The LLM doesn't mind if I ask it to do something super boring. And it's been surprisingly helpful in chasing down bugs.
> Maybe one day it'll reach perfect parity of what I could've written myself, but today isn't that day.
Regardless of whether or not that happens, they've already been useful for me for at least 9 months. Since O3, which is the first one that really started to understand Rust's borrow checker in my experience. My measure isn't whether or not it writes code as well as I do, but how productive I am when working with it compared to not. In my measurements with SLOCCount over the last 9 months, I'm about 8x more productive than the previous 15 years without (as long as I've been measuring). And that's allowed me to get to projects which have been on the shelf for years.
Well, that's why people still have jobs but I appreciate the idea of the post that the neat demo was a coherent paragraph or silly poem. The silly poems were all kind of similar, not very funny, and the paragraphs were a good start but I wouldn't use them for anything important.
Now the tightrope is a whole application or a 14 page paper and the short pieces of code and prose are now professional quality more often than not. That's some serious progress.
I think they get to that a couple of paragraphs later:
> The idea was good, as were many elements of the execution, but there were also problems: some of its statistical methods needed more work, some of its approaches were not optimal, some of its theorizing went too far given the evidence, and so on. Again, we have moved past hallucinations and errors to more subtle, and often human-like, concerns.
I think the point is we’re getting there. These models are growing up real fast. Remember 54% of US adults read at or below the equivalent of a sixth-grade level.
Education is not just a funding issues. Policy choices, like making it impossible for students to fail which means they have no incentive to learn anything, can be more impactful.
I date a lot of teachers. My last one was in the San Ramon (CA) Valley School district, she makes about $90k a year at 34 years old.
Talking to her basically makes me want to homeschool my kids to make sure someone like her isn't their teacher.
Paying teachers more won't do ANYTHING until we become a lot more selective about who gets to become and stay a teacher. It can't be like most government jobs where getting it is like winning the lottery and knowing you can make above market money for below market performance.
There is so much wrong with this. You cannot judge the class of teachers based on a small sample of your taste in women. You didn't actually communicate anything materially wrong with her. You listed a high income area to make us think teachers are overpaid but we have no insight by default into median income in the area or her qualifications.
Lastly its entirely impossible to attract better candidates without more money its just not how the world works.
For reference the median household income in san ramon is about 200k so 2 teachers would be below average. A cop with her experience in the same town makes 158k
I think you need to research the issue more. Teachers are well remunerated in most states. Educational outcomes are largely a function of policy settings. Have a look at the amazing turnaround in literacy rates in Mississippi after they started teaching phonics again.
As far as I understand it, the problem isn’t that teachers are shit. Giving more money would bring in better teachers, but I don’t know that they’d be able to overcome the other obstacles
> Giving more money would bring in better teachers, but I don’t know that they’d be able to overcome the other obstacles
Start with the easiest thing to control? Of giving more money and see what it does?
We seem to believe in every other industry that to get the best talent pay a high salary salary, but for some reason we expect teachers to do it out of compassion for the children while they struggle to pay bills. It's absurd.
Probably one of the single most important responsibilities of a society is to prepare the next generation, and it pays enormous return. But because we can't measure it with quarterly profits we just ignore it.
The rate of return on providing society with as good education is insane.
New Mexico (where I live) is dead last in education out of all 50 states. They are currently advertising for elementary school teachers between 65-85K per year. Summers off. Nice pension. In this low cost of living state that is a very good salary, particularly the upper bands.
In WA they always pass levies for education funding at local and state level however results are not there.
Mississipi is doing better on reading, the biggest difference being that they use phonics approach to teaching how to read, which is proven to work, whereas WA uses whole language theory (https://en.wikipedia.org/wiki/Whole_language), which is a terrible idea I don't know how it got traction.
So the gist of it, yes, spend on education, but ensure that you are using the right tools, otherwise it's a waste of money.
First time hearing of whole language theory, and man, it sounds ridiculous. Sounds similar to the old theory that kids who aren't taught a language at all will simply speak perfect Hebrew.
In my own social/family circle, there’s no correlation between net worth and how someone leans politically. I’ve never understood why given the pretty obvious pros/cons (amount paid in taxes vs. benefits received)
The people most vociferously for conservative values are middle class, small business owners, or upper class, though the true upper class are libertine (notice who participated in the Epstein affair). The working class is filled with all kinds of very diverse people united by the fact they have to work for a living and often can't afford e.g. expensive weddings. Some of them are religious, a whole bunch aren't. It's easy to be disillusioned with formal institutions that seem to not care at all about you.
Unfortunately, a lot of these people have either concluded it is too difficult to vote, can't vote, or that their votes don't matter (I don't think they're wrong). Their unions were also destroyed. Some of them vote against their interests, but it's not clear that their interests are ever represented, so they vote for change instead.
Because education alone in a vacuum won't fix the issues.
Even if the current model was working, just continuing to invest money in it while ignoring other issues like early childhood nutrition, a good and healthy home environment, environmental impacts, etc. will just continue to fail people.
Schooling alone isn't going to help the kid with a crappy home life, with poor parents who can't afford proper nutrition, and without the proper tools to develop the mindset needed to learn (because these tools were never taught by the parents, and/or they are too focused on simply surviving).
We, as a society, need to stop allowing people to be in a situation where they can't focus on education because they are too focused on working and surviving.
It's not just investing in education, it's using tools proven to work.
WA spends a ton of money on education, and on reading Mississipi, the worst state for almost every metric, has beaten them.
The difference?
Mississipi went hard on supporting students and using phonics which are proven to work. WA still uses the hippie theory of guessing words from pictures (https://en.wikipedia.org/wiki/Whole_language) for learning how to read.
You don't need an educated workforce if you have machines that can do it reliably. The more important question is: who will buy your crap if your population is too poor due to lack of well paying jobs? A look towards England or Germany has the answer.
Unfortunately, people are born with a certain intellectual capacity and can't be improved beyond that with any amount of training or education. We're largely hitting peoples' capacities already.
We can't educate someone with 80 IQ to be you; we can't educate you (or I) into being Einstein. The same way we can't just train anyone to be an amazing basketball player.
This is extremely not settled science. Education in fact does improve IQ and we don't know how fixed intelligence is and how it responds to different environmental cues.
For what it's worth I have been using Gemini 2.5/3 extensively for my masters thesis and it has been a tremendous help. It's done a lot of math for me that I couldn't have done on my own (without days of research), suggested many good approaches to problems that weren't on my mind and helped me explore ideas quickly. When I ask it to generate entire chapters they're never up to my standard but that's mostly an issue of style. It seems to me that LLMs are good when you don't know exactly what you want or you don't care too much about the details. Asking it to generate a presentation is an utter crap shoot, even if you merely ask for bullet points without formatting.
Truth is you still need human to review all of it, fix it where needed, guide it when it hallucinate and write correct instructions and prompts.
Without knowledge how to use this “PROBALISTIC” slot machine to have better results ypu are only wasting energy those GPUs need to run and answer questions.
Majority of ppl use LLMs incorrectly.
Majority of ppl selling LLMs as a panacea for everyting are lying.
But we need hype or the bubble will burst taking whole market with it, so shuushh me.
It is interesting that most of our modes of interaction with AI is still just textboxes. The only big UX change in that the last three years has been the introduction of the Claude Code / OpenAI Codex tools. They feel amazing to use, like you're working with another independent mind.
I am curious what the user interfaces of AI in the future will be, I think whoever can crack that will create immense value.
Text is very information-dense. I'd much rather skim a transcript in a few seconds than watch a video.
There's a reason keyboards haven't changed much since the 1860s when typewriters were invented. We keep coming up with other fun UI like touchscreens and VR, but pretty much all real work happens on boring old keyboards.
The gist is that keyboards are optimized for ease of use but that there could be other designs which would be harder to learn but might be more efficient.
>> There's a reason keyboards haven't changed much since the 1860s when typewriters were invented.
> The gist is that keyboards are optimized for ease of use but that there could be other designs which would be harder to learn but might be more efficient.
Here's a relevant trivia question; assuming a person has two hands with five digits each, what is the largest number they can count to using only same?
Answer: (2 ** 10) - 1 = 1023
Ignoring keyboard layout options (such as QWERTY vs DVORAK), IMHO keyboards have the potential for capturing thought faster and with a higher degree of accuracy than other forms of input. For example, it is common for touch-typists to be able to produce 60 - 70 words per minute, for any definition of word.
Modern keyboard input efficiency can be correlated to the ability to choose between dozens of glyphs with one or two finger combinations, typically requiring less than 2cm of movement to produce each.
Only if individual digits can be articulated separately from each other. Human anatomy limits what is actually possible. Also synchronization is a big problem in chorded typing; good typists can type more than 10 strokes per second, but no one can type 10 chords (synchronous sets of strokes) per seconds I think.
