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 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.

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?

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?

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.

How many thing you learned working with LLMs in 2022 are relevant today? How many things you learned now are relevant 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.

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?

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.

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)

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.