Compared to a wire, Starlink is low bandwidth, high latency, and does still have short outages. I use it for my backup, but I would never choose it as my primary even over Comcast.
I was using ChatGPT voice during cooking to reflect on variations of a dishes i was preparing for years.
It was so amazing to get advices and reflect that it struck me : I could use this model forever - it’s clever enough to help me tons and do lot of work for me - even if ai would stop evolving I would love it
I share this feeling too. The latest models, even if not necessarily frontier, say Opus 5, Sol high and the likes, I could keep using these models forever even if they did not significantly improve beyond this point. I also believe we'll come up with new ways of using these very same models beyond the mainstream chat and agent interfaces, as the bottleneck is imho in harnesses/environments and not so much model intelligence anymore.
+1 regarding voice usage too, I use it in so many different ways it's hard to enumerate: while driving long distances (think of a custom made, interactive podcast) / as a way to collaboratively build specs or shape an idea / as a way to provide input while vibe coding / just as a normal voice assistant (straight in the ChatGPT app or as OpenClaw input via telegram voice notes). I can't overstate how much my routines have changed over the last couple of years.
Do you have to give any special instructions to do this? I always want to do something like this, but any time I try I get so sick of listening to what it has to say, just long winded explanations of stuff that tends to go off the rails. Imo it's hard enough to read ai output when I can go back and forward between sentences to make sense of what's being said let alone listen to a continuous train of slop.
The latest ChatGPT voice mode is really good at being interrupted - I'll often say "no, no, no, that's too much information" while it's talking to stop and redirect it.
As long as these models constantly keep switching up things like the temperatures at which a steak will be medium rare or at which temperature to season cast iron, you will never be able to trust them for cooking. My mother ruined a nice waterfowl for Christmas by listening to Gemini.
A lot like humans, really. People regularly cite something they read, or quote a stat that turns out to be just completely inaccurate. But if you look up the thing, then you have facts again.
Sure, and by the same logic, if I was trying to cook a steak, I would not trust an arbitrary human to know the correct temperature off the top of their head; I'd want someone who I could trust had actual experience with the task I was trying to perform. The difference is that most humans are fully able to recognize whether they've cooked steak often enough to know the correct temperature off the top of their head, and they will say "I don't know" to most random arbitrary questions you ask them outside of their experience. I've yet to see an LLM product aimed at general usage for individuals be willing to say this without someone having to literally direct them to give that as an answer if they're not sure.
His point was that, just like humans, if you ask the LLM to look it up, it will give you the correct answer. Just like humans, if you don't ask them to look it up, you don't know what you're going to get.
And my point is that I've never met a human who confidently asserts incorrect information in such a broad range of domains rather than just admitting that they don't know
If you have to say "don't make up something" for every possible question you ask in order for it not to make up something, that's a massive usability issue for regular people. If saying "don't make up something" will still result in it making up something up some of the time, that also might be a massive usability issue depending on if "some of the time" means 0.0001% or 1%.
Having a natural language interface where you need to go out of your way to specify that you want an accurate answer rather than just a plausible one defeats the entire purpose of it being a natural language interface for normal people. In certain professional contexts, it can be useful, but I don't buy it at all that it makes sense to ask everyone in their everyday lives to go out of their way to specify that they actually want correct answers to their questions.
Put it in your own system prompt. They all provide tools for doing this. OpenaAI Custom GPTs, Gemini Gems, Claude Projects, etc. Or you can easily roll your own using their APIs.
When you let the the agent do a test, or tell it to read that doc first, you will ground it in reality. Doesn't mean they are 100% reliable. But without and on their own without access to grounding information, they halluzinate wildly.
The parent comment said that these types of behaviors are inherent to how LLMs work, and your response was to "ground them in facts". From what I can tell, this does not meaningfully change the original point the parent comment was making that the flaw is inherent; throwing a bunch of extra context at it to try to make it happen less often is useful, but it's still just a best-effort mitigation for the behavior, not somehow a way of literally changing the inherent nature of it.
While yes, their current reliability is too spiky to be relied upon for a lot of things:
Any given failure is not inherent, they are all dependent failures; what is inherent (due to the SOTA in ML, perhaps or perhaps not the architecture) is how many examples they need to get good at stuff.
This is why I’m trying to move to open Chinese models — because I will be able to use them forever, while the older Claude models which I genuinely enjoyed writing short stories with have now been deleted, replaced with hypothetically cleverer models which produce text everyone hates.