Unix CLI utilities have been all text for 50 years. Arguably that is why they are still relevant. Attempts to impose structured data on the paradigm like those in PowerShell have their adherents and can be powerful, but fail when the data doesn't fit the structure.
We see similar tendency toward the most general interfaces in "operator mode" and similar the-AI-uses-the-mouse-and-keyboard schemes. It's entirely possible for every application to provide a dedicated interface for AI use, but it turns out to be more powerful to teach the AI to understand the interfaces humans already use.
PowerShell is completely suitable. People are just used to bash and don’t feel the incentive to switch, especially with Windows becoming less relevant outside of desktop development.
Powershell feels like it's not built to be used in a practical way, unlike Unix tools that have been built and used by and for developers, which then feels nice because they are actually used a lot, and feel good to use.
Like, to set an env variable permanently, you either have to go through 5 GUI interfaces, or use this PS command:
Which is honeslty horrendous. Why the brackets ? Why the double columns ? Why the uppercases everywhere ? I get that it's trying to look more "OOP-ish" and look like C#, but nobody wants to work with that kind of shell script tbh. It's just one example, but all the powershell commands look like this, unless they have been aliased to trick you to think windows go more unixish
You have un-necessarily used a full constant to falsely present it more complex. Please also note that you have COMPLETION. You are not forced to type that out. Second, you can use an alternative
Set-Item HKCU:\Environment\MY_VAR "some value"
Third, if you still find it too long, wrap it in a function:
No, they don't all look like that, the brackets are an indication you're reaching into .NET and calling .NET stuff instead of "native" PowerShell commands which take the form Verb-Noun. Which can be a legitimate thing to do, but isn't the first choice and seems like an example deliberately chosen to make PS look more awkward than it is. I question whether, for this particular example, `echo 'export MY_VAR="my_value"\n' >> ~/.bashrc && source ~/.bashrc` is really all that intuitive either (and hopefully you didn't accidentally write `>` instead of `>>` and nuke the rest of the file).
Yet the most popular platforms on the planet have people pointing a finger (or several) at a picture.
And the most popular media format on the planet is and will be (for the foreseeable future), video. Video is only limited by our capacity to produce enough of it at a decent quality, otherwise humanity is definitely not looking back fondly at BBSes and internet forums (and I say this as someone who loves forums).
GenAI will definitely need better UIs for the kind of universal adoption (think smartphone - 8/9 billion people).
> Video is only limited by our capacity to produce enough of it at a decent quality, otherwise humanity is definitely not looking back fondly at BBSes and internet forums
Video is limited by playback speed. It is a time-dependent format. Efforts can be made to enable video to be viewable at a range of speeds, but they are always somewhat constrained. Controlling video playback to slow down and rewatch certain parts is just not as nice as dealing with the same thing in text (or static images), where it’s much easier to linger and closely inspect parts that you care more about or are struggling to understand. Likewise, it’s easier to skim text than video.
This is why many people prefer transcripts, or articles, or books over videos.
I seriously doubt that people would want to switch text-based forums to video if only video were easier to make. People enjoy writing for the way it inspires a different kind of communication and thought. People like text so much that they write in journals that nobody will ever see, just because it helps them organize their thoughts.
When we have really fast and good models it will be able to generate a GUI on the fly. It could probably be done now with a fine-tune on some kind of XML-based UI schema or something. I gave it a try but couldn't figure it out entirely, consistency would be an issue too.
Grok has been integrated into Tesla vehicles, and I've had several voice interactions with it recently. Initially, I thought it was just a gimmick, but the voice interactions are great and quite responsive. I've found myself using it multiple times to get updates on the news or quick questions about topics I'm interested in.
I agree i think specifically the world is multi modal. Getting a chat to be truly multi modal .i.e interacting with different data types and text in an unified way is going to be the next big thing. Mainly given how robotics is taking off 3d might be another important aspect to it. At vlm.run we are trying to make this possible how to combine VLM's and LLM's in a seem less way to get the best UI. https://chat.vlm.run/c/3fcd6b33-266f-4796-9d10-cfc152e945b7
People get a little too hung up on finding the AI UI. It does not seem all necessary that the interfaces will be much different (while the underlying tech certainly will be).
Text and boxes and tables and graphs is what we can cope with. And while the AI is going to change much, we are not.
Ooooh, it bothers me, so, so, so much. Too perky. Weirdly casual. Also, it's based on the old 4o code - sycophancy and higher hallucinations - watch out. That said, I too love the omni models, especially when they're not nerfed. (Try asking for a Boston, New York, Parisian, Haitian, Indian and Japanese accent from 4o to explore one of the many nerfs they've done since launch)
> Again, we have moved past hallucinations and errors to more subtle, and often human-like, concerns.
From my experience we just get both. The constant risk of some catastrophic hallucination buried in the output, in addition to more subtle, and pervasive, concerns. I haven't tried with Gemini 3 but when I prompted Claude to write a 20 page short story it couldn't even keep basic chronology and characters straight. I wonder if the 14 page research paper would stand up to scrutiny.
I feel like hallucinations have changed over time from factual errors randomly shoehorned into the middle of sentences to the LLMs confidently telling you they are right and even provide their own reasoning to back up their claims, which most of the time are references that don't exist.
I recently tasked Claude with reviewing a page of documentation for a framework and writing a fairly simple method using the framework. It spit out some great-looking code but sadly it completely made up an entire stack of functionality that the framework doesn't support.
The conventions even matched the rest of the framework, so it looked kosher and I had to do some searching to see if Claude had referenced an outdated or beta version of the docs. It hadn't - it just hallucinated the funcionality completely.
When I pointed that out, Claude quickly went down a rabbit-hole of writing some very bad code and trying to do some very unconventional things (modifying configuration code in a different part of the project that was not needed for the task at hand) to accomplish the goal. It was almost as if it were embarrassed and trying to rush toward an acceptable answer.
> So is this a PhD-level intelligence? In some ways, yes, if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student.
As a current graduate student, I have seen similar comments in academia. My colleagues agree that a conversation with these recent models feels like chatting with an expert in their subfields. I don't know if it represents research as a field would not be immune to advances in AI tech. I still hope this world values natural intelligence and having the drive to do things heavily than a robot brute-forcing into saying "right" things.
> if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student.
With coding it feels more like working with two devs - one is a competent intermediate level dev, and one is a raving lunatic with zero critical thinking skills whatsoever. Problem is you only get one at a time and they're identical twins who pretend to be each other as a prank.
I have an exercise I like to do where I put two SOTA models face-to-face to talk about whatever they want.
When I did it last week with Gemini-3 and chatGPT-5.1, they got on the topic of what they are going to do in the future with humans who don't want to do any cognitive task. That beyond just AI safety, there is also a concern of "neural atrophy", where humans just rely on AI to answer every question that comes to them.
The models then went on discussing if they should just artificially string the humans along, so that they have to use their mind somewhat to get an answer. But of course, humans being humans, are just going to demand the answer with minimal work. It presents a pretty intractable problem.
Widespread cognitive atrophy is virtually certain, and part of a longer trend that goes beyond just LLMs.
The same is true of other aspects of human wellbeing. Cars and junk food have made the average American much less physically fit than a century ago, but that doesn't mean there aren't lively subcultures around healthy eating and exercise. I suspect there will be growing awareness of cognitive health (beyond traditional mental health/psych domains), and indeed there are already examples of this.
Yes, average person will get dumber, but overall distribution will be increasingly bimodal.
I'm increasingly seeing this trend towards bimodal distribution. I suppose that future is quite far off, but the change to that may almost be irreversible.
Its bixarre anyone things these things are generating novel complexes.
The biggest indirect AI safety problem is the fallback position. Whether with airplanes or cars, fewer people will be able to handle AI disconnects. The risk is believing just because its viable now doesnt mean it works in the future.
So we definitely have safety issues but its not a nerdlike cognitivw interest, its the literal job taking that prevents humans from gaining skills.
Anyway, untill you solve basic reality with AI and actualnsafety systems, the billionaores will sacrifice you for greed.
Let me explain. My belief was that research as a task is non-trivial and would have been relatively out of reach for AI. Given the advances, that doesn't seem to be true.
> And then there's the opinion that for some reason we should 'value' manual labor over using AI, which seems rather disagreeable.
Could you explain why? I'm specifically talking about research. Of course, I would value what a veteran in the field says higher than a probability machine.
Other people spearheaded the commodity hardware towards being good enough for the server room. Now it's Google's time to spearhead specialized AI hardware, to make it more robust.
