What I don't understand about this whole "get on board the AI train or get left behind" narrative, what advantage does an early adopter have for AI tools?
The way I see it, I can just start using AI once they get good enough for my type of work. Until then I'm continuing to learn instead of letting my brain atrophy.
This is a pretty common position: "I don't worry about getting left behind - it will only take a few weeks to catch up again".
I don't think that's true.
I'm really good at getting great results out of coding agents and LLMs. I've also been using LLMs for code on an almost daily basis since ChatGPT's release on November 30th 2022. That's more than three years ago now.
Meanwhile I see a constant flow of complaints from other developers who can't get anything useful out of these machines, or find that the gains they get are minimal at best.
Using this stuff well is a deep topic. These things can be applied in so many different ways, and to so many different projects. The best asset you can develop is an intuition for what works and what doesn't, and getting that intuition requires months if not years of personal experimentation.
I don't think you can just catch up in a few weeks, and I do think that the risk of falling behind isn't being taken seriously enough by much of the developer population.
I'm glad to see people like antirez ringing the alarm bell about this - it's not going to be a popular position but it needs to be said!
It needs to be said that your opinion on this is well understood by the community, respected, but also far from impartial. You have a clear vested interest in the success of _these_ tools.
There's a learning curve to any toolset, and it may be that using coding agents effectively is more than a few weeks of upskilling. It may be, and likely will be, that people make their whole careers about being experts on this topic.
But it's still a statistical text prediction model, wrapped in fancy gimmicks, sold at a loss by mostly bad faith actors, and very far from its final form. People waiting to get on the bandwagon could well be waiting to pick up the pieces once it collapses.
I have a lot of respect from Simon and read a lot of his articles.
But I'm still seeing clear evidence it IS a statistical text prediction model. You ask it the right niche thing and it can only pump out a few variations of the same code, that's clearly someone else's code stolen almost verbatim.
And I just use it 2 or 3 times a day.
How are SimonW and AntiRez not seeing the same thing?
How are they not seeing the propensity for both Claude + ChatGPT to spit out tons of completely pointless error handling code, making what should be a 5 line function a 50 line one?
How are they not seeing that you constantly have to nag it to use modern syntax. Typescript, C#, Python, doesn't matter what you're writing in, it will regularly spit out code patterns that are 10 years out of date. And woe betide you using a library that got updated in the last 2 years. It will constantly revert back to old syntax over and over and over again.
I've also had to deal with a few of my colleagues using AI code on codebases they don't really understand. Wrong sort, id instead of timestamp. Wrong limit. Wrong json encoding, missing key converters. Wrong timezone on dates. A ton of subtle, not obvious, bugs unless you intimately know the code, but would be things you'd look up if you were writing the code.
And that's not even including the bit where the AI obviously decided to edit the wrong search function in a totally different part of the codebase that had nothing to do with what my colleague was doing. But didn't break anything or trigger any tests because it was wrapped in an impossible to hit if clause. And it created a bunch of extra classes to support this phantom code, so hundreds of new lines of code just lurking there, not doing anything but if I hadn't caught it, everyone thinks it does do something.
It's mostly a statistical text model, although the RL "reasoning" stuff added in the past 12 months makes that a slightly less true statement - it has extra tricks now to help it bias bits of code to statistically predict that are more likely to work.
The real unlock though is the coding agent harnesses. It doesn't matter any more if it statistically predicts junk code that doesn't compile, because it will see the compiler error and fix it. If you tell it "use red/green TDD" it will write the tests first, then spot when the code fails to pass them and fix that too.
> How are they not seeing the propensity for both Claude + ChatGPT to spit out tons of completely pointless error handling code, making what should be a 5 line function a 50 line one?
TDD helps there a lot - it makes it less likely the model will spit out lines of code that are never executed.
> How are they not seeing that you constantly have to nag it to use modern syntax. Typescript, C#, Python, doesn't matter what you're writing in, it will regularly spit out code patterns that are 10 years out of date.
I find that if I use it in a codebase with modern syntax it will stick to that syntax. A prompting trick I use a lot is "git clone org/repo into /tmp and look at that for inspiration" - that way even a fresh codebase will be able to follow some good conventions from the start.
Plus the moment I see it write code in a style I don't like I tell it what I like instead.
> And that's not even including the bit where the AI obviously decided to edit the wrong search function in a totally different part of the codebase that had nothing to do with what my colleague was doing.
I usually tell it which part of the codebase to execute - or if it decides itself I spot that and tell it that it did the wrong thing - or discard the session entirely and start again with a better prompt.
You can literally go look at some of antirez's PRs described here in this article. They're not seeing it because it's not there?
Honestly, what you're describing sounds like the older models. If you are getting these sorts of results with Opus 4.5 or 5.2-codex on high I would be very curious to see your prompts/workflow.
> You ask it the right niche thing and it can only pump out a few variations of the same code, that's clearly someone else's code stolen almost verbatim.
There are only so many ways to express the same idea. Even clean room engineers write incidentally identical code to the source sometimes.
I think you are right in saying that there is some deep intuition that takes months, if not years, to hone about current models, however, the intuition some who did nothing but talk and use LLMs nonstop two years ago would be just as good today as someone who started from scratch, if not worse because of antipatterns that don’t apply anymore, such as always starting a new chat and never using a CLI because of context drift.
Also, Simon, with all due respect, and I mean it, I genuinely look in awe at the amount of posts you have on your blog and your dedication, but it’s clear to anyone that the projects you created and launched before 2022 far exceed anything you’ve done since. And I will be the first to say that I don’t think that’s because of LLMs not being able to help you. But I do think it’s because what makes you really, really good at engineering you kept replacing slowly but surely with LLMs more and more by the month.
If I look at Django, I can clearly see your intelligence, passion, and expertise there. Do you feel that any of the projects you’ve written since LLMs are the main thing you focus on are similar?
Think about it this way: 100% of you wins against 100% of me any day. 100% of Claude running on your computer is the same as 100% of Claude running on mine. 95% of Claude and 5% of you, while still better than me (and your average Joe), is nowhere near the same jump from 95% Claude and 5% me.
I do worry when I see great programmers like you diluting their work.
> 95% of Claude and 5% of you, while still better than me (and your average Joe), is nowhere near the same jump from 95% Claude and 5% me.
I see what you're saying, but I'm not sure it is true. Take simonw and tymscar, put them each in charge of a team of 19 engineers (of identical capabilities). Is the result "nowhere near the same jump" as simonw vs. tymscar alone? I think it's potentially a much bigger jump, if there are differences in who has better ideas and not just who can code the fastest.
I agree, however there you don’t compare technical knowledge alone, you also compare managerial skills.
With LLMs its admittedly a bit closer to doing it yourself because the feedback loop is much tighter
My great regret from the past few years is that experimenting with LLMs has been such a huge distraction from my other work! My https://llm.datasette.io/ tool is from that era though, and it's pretty cool.
I do think your datasettes work is fantastic and I genuinely hope you take my previous message the right way. I’m not saying you do something bad, quite the opposite, I feel like we need more of you and I’m afraid because of LLMs we get less of you.