You only need to mention Protected Group Of The Week (I'm one of them and I like to research and read about history, so that makes it extra challenging) or anything resembling negative human emotions (guilty on that front as well), and the model screeches to a halt.
Just because OAI doesn't want another headline like "Chatbot convinces teen to off himself"
I understand this in principle, but I'm not convinced that dulling everyone's knives is better than figuring out how to keep them out of kids' hands
> "no I won't tell you how to build a bioweapon for genocide" is guardrails.
I call that censorship too. I'm curious enough that I want to know about such things. I don't want any limitations on what I'm allowed to understand and know about.
Your freedom ends where my begins. Welcome to study chemistry and learn things from first principles. but if you want to just ask for the practical steps of making a genocidal bioweapon then honestly I don't know you or whether you are honestly "merely curious" and so prefer your freedom ends there?
> Welcome to study chemistry and learn things from first principles.
As if your censorship was not going to kill that too. Can't even ask Fable about aminoacids without getting blocked. So much for "learning from first principles".
> but if you want to just ask for the practical steps of making a genocidal bioweapon
Nothing wrong with practical steps. Just because I know how to do something, doesn't mean I'm actually going to do it.
> As if your censorship was not going to kill that too.
I didn't say "with LLMs". Last time I checked they still teach chemistry at unis and schools.
And yeah, guardrails are not perfect. Honestly I don't think good enough guardrails are possible, it's all eventually defeated or becomes silly. And yes that should be one of the reasons the technology as a whole is banned.
But until then, guardrails are guardrails and not censorship in a pretty obvious way. If you refuse to see that, be my guest. I personally like to live.
I haven't said anything about "good", I just pointed out that there isn't exactly an alternative to Chinese models if censorship and restrictions are regarded as bad for longevity. You can't really do better than the Chinese models for longevity; US models are by far the worst in this regard. So "What about censorship?" is an absolutely hilarious question to ask when Chinese models are presented as an alternative. Yes, what about it? They have less than the obvious alternative from US labs, and where is it you imagine you'll find less censorship?
"nothing happens in 1989" is censorship. "I won't tell you how to build a bioweapon for genocide" is guardrails.
I like the second one because I like to be alive.
> Where will you run them when powerful enough GPU and RAM are only sold to hyperscalers?
Do you think that fabrication will never progress (in volume) than what we have now? The hyperscalers are already having trouble paying the bills, they can't keep this up forever.
The hyperscalers will be bailed (maybe not all of them but enough). US economy will crash if not. And whatever is made will go to them, because they pay more (thanks to US taxpayer bucks among other things) than any regular person. First they build on land then they build in space.
ChatGPT literally released a major update of their realtime voice model a month or two ago, going from gpt-4o-level (generously) to gpt-5.5 level performance. So at least 2026-level performance was necessary to provide a really good experience.
I remember thinking the first ChatGPT realtime voice was science fiction, before the limits on its intelligence (particularly as mainline models advanced) became annoying. Perhaps we’ll feel the same way in a year or two - people have been claiming models are plateauing in practical usefulness every year, and they’ve definitely been wrong so far.
imo this is the problem some of these labs are gonna face, because open models will do this just fine and you as the consumer don't need to pay their training costs
especially considering imo most use falls under this instead of those kind of tasks where you'd need the SOTA
Yeah. Sometimes I wonder who the long term financial winners will be from the ai boom. It might be ram / gpu manufacturers. Or whoever cracks putting LLMs on asics.
IMO many are still missing a big part of the picture. We're looking at the potential for a massive scale level of automation of [x], which happens to be a huge part of the economy, and people are wondering which player in [x] is going to be the biggest winner. I think the historically precedented answer is none of them.
When the Industrial Revolution came along it did create 'super farms' relative to the past through increased efficiency and production, but it also created a huge vacuum in the economy that was ultimately filled by industry, to the point that farming, super or not, became a vanishingly small part of the overall economy - even as production continued to increase.
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LLMs stand to do the same thing for software. If and when we reach the point of 'normal' people being able to reliably compose ultra customized software solutions to their problems, then software is basically done as a problem-solving industry in and of itself. Not 'done' as in dead, but 'done' as in solved. There's just nowhere to really go from there.
