They've tried to play it both ways. Not AI enough for the AI fanboys, but want to keep a toe in there for everyone else. They'd be better placed rejecting AI entirely, and then when the bubble pops they'll be well positioned to sweep in and eat VSCode's lunch with a product that actually works.
> They cite cherry picked announcements showing that LLM usage makes development slower or worse. They opened ChatGPT a couple times a few months ago, asked some questions, and then went “Aha! I knew it was bad!” when they encountered their first bad output instead of trying to work with the LLM to iterate like everyone who gets value out of them.
"Ah-hah you stopped when this tool blew your whole leg off. If you'd stuck with it like the rest of us you could learn to only take off a few toes every now and again, but I'm confident that in time it will hardly ever do that."
To be fair, that does seem be a very common usage pattern for them, to the point where they're even becoming a nuisance to open source projects; e.g. https://news.ycombinator.com/item?id=45330378
I'm impressed that such a short post can be so categorically incorrect.
> For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots
> In 2025 finally almost everybody stopped saying so.
There is still no evidence that LLMs are anything beyond "stochastic parrots". There is no proof of any "understanding". This is seeing faces in clouds.
> I believe improvements to RL applied to LLMs will be the next big thing in AI.
With what proof or evidence? Gut feeling?
> Programmers resistance to AI assisted programming has lowered considerably.
> It is likely that AGI can be reached independently with many radically different architectures.
There continues to be no evidence beyond "hope" that AGI is even possible, yet alone that Transformer models are the path there.
> The fundamental challenge in AI for the next 20 years is avoiding extinction.
Again, nothing more than a gut feeling. Much like all the other AI hype posts this is nothing more than "well LLMs sure are impressive, people say they're not, but I think they're wrong and we will make a machine god any day now".
Strongly agree with this comment. Decoder-only LLMs (the ones we use) are literally Markov Chains, the only (and major) difference is a radically more sophisticated state representation. Maybe "stochastic parrot" is overly dismissive sounding, but it's not a fundamentally wrong understanding of LLMs.
The RL claims are also odd because, for starters, RLHF is not "reinforcement learning" based on any classical definition of RL (which almost always involve an online component). And further, you can chat with anyone who has kept up with the RL field, and quickly realize that this is also a technology that still hasn't quite delivered on the promises it's been making (despite being an incredibly interesting area of research). There's no reason to speculate that RL techniques will work with "agents" where they have failed to achieve wide spread success in similar domains.
I continue to be confused why smart, very technical people can't just talk about LLMs honestly. I personally think we'd have much more progress if we could have conversations like "Wow! The performance of a Markov Chain with proper state representation is incredible, let's understand this better..." rather than "AI is reasoning intelligently!"
I get why non-technical people get caught up in AI hype discussions, but for technical people that understand LLMs it seems counter productive. Even more surprising to me is that this hype has completely destroyed any serious discussions of the technology and how to use it. There's so much oppurtunity lost around practical uses of incorporating LLMs into software while people wait for agents to create mountains of slop.
> why smart, very technical people can't just talk about LLMs honestly
Because those smart people are usually low-rung employees while their bosses are often AI fanatics. Were they to express anti-AI views, they would be fired. Then this mentality slips into their thinking outside of work.
No, it can still be modeled as a finite state machine. Each state just encodes the configuration of your memory. I.e. if you have 8 bits of memory, your state space just encodes 2^8 states for each memory configuration.
Any real-world deterministic thing can be encoded as a FSM if you make your state space big enough, since it by definition there has only a finite number of states.
You could model a specific instance of using your computer this way, but you could not capture the fact that you can execute arbitrary programs with your PC represented as an FSM.
Your computer is strictly more computationally powerful than an FSM or PDA, even though you could represent particular states of your computer this way.
The fact that you can model an arbitrary CFG as an regular language with limited recursion depth does not mean there’s no meaningful distinction between regular languages and CFG.
> you can execute arbitrary programs with your PC represented as an FSM
You cannot execute arbitrary programs with your PC, your PC is limited in how much memory and storage it has access to.
>Your computer is strictly more computationally powerful
The abstract computer is, but _your_ computer is not.
>model an arbitrary CFG as an regular language with limited recursion depth does not mean there’s no meaningful distinction between regular languages and CFG
Yes this I agree. But going back to your argument, claiming that LLMs with a fixed context-window are basically markov chains so they can't do anything useful is reductio ad absurdum in the exact same way as claiming that real-world computers are finite state machines.
A more useful argument on the upper-bound of computational power would be along the lines of circuit complexity I think. But even this does not really matter. An LLM does not need to be turing complete even conceptually. When paired with tool-use, it suffices that the LLM can merely generate programs that are then fed into an interpreter. (And the grammar of turing-complete programming languages can be made simple enough, you can encode Brainfuck in a CFG). So even if an LLM could only ever produce programs with a CFG grammar, the combination of LLM + brainfuck executor would give turing completeness.
I never claimed that. They demonstrate just how powerful Markov chains can be with sophisticated state representations. Obviously LLMs are useful, I have never claimed otherwise.