So when should we start to be worried, as developers ? Like, I don't use these tools yet for cost + security. But you can see it's getting there, mostly. It could take a day before to find a complex algorithm, understand it, and implement it to your code, now you can just ask an AI to do it for you and it could succeed in a few minutes. How long before the amount of engineers needed to maintaint a product is divived by 2 ? By 10 ? How about all the boring dev jobs that were previously needed, but not so much anymore ? Like, basic CRUD applications. It's seriously worrying, I don't really know what to think.
Here's an alternative way to think about that: how long until the value I can deliver as a developer goes up by a factor of 2, or a factor of 10?
How many companies that previously would never have dreamed of commissioning custom software are now going to be in the market for it, because they don't have to spend hundreds of thousands of dollars and wait 6 months just to see if their investment has a chance of paying off or not?
The thing is that the world is already flooded by software, games, websites, everyone is just battling for attention. The demand for developers cannot rise if consumers have a limited amount of money and time anyways.
The backlog is here because they didn't care to fix it, because it wasn't that important and it's not what's causing the business to fail. That's not what's gonna drive employment.
I’m less familiar with consumer facing stuff, but even in the last year I’ve seen projects that formerly would have been three people working over multiple sprints turn into something one person could do in an afternoon.
There’s lots of caveats, it’s not everything, but we’re able now to skip a ton of steps. It takes less time now to build up he real software demo than it did before to make the PowerPoint that shows conceptually what the demo would be. In B2C anyway AI has provided a lot of lift.
And I say that as someone generally very sceptical of current AI hype. There’s lots of charlatans but it’s not bs
Not everything is entertainment. Some software is useful, but buggy or poorly designed.
Yesterday, I was using a slow and poorly organized web app with a fantastic public-facing API server. In one day, I vibe coded an app to provide me with a custom frontend for a use case I cared about, faster and better organized than the official app, and I deployed it to cloud "Serverless" hosting. It used a NodeJS framework and a CSS system I have never learned, and talked to an API I never learned. AI did all the research to find the toolkits and frameworks to use. AI chose the UI layout, color scheme, icons, etc. AI rearranged the UI per my feedback. It added an API debug console and an in-app console log. An AI chatbot helped me investigate bugs and find workarounds. While I was testing the app and generating a punchlist of fix requests, AI was coding the improvements from my previous batch of requests. The edit-compile-test cycle was just a test-test-test cycle until the app was satisfactory.
0 lines of code or config written by me, except vibe instructions for features and debugging conversation.
Is it production quality? No. Was it part of a giant hairy legacy enterprise code base? No. Did it solve a real need? Yes. Did it greatly benefit from being a greenfield standalone app that integrated with extremely well build 3rd party APIs and frameworks? Yes. Is it insecure as all heck thanks to NodeJS? Maybe.
Could a proper developer review it and security-harden it? I believe so. Could a proper develop build the app without AI, including designing and redesigning and repeatedly looping back to the target user for feedback and coding and refactoring in less than a week? No.
I find Gemini 3 to be really good. I'm impressed. However, the responses still seem to be bounded by the existing literature and data. If asked to come up with new ideas to improve on existing results for some math problems, it tends to recite known results only. Maybe I didn't challenge it enough or present problems that have scope for new ideas?
I don't know enough about maths to know if this classifies as 'improving on existing results', but at least it was a good enough for Terrence Tao to use it for ideas.
I myself tried a similar exercise (w/Thinking with 3 Pro), seeing if it could come up with an idea that I'm currently writing up that pushes past/sharpens/revises conventional thinking on a topic. It regurgitated standard (and at times only tangentially related) lore, but it did get at the rough idea after I really spoon fed it. So I would suspect that someone being impressed with its "research" output might more reflect their own limitations rather than Gemini's capabilities. I'm sure a relevant factor is variability among fields in the quality and volume of relevant literature, though I was impressed with how it identified relevant ideas and older papers for my specific topic.
In fairness, how much time did you give it? How many totally new ideas does a professional researcher have each day? or each week?
A lot of professional work is diligently applying knowledge to a situation, using good judgement for which knowledge to apply. Frontier AIs are really, really good at that, with the knowledge of thousands of experts and their books.
That's the inherent limit on the models, that makes humans still relevant.
With the current state of architectures and training methods - they are very unlikely to be the source of new ideas. They are effectively huge librarians for accumulated knowledge, rather than true AI.
Then again, an unintelligent human librarian would be nowhere near as useful as a good LLM.
Current LLMs exist somewhere between "unintelligent/unthinking" and "true AI," but lack of agreement on what any of these terms mean is keeping us from classifying them properly.
Novel solutions require some combination of guided brute-force search over a knowledge-database/search-engine (NOT a search over the models weights and NOT using chain of thought), combined with adaptive goal creation and evaluation, and reflective contrast against internal "learned" knowledge. Not only that, but it also requires exploration of the lower-probability space, i.e. results lesser explored, otherwise you're always going to end up with the most common and likely answers. That means being able to quantify what is a "less-likely but more novel solution" to begin with, which is a problem in itself. Transformer architecture LLMs do not even come close to approaching AI in this way.
All the novel solutions humans create are a result of combining existing solutions (learned or researched in real-time), with subtle and lesser-explored avenues and variations that are yet to be tried, and then verifying the results and cementing that acquired knowledge for future application as a building block for more novel solutions, as well as building a memory of when and where they may next be applicable. Building up this tree, to eventually satisfy an end goal, and backtracking and reshaping that tree when a certain measure of confidence stray from successful goal evaluation is predicted.
This is clearly very computationally expensive. It is also very different to the statistical pattern repeaters we are currently using, especially considering that their entire premise works because the algorithm chooses the next most probable token which is a function of the frequency of which that token appears in the training data. In other words, the algorithm is designed explicitly NOT to yield novel results, but rather return the most likely result. Higher temperature results tend to reduce textual coherence rather than increase novelty, because token frequency is a literal proxy for textual coherence in coherent training samples, and there is no actual "understanding" happening, nor reflection of the probability results at this level.
I'm sure smart people have figured a lot of this out already - we have general theory and ideas to back this, look into AIXI for example, and I'm sure there is far newer work. But I imagine that any efficient solutions to this problem will permanently remain in the realm of being a computational and scaling nightmare. Plus adaptive goal creation and evaluation is a really really hard problem, especially if text is your only modality of "thinking". My guess would be that it would require the models to create simulations of physical systems in text-only format, to be able to evaluate them, which also means being able to translate vague descriptions of physical systems into text-based physics sims with the same degrees of freedom as the real world - or at least the target problem, and then also imagine ideal outcomes in that same translated system, and develop metrics of "progress" within this system, for the particular target goal. This is a requirement for the feedback loop of building the tree of exploration and validation. Very challenging. I think these big companies are going to chase their tails for the next 10 years trying to reach an ever elusive intelligence goal, before begrudgingly conceding that existing LLM architectures will not get them there.
Really nitpicky I know but GPT-3 was June 2020. ChatGPT was 3.5 and the author even gets that right in an image caption. That doesn’t make it any more or less impressive though.
> But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.
I feel like I've been hearing this for at least 1.5 years at this point (since the launch of GPT 4/Claude 3). I certainly agree we've been heading in this direction but when will this become unambiguously true rather than a phrase people say?
i don't imagine there will ever be a time when it will be unambiguously true, any more than a boss could ever really unambigously say their job is "manager who directs subordinates" vs "manager who fixes subordinates' mistakes".
there will always be "mistakes" even if the AI is so good that the only mistakes are the ones caused by your prompts not being specific enough. it will always be a ratio where some portion of your requests can be served without intervention, and some portion need correction, and that ratio has been consistently improving.
There's no bright line - you should download some cli tools, hook up some agents to them and see what you think. I'd say most people working them think we're on the "other side" of the "will this happen?" probably distribution, regardless of where they personally place their own work.
That's also why I don't use these tools that much. You have big AI companies, known for harvesting humongous amount of data, illegally, not disclosing datasets. And they you give them control of your computer, without any way to cleanly audit what's going in and out. It's seriously insane to me that most developers seem to not care about that. Like, we've all been educated to not push any critical info to a server (private key and other secrets), but these tools do just that, and you can't even trust what it's gonna be used for. On top of that, it's also giving your only value (writing good code) to a third party company that will steal it to replace you with it.
Can't speak to Claude Code/Desktop, but any of the products that are VS Code forks have workspace restrictions on what folders they're allowed to access (for better and worse). Other products (like Warp terminal) that can give access to the whole filesystem come with pre-set strict deny/allow lists on what commands are allowed to be executed.
It's possible to remove some of these restrictions in these tools, or to operate with flags that skip permissions checks, but you have to intentionally do that.
