buu700

To be fair, if I choose to buy something, it's almost by definition because I consider the thing useful. It's pretty rare that I purchase something I'd learned about from an ad, but I have done so a few times and benefited from doing so. How is anyone else to determine whether I've been adversely manipulated, i.e. whether the cost of the thing outweighs its benefit to me?

Buying something from an ad isn't really fundamentally different from being influenced by an HN post. For example, thanks to https://news.ycombinator.com/item?id=46294574, I read up on and have decided to experiment with TLA+ on my next project. It's really no concern of mine what Martin Kleppman's commercial interests may ultimately have been in publishing that blog post; I received value from the information all the same.

Personally, I'm not particularly bothered by ads per se. I'd be more bothered by information being withheld from me during searches, e.g. if Brand A could pay Amazon to delist Brands B and C from organic search results, since that would directly guide me toward less optimal purchases. But as far as simply going about my day and seeing a billboard or promoted social media post every now and then, I don't see the big deal. It's generally easy to ignore, it costs me almost nothing, it occasionally helps me, and ultimately it funds a lot of things I like and take for granted (e.g. Chromium, Firefox, and Android).

I'm not saying that people who routinely waste money on irrational purchases don't exist. I just don't find that to be a compelling argument against the existence of a particular market which overwhelmingly benefits almost all of us.

I do have quite strong concerns regarding aggressive data collection, however, and I certainly wouldn't opt in to greater erosion of my privacy — but I see that as a separate issue. To the extent that ad-driven revenue models provide an incentive for companies to facilitate greater privacy invasion, I agree that it's a significant concern which warrants much stronger pushback from the public than it receives. I just think it's important to highlight that mass data harvesting per se is the major issue, more so than any perceived manipulativeness of the fact that brands pay money for exposure.

Then of course there's the issue raised in this post, which is yet again another matter entirely. I'm all for using AI to optimize pricing and efficiency, but "dynamic pricing" as described in the article sounds like a euphemism for price discrimination, and should be more strictly regulated IMO regardless of whether or not AI is involved.

That's definitely cool too. I was just suggesting an intermediary text prompt step as a compromise between 100% writing and 100% voice. So instead of getting home to actual code, you'd get home to a draft of relatively detailed requirements to review and revise before incurring the cost of throwing a coding agent at it.

Sure, but that doesn't prove anything about the properties of the output. Change a few words, and this could be an argument against the possibility of what we now refer to as LLMs (which do, of course, exist).

That may be what most or all current LLMs do by default, but it isn't self-evident that it's what LLMs inherently must do.

A reasonable human, given the same task, wouldn't just make arbitrary changes to an already-well-composed sentence with no identified typos and hope for the best. They would clarify that the sentence is already generally high-quality, then ask probing questions about any perceived issues and the context in and ends to which it must become "better".

I've been using em dashes for much longer than transformers have existed. It's easily accessible on at least the Android and macOS keyboards.

Turns out "God" was just a convenient shorthand for "alien AI", and Genesis was about terraforming and seeding life on Earth.

I prefer writing myself, but I could see the appeal of producing a first draft of a prompt by dumping a verbal stream of consciousness into ChatGPT. That might actually be kind of fun to try while going on a walk or something.

It's kind of funny to see "a computer in every home" invoked when we're talking about the equivalent of ~$100 buying a non-trivial percentage of all computational power in existence at the time of the quote. By the standards of that time, we don't just have a computer in every home, we have a supercomputer in every pocket.

That's more of a management problem than an AI problem. You could get the same result by replacing "model" with "intern" or "dude from Fiverr".

Neurospora crassa is also pretty good. Meati sells slabs of it.

I use Grok pretty heavily, and Elon doesn't factor into it any more than Sam and Sundar do when I use GPT and Gemini. A few use cases where it really shines:

* Research and planning

* Writing complex isolated modules, particularly when the task depends on using a third-party API correctly (or even choosing an API/library at its own discretion)

* Reasoning through complicated logic, particularly in cases that benefit from its eagerness to throw a ton of inference at problems where other LLMs might give a shallower or less accurate answer without more prodding

I'll often fire off an off-the-cuff message from my phone to have Grok research some obscure topic that involves finding very specific data and crunching a bunch of numbers, or write a script for some random thing that I would previously never have bothered to spend time automating, and it'll churn for ~5 minutes on reasoning before giving me exactly what I wanted with few or no mistakes.

As far as development, I personally get a lot of mileage out of collaborating with Grok and Gemini on planning/architecture/specs and coding with GPT. (I've stopped using Claude since GPT seems interchangeable at lower cost.)

For reference, I'm only referring to the Grok chatbot right now. I've never actually tried Grok through agentic coding tooling.