Additionally, it doesn’t require any logical leaps to understand decoder only LLMs as Markov Chains, they preserve the Markov Property and otherwise be have exactly like them. It’s worth noting that encoder-decoder LLMs do not preserve the Markov property and can not be considered Markov chains.
Edit: I saw that post and at the time was disappointed by how confused the author was about those topics and how they apply to the subject.
> A2UI lets agents send declarative component descriptions that clients render using their own native widgets. It's like having agents speak a universal UI language.
Why the hell would anyone want this? Why on earth would you trust an LLM to output a UI? You're just asking for security bugs, UI impersonation attacks, terrible usability, and more. This is a nightmare.
If done in chat, it's just an alternative to talking to you freeform. Consider Claude Code's multiple-choice questions, which you can trigger by asking it to invoke the right tool, for example.
None of the issues go away just because it's in chat?
Freeform looks and acts like text, except for a set of things that someone vetted and made work.
If the interactive diagram or UI you click on now owns you, it doesn't matter if it was inside the chat window or outside the chat window.
Now, in this case, it's not arbitrary UI, but if you believe that the parsing/validation/rendering/two way data binding/incremental composition (the spec requires that you be able to build up UI incrementally) of these components: https://a2ui.org/specification/v0.9-a2ui/#standard-component...
as transported/renderered/etc by NxM combinations of implementations (there are 4 renderers and a bunch of transports right now), is not going to have security issues, i've got a bridge to sell you.
Here, i'll sell it to you in gemini, just click a few times on the "totally safe text box" for me before you sign your name.
My friend once called something a babydoggle - something you know will be a boondoggle, but is still in its small formative stages.
> None of the issues go away just because it's in chat?
There is a wast difference in risk between me clicking a button provided by Claude in my Claude chat, on the basis of conversations I have had with Claude, and clicking a random button on a random website. Both can contain a malicious. One is substantially higher risk. Separately, linking a UI constructed this way up to an agent and let third parties interact with it, is much riskier to you than to them.
> If the interactive diagram or UI you click on now owns you, it doesn't matter if it was inside the chat window or outside the chat window.
In that scenario, the UI elements are irrelevant barring a buggy implementation (yes, I've read the rest, see below), as you can achieve the same things as you can do that way with just presenting the user with a basic link and telling them to press it.
> as transported/renderered/etc by NxM combinations of implementations (there are 4 renderers and a bunch of transports right now), is not going to have security issues, i've got a bridge to sell you.
I very much doubt we'll see many implementations that won't just use a web view for this, and I very much doubt these issues will even fall in the top 10 security issues people will run into with AI tooling. Sure, there will be bugs. You can use this argument against anything that requires changes to client software.
But if you're concerned about the security of clients, mcp and hooks is a far bigger rats nest of things that are inherently risky due to the way they are designed.
This article seems to suggest that businesses are going to swap domain-specific SaaS tools, written and tested by people knowledgable in the domain with specific SLAs for vibe coding everything. But your AI subscription is still a SaaS?
All you've done is swapped a SaaS built for your problem domain with another, more expensive SaaS that has no support at all for your actual problem. Why would anyone want that? People buy SaaS products because they don't want to solve the problem, they just want it fixed. AI changes nothing about that.
> Vibe coding actually works. It creates robust, complex systems that work. You can tell yourself (as I did) that it can’t possibly do that, but you are wrong.
This is such a bad take. I'm convinced that engineers simply don't understand what the job is. The point was never "does it output code that works", the point is "can it build the right thing in a way that is maintainable and understandable". If you need an LLM to understand the output then you have failed to engineer software.
If all you're doing is spitting out PoCs and pure greenfield development then I'm sure it looks very impressive, as the early language models did when it looked like they were capable of holding a conversation. But 99% of software engineering is not that kind of work.
> With an LLM, you have to specifically setup a convoluted (and potentially financially and electrical power expensive) system in order to provide MANY MORE examples of how to improve via fine tuning or other training actions.
The only way that an AI model can "learn" is during model creation, which is then fixed. Any "instructions" or other data or "correcting" you give the model is just part of the context window.
Fine tuning is additional training on specific things for an existing model. It happens after a model already exists in order to better suit the model to specific situations or types of interactions. It is not dealing with context during inference but actually modifying the weights within the model.
> Humans are on the verge of building machines that are smarter than we are.
You're not describing a system that exists. You're describing a system that might exist in some sci-fi fantasy future. You might as well be saying "there's no point learning to code because soon the rapture will come".
That particular future exists now, it's just not evenly distributed. Gemini 2.5 Pro Thinking is already as good at programming as I am. Architecture, probably not, but give it time. It's far better at math than I am, and at least as good at writing.
Computers beat us in maths decades ago, yet LLMs are not able to beat a calculator half of the time. The maths benchmarks that companies so proudly show off are still the realm of a traditional symbolic solvers. You claiming much success in asking LLMS for math makes me question if you have actually asked an LLM about maths.