So where’s all of this cutting edge amazing and flawless stuff you’ve built in a weekend that everybody else couldn’t because they were too dumb or slow or clueless?
This is such a tired response at this point.
People are under zero obligation to release their work to the public. Simon actually publishes and writes about a remarkable amount of the side projects he builds with AI.
The rest of us just build tons of cool stuff for personal use or for $JOB. Releasing stuff to the public is, in general, a massive amount of extra work for very little benefit. There are loads of FOSS maintainers trapped spending as much time managing their communities as they do their actual projects and many of us just don't have time for that.
> The rest of us just build tons of cool stuff for personal use or for $JOB. Releasing stuff to the public is, in general, a massive amount of extra work for very little benefit. There are loads of FOSS maintainers trapped spending as much time managing their communities as they do their actual projects and many of us just don't have time for that.
I wouldn't worry about this.
There are many examples of people sharing a project they've used LLMs to help write, and the result was not a huge amount of attention & expectation of burden.
Perhaps "I don't share it because I'm worried people will love it too much" even suggests the opposite: you can concretely demonstrate the kinds of things you've been able to build using LLMs.
> This is such a tired response at this point.
Lack of specificity & concrete examples frequently mean all that's left for discussion is emotion for hype and anti-hype, though.
In this thread, the discussion was:
I like LLMs. But making big claims while being reticent about concrete claims and demonstrations is irksome.The response may be tired when asked in this personal way, but in general, it's a fair question. Nobody is forced to share their work. But with all the high praises, we'd expect to see at least some uptick in the software world. But there is no surge in open source projects. No surge in app store entries. And for the bigger companies claiming high GenAI use, they're not iterating faster or building more. They are continually removing features and their software is getting worse, slower, less robust, and less secure.
Software quality has been on a step downwards curve as far as quality and capabilities are concerned, for years before LLM coding had its breakthrough. For all the promises I'd have expected to, three years later, at least notice the downward trajectory easing off. But it hasn't been happening.
All I took from your reply was
> I could if I wanted to, but I just don't feel like it.
What am I missing where I can understand that's not what you meant?
I wouldn't call these flawless but here you go:
- https://github.com/simonw/denobox is a new Python library that gives you the ability to run arbitrary JavaScript and WASM in a sandbox provided by Deno, because it turns out a Python library can depend on deno these days. I built that on my phone in bed yesterday morning.
- https://github.com/simonw/pwasm is a WebAssembly runtime written in pure Python with no dependencies, built by feeding Claude Code the official WASM specification along with its conformance test suite and having it hack away at that (again via my phone) to get as many of the tests to pass as possible. It's pretty slow and not really useful yet but it's certainly interesting.
- https://github.com/datasette/datasette-transactions is a Datasette plugin which provides a JSON API for starting a SQLite transaction, running multiple queries within it and then executing or rolling back that transaction. I built that one on my phone on a BART (SF Bay Area metro) trip.
- https://github.com/simonw/micro-javascript is a pure Python, no dependency JavaScript interpreter which started as a port of MicroQuickJS. Here's a demo of that one running in a browser https://simonw.github.io/micro-javascript/playground.html - that's my JavaScript interpreter running inside Python running in Pyodide in WebAssembly in your browser of choice, which I find inherently amusing.
All of those are from the past three weeks. Most of them were built on my phone while I was doing other things.
I am not at all an AI sceptic, but probably less impressed by what LLMs are capable of.
Looking at these projects, I have a few questions:
1. These seem to be fairly self-contained and well specified problems, which is the best case scenario for “vibe coding”. Do you have any examples of projects where the solution was somewhat vague and open-ended? If not, how do you think Claude Code or similar would perform?
2. Did you feel excited or energized by having an LLM implement these projects end-to-end? Personally, I find LLMs useful as a closely guided assistant, particularly to interactively explore the space of solutions. I also don’t feel energized at all by having it implement anything non-trivial end to end, outside of writing tests (and even then, not all types of tests!).
3. Do you think others would find these projects useful? In particular, if you vibe coded them, why couldn’t someone else do the same thing? And once these projects are picked up by future model training runs, they’ll probably be even easier to one shot, reducing the value even further.
Let me provide an example of what I mean by (2), at least in the context of hobbyist dev. I could have Claude Code vibe code a Gameboy emulator and it would probably do a fine job given that it’s a well specified problem that is likely well represented in its training data. But the process would neither be exciting nor energizing. I would rather spend hours gradually getting more and more working and experience the fruits of my labor (I did this already btw).
At $DAYJOB, I simply do not have confidence in an LLM doing anything non-trivial end to end. Besides, the complexity remains in defining the requirements and constraints, designing the solution, gaining consensus, and devising a plan for implementation. The goal would be for the LLM to pick up discrete, well defined chunks of work.
Based on those, it seems you are not actually using them to create big codebases from scratch, but rather for problems that would normally take quite a while, not because they are inherently difficult to implement, but because you would normally have to spend considerable time on the finicky implementation details.
I think that's the reason why LLMs work so well for some like you, and generate slop for others, because if you let them alone with projects that require opinionated code and actual decision making they most often don't grasp the users intention well or worse misinterpret it so confidently that you end up with something with all the wrong opinions and decisions compounding path-dependently into the strangest and most useless slop.
"for problems that would normally take quite a while, not because they are inherently difficult to implement, but because you would normally have to spend considerable time on the finicky implementation details"
Yes, exactly! How amazing is it that we have technology now that lets us quickly build projects where we would normally have to spend considerable time on the finicky implementation details?
Another lens is that many people either have terrible written communication skills, do not intuitively grasp how to describe a complex system design, or both. And yet, since everyone is a genius with 100% comprehensibility in their own mind, they simply aren't aware that the problem starts with them.
Well I think it also has to do with communication with LLMs being different to communication with humans. If you tell a developer "don't do busywork" they surely wouldn't say "Oh the repo looks like a trash dump, but no busywork so I'm not going to clean it up, quickly document that as canonical structure, then continue"
I find it increasingly confusing that some people seem to believe, that other people not subjecting themselves to this continued interrogation, gives any credence to their position.
People seem to believe that there is a burden of proof. There is not. What do I care if you are on board?
I don't know what could change your mind, but of course the answer is "nothing" as long as you aer not open to it. Just look around. There is so much stuff, from so many credible people in all domains. If you can't find anything that is convincing or at least interesting to you, you are simply not looking.
> What do I care if you are on board?
Without enough adoption expect some companies you are a client of to increase prices more, or close entirely down the road, due to insufficient cash inflow.
So, you would care, if you want to continue to use these tools and see them evolve, instead of seeing the bubble pop.
Over the last few days I made this ggplot2-looking plotting DSL as a CLI tool and a Rust library.
https://github.com/williamcotton/gramgraph
The motivation? I needed a declarative plotting language for another DSL I'm working on called Web Pipe:
"Web Pipe is an experimental DSL and Rust runtime for building web apps via composable JSON pipelines, featuring native integration of GraphQL, SQL, and jq, an embedded BDD testing framework, and a sophisticated Language Server."https://github.com/williamcotton/webpipe
https://github.com/williamcotton/webpipe-lsp
https://williamcotton.com/articles/basic-introduction-to-web...
