Uhuh, but if it was so easy, we also wouldn't be funding billions of euros and dollars in anti-AI-disinformation systems, holding entire conferences about deepfakes and hybrid attacks on civilians, and have dozens of governments opening entirely new defense departments to study and counter the capabilities of AI to disseminate human-like disinformation at scale (which has been affecting elections across the world).
But yeah, your AI slop take about the local burger joint that used free chatgpt to generate a menu filled with typos and bad images is A+.
If you're saying I don't see LLMs for what they are, you are wrong. I have worked in the AI sector for over a decade and I know exactly how the sausage is made. As of today, I run an AI lab (ingram.tech), and we see daily not just the theoretical of what's possible, but how these systems get deployed and who's really at risk. We're head to head with the reality of the terrain.
But it's completely irrelevant. The emergent properties of LLMs, what was built on top of those emergent properties, and the emergent properties of that, are all together building a world nobody is ready for.
If you don't think this, you haven't seen what these things are truly capable of yet. Either that, or you have a romanticized view of what humans actually do in 99% of non-manual jobs.
I'm blown away by how so many people on HN are just... idk, "blind" is the politically correct way to say it, I think. With zero ability to understand the transitive aspects of what they are looking at. For example, these HN threads are so often polluted with comments claiming some random use case cannot possibly be automated.
Sometimes I feel like I'm showing somebody how a spreadsheet can calculate 1+1, and they ask "Yes, but can it do 1+2?".
You have short memory if you think we've not blown past at least 5 different AGI goalposts. They're being moved every time and we're hitting them every time.
Or maybe you just don't know exactly how capable these models are. Most people's experience of AI is a stupid chatbot, it's no wonder they don't understand how these things are coming for their jobs.
On my end, I have a software that is designed and built by Claude, that I did a strategy session on (with claude), and prepared a fundraise for (with claude). My only role, other than "knowing what to aim for", has been to feed the AI some fairly basic english prompts for a few weeks... which is also easily automatable.
Caring for memory mattered more when memory was sparse.
But then again, it's crazy how people are disingenuous. You're in a thread about Bun being rewritten from a non-memory-safe to a memory-safe language and everyone's shitting on it because it's a useless rewrite. How does this mesh with what you say about caring for stability?
Using AI you can actually start caring about things such as memory, footprint, stability and speed because it's crazy cheap to start optimizing for this, when before you couldn't afford to make the tradeoff.
Yes I also used Bubbletea. I don’t know about ratatui but honestly I think the hate for react is very blind. It’s not like CC is shipping an embedded web browser. It’s a framework on top of an embedded language that has seen a crazy amount of optimisation.
It sounds like you completely missed the point I was making: that actually implementing such a highly dynamic TUI application normally requires a significant amount of state tracking and it is significantly easier to write such apps using a functional, descriptive UI framework instead of something like ncurses.
Take it from someone who, in those 24 years, has written UIs in jquery, react and ncurses (and yeah I've tried out Ink and it's great).
"all the downsides that come with that" yeah well, while you're stuck implementing your app in the Best Possible Library, millions of people are happy using claude code and it's crazy useful.
I have ~24 years of experience coding and the LAST thing I think about when opening claude code is "why is this written in react".
Tell you what I sometimes think about though: The fact it has clickable links and complex formatting rules for markdown, the most interactive and highest-quality clickable interface I've ever seen in a terminal, and somehow manages to work. That actually blows my mind.
THEN, I'm reminded that it's written in React, and I think "Huh, guess that does make it a ton easier than using ncurses or something."
Again, you are comparing things which are not comparable.
"Working on a feature" and "Working on a language-to-language port of 1M LOC" are NOT the same thing.
You're also still bringing up "regression per day", while ignoring the fact that without AI, this would have taken easily ~4000 days. Which is 1 regression per 0.003 calendar day. Not the same.
And you still aren't addressing sibling comments reminding you that these aren't shipped migrations. I'll second the comment asking you whether your code is always perfect first-shot, because if we're going to count those bugs, we might be well above your 1/day number.
Migrating a million lines of code is how many human-engineer-years worth of work? Some quick fermi estimates place it at between 15-100 years depending on how much automated tooling is used (without AI).
It really blows my mind how some AI naysayers like you fail the absolute basic common sense test of comparing the comparable. If you, without AI, merged nineteen regressions in 2 weeks, how much work has actually been done? How many features did you rewrite? How many refactors did you make and how difficult were they?
I don't know why you're saying you're "tired of people's hateful bullshit". I originally responded that your point was off, and you're the one writing these comments: https://news.ycombinator.com/item?id=48886350
Take a breather, my guy. Stop saying in every comment that "you're done" and "this is your last response" and actually go touch a bit of grass. You do not owe anyone on this website a response, and the only thing your comments are for (when they're good and not hostile) is contributing to a discussion. Nobody here cares whether you respond or not.
Anyway, not to detract - this last comment was good. I just think maybe you're putting intent in people's mouths where there is none. For example, I certainly don't believe the bullshit Altman and Amodei are putting out. I just disagree the LLMs don't think.
And yes I also understand the math behind them. But it being math doesn't mean there cannot be emergent behaviour, just like there is emergent behaviour after the layers upon layers of biology in humans, resulting in thinking. "It's not magic, it's just [biology]" applies to us as well.
"But brains are the most complex machines in the universe" some might say -> Right, but who's to say the thinking we do requires machines as complex as our brains are? Our brains move muscles, help us breathe and manage millions of other invisible innate processes, many things LLMs do not need to do. And LLMs have a very, very deep access to and understanding of language, which is argued to be a significant contributor to how humans think (based on studies on nonverbal humans).
> As soon as the math stops being done, the "thinking" stops. Does a brain ever stop thinking?
Right, so I understand your point in this and there's something to that, but it enters in how thinking is defined in both ways.
First, the calculation not running is at best equivalent to time stopping. It's not like the LLM actually sits there waiting for input. As you said: we continuously receive input, and we do so because our biology is built this way and we are damn energy efficient, so we can afford to continuously and asynchronously process that input. But this is not really related to the process of thinking itself.
So it comes down to what happens during the calculation. And Anthropic's published research on the J-space is pretty damning evidence, IMO, that thinking does happen during the calculation.
You phrased it better (and with a lot more patience) than I ever could muster.
So many people I meet are so deeply convinced LLMs absolutely cannot physically think, because they define "thinking" as "that thing you do with your human brain where neurons are involved", and they define "LLM thinking" as "that thing ChatGPT does where it says it's thinking but it's actually just detached inference".
The underlying assumption is usually two-fold:
1. That simulated thinking is not thinking.
2. That "LLM thinking" is always only defined as Chain of Thought.
Well, 1 is a pretty useless stance to have, because it removes space for any useful definition of what thinking is. And 2 is simply false, as presented by Anthropic here: https://www.anthropic.com/research/global-workspace
I’m based in Brussels, working in tech, AI, and startup funding. I run an incubator for US founders who want to move to EU (https://sevenseed.eu)
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