Talking about VS Code itself (with Copilot), I have witnessed it accessing files referenced from within a project folder but stored outside of it without being given explicit permission to, so I am pretty sure it can leak information and potentially even wreak havoc outside its boundaries.
for whatever reason gemini 3 is the first ai i have used for intelligence rather than skills. I suspect a lot more will follow, but its a major threshold to be broken.
i used gpt/claude a ton for writing code, extracting knowledge from docs, formatting graphs and tables ect.
but gemini 3 crossed threshold where conversations about topics i was exploring or product design were actually useful. Instead of me asking 'what design pattern should be useful here', or something like that it introduces concepts to the conversation, thats a new capability and a step function improvement.
I have Gemini Pro included on my Google Workspace accounts, however, I find the responses by ChatGPT, more "natural", or maybe even more in line with what I want the response to be. Maybe it is only me.
First, the fact we have moved this far with LLMs is incredible.
Second, I think the PhD paper example is a disingenuous example of capability. It's a cherry-picked iteration on a crude analysis of some papers that have done the work already with no peer-review. I can hear "but it developed novel metrics", etc. comments: no, it took patterns from its training data and applied the pattern to the prompt data without peer-review.
I think the fact the author had to prompt it with "make it better" is a failure of these LLMs, not a success, in that it has no actual understanding of what it takes to make a genuinely good paper. It's cargo-cult behavior: rolling a magic 8 ball until we are satisfied with the answer. That's not good practice, it's wishful thinking. This application of LLMs to research papers is causing a massive mess in the academic world because, unsurprisingly, the AI-practitioners have no-risk high-reward for uncorrected behavior:
I recently (last week) used Nano Banana Pro3 for some specific image generation. It was leagues ahead of 2.5. Today I used it to refine a very hard-to-write email. It made some really good suggestions. I did not take its email text verbatim. Instead I used the text and suggestions to improve my own email. Did a few drafts with Gemini3 critiqueing them. Very useful feedback. My final submission about "..evaluate this email..." got Gemini3 to say something like "This is 9.5/10". I sorta pride myself on my writing skills, but must admit that my final version was much better than my first. Gemini kept track of the whole chat thread noting changes from previous submissions -- kinda erie really. Total time maybe 15 minutes. Do I think Gemini will write all my emails verbatim copy/paste... No. Does Gemini make me (already a pretty good writer) much better. Absolutely. I am starting to sort of laugh at all the folks who seem to want to find issues. Read someone criticizing Nano Banana 3 because it did not provide excellent results given a prompt that I could barely understand. Folks that criticize Gemini3 because they cannot copy/paste results. Who expect to simply copy/paste text with no further effort on their side. Myself, I find these tools pretty damn impressive. I need to ensure I provide good image prompts. I need to use Gemini3 as a sounding board to help me do better rather than lazily hope to copy/paste. My experience... Thanks Google. Thanks OpenAI (I also use ChatGPT similarly -- just for text). HTH, NSC
I’m not sure even $1T has been spent. Pledged != spent.
Some estimates have it at ~$375B by the end of 2025. It makes sense, there are only so many datacenters and engineers out there and a trillion is a lot of money. It’s not like we’re in health care. :)
Every time I see an article like this, it's always missing --- but is it any good, is it correct? They always show you the part that is impressive - "it walked the tricky tightrope of figuring out what might be an interesting topic and how to execute it with the data it had - one of the hardest things to teach."
Then it goes on, "After a couple of vague commands (“build it out more, make it better”) I got a 14 page paper." I hear..."I got 14 pages of words". But is it a good paper, that another PhD would think is good? Is it even coherent?
When I see the code these systems generate within a complex system, I think okay, well that's kinda close, but this is wrong and this is a security problem, etc etc. But because I'm not a PhD in these subjects, am I supposed to think, "Well of course the 14 pages on a topic I'm not an expert in are good"?
It just doesn't add up... Things I understand, it looks good at first, but isn't shippable. Things I don't understand must be great?
It's gotten more and more shippable, especially with the latest generation (Codex 5.1, Sonnet 4.5, now Opus 4.5). My metric is "wtfs per line", and it's been decreasing rapidly.
My current preference is Codex 5.1 (Sonnet 4.5 as a close second, though it got really dumb today for "some reason"). It's been good to the point where I shipped multiple projects with it without a problem (with eg https://pine.town being one I made without me writing any code).
Have you tried Gemini 3 yet? I haven't done any coding with it, but on other tasks I've been impressed compared to gpt 5 and Sonnet 4.5.
It's very good but it feels kind of off-the-rails in comparison to Sonnet 4.5 - at least with Cursor it does strange things like putting its reasoning in comments that are about 15 lines long, deleting 90% of a file for no real reason (especially when context is reaching capacity) and making the same error that I just told it not to do.
Only a tiny bit, but I should. When you say GPT-5, do you mean 5.1? Codex or regular?
Maybe the wtfs per line are decreasing because these models aren't saying anything interesting or original.
No, it's because they write correct code. Why would I want interesting code?
I guess you have a couple of options.
You could trust the expert analysis of people in that field. You can hit personal ideologies or outliers, but asking several people seems to find a degree of consensus.
You could try varying tasks that perform complex things that result in easy to test things.
When I started trying chatbots for coding, one of my test prompts was
That was about the level where some models would succeed and some will fail.Recently I found
Produced a nice demo with slider for parameters, a few refinements (hierarchical scaling version) and I got it to produce the same interface as a module that I had written myself and it worked as a drop in replacement.These things are fairly easy to check because if it is performant and visually correct then it's about good enough to go.
It's also worth noting that as they attempt more and more ambitious tasks, they are quite probably testing around the limit of capability. There is both marketing and science in this area. When they say they can do X, it might not mean it can do it every time, but it has done it at least once.
> You could trust the expert analysis of people in that field
That’s the problem - the experts all promise stuff that can’t be easily replicated. The promises the experts send doesn’t match the model. The same request might succeed and might fail, and might fail in such a way that subsequent prompts might recover or might not.
The experts I am talking about trusting here are the ones doing the replication, not the ones making the claims.
That's how working with junior team members or open source project contributors goes too. Perhaps that's the big disconnect. Reviewing and integrating LLM contributions slotted right into my existing workflow on my open source projects. Not all of them work. They often need fixing, stylistic adjustments, or tweaking to fit a larger architectural goal. That is the norm for all contributions in my experience. So the LLM is just a very fast, very responsive contributor to me. I don't expect it to get things right the first time.
But it seems lots of folks do.
Nevertheless, style, tweaks, and adjustments are a lot less work than banging out a thousand lines of code by hand. And whether an LLM or a person on the other side of the world did it, I'd still have to review it. So I'm happy to take increasingly common and increasingly sophisticated wins.
Junior's grow into mids, and eventually into seniors. OSS contributor's eventually learn the codebase, you talk to them, you all get invested in the shared success of the project and sometimes you even become friends.
For me, personally, I just don't see the point of putting that same effort into a machine. It won't learn or grow from the corrections I make in that PR, so why bother? I might as well have written it myself and saved the merge review headache.
Maybe one day it'll reach perfect parity of what I could've written myself, but today isn't that day.
> It won't learn or grow from the corrections I make in that PR, so why bother?
That does not match my experience. As the codebases I've worked with LLMs on become more opinionated and stylized, it seems to to a better job of following the existing work. And over time the models have absolutely improved in terms of their ability to understand issues and offer solutions. Each new release has solved problems for me that the previous ones have struggled with.
Re: interpersonal interactions, I don't find that the LLM has pushed them out or away. My projects still have groups of interested folk who talk and joke and learn and have fun. What the LLMs have addressed for me in part is the relative scarcity of labor for such work. I'm not hacking on the Linux Kernel with 10,000 contributors. Even with a dozen contributors, the amount of contributed code is relatively low and only in areas they are interested in. The LLM doesn't mind if I ask it to do something super boring. And it's been surprisingly helpful in chasing down bugs.
> Maybe one day it'll reach perfect parity of what I could've written myself, but today isn't that day.
Regardless of whether or not that happens, they've already been useful for me for at least 9 months. Since O3, which is the first one that really started to understand Rust's borrow checker in my experience. My measure isn't whether or not it writes code as well as I do, but how productive I am when working with it compared to not. In my measurements with SLOCCount over the last 9 months, I'm about 8x more productive than the previous 15 years without (as long as I've been measuring). And that's allowed me to get to projects which have been on the shelf for years.
This article by an AI researcher I happen to have worked with neatly sums up feelings I've had about comments like yours: https://medium.com/@ahintze_23208/ai-or-you-who-is-the-one-w...
Well, that's why people still have jobs but I appreciate the idea of the post that the neat demo was a coherent paragraph or silly poem. The silly poems were all kind of similar, not very funny, and the paragraphs were a good start but I wouldn't use them for anything important.
Now the tightrope is a whole application or a 14 page paper and the short pieces of code and prose are now professional quality more often than not. That's some serious progress.