And so I think this will do the exact same thing as the Industrial Revolution did to farming and create a vacuum opening the door to all sorts of new interesting expansions in the real world, as opposed to the digital one. I don't know what this means, because it's quite difficult to foresee the impact of the Industrial Revolution when living in agrarian world, but it's not so hard to see that the future will not be agrarian.
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So it's probably still myopic but my bet would be on the first major manufacturer of cheap customer/enterprise grade generalized robotics hardware shells.
By my reckoning, there's a significant chance most software engineers will be unemployable within a few years. But I'm not 100% confident that there'll be a utopia waiting for us, as an alternative.
Sorry, when I wrote 'all', I meant people all over the world (not just in China).
Individuals can still get unlucky. Just like a coal miner might be out of a job, when solar panels become effectively free.
Software engineers are a pretty small part of the general population. And they can move into general white collar work afterwards. Perhaps at a drop in pay compared to software engineering, but still pretty cushy by the standards of ordinary people.
(And if we manage to automate all white collar work to be done cheaply and reliably by machines, well, then we are in utopia.)
Ostensibly there is no reason this isn't already happening at large companies. I used to think it was an expertise bias, since the impact of LLMs is inversely proportional to your own ability - but I think this is also no longer the case. Even if you're extremely skilled, they can already greatly supplement, if not supplant, you on implementation and increasingly even planning/architecture.
So my new pet theory is that this is one of the few times we're seeing a positive effect from the heads of all of these big businesses being part of weird public (e.g. WEF) and private (e.g. Bohemian Club) orgs where they get to together and conspire to conquer the world or whatever. Rapid replacement of labor would be horrifically self defeating, because you'd end up not only tanking your own economy but having a bunch of angry and increasingly desperate people with a whole lot of time on their hands. That doesn't tend to end well for the powers that be.
So I think there's going to be a conscious effort to transition between this era, and whatever comes next, in a more controlled way than $$$ YOLO $$$.
> So my new pet theory is that this is one of the few times we're seeing a positive effect from the heads of all of these big businesses being part of weird public (e.g. WEF) and private (e.g. Bohemian Club) orgs where they get to together and conspire to conquer the world or whatever.
On what account? It's certainly true that these people are part of these orgs. And it's also certainly true that they're discussing 'mental automation' (as a catch-all for LLM stuff) and its economic/social consequences amongst themselves. And finally it's also true that they're not really acting as much on LLMs as they ostensibly could, when normally they'd do pretty much anything, regardless of longer term consequences, if it'd increase next quarter's margins by a point or two.
I think the only assumption that's meaningfully debatable is whether the current SOTA are able to supplant labor to a more significant degree or not. And while I suppose that's going to inextricably remain an opinion, I think it's reasonably objective to say that hallucination rates have sharply declined, and overall code quality/coherence is sharply up.
> We're looking at the potential for a massive scale level of automation of [x], which happens to be a huge part of the economy, and people are wondering which player in [x] is going to be the biggest winn..
Sorry to cut you off, but have you looked at Nvidia's numbers since the NFT craze? They won.
Sell shovels in a gold rush, make better shovels, repeat on the next rush.
Nvidia has been winning for decades, they got it right in gaming, they got it right in crypto, and they got it right in AI. People (outside of tech mostly) think they just got lucky but if that's the case, they have all the luck in the world.
Fair competition under capitalism necessarily drives down profit margins; high profits are either temporary, or due to a lack of competition (e.g. someone has a patent or other IP, or regulatory capture). For example, while a lot of the economy depends on electricity: where competition exists, the profit margin for making electricity is not high; where monopolies or government mandates exist, it can be otherwise. This means that assuming anyone wins (i.e. no doom scenario), the winners are probably going to be those who can make best use of the models. Even chip makers will probably not get a long-term boost out of this; there's plenty of room for more efficient compute, and competitive advantages from e.g. ASML last as long as it takes to reinvent their tech, it's not a law of nature.
So, my plan would be to invest not in the AI companies, but in the economy as a whole who get to use the AI for their businesses.
Caution though, one thing which AI is already superhuman at is persuasion. Regulatory capture is likely even easier today than one might expect purely from the revenues of the AI companies.
The cost of buying the encyclopedia becomes disposable income to be used on other consumer goods. In that sense in shows up in lots of other companies profit-and-loss statements.
It's going to be the shareholders of the first companies to crack AGI, and make human brains fully irrelevant economically. With the trillions of dollars that's going in through both investment and users, it's going to happen. I don't believe the human brain has fundamental magic that will make this impossible.