I've been working at quite a clip for a solo developer who is building a new language with a full featured set of tooling.
I'd like to think that the approach to building the BDD-testing framework directly into the language itself and having the test runner using the production request handlers is at least somewhat novel!
I'm married with two young kids and I have a full-time job. Before these tools there was no way I could build all of these experiments with such limited resources.Where is all the amazing, much better stuff you implemented manually meanwhile?
He's built lots of cool stuff with AI. Here is four random ones pulled from https://tools.simonwillison.net
- https://tools.simonwillison.net/bullish-bearish
- https://tools.simonwillison.net/user-agent
- https://tools.simonwillison.net/gemini-chat
- https://tools.simonwillison.net/token-usage
All of the linked apps look trivial to me. Also, the first one, the UI has no feedback once you click the answer (plus some questions don't really make sense as they have the answer in them). There is more on the website, so there could be something interesting, but I'm having trouble finding it among all the noise. Not saying simple apps have no value. Even simple throwaway UIs can have value, especially if you develop them quickly.
How about these ones, are these trivial too? https://news.ycombinator.com/item?id=46582192
This is not really cool or impressive at all?
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A page that outputs your user agent as an example of 'cool stuff built with AI'?
See my comment here - I suspect that those were deliberately picked by llmslave3 to NOT be impressive: https://news.ycombinator.com/item?id=46582209
For more impressive examples see https://simonwillison.net/2025/Dec/10/html-tools/ and https://news.ycombinator.com/item?id=46574276#46582192
llmslave3 appears to have deliberately picked the least interesting from my HTML+JavaScript tools collection here. This post describes a bunch of much more interesting ones: https://simonwillison.net/2025/Dec/10/html-tools/
> Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith.
Did you genuinely select those examples in good faith?
If you're here to converse in good faith, what's your opinion of the examples I shared in this post over here? https://news.ycombinator.com/item?id=46574276#46582192
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Show me what you've made with AI?
What's the impressive thing that can convince me it's equivalent, or better than anything created before, or without it?
I understand you've produced a lot of things, and that your clout (which depends on the AI ferver) is based largely because of how refined a workflow you've invented. But I want to see the product, rather than the hype.
Make me say; I wish I was good enough to create this!
Without that, all I can see is the cost, or the negative impact.
edit: I've read some of your other posts, and for my question, I'd like to encourage you to pick only one. Don't use the scatter shot approach that LLMs love, giving plenty of examples, hoping I'll ignore the noise for the single that sounds interesting.
Pick only one. What project have you created that you're truly proud of?
I'll go first, (even though it's unfinished): Verse
> Using this stuff well is a deep topic.
Just like the stuff LLMs are being used for today. Why wouldn't "using LLMs well" be not just one of the many things LLMs will simplify too?
Or do you believe your type of knowledge is somehow special and is resistant to being vastly simplified or even made obsolete by AI?
An interesting trend over the past year is that LLMs have learned how to prompt each other.
Back in ~2024 a lot of people were excited about having "LLMs write the prompt!" but I found the results to be really disappointing - they were full of things like "You are the world's best expert in marketing" which was superstitious junk.
As of 2025 I'm finding they actually do know how to prompt, which makes sense because there's a ton more information about good prompting approaches in the training data as opposed to a couple of years ago. This has unlocked some very interesting patterns, such as Claude Code prompting sub-agents to help it explore codebases without polluting the top level token window.
But learning to prompt is not the key skill in getting good results out of LLMs. The thing that matters most is having a robust model of what they can and cannot do. Asking an LLM "can you do X" is still the kind of thing I wouldn't trust them to answer in a useful way, because they're always constrained by training data that was only aware of their predecessors.
Unless we figure out how to make 1 billion+ tokens multimodal context windows (in a commercially viable way) and connect them to Google Docs/Slack/Notion/Zoom meetings/etc, I don't think it will simplify that much. Most of the work is adjusting your mental model to the fact that the agent is a stateless machine that starts from scratch every single time and has little-to-no knowledge besides what's in the code, so you have to be very specific about the context of the task in some ways.
It's different from assigning a task to a co-worker who already knows the business rules and cross-implications of the code in the real world. The agent can't see the broader picture of the stuff it's making, it can go from ignoring obvious (to a human that was present in the last planning meeting) edge cases to coding defensively against hundreds of edge cases that will never occur, if you don't add that to your prompt/context material.
Strongly disagree. Claude Code is the most intuitive technology I've ever used-- way easier than learning to use even VS Code for example. It doesn't even take weeks. Maybe a day or two to get the hang of it and you're off to the races.
The difference is AI tooling lies to you. Day 0 you think it's perfect but the more you use ai tools you realize using them wrong can give you gnarly bugs.
It's intuitive to use but hard to master
It took me a couple of days to find the right level of detail to prompt it. Too high level, and the codebase gets away from me/the tooling goes off the rails. Too low level, and I may as well do it myself. Maybe also learn the sorts of things Claude Code isn't good at yet. But once I got in the groove it was very easy from there. I think the whole process took 2-3 days.
Don't underestimate the number of developers who aren't comfortable with tools that live in the terminal.
I actually don't use it in the terminal, I use the vs code extension. It's a better experience (bringing up the file being edited, nicer diffs, etc.) But both are trivial to pick up.
Well these people are left behind either way. Competent devs can easily learn to use coding assistants in a day or two
How many thing you learned working with LLMs in 2022 are relevant today? How many things you learned now are relevant in the future?
This question misses the point. Everything you learn today informs how you learn in the future.
I don't disagree, knowing how to use the tools is important. But I wanted to add that great prompting skill nowadays are far far less necessary for top-tier models that it was years ago. If I'm clear about what I want and how I want it to behave, Claude Opus 4.5 almost always nails it first time. The "extra" that I do often, that maybe newcomers don't, is to setup a system where the LLM can easily check the results of its changes (verbose logs in terminal and, in web, verbose logs in console and playwright).
I think I'm also very good at getting great results out of coding agents and LLMs, and I disagree pretty heavily with you.
It is just way easier for someone to get up to speed today than it was a year ago. Partly because capabilities have gotten better and much of what was learned 6+ months ago no longer needs to be learned. But also partly because there is just much more information out there about how to get good results, you might have coworkers or friends you can talk to who have gotten good results, you can read comments on HN or blog posts from people who have gotten good results, etc.
I mean, ok, I don't think someone can fully catch up in a few weeks. I'll grant that for sure. But I think they can get up to speed much faster than they could have a year ago.
Of course, they will have to put in the effort at that time. And people who have been putting it off may be less likely to ever do that. So I think people will get left behind. But I think the alarm to raise is more, "hey, it's a deep topic and you're going to have to put in the effort" rather than "you better start now or else it's gonna be too late".
So far every new AI product and even model update has required me to relearn how to get decent results out of them. I'm honestly kind of sick of having to adjust my work flow every time.