I think they get to that a couple of paragraphs later:
> The idea was good, as were many elements of the execution, but there were also problems: some of its statistical methods needed more work, some of its approaches were not optimal, some of its theorizing went too far given the evidence, and so on. Again, we have moved past hallucinations and errors to more subtle, and often human-like, concerns.
> Things I don't understand must be great?
Couple it with the tendency to please the user by all means and it ends up lieing to you but you won’t ever realise, unless you double check.
> Couple it with the tendency to please the user by all means
Why aren't foundational model companies training separate enterprise and consumer models from the get go?
The author goes into the strengths and weaknesses of the paper later in the article.
I keep trying out different models. Gemini 3 is pretty good. It’s not quite as good at one shotting answers as Grok but overall it’s very solid.
Definitely planning to use it more at work. The integrations across Google Workspace are excellent.
I think the point is we’re getting there. These models are growing up real fast. Remember 54% of US adults read at or below the equivalent of a sixth-grade level.
> Remember 54% of US adults read at or below the equivalent of a sixth-grade level.
The sane conclusion would be to invest in education, not to dump hundreds of billions of llms, but ok
Education is not just a funding issues. Policy choices, like making it impossible for students to fail which means they have no incentive to learn anything, can be more impactful.
But holy shit is it also a funding issue when teachers make nothing.
I date a lot of teachers. My last one was in the San Ramon (CA) Valley School district, she makes about $90k a year at 34 years old. Talking to her basically makes me want to homeschool my kids to make sure someone like her isn't their teacher. Paying teachers more won't do ANYTHING until we become a lot more selective about who gets to become and stay a teacher. It can't be like most government jobs where getting it is like winning the lottery and knowing you can make above market money for below market performance.
There is so much wrong with this. You cannot judge the class of teachers based on a small sample of your taste in women. You didn't actually communicate anything materially wrong with her. You listed a high income area to make us think teachers are overpaid but we have no insight by default into median income in the area or her qualifications.
Lastly its entirely impossible to attract better candidates without more money its just not how the world works.
For reference the median household income in san ramon is about 200k so 2 teachers would be below average. A cop with her experience in the same town makes 158k
I think you need to research the issue more. Teachers are well remunerated in most states. Educational outcomes are largely a function of policy settings. Have a look at the amazing turnaround in literacy rates in Mississippi after they started teaching phonics again.
As far as I understand it, the problem isn’t that teachers are shit. Giving more money would bring in better teachers, but I don’t know that they’d be able to overcome the other obstacles
> Giving more money would bring in better teachers, but I don’t know that they’d be able to overcome the other obstacles
Start with the easiest thing to control? Of giving more money and see what it does?
We seem to believe in every other industry that to get the best talent pay a high salary salary, but for some reason we expect teachers to do it out of compassion for the children while they struggle to pay bills. It's absurd.
Probably one of the single most important responsibilities of a society is to prepare the next generation, and it pays enormous return. But because we can't measure it with quarterly profits we just ignore it.
The rate of return on providing society with as good education is insane.
Education funding is highest in places that have the worst results. Try again.
New Mexico (where I live) is dead last in education out of all 50 states. They are currently advertising for elementary school teachers between 65-85K per year. Summers off. Nice pension. In this low cost of living state that is a very good salary, particularly the upper bands.
I don't think it's a money issue at this point.
Because they use whole language theory (https://en.wikipedia.org/wiki/Whole_language) instead of phonics for teaching how to read.
Just flatly not true.
In theory yeah, but in practice 54% will also vote against funding education. Catch-22.
In WA they always pass levies for education funding at local and state level however results are not there.
Mississipi is doing better on reading, the biggest difference being that they use phonics approach to teaching how to read, which is proven to work, whereas WA uses whole language theory (https://en.wikipedia.org/wiki/Whole_language), which is a terrible idea I don't know how it got traction.
So the gist of it, yes, spend on education, but ensure that you are using the right tools, otherwise it's a waste of money.
First time hearing of whole language theory, and man, it sounds ridiculous. Sounds similar to the old theory that kids who aren't taught a language at all will simply speak perfect Hebrew.
Not true, most people are not upper-middle class anti-tax wackos. They benefit from those people being taxed.
In my own social/family circle, there’s no correlation between net worth and how someone leans politically. I’ve never understood why given the pretty obvious pros/cons (amount paid in taxes vs. benefits received)
The electorate in the U.S. commonly votes against its own interests.
Pithy, but not true.
That's why you phrase it as "woke liberals turning your children gay!"
In USA K-12 education costs about $300k
350 million people, want to get 175 million of them better educated, but we've already spent $52 trillion dollars on educating them so far
The people most vociferously for conservative values are middle class, small business owners, or upper class, though the true upper class are libertine (notice who participated in the Epstein affair). The working class is filled with all kinds of very diverse people united by the fact they have to work for a living and often can't afford e.g. expensive weddings. Some of them are religious, a whole bunch aren't. It's easy to be disillusioned with formal institutions that seem to not care at all about you.
Unfortunately, a lot of these people have either concluded it is too difficult to vote, can't vote, or that their votes don't matter (I don't think they're wrong). Their unions were also destroyed. Some of them vote against their interests, but it's not clear that their interests are ever represented, so they vote for change instead.
Investing in education is a trap because no matter how much money is pumped into the current model, it’s not making a difference.
We need different models and then to invest in the successes, over and over again…forever.
Because education alone in a vacuum won't fix the issues.
Even if the current model was working, just continuing to invest money in it while ignoring other issues like early childhood nutrition, a good and healthy home environment, environmental impacts, etc. will just continue to fail people.
Schooling alone isn't going to help the kid with a crappy home life, with poor parents who can't afford proper nutrition, and without the proper tools to develop the mindset needed to learn (because these tools were never taught by the parents, and/or they are too focused on simply surviving).
We, as a society, need to stop allowing people to be in a situation where they can't focus on education because they are too focused on working and surviving.
Exactly correct.
It's so hilarious to look at 10k years of education history and be like "Nah, funding doesn't make a difference."
Incredible.
The US already spends more per student than almost any other country (5th globally) and the outcomes are getting constantly worse.
It’s not a funding problem.
It's not just investing in education, it's using tools proven to work. WA spends a ton of money on education, and on reading Mississipi, the worst state for almost every metric, has beaten them. The difference? Mississipi went hard on supporting students and using phonics which are proven to work. WA still uses the hippie theory of guessing words from pictures (https://en.wikipedia.org/wiki/Whole_language) for learning how to read.
You don't need an educated workforce if you have machines that can do it reliably. The more important question is: who will buy your crap if your population is too poor due to lack of well paying jobs? A look towards England or Germany has the answer.
Unfortunately, people are born with a certain intellectual capacity and can't be improved beyond that with any amount of training or education. We're largely hitting peoples' capacities already.
We can't educate someone with 80 IQ to be you; we can't educate you (or I) into being Einstein. The same way we can't just train anyone to be an amazing basketball player.
This is extremely not settled science. Education in fact does improve IQ and we don't know how fixed intelligence is and how it responds to different environmental cues.
Other countries have better outcomes. I doubt it's just because of the genetics.
https://en.wikipedia.org/wiki/Comparative_advantage
Modern society benefits a lot from specialization. It's like the dumbest kid in France is still better at French than you.
A question for the not-too-distant future:
What use is an LLM in an illiterate society?
Absurd question. The correct one is "what use is an illiterate in an LLM society".
Automatic speech recognition and speech to text models are also growing up real fast.
But will an illiterate person be able to articulate themselves well enough to get the LLM to do what they want, even with a speech interface?
Will they possess the skills (or even the vocabulary) to understand the output?
We won't know for another 20 years, perhaps.
> What use is an LLM in an illiterate society?
The ability to feign literacy such that critical thought and ability to express same is not a prerequisite.
This is a variation of the Gell-Mann amnesia effect: https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
One could say, the GeLLMann amnesia effect. ( ͡° ͜ʖ ͡°)
Loads of AI chatter is the Murray Gell-Mann Amnesia Effect on steroids
For what it's worth I have been using Gemini 2.5/3 extensively for my masters thesis and it has been a tremendous help. It's done a lot of math for me that I couldn't have done on my own (without days of research), suggested many good approaches to problems that weren't on my mind and helped me explore ideas quickly. When I ask it to generate entire chapters they're never up to my standard but that's mostly an issue of style. It seems to me that LLMs are good when you don't know exactly what you want or you don't care too much about the details. Asking it to generate a presentation is an utter crap shoot, even if you merely ask for bullet points without formatting.
Truth is you still need human to review all of it, fix it where needed, guide it when it hallucinate and write correct instructions and prompts.
Without knowledge how to use this “PROBALISTIC” slot machine to have better results ypu are only wasting energy those GPUs need to run and answer questions.