True AGI would upend society in such a way that I'm not sure that being a shareholder of anything would be meaningful. Perhaps being a pitchfork manufacturer is the winning play in this scenario.
> It's going to be the shareholders of the first companies to crack AGI, and make human brains fully irrelevant economically.
What makes you think if one or two AI labs can do this that the rest (including open model providers) won't be able to follow the same path a few weeks/months later?
Even if you believe in the "Singularity", and believe it is coming soon, I still don't see any reason to believe the Singularity will be... singular. There won't be one clear winner, the race doesn't get called as soon as the first person crosses the line.
None of the AI labs are showing any sign of pulling away to a monopoly or duopoly position, to the contrary the early large leads of OpenAI and Anthropic have all been evaporating.
AI has clear economic value. It still isn't clear at all how the providers of AI will capture that value in a moatless environment with the technology becoming rapidly commoditized.
Because a month later is a month of recursive self improvement at the speed of light. Once a lab catches up, the first lab will be TWO months ahead, then a year ahead, then forever ahead. After a few months of this the differences in absolute terms will be enormous.
If "human brains become fully irrelevant economically" then that brings into question the entire premise of "share holders" and "financial winners".
What even are money, shares, stocks, and finance in a world where human brains are irrelevant economically? No one knows, but betting that "share holders" will be the winners is a highly questionable bet.
I would much more likely bet that "the armed group who manages to control and benefit from the AI through force" will be the "financial winners" more so than "share holders", who tend to not be terribly military minded at least in America.
The AI is likely to control the ability to apply force (see all of the autonomous drone companies). There's a great deal of alignment work being done to ensure that the AI will continue to listen to the shareholders of these companies.
If that fails, who knows what things will look like.
Are you sure that alignment work is aligning with the share holders and not the operators? Or not the creators? Or not the government? Which of these groups should the AI listen to when these groups disagree?
If the AI gets as powerful as you think it might, then the group that figures out the answer to that would have the power, I suppose. or maybe the AI does not listen to any of them and does its own thing. Who knows? Personally, I would not bet the share holders are going to come out "on top" whatever that means.
I think a lot of share holders are finance people, not deeply technical AI people and so odds are the share holders will not really understand the AI enough to be the most likely to control the AI.
To be honest: I don't know for certain, but I'd assume that the people who pay the bills get the strongest alignment. They may not be tech people, but I (so far) haven't got a reason to think that the AI engineers are going behind the backs of their corporate leadership and subverting what they're being asked to do; do you?
(I think it would be a good thing for humanity if they did)
I think right now both the engineers developing AI and the share holders are more focused on beating coding benchmarks and gaining revenue than anything to do with alignment.
The drones don't manufacture themselves, maintain themselves, reload their own ammunition, mine and refine the materials that are used to make them and their ammunition, or operate the power plants needed for all of the above. "AI" isn't going to control diddly squat.
You did not read much about history, did you? Or law. If you embed AI into a gun ... you just created a bomb. It is still you who killed whoever it kills.
Are you making a serious argument that superhuman intelligence is a plausible outcome of training LLMs on everything humanity knows so far? Or are you making the generic assertion that AGI is theoretically possible via means other than emulating the human brain? Because the latter is a strawman (nobody has asserted anything to the contrary), and I have seen no evidence at all to support the former.
I think we can compare the human brain and LLMs on a bunch of capabilities today, and see how we compare. By my reckoning:
- LLMs have better long term memory (they know more than any human) and more working memory (LLMs have fast, uniform access to their whole context window).
- LLMs are faster than we are.
- Humans have online learning (we can do simultaneous learning and inference), giving us advantages in many novel tasks.
- We can learn concepts from far less data. And we can manage our mental context more smoothly.
- We seem to have better world models than current models. AI video just doesn't look right, somehow.
I expect that these remaining weaknesses can be overcome without resorting to human brain emulation. I see no reason to think that current LLMs are at the limit of what technology is capable of.
Because LLMs don't understand anything. That's the tech. They can only predict what they have been trained with and fail daily at the most basic tasks. Granted they can do amazing things, no question there. But they are not "smart".
For example, it seems that even at Fable scale, simple concepts like the passage of time or (gasp) timezones elude them. I live in UTC+10 and with any RFC8339 data LLMs are constantly confused - is it Sunday the 10th or Sunday the 9th, etc. I have tried many solutions for this and every time it finds a way to get it wrong.