The intuition just doesn't hold. The LLM gets trained and retrained by other LLM users so what works for me suddenly changes when the LLM models refresh.
LLMs have only gotten easier to learn and catch up on over the years. In fact, most LLM companies seem to optimise for getting started quickly over getting good results consistently. There may come a moment when the foundations solidify and not bothering with LLMs may put you behind the curve, but we're not there yet, and with the literally impossible funding and resources OpenAI is claiming they need, it may never come.
Really? Claude Code upgrades for me have been pretty seamless- basically better quality output, given the same prompts, with no discernible downsides.
Why can't both be true at the same time? Maybe their problems are more complex than yours. Why do you assume it's a skill issue and ignore the contextual variables?
On the rare occasions that I can convince them to share the details of the problems they are tackling and the exact prompts they are using it becomes very clear that they haven't learned how to use the tools yet.
I'm kind of curious about the things you're seeing since I find the best way is to have them come up with a plan for the work they're about to do and then make sure they actually finish it because they like to skip stuff if it requires too much effort.
I mean, I just think of them like a dog that'll get distracted and go off doing some other random thing if you don't supervise them enough and you certainly don't want to trust them to guard your sandwich.
I've been building Ai apps since gpt 3 so 5 years now.
The pro AI people don't understand what quadratic attention means and the anti-ai people don't understand how much information can be contained in a tb of weights.
At the end of the day both will be hugely disappointed.
>The best asset you can develop is an intuition for what works and what doesn't, and getting that intuition requires months if not years of personal experimentation.
Intuition does not translate between models. Whatever you think dense llms were good at deepseek completely upended it in an afternoon. The difference between major revisions of model families is substantial enough that intuition is a drawback not an asset.
What does quadratic attention mean?
I've so far found that intuition travels between models of a similar generation remarkably well. The conformance suite trick (find a 9,200 test existing conformance suite and tell an agent to build a fresh implementation that passes all those tests) I first found with GPT-5.2 turned out to work exactly as well against Claude Opus 4.5, for example.
https://arxiv.org/abs/2209.04881
To save anyone else the click, this is the paper "On The Computational Complexity of Self-Attention" from September 2022, with authors from NYU and Microsoft.
It argues that the self-attention mechanism in transformers works by having every token "attend to" every other token in a sequence, which is quadratic - n^2 against input - which should limit the total context length available to models.
This would explain why the top models have been stuck at 1 million tokens since Gemini 1.5 in February 2024 (there has been a 2 million token Gemini but it's not in wide use, and Meta claimed their Llama 4 Scout could do 10 million but I don't know of anyone who's seen that actually work.)
My counter-argument here is that Claude Opus 4.5 has a comparatively tiny 200,000 token window which turns out to work incredibly well for the kinds of coding problems we're throwing at it, when accompanied by a cleverly designed harness such as Claude Code. So this limit from 2022 has been less "disappointing" than people may have expected.
The quadratic attention problem seems to be largely solved by practical algorithmic improvements. (Iterations on flash attention, etc.)
What's practically limiting context size IME is that results seem to get "muddy" and get off track when you have a giant context size. For a single-topic long session, I imagine you get a large number of places in the context which may be good matches for a given query, leading to ambiguous results.
I'm also not sure how much work is being put into reinforcement in extremely large context inference, as it's presumably quite expensive to do and hard to reliably test.
Indeed, filling the adversitsed context more than 1/4 full is a bad idea in general. 50k tokens is a fair bit, but works out to between 1 and 10k lines of code.
Perfect for a demo or work on a single self contained file.
Disastrous for a large code base with logic scattered all throughout it.
I guess this applies to the type of developer who needs years, not weeks, to become proficient in say Python?
What are your tips? Any resources you would recommend? I use Claude code and all the chat bots, but my background isn't programming, so I sometimes feel like I'm just swimming around.
I can't buy it because for many people like you it's always the other that uses the tools wrong, proving the contrary for skeptics that keep getting bad results from llms it simply is impossible with this narrative as the base of the discourse, eg "you're not using it well". I don't even get why you need to praise yourself so much being really good at using these tools, if not for building some tech influencer status around here... same thing I believe antirez is trying to do(who knows why)
Have you considered that maybe you aren't using it well? It's something that can and should be learned. It's a tool, and you can't expect to get the most out of a tool without really learning how to use it.
I've had this conversation with a few people so far, and I've offered to personally walk through a project of their choosing with them. Everyone who has done this has changed their perspective. You may not be convinced it will change the world, but if you approach it with an open mind and take the time to learn how to best use it, I'm 100% sure you will see that it has so much potential.
There are tons of youtube videos and online tutorials if you really want to learn.
> Have you considered that maybe you aren't using it well?
Here we go, as I said, and again and again and again it's always out fault we're not using well. It is impossible to counter argument. Btw to reply to your question, yes many times and proved to be useful in very small specialized tasks and a couple of migrations. I really like how LLMs are helping me in my day to day, but still so far away from all this astroturfing
I don't see how your position is compatible with the constant hype about the ever-growing capabilities of LLMs. Either they are improving rapidly, and your intuition keeps getting less and less valuable, or they aren't improving.
They're improving rapidly, which means your intuition needs to be constantly updated.
Things that they couldn't do six months go might now be things that they can do - and knowing they couldn't do X six months ago is useful because it helps systematize your explorations.
A key skill here is to know what they can do, what they can't do and what the current incantations are that unlock interesting capabilities.
A couple I've learned in the past week:
1. Don't give Claude Code a URL to some code and tell it to use that, because by default it will use its WebFetch tool but that runs an extra summarization layer (as a prompt injection defense) which loses details. Telling it to use curl sometimes works but a guaranteed trick is to have it git clone the relevant repo to /tmp and look at the code there instead.
2. Telling Claude Code "use red/green TDD" is a quick to type shortcut that will cause it to write tests first, run them and watch them fail, then implement the feature and run the test again. This is a wildly effective technique for getting code that works properly while avoiding untested junk code that isn't needed.
Now multiply those learnings by three years. Sure, the stuff I figure out in 2023 mostly doesn't apply today - but the skills I developed in learning how to test and iterate on my intuitions from then still count and still keep compounding.
The idea that you don't need to learn these things because they'll get better to the point that they can just perfectly figure out what you need is AGI science fiction. I think it's safe to ignore.
Personally I think this is an extreme waste of time. Every week you're learning something new that is already outdated the next week. You're telling me AI can write complex code but isn't able to figure out how to properly guide the user into writing usable prompts?
A somewhat intelligent junior will dive deep for one week and be on the same knowledge level as you in roughly 3 years.
No matter how good AI gets we will never be in a situation where a person with poor communication skills will be able to use it as effectively as someone who's communication skills are razor sharp.
But the examples you've posted have nothing to do with communication skills, they're just hacks to get particular tools to work better for you, and those will change whenever the next model/service decides to do things differently.
I'm generally skeptical of Simon's specific line of argument here, but I'm inclined to agree with the point about communication skill.
In particular, the idea of saying something like "use red/green TDD" is an expression of communication skill (and also, of course, awareness of software methodology jargon).