As I recall, Randy Pausch had a lot of positive things to say about Disney Imagineering.

Whether or not it will one day get there is anyone's guess, but it sounds like we agree that it at least isn't currently there. I brought up that goalpost to illustrate why more efficient models will only improve the aggregate volume and/or quality of output for the foreseeable future, as opposed to creating a glut of supply that destroys the economics of data centers.

I'd say the simple answer is that the buck always stops with the vendor. If Acme Co sells a jetpack that explodes and kills someone, the entity doesn't get to deflect liability by saying one of their engineers made a mistake; that may be an explanation, but not an excuse. Swapping out Acme Co and its employees with your grandma and her AI/robots doesn't change the fundamental principle.

Just in case it was unclear, I extensively use AI and agentic coding with current models on a daily basis. The only thing I haven't tried in a few months is specifically one-shotting a greenfield project.

I know computer-use agents exist, and theoretically have tooling and permission to do all the things a human sitting in front of a computer can. I just haven't heard of anyone successfully claiming to have had one do exactly what I described for a non-toy project in one shot with zero mistakes, or of any tool like Replit claiming to support such a capability.

I'd be very interested to know if my impression is out of date. As in, if I could send a single message to some AI service and say "Here's my credit card, banking info, and entity info/EIN; build me a production-ready Google Drive clone with religious branding and 10x higher pricing called God Drive with native Android/iOS/Linux/macOS/Windows apps, then deploy it to production on an optimal cloud architecture capable of scaling to a billion users at whatever domain name you like best and release the apps to all major app stores/repositories", then go to bed with high confidence that I'd be able to start creating God Drive docs/spreadsheets/presentations for work the following morning.

If that isn't the case, it isn't a criticism of the technology. The fact that we're even seriously discussing the scenario is incredible.

Thanks for the recommendations. Regarding your other comment, Flutter is what I've landed on as well for my next cross-platform app project, and I'm currently in the middle of developing a spec for a fairly complex agentic system that I'm going to try having Codex two-shot (basic project setup + file stubs + exhaustive tests -> manual checkpoint -> TDD the rest).

I haven't tried Lovable, V0, or Jules, but I really like Replit for certain things. Having said that, based on my experience, I would characterize it as an amazing tool for rapid frontend iteration with prototype-level backend creation. I'm sure it's gotten better at one-shotting since I tried Agent 2 with Sonnet 3.7 in May, but would still be very (pleasantly) surprised to see that Agent 3 with current models could meet the incredibly high bar of wholly replacing a human dev team.

The fact that tools like Replit also include their own hosting environments is definitely neat, but not really what I was getting at as far as deployment. What I had in mind was managing arbitrary cloud platforms, setting up an optimal architecture for your anticipated scale and usage patterns — whether that's a single Hetzner instance with SQLite or horizontally scaled app servers behind an API gateway with Kafka, Valkey, and Spanner or ScyllaDB — and doing all the DevOps to handle that along with things like CI/CD.

I'm not downplaying how amazing these capabilities are. Being able to generate high-quality code from natural language feels like magic. But all the parts beyond narrow application code are half of the thing I described:

* I'm saying you should be able to send a single off-the-cuff drunk text to an AI and later find a complete production-ready SaaS startup that fully aligns with a reasonable interpretation of your message.

* The other half of the whole thing is >=human-level execution. If the AI can't autonomously deliver work comparable to what an experienced CTO would (given the same requirements, an arbitrarily large hiring budget, and a stipulation to never contact you again until the work was done), it's not there yet.

Again, none of this is to dunk on agentic coding. My point is that I set an absurdly high bar because I want it to one day be met. Just as a $100 storage budget today is equivalent to $100m a few decades ago, I want to live to see a $100 engineering budget reach equivalency with last decade's $100m.

Just to be clear, what I was proposing was a single tool which would, on the basis of a single ~30-minute interaction, purchase a domain name, set up a cloud environment, build a full-stack application + cross-platform native apps + useful tests with near-100% coverage, deploy a live test environment, and compile each platform's native app — all entirely autonomously. Are you saying you've used or built something similar to that? That is super interesting if so, even if you're unable to share. A major subset of that could also still be incredibly useful, but the whole solution I described is a very high bar.

I've been very successful building with custom LLM workflows and automation myself, but that's beyond the capabilities of any tooling I've seen, and I wouldn't necessarily expect great results with current models even if current tooling were fully capable of what I described. Even with such tooling, the cost of inference is high enough to deter careless usage without much more rigorous work on the initial spec and/or micromanagement of the development process.