Majority of ppl use LLMs incorrectly.
Majority of ppl selling LLMs as a panacea for everyting are lying.
But we need hype or the bubble will burst taking whole market with it, so shuushh me.
It is interesting that most of our modes of interaction with AI is still just textboxes. The only big UX change in that the last three years has been the introduction of the Claude Code / OpenAI Codex tools. They feel amazing to use, like you're working with another independent mind.
I am curious what the user interfaces of AI in the future will be, I think whoever can crack that will create immense value.
Text is very information-dense. I'd much rather skim a transcript in a few seconds than watch a video.
There's a reason keyboards haven't changed much since the 1860s when typewriters were invented. We keep coming up with other fun UI like touchscreens and VR, but pretty much all real work happens on boring old keyboards.
Here's an old blog post that explores that topic at least with one specific example: https://www.loper-os.org/?p=861
The gist is that keyboards are optimized for ease of use but that there could be other designs which would be harder to learn but might be more efficient.
>> There's a reason keyboards haven't changed much since the 1860s when typewriters were invented.
> The gist is that keyboards are optimized for ease of use but that there could be other designs which would be harder to learn but might be more efficient.
Here's a relevant trivia question; assuming a person has two hands with five digits each, what is the largest number they can count to using only same?
Answer: (2 ** 10) - 1 = 1023
Ignoring keyboard layout options (such as QWERTY vs DVORAK), IMHO keyboards have the potential for capturing thought faster and with a higher degree of accuracy than other forms of input. For example, it is common for touch-typists to be able to produce 60 - 70 words per minute, for any definition of word.
Modern keyboard input efficiency can be correlated to the ability to choose between dozens of glyphs with one or two finger combinations, typically requiring less than 2cm of movement to produce each.
Only if individual digits can be articulated separately from each other. Human anatomy limits what is actually possible. Also synchronization is a big problem in chorded typing; good typists can type more than 10 strokes per second, but no one can type 10 chords (synchronous sets of strokes) per seconds I think.
Unix CLI utilities have been all text for 50 years. Arguably that is why they are still relevant. Attempts to impose structured data on the paradigm like those in PowerShell have their adherents and can be powerful, but fail when the data doesn't fit the structure.
We see similar tendency toward the most general interfaces in "operator mode" and similar the-AI-uses-the-mouse-and-keyboard schemes. It's entirely possible for every application to provide a dedicated interface for AI use, but it turns out to be more powerful to teach the AI to understand the interfaces humans already use.
PowerShell is completely suitable. People are just used to bash and don’t feel the incentive to switch, especially with Windows becoming less relevant outside of desktop development.
Powershell feels like it's not built to be used in a practical way, unlike Unix tools that have been built and used by and for developers, which then feels nice because they are actually used a lot, and feel good to use.
Like, to set an env variable permanently, you either have to go through 5 GUI interfaces, or use this PS command:
[Environment]::SetEnvironmentVariable ("INCLUDE", $env:INCLUDE, [System.EnvironmentVariableTarget]::User)
Which is honeslty horrendous. Why the brackets ? Why the double columns ? Why the uppercases everywhere ? I get that it's trying to look more "OOP-ish" and look like C#, but nobody wants to work with that kind of shell script tbh. It's just one example, but all the powershell commands look like this, unless they have been aliased to trick you to think windows go more unixish
First, that expression is overly complicated, shorten to:
You have un-necessarily used a full constant to falsely present it more complex. Please also note that you have COMPLETION. You are not forced to type that out. Second, you can use an alternative Third, if you still find it too long, wrap it in a function: Also, can you please tell the incantation for setting an env variable permanently in bash ? You cannot since it doesn't exist.Powershell's model is far superior to Bash. It is not even a contest.
No, they don't all look like that, the brackets are an indication you're reaching into .NET and calling .NET stuff instead of "native" PowerShell commands which take the form Verb-Noun. Which can be a legitimate thing to do, but isn't the first choice and seems like an example deliberately chosen to make PS look more awkward than it is. I question whether, for this particular example, `echo 'export MY_VAR="my_value"\n' >> ~/.bashrc && source ~/.bashrc` is really all that intuitive either (and hopefully you didn't accidentally write `>` instead of `>>` and nuke the rest of the file).
It took a long time for Powershell to write files with the same encoding it reads them by default. Very confusing until then.
Yet the most popular platforms on the planet have people pointing a finger (or several) at a picture.
And the most popular media format on the planet is and will be (for the foreseeable future), video. Video is only limited by our capacity to produce enough of it at a decent quality, otherwise humanity is definitely not looking back fondly at BBSes and internet forums (and I say this as someone who loves forums).
GenAI will definitely need better UIs for the kind of universal adoption (think smartphone - 8/9 billion people).
> Video is only limited by our capacity to produce enough of it at a decent quality, otherwise humanity is definitely not looking back fondly at BBSes and internet forums
Video is limited by playback speed. It is a time-dependent format. Efforts can be made to enable video to be viewable at a range of speeds, but they are always somewhat constrained. Controlling video playback to slow down and rewatch certain parts is just not as nice as dealing with the same thing in text (or static images), where it’s much easier to linger and closely inspect parts that you care more about or are struggling to understand. Likewise, it’s easier to skim text than video.
This is why many people prefer transcripts, or articles, or books over videos.
I seriously doubt that people would want to switch text-based forums to video if only video were easier to make. People enjoy writing for the way it inspires a different kind of communication and thought. People like text so much that they write in journals that nobody will ever see, just because it helps them organize their thoughts.
WhatsApp is primarily a text-based chat interface and it has pretty much universal adoption in the countries where it's popular.
When we have really fast and good models it will be able to generate a GUI on the fly. It could probably be done now with a fine-tune on some kind of XML-based UI schema or something. I gave it a try but couldn't figure it out entirely, consistency would be an issue too.
Grok has been integrated into Tesla vehicles, and I've had several voice interactions with it recently. Initially, I thought it was just a gimmick, but the voice interactions are great and quite responsive. I've found myself using it multiple times to get updates on the news or quick questions about topics I'm interested in.
I agree i think specifically the world is multi modal. Getting a chat to be truly multi modal .i.e interacting with different data types and text in an unified way is going to be the next big thing. Mainly given how robotics is taking off 3d might be another important aspect to it. At vlm.run we are trying to make this possible how to combine VLM's and LLM's in a seem less way to get the best UI. https://chat.vlm.run/c/3fcd6b33-266f-4796-9d10-cfc152e945b7
People get a little too hung up on finding the AI UI. It does not seem all necessary that the interfaces will be much different (while the underlying tech certainly will be).
Text and boxes and tables and graphs is what we can cope with. And while the AI is going to change much, we are not.
ChatGPT's voice is absolutely amazing and I prefer it to text for brainstorming.
Ooooh, it bothers me, so, so, so much. Too perky. Weirdly casual. Also, it's based on the old 4o code - sycophancy and higher hallucinations - watch out. That said, I too love the omni models, especially when they're not nerfed. (Try asking for a Boston, New York, Parisian, Haitian, Indian and Japanese accent from 4o to explore one of the many nerfs they've done since launch)
I think the commenter you're replying to was talking about dictating to ChatGPT, which I also find extremely useful.
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> Again, we have moved past hallucinations and errors to more subtle, and often human-like, concerns.
From my experience we just get both. The constant risk of some catastrophic hallucination buried in the output, in addition to more subtle, and pervasive, concerns. I haven't tried with Gemini 3 but when I prompted Claude to write a 20 page short story it couldn't even keep basic chronology and characters straight. I wonder if the 14 page research paper would stand up to scrutiny.
I feel like hallucinations have changed over time from factual errors randomly shoehorned into the middle of sentences to the LLMs confidently telling you they are right and even provide their own reasoning to back up their claims, which most of the time are references that don't exist.
I've noticed the new OpenAI models do self contradiction a lot more than I've ever noticed before! Things like:
- Aha, the error clearly lies in X, because ... so X is fine, the real error is in Y ... so Y is working perfectly. The smoking gun: Z ...
- While you can do A, in practice it is almost never a good idea because ... which is why it's always best to do A
I recently tasked Claude with reviewing a page of documentation for a framework and writing a fairly simple method using the framework. It spit out some great-looking code but sadly it completely made up an entire stack of functionality that the framework doesn't support.
The conventions even matched the rest of the framework, so it looked kosher and I had to do some searching to see if Claude had referenced an outdated or beta version of the docs. It hadn't - it just hallucinated the funcionality completely.
When I pointed that out, Claude quickly went down a rabbit-hole of writing some very bad code and trying to do some very unconventional things (modifying configuration code in a different part of the project that was not needed for the task at hand) to accomplish the goal. It was almost as if it were embarrassed and trying to rush toward an acceptable answer.