Ehhh, I don't know. "Communication" is for sapients. I'd call that "knowing the right keywords".
And if the hype is right, why would you need to know any of them? I've seen people unironically suggest telling the LLM to "write good code", which seems even easier.
I sympathize with your view on a philosophical level, but the consequence is really a meaningless semantic argument. The point is that prompting the AI with words that you'd actually use when asking a human to perform the task, generally works better than trying to "guess the password" that will magically get optimum performance out of the AI.
Telling an intern to care about code quality might actually cause an intern who hasn't been caring about code quality to care a little bit more. But it isn't going to help the intern understand the intended purpose of the software.
I'm going to resist the temptation to spend more time coming up with more examples. I'm sorry those weren't to your liking!
Why do you bother with all this discussion? Like, I get it the first x times for some low x, it's fun to have the discussion. But after a while, aren't you just tired of the people who keep pushing back? You are right, they are wrong. It's obvious to anyone who has put the effort in.
Trying to have a discussion with people who aren't actually interested in being convinced is exhausting. Simon has a lot more patience than I do.
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I feel like both of these examples are insights that won't be relevant in a year.
I agree that CC becoming omniscient is science fiction, but the goal of these interfaces is to make LLM-based coding more accessible. Any strategies we adopt to mitigate bad outcomes are destined to become part of the platform, no?
I've been coding with LLMs for maybe 3 years now. Obviously a dev who's experienced with the tools will be more adept than one who's not, but if someone started using CC today, I don't think it would take them anywhere near that time to get to a similar level of competency.
I base part of my skepticism about that on the huge number of people who seem to be unable to get good results out of LLMs for code, and who appear to think that's a commentary on the quality of the LLMs themselves as opposed to their own abilities to use them.
> huge number of people who seem to be unable to get good results out of LLMs for code
Could it be, they use other definition of "good"?
I suspect that's neither a skill issue nor a technical issue.
Being "a person who can code" carries some prestige and signals intelligence. For some, it has become an important part of their identity.
The fact that this can now be said of a machine is a grave insult if you feel that way.
It's quite sad in a way, since the tech really makes your skills even more valuable.
You're right, it's difficult to get "left behind" when the tools and workflows are being constantly reinvented.
You'd be sage with your time just to keep a high-level view until workflows become stable and aren't advancing every few months.
The time to consider mastering a workflow is when a casual user of the "next release" wouldn't trivially supersede your capabilities.
Similarly we're still in the race to produce a "good enough" GenAI, so there isn't value in mastering anything right now unless you've already got a commercial need for it.
This all reminds me of a time when people were putting in serious effort to learn Palm Pilot's Graffiti handwriting recognition, only for the skill to be made redundant even before they were proficient at it.
I think that who says that you need to be accustomed to the current "tools" related to AI agents, is suffering from a horizon effect issue: these stuff will change continuously for some time, and the more they evolve, the less you need to fiddle with the details. However, the skill you need to have, is communication skills. You need to be able to express yourself and what matters for your project fast and well. Many programmers are not great at communication. In part this is a gift, something you develop at small age, and this will, I believe, kinda change who is good at programming: good communicators / explorers may not have a edge VS very strong coders that are bad at explaining themselves. But a lot of it is attitude, IMHO. And practice.
> Many programmers are not great at communication.
This is true, but still shocking. Professional (working with others at least) developers basically live or die by their ability to communicate. If you're bad at communication, your entire team (and yourself) suffer, yet it seems like the "lone ranger" type of programmer is still somewhat praised and idealized. When trying to help some programmer friends with how they use LLMs, it becomes really clear how little they actually can communicate, and for some of them I'm slightly surprised they've been able to work with others at all.
An example the other day, some friend complained that the LLM they worked with was using the wrong library, and using the wrong color for some element, and surprised that the LLM wouldn't know it from the get go. Reading through the prompt, they never mentioned it once, and when asked about it, they thought "it should have been obvious" which yeah, to someone like you who worked for 2 years on this project that might be obvious, but for some with zero history and zero context about what you do? How you expect it to know this? Baffling sometimes.
Yup. I'd take a gander than most complaints by people who have even used LLMs for long time can be resolved by "describe your thing in detail". LLM's are such a relief on my wrists that I often get tempted to write short prompts and pray that the LLM divines my thoughts. I always get much better results in a lot faster time when i just turn on the mic and have whisper transcribe a couple minutes of my speaking though.
I am using Google Antigravity for the same type of work you mention, such as many things and ideas I had over the years but I couldn't justify the time I needed to invest into them. Pretty non-trivial ideas and yet with a good problem definition communication skills I am getting unbelievable results. I am even intentionally sometimes being too vague in my problem definition to avoid introducing the bias to the model and the ride has been quite crazy so far. In 2 days I've implemented several substantial improvements that i had in my head for years.
The world changed for good and we will need to adapt. The bigger and more important question at this point isn't anymore if LLMs are good enough, for the ones who want to see, but, as you mention in your article, is what will happen to people who will get unemployed. There's a reality check for all of us.
I thought this way for a while. I still do to a certain degree, but I'm starting to see the wisdom in hurrying off into the change.
The most advanced tooling today looks nothing like the tooling for writing software 3 years ago. We've got multi-agent orchestration with built in task and issue tracking, context management, and subagents now. There's a steep learning curve!
I'm not saying that everyone has to do it, as the tools are so nascent, but I think it is worthwhile to at least start understanding what the state of the art will look like in 12-24 months.
My take: learning how to do LLM-assisted coding at a basic level gets you 80% of the returns, and takes about 30 minutes. It's a complete no-brainer.
Learning all of the advanced multi-agent worklows etc. etc... Maybe that gets you an extra 20%, but it costs a lot more time, and is more likely to change over time anyway. So maybe not very good ROI.
What would be the type of work you're doing where you wouldn't benefit from one or multiple of the following:
- find information about APIs without needing to open a browser
- writing a plan for your business-logic changes or having it reviewed
- getting a review of your code to find edge cases, potential security issues, potential improvements
- finding information and connecting the dots of where, what and why it works in some way in your code base?
Even without letting AI author a single line of code (where it can still be super useful) there are still major uses for AI.
It took me a few months of working with the agents to get really productive with it. The gains are significant. I write highly detailed specs (equiv multiple A4 pages) in markdown and dicate the agent hierarchy (which agent does what, who reports to who).
I've learned a lot of new things this year thanks to AI. It's true that the low levels skills with atrophy. The high level skills will grow though; my learning rate is the same, just at a much higher abstraction level; thus covering more subjects.
The main concern is the centralisation. The value I can get out of this thing currently well exceeds my income. AI companies are buying up all the chips. I worry we'll get something like the housing market where AI will be about 50% of our income.
We have to fight this centralisation at all costs!
This is something I think a lot of people don't seem to notice, or worry about, the moving of programming as a local task, to one that is controlled by big corporations, essentially turning programming into a subscription model, just like everything else, if you don't pay the subscription you will no longer be able to code i.e. PaaS (Programming as a Service). Obviously at the moment most programmers can still code without LLMs, but when autocomplete IDEs became main stream, it didn't take long before a large proportion of programmers couldn't program without an autocomplete IDE, I expect most new programmers coming in won't be able to "program" without a remote LLM.