I'm not necessarily advocating for one-shotting in any given context. I'm simply pointing out that there would be huge advantages to LLMs and tooling sufficiently advanced to be fully capable of doing so end-to-end, especially at dramatically lower cost than current models and at superhuman quality. Such an AI could conceivably one-shot any possible project idea, in the same sense that a competent human dev team with nothing but a page of vague requirements and unlimited time could at least eventually produce something functional.

The value of such an AI is that we'd use it in ways that sound ridiculous today. Maybe a chat with some guy at a bar randomly inspires a neat idea, so you quickly whip out your phone and fire off some bullet point notes; by the time you get home, you have 10 different near-production-ready variations to choose from, each with documentation on the various decisions its agent made and why, and each one only cost $5 in account credit. None is quite perfect, but through the process you've learned a lot and substantially refined the idea; you give it a second round of notes and wake up to a new testable batch. One of those has the functional requirements just right, so you make the final decisions on non-functional requirements and let it roll one last time with strict attention to detail on code quality and a bunch of cycles thrown at security review.

That evening, you check back in and find a high-quality final implementation that meets all of your requirements with a performant and scalable architecture, with all infrastructure deployed and apps submitted to all stores/repositories. You subsequently allocate a sales and marketing budget to the AI, and eventually notice that you suddenly have a new source of income. Now imagine that instead of you, this was actually your friend who's never written a line of code and barely knows how to use a computer.

I still agree with you that current models have been "good enough" for some time, in the sense that if LLMs froze today we could spend the next decade collectively building on and with them and it would totally transform the economy. But at the same time, there's definitely latent demand for more and/or better inference. If LLMs were to become radically more efficient, we wouldn't start shuttering data centers; the economy would just become that much more productive.

I'm not aware that we are there, but would be very interested if you have information to the contrary. Even if we were there, the product/service that does it would have to be at a reasonable cost in order to be useful for most people.

As I said, a lot of the gap may be tooling. But I'm skeptical that even the models themselves are capable of that given sufficiently advanced tooling. I'm not saying we're not close (certainly much closer than we were at the start of the decade), but if we were actually there, you would have zero reservations about removing the human from the loop of an initial prototype.

Coding capability in and of itself may be "good enough" or close to it, but there's a long way to go before AI can build and operate a product end-to-end. In fairness, a lot of the gap may be tooling.

But the end state in my mind is telling an AI "build me XYZ", having it ask all the important questions over the course of a 30-minute chat while making reasonable decisions on all lower-level issues, then waking up the next morning to a live cloud-hosted test environment at a subdomain of the domain it said it would buy along with test builds of native apps for Android, iOS, Linux, macOS, and Windows, all with near-100% automated test coverage and passing tests. Coding agents feel like magic, but we're clearly not there yet.

And that's just coding. If someone wanted to generate a high-quality custom feature-length movie within the usage limits of a $20/mo AI plan, they'd be sorely disappointed.

If it is satire, it's very good. They pointed out exactly how their proposed dystopia is consistent with policies that already exist.

Withholding autonomy from anyone with a diagnosed mental/neurodevelopmental disorder or an IQ below 100 is the logical conclusion of banning drugs, alcohol, or prostitution. It's all the product of a mindset which presumes that adults aren't sufficiently competent to make their own personal life decisions, and need to be forced into the correct decisions through threats of violence.

I've been using a functionally identical implementation of this since I wrote it in my startup's codebase a decade ago. It's really handy, but definitely not without edge case issues. I've occasionally had to put in workarounds for false positive TypeScript/lint errors or a tool in the bundling pipeline trying to be too clever and breaking the output.

Overall it's great, and I'm glad to see a generic implementation of it which will hopefully become a thriving open source project, but ultimately it's a kludge. What's really needed is for JS to introduce a native standardized version of this construct which TypeScript and the rest of the ecosystem have to play nice with.

It's easy for us as humans to correct dumb mistakes made by AI. It's less easy for AI to correct mistakes made by AI.

What's remarkable on Grok's part is when it spends five minutes churning through a few thousand lines of code (not the whole codebase, just the relevant files) and correctly arrives at the correct root cause of a complex bug in one shot.

Grok as a model may or may not be uniquely amazing per se, but the service's eagerness to throw compute at problems that genuinely demand it is a superpower that makes at least makes it uniquely amazing in practice. By comparison, even Gemini 3 often returns lazy/shallow/wrong responses (and I say that as a regular user of Gemini).

They can be, but in this case they don't seem to be. Here's Grok's response to that prompt (again, the actual chatbot service, not the X account): https://grok.com/share/c2hhcmQtMw_2b46259a-5291-458e-9b85-0c....

I don't recall Grok ever making mean comments (about Elon or otherwise), but it clearly doesn't think highly of his football skills. The chain of thought shows that it interpreted the question as a joke.