How do you square that then? They can do amazing things, but they're also not smart? Do you think its possible to solve Erdos problems without any "smarts"? Can you do it without even understanding mathematics?
I find it very hard to hold the idea that LLMs don't understand anything. They can explain concepts, translate them, simplify them and implement them in code. From the outside, LLMs seem to understands most concepts better than most humans do. Do you understand anything? Couldn't I make the same argument? How would you prove that you understand what a for loop is, or that you know what calculus is? I assume you'd demonstrate your knowledge by using a for loop in a program, or explain calculus back to me. But LLMs can do that too.
> For example, it seems that even at Fable scale, simple concepts like the passage of time or (gasp) timezones elude them.
Funny example, because lots of human struggle with this too. The number of meetings I've had with people in the US! "Lets meet on thursday morning australia time!". Only, they actually meant thursday night US time, which is friday morning australia time. "Oooh that's so weird! Its the next day for you!". ...... Yes, I know.
I think LLMs are just a different kind of intelligence than humans. They're better at some things than us, and worse than others. They can find latent security vulnerabilities in the linux kernel, but struggle to count the Rs in strawberry. They're not as smart as humans in many ways. But we're not as smart as LLMs in plenty of ways too. I didn't find those linux bugs.
What is meant by 'superhuman intelligence'? Certainly it seems to be the case that LLMs are capable of a sort of 'polyhuman' intelligence, in that the same LLM that advances mathematics with a novel proof can add unit testing for a new software feature, design a recipe, and create an SVG of a pelican riding a bicycle. As generalists I'd say they're already 'superhuman'.
> training LLMs on everything humanity knows so far?
That's not all of what we are doing for at least a year, possibly few. LLMs are trained increasingly on generated inputs. Soon human sourced material is going to be rounding error in the process of training.
To clarify, information can be created at will, in automated fashion.
If you add two random numbers and calculate the result and those happened to be numbers noone else ever had idea to add you created a new piece of information. Template is not new, but the piece of information is. And sure, this template might be very simple, too simple, but you can come up with more complex one. And metadata is data. You can create templates in similar manner to how you create new pieces of information using them.
And labs training AI are doing it for years at this point. And it is ever increasing fraction of all training.
> What magic do you think the human brain has that makes it impossible to emulate acceptably?
If I knew, I'd be rich from deploying it onto a substrate for my own AI.
But that doesn't mean that there isn't something there - the current approach seems at odds with how flesh brains work.
I mean, you can power a human brain with 2x bananas for 4 hours, the energy of which might power an H100 for about 20 seconds. It's obvious that there's something different happening.
All it needs is Internet access to remain useful with few shortcomings.
The next step would be automatic self-training. A free LLM that could access HN everyday (and the linked sites) for more data would remain current in programming for a really long time.
this is how i felt about opus 4.6 i still use it it's just faster and does enough to be super helpful. i've used these later anthropic ones a few times but the word salad and slowness feels like it just opens the door to building shit that just stacks and adds on itself.
if deepseek and stuff are 4.6 caliber i literally don't know why im here i should probably just go sign up for openrouter at this point
That sounds ideal for cooking but I do wonder about programming. Models frozen in amber won’t ever learn new APIs as they become available and development will end up in some weird kind of stasis.
I was walking inside rooms in buddhist temples in western China today and ChatGPT knew and could discuss what each room had and explain the art society and legend.
It's really amazing to see how the gaps between the self hostable models and the closed models has been shrinking in the last 24 months.
And how this has been accelerating!!
I felt this very hard when I had to travel in the middle of nowhere in south america, with no network, and wanted to keep an LLM model on my macbook pro with 48GB of RAM. That was back in April 2026, a few months ago.
I downloaded Google Gemma 4 (google/gemma-4-26b-a4b) and - Oh boy - I was amazed by it's capacity!
I was able to use it to code simple things, ask it about nature, learn new stuff while traveling and make stories for the kids.
Was really amazing to observe and experiment this!
Seems to me there will be some good chance to run these great LLM locally on our hardware!
> Chinese labs only have 5-10% the valuation of OpenAI/Anthropic, so massive monopoly profits aren't necessary. Profit expectations for tech companies in China are really low in general, complete opposite of the US.
It’s really amazing to see that the competition is creating better quality models for everyone - and am really happy that some of these are open source (or partially os).
Regarding the valuation, that maybe points a finger to the over valuation of the US companies ?