That ignores the possibility that local inference gets good enough to run without a subscription on reasonably priced hardware.
I don't think that's too far away. Anthropic, OpenAI, etc. are pushing the idea that you need a subscription but if opensource tools get good enough they could easily become an expensive irrelivance.
There is that, but the way this usually works is that there is always a better closed service you have to pay for, and we see that with LLMs as well. Plus there is the fact that you currently need a very powerful machine to run these models at anywhere near the speed of the PaaS systems, and I'm not convinced we'll be able to do the Moore's law style jumps required to get that level of performance locally, not to mention the massive energy requirements, you can only go so small, and we are getting pretty close to the limit. Perhaps I'm wrong, but we don't see the jumps in processing power we used to see in the 80s and 90s, due to clock speed jumps, the clock speed of most CPUs has stayed pretty much the same for a long time. As LLMs are essentially probabilistic in nature, this does open up options not available to current deterministic CPU designs, so that might be an avenue which gets exploited to bring this to local development.
Local inference is already very good on open models if you have the hardware for it.
My concern is that inference hardware is becoming more and more specialized and datacenter-only. It won’t be possible any longer to just throw in a beefy GPU (in fact we’re already past that point).
Yep, good point. If they don't make the hardware available for personal use, then we wouldn't be able to buy it even it could be used in a personal system.
This is the most valid criticism. Theoretically in several years we may be able to run Opus quality coding models locally. If that doesn't happen then yes, it becomes a pay to play profession - which is not great.
I have found that using more REPLs and doing leetcodes/katas prevents the atrophy to be honest.
In fact, I'd say I code even better since I started doing one hour per day of a mixture of fun coding and algo quizzes while at work I mostly focus on writing a requirements plan and implementation plan later and then letting the AI cook while I review all the output multiple times from multiple angles.
The hardware needs to catch up I think. I asked ChatGPT (lol) how much it would cost to build a Deepseek server that runs at a reasonable speed and it quoted ~400k-800k(8-16 H100 + the rest of the server).
Guess we are still in the 1970s era of AI computing. We need to hope for a few more step changes or some breakthrough on model size.
The problem is that Moore's law is dead, silicon isn't advancing as fast as what we've envisioned in the past, we're experiencing all sorts of quantum tunneling effects in order to cram as much microstructure as possible into silicon, and R&D for manufacturing these chips are climbing at a rapid rate. There's a limit to how we can fight against Physics, and unless we discover a totally new paradigm to alleviate this issues (ex. optical computing?) we're going to experience diminishing returns at the end of the sigmoid-like tech advancement cycle.
You can run most open models (excluding kimi-k2) on hardware that costs anywhere from 45 - 85k (tbf, specced before the vram wars of late 2025 so +10k maybe?). 4-8 PRO6000s + all the other bits and pieces gives you a machine that you can host locally and run very capable models, at several quants (glm4.7, minimax2.1, devstral, dsv3, gpt-oss-120b, qwens, etc.), with enough speed and parallel sessions for a small team (of agents or humans).
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Well, if you're programming without AI you need to understand what you're building too, lest you program yourself into a corner. Taking 3-5 minutes to speech-to-text an overview of why you want to build what exactly, using which general philosophies/tool seems like it should cost you almost zero extra time and brainpower
I've used cursor and claude code both daily[0] within a month of their releases - i'm learning something new on how to work with and apply the tools almost every day.
I don't think it's a coincidence that some of the best developers[1] are using these tools and some openly advocating for them because it still requires core skills to get the most out of them
I can honestly say that building end-to-end products with claude code has made me a better developer, product designer, tester, code reviewer, systems architect, project manager, sysadmin etc. I've learned more in the past ~year than I ever have in my career.
[0] abandoned cursor late last year
[1] see Linus using antigravity, antirez in OP, Jared at bun, Charlie at uv/ruff, mitushiko, simonw et al
I started heavy usage in April 2025 (Codex CLI -> some Claude Code and trying other CLIs + a bit of Cursor -> Warp.dev -> Claude Code) and I’m still learning as well (and constantly trying to get more efficient)
(I had been using GitHub Copilot for 5+ years already, started as an early beta tested, but I don’t really consider that the same)
I like to say it’s like learning a programming language. it takes time, but you start pattern matching and knowing what works. it took me multiple attempts and a good amount of time to learn Rust, learning effective use of these tools is similar
I’ve also learned a ton across domains I otherwise wouldn’t have touched
AI development is about planning, orchestration and high throughput validation. Those skills won't go away, the quality floor of model output will just rise over time.
The idea, I think, is to gain experience with the loop of communicating ideas in natural language rather than code, and then reading the generated code and taking it as feedback.
It's not that different overall, I suppose, from the loop of thinking of an idea and then implementing it and running tests; but potentially very disorienting for some.
By their promises it should get so good that basically you do not need to learn it. So it is reasonable to wait until that point.
If you listen to promises like that you're going get burned.
One of the key skills needed in working with LLMs is learning to ignore the hype and marketing and figure out what these things are actually capable of, as opposed to LinkedIn bluster and claims from CEOs who's net worth are tied to investor sentiment in their companies.
If someone spends more time talking about "AGI" then what they're actually building, filter that person out.
>One of the key skills needed in working with LLMs is learning to ignore the hype and marketing and figure out what these things are actually capable of
This is precisely what led me to realize that while they have some use for code review and analyzing docs, for coding purposes they are fairly useless.
The hypesters responses' to this assertion exclusively into 5 categories. Ive never heard a 6th.
this is a straw man, nobody serious is promising that. it is a skill like any other that requires learning
Nobody serious, like every single AI CEO out there? I mean I agree, nobody should be taking them seriously, yet we're fast on track for a global financial meltdown because of these fraudsters and their "non-serious" words.
I agree about skills actually, but it's also obvious that parent is making a very real point that you cannot just dismiss. For several years now and far short of wild AGI promises, the answer to literally every issue with casual or production AI has been something like "but the rate of model improvement.." or "but the tools and ecosystem will evolve.."
If you believe that uncritically about everything else, then you have to answer why agentic workflows or MCP or whatever is the one thing that it can't evolve to do for us. There's a logical contradiction here where you really can't have it both ways.
I’m not understanding your point… (and would be genuinely curious to)? the models and systems around them have evolved and gotten better (over the past few years for LLMs and decades for “AI” more broadly)
oh I think I do get your point now after a few rereads (correct if wrong but you’re saying it should keep getting better until there’s nothing for us to do). “AI”, and computer systems more broadly, are not and cannot be viable systems. they don’t have agency (ironically) to affect change in their environment (without humans in the loop). computer systems don’t exist/survive without people. all the human concerns around what/why remain, AI is just another tool in a long line of computer systems that make our lives easier/more efficient
AI Engineer to Software Engineer: Humans writing code is a waste of time, you can only hope to add value by designing agentic workflows
Prompt Engineer to AI Engineer: Designing agentic workflows is a waste of time, just pre/postfix whatever input you'd normally give to the agentic system with the request to "build or simulate an appropriate agentic workflow for this problem"
> nobody serious is promising that
There is a staggering number of unserious folks in the ears of people with corporate purchasing power.