Most AI experts not heavily invested in the stocks of inflated tech companies seem to agree that current architectures cannot reach AGI. It's a sci-fi dream, but hyping it is real profitable. We can destroy ourselves plenty with the tech we already have, but it won't be a robot revolution that does it.
The maths benchmarks that companies so proudly show off are still the realm of a traditional symbolic solvers. You claiming much success in asking LLMS for math makes me question if you have actually asked an LLM about maths.
What I really need to ask an LLM for is a pointer to a forum that doesn't cultivate proud exhibition of ignorance, Luddism, and general stupidity at the level exhibited by commenters in this entire HN story, and in this subthread in particular.
> Computing machines are instruments of creativity, companions in learning, and partners in thought. They should amplify human intention.
An admirable goal. However putting that next to a bunch of AI slop artwork and this statement...
> One of our goals is to explore how an A.I.-native computer system can enhance the creative process, all while keeping data private.
...is comically out of touch.
The intersection between "I want simple and understandable computing systems" and "I want AI" is basically zero. (Yes, I'm sure some of you exist, my point is that you're combining a slim segment of users who want this approach to tech with another slim segment of users who want AI.)
In five years time, "I want AI" will be 99% of computer users. Sure, neural nets are opaque, but having an AI assistant running locally and helping you with your tasks does not make your computer any harder to understand.
What is it about large language models that makes otherwise intelligent and curious people assign them these magical properties. There's no evidence, at all, that we're on the path to AGI. The very idea that non-biological consciousness is even possible is an unknown. Yet we've seen these statistical language models spit out convincing text and people fall over themselves to conclude that we're on the path to sentience.
We don’t understand our own consciousness first off. Second, like the old saying, sufficiently advanced science will be indistinguishable from magic, if it is completely convincing as agi, even if we skeptical of its methods, how can we know it isn’t?
I think we can all agree that LLMs can mimick consciousness to the point that it is hard for most people to discern them from humans. Like the turing test isn't even really discussed anymore.
There are two conclusions you can draw: Either the machines are conscious, or they aren't.
If they aren't, you need a really good argument that shows how they differ from humans or you can take the opposite route and question the consciousness of most humans.
Since I neither heard any really convincing arguments besides "their consciousness takes a form that is different from ours so it's not conscious" and I do think other humans are conscious, I currently hold the opinion that they are conscious.
(Consciousness does not actually mean you have to fully respect them as autonomous beings with a right to live, as even wanting to exist is something different from consciousness itself. I think something can be conscious and have no interest in its continued existence and that's okay)
> I think we can all agree that LLMs can mimick consciousness to the point that it is hard for most people to discern them from humans.
No, their output can mimic language patterns.
> If they aren't, you need a really good argument that shows how they differ from humans or you can take the opposite route and question the consciousness of most humans.
The burden of proof is firmly on the side of proving they are conscious.
> I currently hold the opinion that they are conscious.
There is no question, at all, that the current models are not conscious, the question is “could this path of development lead to one that is”. If you are genuinely ascribing consciousness to them, then you are seeing faces in clouds.
That's true and exactly what I mean. The issue is we have no measure to delineate things that mimic conscousness from things that have consciousness. So far the beings that I know have consciousness is exactly one: Myself. I assume that others have consciousness too exactly because they mimic patterns that I, a verified conscious being, has. But I have no further proof that others aren't p-Zombies.
I just find it interesting that people say that LLMs are somehow guaranteed p-Zombies because they mimic language patterns, but mimicing language patterns is also literally how humans learn to speak.
Note that I use the term consciousness somewhat disconnected from ethics, just as a descriptor for certain qualities. I don't think LLMs have the same rights as humans or that current LLMs should have similar rights.
I think it's like seeing shapes in clouds. Some people just fundamentally can't decouple how a thing looks from what it is. And not in that they literally believe chatgpt is a real sentient being, but deep down there's a subconscious bias. Babbling nonsense included, LLMs look intelligent, or very nearly so. The abrupt appearance of very sophisticated generative models in the public consciousness and the velocity with which they've improved is genuinely difficult to understand. It's incredibly easy to form the fallacious conclusion that these models can keep improving without bound.
The fact that LLMs are really not fit for AGI is a technical detail divorced from the feelings about LLMs. You have to be a pretty technical person to understand AI enough to know that. LLMs as AGI is what people are being sold. There's mass economic hysteria about LLMs, and rationality left the equation a long time ago.
What we do have, for whatever reason (usually money related: either making money or getting more funding) many companies/people focused on making AI. It might take another winter (I believe it will unless we find a way to retrain the NNs on the fly instead of storing new knowledge in RAG: and many other things we currently don't have, but this would he a step) or not, people will keep pushing toward that goal.
I mean, we went from worthless chatbots which basically pattern matched to me waiting for a plane and seeing a fairly large amount of people charting to chatgpt, not insta, whatsapp etc. Or sitting in a plane next to a person who is using local ollama in cursor to code and brainstorm. This took us about 10 years to go from some ideas that no one but scientists could use to stuff everyone uses. And many people already find human enough. What in 100 years?