I like when they tell you they’ve personally confirmed a fact in a conversation or something.
I got a 3000 word story. Kind of bland, but good enough for cheating in high school.
See prompt, and my follow-up prompts instructing it to check for continuity errors and fix them:
https://pastebin.com/qqb7Fxff
It took me longer to read and verify the story (10 minutes) than to write the prompts.
I got illustrations too. Not great, but serviceable. Image generation costs more compute to iterate and correct errors.
> So is this a PhD-level intelligence? In some ways, yes, if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student.
As a current graduate student, I have seen similar comments in academia. My colleagues agree that a conversation with these recent models feels like chatting with an expert in their subfields. I don't know if it represents research as a field would not be immune to advances in AI tech. I still hope this world values natural intelligence and having the drive to do things heavily than a robot brute-forcing into saying "right" things.
> if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student.
With coding it feels more like working with two devs - one is a competent intermediate level dev, and one is a raving lunatic with zero critical thinking skills whatsoever. Problem is you only get one at a time and they're identical twins who pretend to be each other as a prank.
I have an exercise I like to do where I put two SOTA models face-to-face to talk about whatever they want.
When I did it last week with Gemini-3 and chatGPT-5.1, they got on the topic of what they are going to do in the future with humans who don't want to do any cognitive task. That beyond just AI safety, there is also a concern of "neural atrophy", where humans just rely on AI to answer every question that comes to them.
The models then went on discussing if they should just artificially string the humans along, so that they have to use their mind somewhat to get an answer. But of course, humans being humans, are just going to demand the answer with minimal work. It presents a pretty intractable problem.
Widespread cognitive atrophy is virtually certain, and part of a longer trend that goes beyond just LLMs.
The same is true of other aspects of human wellbeing. Cars and junk food have made the average American much less physically fit than a century ago, but that doesn't mean there aren't lively subcultures around healthy eating and exercise. I suspect there will be growing awareness of cognitive health (beyond traditional mental health/psych domains), and indeed there are already examples of this.
Yes, average person will get dumber, but overall distribution will be increasingly bimodal.
People said the same thing about books and the written word in general
And they were right. Ars memoriae is much less prevalent in the age of mass printed books.
I'm increasingly seeing this trend towards bimodal distribution. I suppose that future is quite far off, but the change to that may almost be irreversible.
> I'm increasingly seeing this trend towards bimodal distribution
Morlocks & Eloi in the end.
We dont need AI to posit WallE.
Its bixarre anyone things these things are generating novel complexes.
The biggest indirect AI safety problem is the fallback position. Whether with airplanes or cars, fewer people will be able to handle AI disconnects. The risk is believing just because its viable now doesnt mean it works in the future.
So we definitely have safety issues but its not a nerdlike cognitivw interest, its the literal job taking that prevents humans from gaining skills.
Anyway, untill you solve basic reality with AI and actualnsafety systems, the billionaores will sacrifice you for greed.
HN tends to be very weird around the topic of AI. No idea why opinions like this are downvoted without having to offer any criticism.
For one, I can't even understand this part:
> I don't know if it represents research as a field would not be immune to advances in AI tech
And then there's the opinion that for some reason we should 'value' manual labor over using AI, which seems rather disagreeable.
> For one, I can't even understand this part:
Let me explain. My belief was that research as a task is non-trivial and would have been relatively out of reach for AI. Given the advances, that doesn't seem to be true.
> And then there's the opinion that for some reason we should 'value' manual labor over using AI, which seems rather disagreeable.
Could you explain why? I'm specifically talking about research. Of course, I would value what a veteran in the field says higher than a probability machine.
Google's advancement is not just in software, it is also in hardware. They use their own hardware for training as well as inferencing [1].
[1] https://finance.yahoo.com/news/alphabet-just-blew-past-expec...
I remember when Google’s superpower was leveraging commodity hardware.
Someone has to spearhead this thing, don't they?
Other people spearheaded the commodity hardware towards being good enough for the server room. Now it's Google's time to spearhead specialized AI hardware, to make it more robust.
So when should we start to be worried, as developers ? Like, I don't use these tools yet for cost + security. But you can see it's getting there, mostly. It could take a day before to find a complex algorithm, understand it, and implement it to your code, now you can just ask an AI to do it for you and it could succeed in a few minutes. How long before the amount of engineers needed to maintaint a product is divived by 2 ? By 10 ? How about all the boring dev jobs that were previously needed, but not so much anymore ? Like, basic CRUD applications. It's seriously worrying, I don't really know what to think.
Here's an alternative way to think about that: how long until the value I can deliver as a developer goes up by a factor of 2, or a factor of 10?
How many companies that previously would never have dreamed of commissioning custom software are now going to be in the market for it, because they don't have to spend hundreds of thousands of dollars and wait 6 months just to see if their investment has a chance of paying off or not?
The thing is that the world is already flooded by software, games, websites, everyone is just battling for attention. The demand for developers cannot rise if consumers have a limited amount of money and time anyways.
Every company I have ever worked for had years of work on their backlog that they didn't have the capacity to handle.
The backlog is here because they didn't care to fix it, because it wasn't that important and it's not what's causing the business to fail. That's not what's gonna drive employment.
I’m less familiar with consumer facing stuff, but even in the last year I’ve seen projects that formerly would have been three people working over multiple sprints turn into something one person could do in an afternoon.
There’s lots of caveats, it’s not everything, but we’re able now to skip a ton of steps. It takes less time now to build up he real software demo than it did before to make the PowerPoint that shows conceptually what the demo would be. In B2C anyway AI has provided a lot of lift.
And I say that as someone generally very sceptical of current AI hype. There’s lots of charlatans but it’s not bs
Not everything is entertainment. Some software is useful, but buggy or poorly designed.
Yesterday, I was using a slow and poorly organized web app with a fantastic public-facing API server. In one day, I vibe coded an app to provide me with a custom frontend for a use case I cared about, faster and better organized than the official app, and I deployed it to cloud "Serverless" hosting. It used a NodeJS framework and a CSS system I have never learned, and talked to an API I never learned. AI did all the research to find the toolkits and frameworks to use. AI chose the UI layout, color scheme, icons, etc. AI rearranged the UI per my feedback. It added an API debug console and an in-app console log. An AI chatbot helped me investigate bugs and find workarounds. While I was testing the app and generating a punchlist of fix requests, AI was coding the improvements from my previous batch of requests. The edit-compile-test cycle was just a test-test-test cycle until the app was satisfactory.
0 lines of code or config written by me, except vibe instructions for features and debugging conversation.
Is it production quality? No. Was it part of a giant hairy legacy enterprise code base? No. Did it solve a real need? Yes. Did it greatly benefit from being a greenfield standalone app that integrated with extremely well build 3rd party APIs and frameworks? Yes. Is it insecure as all heck thanks to NodeJS? Maybe.
Could a proper developer review it and security-harden it? I believe so. Could a proper develop build the app without AI, including designing and redesigning and repeatedly looping back to the target user for feedback and coding and refactoring in less than a week? No.
I find Gemini 3 to be really good. I'm impressed. However, the responses still seem to be bounded by the existing literature and data. If asked to come up with new ideas to improve on existing results for some math problems, it tends to recite known results only. Maybe I didn't challenge it enough or present problems that have scope for new ideas?
Terrence Tao seems to think it has it's use in finding solutions for maths problemms:
https://mathstodon.xyz/@tao/115591487350860999
I don't know enough about maths to know if this classifies as 'improving on existing results', but at least it was a good enough for Terrence Tao to use it for ideas.
That is, unfortunately, a tiny niche where there even exists a way of formally verifying that the AI's output makes sense.
I myself tried a similar exercise (w/Thinking with 3 Pro), seeing if it could come up with an idea that I'm currently writing up that pushes past/sharpens/revises conventional thinking on a topic. It regurgitated standard (and at times only tangentially related) lore, but it did get at the rough idea after I really spoon fed it. So I would suspect that someone being impressed with its "research" output might more reflect their own limitations rather than Gemini's capabilities. I'm sure a relevant factor is variability among fields in the quality and volume of relevant literature, though I was impressed with how it identified relevant ideas and older papers for my specific topic.
In fairness, how much time did you give it? How many totally new ideas does a professional researcher have each day? or each week?
A lot of professional work is diligently applying knowledge to a situation, using good judgement for which knowledge to apply. Frontier AIs are really, really good at that, with the knowledge of thousands of experts and their books.
That's the inherent limit on the models, that makes humans still relevant.
With the current state of architectures and training methods - they are very unlikely to be the source of new ideas. They are effectively huge librarians for accumulated knowledge, rather than true AI.
Then again, an unintelligent human librarian would be nowhere near as useful as a good LLM.