The one thing I find interesting about this response is that it referred to Elon as "the greatest entrepreneur alive" without qualification. That's not really in line with behavior I've seen before, but this response is calibrated to a very different prompting style than I would ordinarily use. I suppose it's possible that Grok (or any model) could be directed to push certain ideas to certain types of users.

Anyone who hasn't used Grok might be surprised to learn that it isn't shy about disagreeing with Elon on plenty of topics, political or otherwise. Any insinuation to the contrary seems to be pure marketing spin on his part.

Grok is often absurdly competent compared to other SOTA models, definitely not a tool I'd write off over its supposed political leanings. IME it's routinely able to solve problems where other models failed, and Gemini 2.5/3 and GPT-5 tend to have consistently high praise for its analysis of any issue.

That's as far as the base model/chatbot is concerned, at least. I'm less familiar with the X bot's work.

The "it's not X, it's Y" construction seems pretty neutral to me. Almost no one minds when the phrase "it's not a bug, it's a feature" is used idiomatically, for example.

The main thing that's annoying about typical AI writing style is its repetitiveness and fixation on certain tropes. It's like if you went to a comedy club and noticed a handful of jokes that each comedian used multiple times per set. You might get tired of those jokes quickly, but the jokes themselves could still be fine.

Related: https://www.nytimes.com/2025/12/03/magazine/chatbot-writing-...

This just reminded me of the time I thought Firefox was cooked when tabs were finally added in IE7. The idea that IE was ever suddenly going to have a huge resurgence is amusing in hindsight, but it really felt like we were stuck with it for a while there.

You're imagining an exchange that didn't happen. At no point have I declined to provide a definition upon request. In fact, I went out of my way to provide both a Wikipedia link and my own off-the-cuff paraphrasing.

That being said, here is my answer to your question: I would broadly define cancel culture as a culture of engaging in grassroots campaigns to materially punish, ostracize, and/or silence sources of speech which one finds disagreeable.

I'm not sure what you think celebrities being the most prominent (debatable) examples proves. That's practically a tautology. It's not difficult to find specific known impacted individuals who weren't celebrities, whom I'm hesitant to name out of respect. Having said that, here are two general examples off the top of my head:

1. Ostensibly non-political major subreddits setting automod to indiscriminately ban anyone with a past comment in /r/conservative. This quietly hits large numbers of nameless people on a daily basis, and manual moderation activity isn't much better (particularly on subreddits that are actually related to politics).

2. A recent campaign of targeted doxxing and harassment against authors of distasteful remarks regarding Charlie Kirk's assassination, including reports to employers with intent to cause financial harm. This is one current prominent example, but there are many others in relation to pretty much any controversial political issue.

I've given no indication of bad faith, so I'm not sure why you insist on accusing me of lying about my preference that cancel culture not exist. It's an anathema to free speech and privacy, and ultimately bad for everyone.

You're attacking a straw man. I didn't say I supported Harvey Weinstein, and I don't have any strong opinions on J.K. Rowling.

Why are you so focused on wealthy celebrities? They're a tiny minority of the population and inherently the ones least harmed by cancel culture. You can't really doxx a celebrity, and trying to have them censored or financially ruined is a much larger hurdle than for ordinary people.

I don't intend to silence anyone, critics or otherwise. I welcome all constructive criticism. You're just inventing a motive and arbitrarily assigning it to a phrase you don't like.

On a meta level, this whole subthread proves my original point. Whether or not you actually support cancel culture, what you're attacking right now is vocabulary. It sounds a lot like how certain people online react to the term "cisgender". Having a commonly understood term for a thing that exists isn't offensive; it's just how language works.

Whether the ideal works and the extent to which it's commonly held are two different issues. My view is that both major parties in the US are somewhat illiberal, but average out to a moderately liberal status quo.

Despite many flaws, e.g. the Wars on Drugs and Prostitutes, the US is arguably still the world's greatest stronghold of liberalism (for the moment).

The War on Drugs is actually a perfect microcosm of how illiberal policy doesn't work. Instead of learning our lesson from the War on Alcohol, we doubled down and funneled untold billions of dollars into Mexican drug cartels via US markets — funding the very problem we wanted to solve. By contrast, our more liberal tobacco policy has been a huge success.

Social media is full of politically motivated bullying, harassment, and censorship. That should be readily apparent to anyone who's ever used the internet. That's what cancel culture is, not a dispute with rude houseguests.

I'm not sure why you're so insistent on denying this that you'd compare Wikipedia — and everyone quoted on the subject therein, including presidents from both parties and the former pope — with antisemitic conspiracy theorists.

Who are you implying I intend to silence? I'm commenting on it because I oppose cancel culture, which is the opposite of wanting to silence opposing viewpoints.