Fully agree. It takes months to learn how to use LLMs properly. There is an initial honeymoon where the LLMs blow your mind out. Then you get some disappointments. But then you start realizing that there are some things that LLMs are good at and some that they are bad at. You start creating a feel for what you can expect them to do. And more importantly, you get into the habit of splitting problems into smaller problems that the LLMs are more likely to solve. You keep learning how to best describe the problem, and you keep adjusting your prompts. It takes time.

> Learning how to use LLMs in a coding workflow is trivial. There is no learning curve. You can safely ignore them if they don’t fit your workflows at the moment.

That's a wild statement. I'm now extremely productive with LLMs in my core codebases, but it took a lot of practice to get it right and repeatable. There's a lot of little contextual details you need to learn how to control so the LLM makes the right choices.

Whenever I start working in a new code base, it takes a a non-trivial amount of time to ramp back up to full LLM productivity.

> I have never heard anybody successfully using LLMs say this before. Most of what I've learned from talking to people about their workflows is counterintuitive and subtle.

Because for all our posturing about being skeptical and data driven we all believe in magic.

Those "counterintuitive non-trivial workflows"? They work about as well as just prompting "implement X" with no rules, agents.md, careful lists etc.

Because 1) literally no one actually measures whether magical incarnations work and 2) it's impossible to make such measurements due to non-determinism

I agree with your assessment about this statement. I actually had to reread it a few times to actually understand it.

He is actually recommending Copilot for price/performance reasons and his closing statement is "Don’t fall for the hype, but also, they are genuinely powerful tools sometimes."

So, it just seems like he never really gave a try at how to engineer better prompts that these more advanced models can use.

The OPs point seems to be: it's very quick for LLMs to be a net benefit to your skills, if it is a benefit at all. That is, he's only speaking of the very beginning part of the learning curve.

The first two points directly contradict each other, too. Learning a tool should have the outcome that one is productive with it. If getting to "productive" is non-trivial, then learning the tool is non-trivial.

Agreed. This is an astonishingly bad article. It's clear that the only reason it made it to the front page is because people who view AI with disdain or hatred upvoted it. Because as you say: how can anyone make authoritative claims about a set of tools not just without taking the time to learn to use them properly, but also believing that they don't even need to bother?

Would it be more appropriate to compare LLMs to Autotunes rather than pianos?

I've said it before, I feel like I'm some sort of lottery winner when it comes to LLM usage.

I've tried a few things that have mostly been positive. Starting with copilot in-line "predictive text on steroids" which works really well. It's definitely faster and more accurate than me typing on a traditional intellisense IDE. For me, this level of AI is cant-lose: it's very easy to see if a few lines of prediction is what you want.

I then did Cursor for a while, and that did what I wanted as well. Multi-file edits can be a real pain. Sometimes, it does some really odd things, but most of the time, I know what I want, I just don't want to find the files, make the edits on all of them, see if it compiles, and so on. It's a loop that you have to do as a junior dev, or you'll never understand how to code. But now I don't feel I learn anything from it, I just want the tool to magically transform the code for me, and it does that.

Now I'm on Claude. Somehow, I get a lot fewer excursions from what I wanted. I can do much more complex code edits, and I barely have to type anything. I sort of tell it what I would tell a junior dev. "Hey let's make a bunch of connections and just use whichever one receives the message first, discarding any subsequent copies". If I was talking to a real junior, I might answer a few questions during the day, but he would do this task with a fair bit of mess. It's a fiddly task, and there are assumptions to make about what the task actually is.

Somehow, Claude makes the right assumptions. Yes, indeed I do want a test that can output how often each of the incoming connections "wins". Correct, we need to send the subscriptions down all the connections. The kinds of assumptions a junior would understand and come up with himself.

I spend a lot of time with the LLM critiquing, rather than editing. "This thing could be abstracted, couldn't it?" and then it looks through the code and says "yeah I could generalize this like so..." and it means instead of spending my attention on finding things in files, I look at overall structure. This also means I don't need my highest level of attention, so I can do this sort of thing when I'm not even really able to concentrate, eg late at night or while I'm out with the kids somewhere.

So yeah, I might also say there's very little learning curve. It's not like I opened a manual or tutorial before using Claude. I just started talking to it in natural language about what it should do, and it's doing what I want. Unlike seemingly everyone else.

I agree with you and I have seen this take a few times now in articles on HN, which amounts to the classic: "We've tried nothing and we're all out of ideas" Simpson's joke.

I read these articles and I feel like I am taking crazy pills sometimes. The person, enticed by the hype, makes a transparently half-hearted effort for just long enough to confirm their blatantly obvious bias. They then act like the now have ultimate authority on the subject to proclaim their pre-conceived notions were definitely true beyond any doubt.

Not all problems yield well to LLM coding agents. Not all people will be able or willing to use them effectively.

But I guess "I gave it a try and it is not for me" is a much less interesting article compared to "I gave it a try and I have proved it is as terrible as you fear".

Pianists' results are well known to be proportional to their talent/effort. In open source hardly anyone is even using LLMs and the ones that do have barely any output, In many cases less output than they had before using LLMs.

The blogging output on the other hand ...