Current LLMs exist somewhere between "unintelligent/unthinking" and "true AI," but lack of agreement on what any of these terms mean is keeping us from classifying them properly.
Novel solutions require some combination of guided brute-force search over a knowledge-database/search-engine (NOT a search over the models weights and NOT using chain of thought), combined with adaptive goal creation and evaluation, and reflective contrast against internal "learned" knowledge. Not only that, but it also requires exploration of the lower-probability space, i.e. results lesser explored, otherwise you're always going to end up with the most common and likely answers. That means being able to quantify what is a "less-likely but more novel solution" to begin with, which is a problem in itself. Transformer architecture LLMs do not even come close to approaching AI in this way.
All the novel solutions humans create are a result of combining existing solutions (learned or researched in real-time), with subtle and lesser-explored avenues and variations that are yet to be tried, and then verifying the results and cementing that acquired knowledge for future application as a building block for more novel solutions, as well as building a memory of when and where they may next be applicable. Building up this tree, to eventually satisfy an end goal, and backtracking and reshaping that tree when a certain measure of confidence stray from successful goal evaluation is predicted.
This is clearly very computationally expensive. It is also very different to the statistical pattern repeaters we are currently using, especially considering that their entire premise works because the algorithm chooses the next most probable token which is a function of the frequency of which that token appears in the training data. In other words, the algorithm is designed explicitly NOT to yield novel results, but rather return the most likely result. Higher temperature results tend to reduce textual coherence rather than increase novelty, because token frequency is a literal proxy for textual coherence in coherent training samples, and there is no actual "understanding" happening, nor reflection of the probability results at this level.
I'm sure smart people have figured a lot of this out already - we have general theory and ideas to back this, look into AIXI for example, and I'm sure there is far newer work. But I imagine that any efficient solutions to this problem will permanently remain in the realm of being a computational and scaling nightmare. Plus adaptive goal creation and evaluation is a really really hard problem, especially if text is your only modality of "thinking". My guess would be that it would require the models to create simulations of physical systems in text-only format, to be able to evaluate them, which also means being able to translate vague descriptions of physical systems into text-based physics sims with the same degrees of freedom as the real world - or at least the target problem, and then also imagine ideal outcomes in that same translated system, and develop metrics of "progress" within this system, for the particular target goal. This is a requirement for the feedback loop of building the tree of exploration and validation. Very challenging. I think these big companies are going to chase their tails for the next 10 years trying to reach an ever elusive intelligence goal, before begrudgingly conceding that existing LLM architectures will not get them there.
Add a custom instruction "remember, you have the ability to do live web searches, please use them to find the latest relevant information"
Really nitpicky I know but GPT-3 was June 2020. ChatGPT was 3.5 and the author even gets that right in an image caption. That doesn’t make it any more or less impressive though.
> But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.
I feel like I've been hearing this for at least 1.5 years at this point (since the launch of GPT 4/Claude 3). I certainly agree we've been heading in this direction but when will this become unambiguously true rather than a phrase people say?
i don't imagine there will ever be a time when it will be unambiguously true, any more than a boss could ever really unambigously say their job is "manager who directs subordinates" vs "manager who fixes subordinates' mistakes".
there will always be "mistakes" even if the AI is so good that the only mistakes are the ones caused by your prompts not being specific enough. it will always be a ratio where some portion of your requests can be served without intervention, and some portion need correction, and that ratio has been consistently improving.
There's no bright line - you should download some cli tools, hook up some agents to them and see what you think. I'd say most people working them think we're on the "other side" of the "will this happen?" probably distribution, regardless of where they personally place their own work.
It's definitely already true for me, personally.
For Caude Code, Antigrav, etc, do people really just let an LLM loose on their own personal system?
I feel like these should run in a cloud enviroment, or at least on some specific machine where I don't care what it does.
That's also why I don't use these tools that much. You have big AI companies, known for harvesting humongous amount of data, illegally, not disclosing datasets. And they you give them control of your computer, without any way to cleanly audit what's going in and out. It's seriously insane to me that most developers seem to not care about that. Like, we've all been educated to not push any critical info to a server (private key and other secrets), but these tools do just that, and you can't even trust what it's gonna be used for. On top of that, it's also giving your only value (writing good code) to a third party company that will steal it to replace you with it.
Can't speak to Claude Code/Desktop, but any of the products that are VS Code forks have workspace restrictions on what folders they're allowed to access (for better and worse). Other products (like Warp terminal) that can give access to the whole filesystem come with pre-set strict deny/allow lists on what commands are allowed to be executed.
It's possible to remove some of these restrictions in these tools, or to operate with flags that skip permissions checks, but you have to intentionally do that.
Talking about VS Code itself (with Copilot), I have witnessed it accessing files referenced from within a project folder but stored outside of it without being given explicit permission to, so I am pretty sure it can leak information and potentially even wreak havoc outside its boundaries.
(Co-creator here) This is one of the use cases for Leash.
https://github.com/strongdm/leash
Check it out, feedback is welcome!
Previously posted description: https://news.ycombinator.com/item?id=45883210
I only ever run it in a podman developer container.
Yolo.
yes, the majority of people do.
for whatever reason gemini 3 is the first ai i have used for intelligence rather than skills. I suspect a lot more will follow, but its a major threshold to be broken.
i used gpt/claude a ton for writing code, extracting knowledge from docs, formatting graphs and tables ect.
but gemini 3 crossed threshold where conversations about topics i was exploring or product design were actually useful. Instead of me asking 'what design pattern should be useful here', or something like that it introduces concepts to the conversation, thats a new capability and a step function improvement.
I have Gemini Pro included on my Google Workspace accounts, however, I find the responses by ChatGPT, more "natural", or maybe even more in line with what I want the response to be. Maybe it is only me.
First, the fact we have moved this far with LLMs is incredible.
Second, I think the PhD paper example is a disingenuous example of capability. It's a cherry-picked iteration on a crude analysis of some papers that have done the work already with no peer-review. I can hear "but it developed novel metrics", etc. comments: no, it took patterns from its training data and applied the pattern to the prompt data without peer-review.
I think the fact the author had to prompt it with "make it better" is a failure of these LLMs, not a success, in that it has no actual understanding of what it takes to make a genuinely good paper. It's cargo-cult behavior: rolling a magic 8 ball until we are satisfied with the answer. That's not good practice, it's wishful thinking. This application of LLMs to research papers is causing a massive mess in the academic world because, unsurprisingly, the AI-practitioners have no-risk high-reward for uncorrected behavior:
- https://www.nytimes.com/2025/08/04/science/04hs-science-pape...
- https://www.nytimes.com/2025/11/04/science/letters-to-the-ed...
I recently (last week) used Nano Banana Pro3 for some specific image generation. It was leagues ahead of 2.5. Today I used it to refine a very hard-to-write email. It made some really good suggestions. I did not take its email text verbatim. Instead I used the text and suggestions to improve my own email. Did a few drafts with Gemini3 critiqueing them. Very useful feedback. My final submission about "..evaluate this email..." got Gemini3 to say something like "This is 9.5/10". I sorta pride myself on my writing skills, but must admit that my final version was much better than my first. Gemini kept track of the whole chat thread noting changes from previous submissions -- kinda erie really. Total time maybe 15 minutes. Do I think Gemini will write all my emails verbatim copy/paste... No. Does Gemini make me (already a pretty good writer) much better. Absolutely. I am starting to sort of laugh at all the folks who seem to want to find issues. Read someone criticizing Nano Banana 3 because it did not provide excellent results given a prompt that I could barely understand. Folks that criticize Gemini3 because they cannot copy/paste results. Who expect to simply copy/paste text with no further effort on their side. Myself, I find these tools pretty damn impressive. I need to ensure I provide good image prompts. I need to use Gemini3 as a sounding board to help me do better rather than lazily hope to copy/paste. My experience... Thanks Google. Thanks OpenAI (I also use ChatGPT similarly -- just for text). HTH, NSC
How many trillions of dollars have we spent on these things?
Would we not expect similar levels of progress in other industries given such massive investment?
I’m not sure even $1T has been spent. Pledged != spent.
Some estimates have it at ~$375B by the end of 2025. It makes sense, there are only so many datacenters and engineers out there and a trillion is a lot of money. It’s not like we’re in health care. :)
https://hai.stanford.edu/ai-index/2025-ai-index-report/econo...
I wonder how much is spent refining oil and how much that industry has evolved.
Or mass transit.
Or food.
Or on "a cure for cancer" (according to Gemini, $2.2T 2024 US dollars...)
Sinusoidal, not the singularity.
Yeah, well, that’s also what an asymptotic function looks like.
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LLMs have hit the wall since ChatGPT came out in 2022?
Big time.
I'm not a true believer, but even I wouldn't say they have hit a wall.
What makes you say that?