OpenAI is going to get to AGI. And AGI should in minutes build a system that takes vague input and produces fully functioning product out of it. Isn't singularity being promised by them?
you’re just repeating the straw man. if you can’t think critically and just regurgitate every dumb thing you hear idk what to tell you. nobody serious thinks a “singularity” is coming. there’s not even a proper definition of “AGI”
your argument amounts to “some people said stupid shit one time and I took it seriously”
An ecosystem is being built around AI : Best prompting practices, mcps, skills, IDE integration, how to build a feedback loop so that LLM can test its output alone, plug to the outside world with browser extensions, etc...
For now i think people can still catch up quickly, but at the end of 2026 it's probably going to be a different story.
Okay, end of 2026 then what? No one ever learns how to use the tools after that? No one gets a job until the pre-2026 generation dies?
For now i think people can still catch up quickly, but at the end of 2027 it's probably going to be a different story.
> probably going to be a different story
Can you elaborate? Skill in AI use will be a differentiator?
Yes.
At some point you will need to combine multiple skills together:
- communication
- engineering skills (understanding requirements, finding edge cases, etc)
- architectural proficiency
- prompting
- agentic workflows and skills
- context management
- and yes, proper old fashioned coding skills to keep things tidy and consistent
> Best prompting practices, mcps, skills, IDE integration, how to build a feedback loop so that LLM can test its output alone, plug to the outside world with browser extensions, etc...
Ah yes, an ecosystem that is fundamentally inherently built on probabilisitic quick sand and even with the "best prompting practices", you still get agents violating the basics of security and committing API keys when they were told not to. [0]
[0] https://xcancel.com/valigo/status/2009764793251664279
One of the skills needed to effectively use AI for code is to know that telling AI "don't commit secrets" is not a reliable strategy.
Design your secrets to include a common prefix, then use deterministic scanning tools like git hooks to prevent then from being checked in.
Or have a git hook that knows which environment variables have secrets in and checks for those.
That's such an incredibly basic concept, surely AIs have evolved to the point where you don't need to explicitly state those requirements anywhere?
They can still make mistakes.
For example, what if your code (that the LLM hasn't reviewed yet) has a dumb feature in where it dumps environment variables to log output, and the LLM runs "./server --log debug-issue-144.log" and commits that log file as part of a larger piece of work you ask it to perform.
If you don't want a bad thing to happen, adding a deterministic check that prevents the bad thing to happen is a better strategy than prompting models or hoping that they'll get "smarter" in the future.
Part of why these things feel "not fit for purpose" is that they don't include the things Simon has spent three years learning? (I know someone else who's doing multi-LLM development where he uses job-specialty descriptions for each "team member" that lets them spend context on different aspects of the problem; it's a fascinating exercise to watch, but it feels even more like "if this is how the tools should be used, why don't they just work that way"?)
Doesn't seem to work for humans all the time either.
Some of this negativity I think is due to unrealistic expectations of perfection.
Use the same guardrails you should be using already for human generated code and you should be fine.
I have tons of examples of AI not committing secrets. this is one screenshot from twitter? I don’t think it makes your point
CPUs are billions of transistors. sometimes one fails and things still work. “probabilistic quicksand” isn’t the dig you think it is to people who know how this stuff works
I have tons of examples of drivers not running into objects.
like my other comment, my point is one screenshot from twitter vs one anecdote. neither proves anything. cool snarky response though!
> I have tons of examples of AI not committing secrets.
"Trust only me bro".
It takes 10 seconds to see the many examples of API keys + prompts on GitHub to verify that tweet. The issue with AI isn't limited to that tweet which demonstrates its probabilistic nature; Otherwise why do need a sandbox to run the agent in the first place?
Nevermind, we know why: Many [0] such [1] cases [2]
> CPUs are billions of transistors. sometimes one fails and things still work. “probabilistic quicksand” isn’t the dig you think it is to people who know how this stuff works
Except you just made a false equivalence. CPUs can be tested / verified transparently and even if it does go wrong, we know exactly why. Where as you can't explain why the LLM hallucinated or decided to delete your home folder because the way it predicts what it outputs is fundamentally stochastic.
[0] https://old.reddit.com/r/ClaudeAI/comments/1pgxckk/claude_cl...
[1] https://old.reddit.com/r/ClaudeAI/comments/1jfidvb/claude_tr...
[2] https://www.google.com/search?q=ai+deleted+files+site%3Anews...
you could find tons of API keys on GitHub before these “agentic” tools too. that was my point, one screenshot from twitter vs one anecdote from me. I don’t think either proves the point, but posting a screenshot from twitter like it’s proof of some widespread problem is what I was responding to (N=2, 1 vs 1)
my point is more “skill issue” than “trust me this never happens”
my point on CPUs is people who don’t understand LLMs talk like “hallucinations” are a real thing — LLMs are “deciding” to make stuff up rather than just predicting the next token. yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are. can you really explain in detail how everything you use works? I’m guessing I can explain failure modes of agentic systems (and how to avoid them so you don’t look silly on twitter/github) and how neural networks work better than most people can explain the technology they use every day
> you could find tons of API keys on GitHub before these “agentic” tools too. that was my point, one screenshot from twitter vs one anecdote from me. I don’t think either proves the point, but posting a screenshot from twitter like it’s proof of some widespread problem is what I was responding to (N=2, 1 vs 1)
That doesn't refute the probabilistic nature of LLMs despite best prompting practices. In fact it emphasises it. More like your 1 anecdotal example vs my 20+ examples on GitHub.
My point tells you that not only it indeed does happen, but a previous old issue is now made even worse and more widespread, since we now have vibe-coders without security best practices assuming the agent should know better (when it doesn't).
> my point is more “skill issue” than “trust me this never happens”
So those that have this "skill issue" are also those who are prompting the AI differently then? Either way, this just inadvertently proves my whole point.
> yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are.
The additional problem is can you explain why it went wrong as you scale the technology? CPUs circuit design go through formal verification and if a fault happens, we know exactly why; hence it is deterministic in design which makes them reliable.
LLMs are not and don't have this. Which is why OpenAI had to describe ChatGPT's misaligned behaviour as "sycophancy", but could not explain why it happened other than tweaking the hyper-parameters which got them that result.
So LLMs being fundamentally probabilistic and are hence, more unexplainable being the reason why you have the screenshot of vibe-coders who somehow prompted it wrong and the agent committed the keys.
Maybe that would never have happened to you, but it won't be the last time we see more of this happening on GitHub.
I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point.
yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution
I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. you can still practically use the tools to great effect, just like we use everything else that has underlying probabilities
OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice
and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
> I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point.
Verses your anecdote being a proof of what? Skill issue for vibe coders? Someone else prompting it wrong?
You do realize you are proving my entire point?
> yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution
Again, it exacerbates my point such that it makes the existing issue even worse. Additionally, that wasn't even the only point I made on the subject.
> I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic.
When you scale neural networks to become say, production-grade LLMs, then it does matter. Just like it does matter for CPUs to be reliable when you scale them in production-grade data centers.
But your earlier (fallacious) comparison ignores the reliability differences between them (CPUs vs LLMs.) and determinism is a hard requirement for that; which the latter, LLMs are not.
> OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice
For the press, they had to, but no-one knows the real reason, because it is unexplainable; going back to my other point on reliability.
> and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
It is indeed wrong for LLMs because not even the researchers can practically give an explanation why a single neuron (for every neuron in the network) gives different values on every fine-tune or training run. Even if it is "good enough", it can still go wrong at the inference-level for other unexplainable reasons other than it "overfitted".
CPUs on the other hand, have formal verification methods which verify that the CPU conforms to its specification and we can trust that it works as intended and can diagnose the problem accurately without going into atomic-level details.
…what is your point exactly (and concisely)? I’m saying it doesn’t matter it’s probabilistic, everything is, the tech is still useful
No one is arguing that it isn't useful. The problem is this:
> I’m saying it doesn’t matter it’s probabilistic, everything is,
Maybe it doesn't matter for you, but it generally does matter.
The risk level of a technology failing is far higher if it is more random and unexplainable than if it is expected, verified and explainable. The former eliminates many serious use-cases.
This is why your CPU, or GPU works.
LLMs are neither deterministic, no formal verification exists and are fundamentally black-boxes.
That is why many vibe-coders reported many "AI deleted their entire home folder" issues even when they told it to move a file / folder to another location.
If it did not matter, why do you need sandboxes for the agents in the first place?
I think we agree then? the tech is useful; you need systems around them (like sandboxes and commit hooks that prevent leaking secrets) to use them effectively (along with learned skills)
very little software (or hardware) used in production is formally verified. tons of non-deterministic software (including neural networks) are operating in production just fine, including in heavily regulated sectors (banking, health care)
> I think we agree then? the tech is useful; you need systems around them (like sandboxes and commit hooks that prevent leaking secrets) to use them effectively (along with learned skills)
No.
> very little software (or hardware) used in production is formally verified. tons of non-deterministic software (including neural networks) are operating in production just fine, including in heavily regulated sectors (banking, health care)
It's what happens when it all goes wrong.
You have to explain exactly why, a system failed in heavily regulated sectors.
Saying 'everything is probabilistic' as the reason for the cause of an issue, is a non answer if you are a chip designer, air traffic controller, investment banker or medical doctor.
So your point does not follow.
that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point (in the second part of your response; I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine. they can be explained despite your repeated assertions they cannot be. also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf
> that’s not what I said. you honestly seem like you just want to argue about stuff (e.g. not elaborating on the “no” when I basically repeated and agreed with what you said). and you seem to consistently miss my point
I have repeated myself many times and you decide to continue to ignore the reliability points that inherently impede LLMs in many use-cases which exclude them in areas where predictability in critical systems is required in production.
Vibe coders can use them, but the gulf between useful for prototyping and useful for production is riddled with hard obstacles as such a software like LLMs are fundamentally unpredictable hence the risks are far greater.
> I’m saying these non-deterministic neural networks are already widespread in industry with these regulations, and it’s fine.
So when a neural network scales beyond hundreds of layers and billions of parameters, equivalent to a production-grade LLM, explain exactly how is such a black-box on that scale explainable when it messes up and goes wrong?
> they can be explained despite your repeated assertions they cannot be.
With what methods exactly?
Early on, I said formal verification and testing on CPUs for explaining when they go wrong at scale. It is you that provided absolutely nothing of your own assertions with the equivalent for LLMs other than "they can be explained" without providing any evidence.
> also the entire point on CPUs which you may have noticed I dropped from my responses because you seemed distracted arguing about it). this is not productive and we’re both clearly stubborn, glhf
You did not make any point with that as it was a false equivalence, and I explained why the reliability of a CPU isn't the same as the reliability of a LLM.
> What I don't understand about this whole "get on board the AI train or get left behind" narrative, what advantage does an early adopter have for AI tools?
Replace that with anything and you will notice that people who are building startups in this area will want to bring the narrative like that as it usually highly increases the value of their companies. When narrative gets big enough, then big companies must follow - or they look like "lagging behind". Whether the current thing brings value or not. It is a fire that keeps feeding itself. In the end, when it gets big enough - we call it as bubble. Bubble that may explode. Or not.
Whether the end user gets actual value or not, is just side effect. But everyone wants to believe that that it brings value - otherwise they were foolish to jump in the train.
> What I don't understand about this whole "get on board the AI train or get left behind" narrative, what advantage does an early adopter have for AI tools?
The ones pushing this narrative have either the following:
* Invested in AI companies (which they will never disclose until they IPO / acquired)
* Employees at AI companies that have stock options which they are effectively paid boosters around AGI nonsense.
* Mid-life crisis / paranoia that their identity as a programmer is being eroded and have to pivot to AI.
It is no different to the crypto web3 bubble of 2021. This time, it is even more obvious and now the grifters from crypto / tech are already "pivoting to ai". [0]
[0] https://pivot-to-ai.com/
I'm not an AI booster, but I can't argue with Opus doing lots of legwork
> It is no different to the crypto web3 bubble of 2021
web3 didn't produce anything useful, just noise. I couldn't take a web3 stack to make an arbitrary app. with the PISS machine I can.
Do I worry about the future, fuck yeah I do. I think I'm up shit creek. I am lucky that I am good at describing in plain English what I want.
Web3 generated plenty of use if you're in on it. Pension funds, private investors, public companies, governments, gambling addicts, teenagers with more pocket money than sense, they've all moved billions into the pockets of Web3 grifters. You follow a tutorial on YouTube, spam the right places, maybe buy a few illegal ads, do a quick rugpull, and if you did your opsec right, you're now a millionaire. The major money sources have started to dry up (although the current American regime has been paid off by crypto companies so a Web3 revival might just happen).
With AI companies still selling services far below cost, it's only a matter of time before the money runs out and the true value of these tools will be tested.
> Pension funds, private investors, public companies
As someone who was at a large company that was dabbling in NFTs, there was no value apart from pure gambling. At the time that we were doing it, it was also too late, so it was just a jinormous
My issue with GenAI is the rampant copyright violation, and the effect it will have on the economy. Its also replacing all of the fun bits of the world that I inhabit.
At least with web3 it was mostly contained with in the BO infested basement that crypto bros inhabit. AI bollocks has infected half the world.
Comparing crypto and web3 scam with AI advancements is disingenuous at its best. I am a long time C and C++ systems programming engineer oriented at (sometimes novel) algorithmic design and high-performance large-scale systems operating at the scale of internet. I am specializing in low-level details that generally very small amount of engineers around the globe are familiar with. We can talk at the level of CPU microarchitectural details or memory bank conflicts or OS internals, and all the way up to the line of code we are writing. AI is the most transformative technology ever designed. I'd go that far and say that not even industrial revolution is going to be comparable to it. I have no stakes in AI.
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