Why do you think what you describe being excited about does not warrant the current level of AI hype? I agree with your assessment and sometimes I think there is too much cynicism and not enough excitement.

They go beyond merely "return something that looks as close to the thing I’ve asked for as it can find". Eg: Say we asked for "A todo app that has 4 buttons on the right that each play a different animal sound effect for no good reason and also you can spin a wheel and pick a random task to do". That isn't something that already exists, so in order to build that, the LLM has to break that down, look for appropriate libraries and source and decide on a framework to use, and then glue those pieces together cohesively. That didn't come from a singular repo off GitHub. The machine had to write new code in order to fulfill my request. Yeah, some if it existed in the training data somewhere, but not arranged exactly like that. The LLM had to do something in order to glue those together in that way.

Some people can't see past how the trick is done (take training data and do a bunch of math/statistics on it), but the fact that LLMs are able to build the thing is in-and-of-itself interesting and useful (and fun!).

Wysiwyg kind of fell apart once we had to stop assuming everyone had an 800x600 or 1024x768 screen, because what you saw was no longer what others got.

I still miss my days of programming Visual Basic 6. Nothing since then ever compares.

4gl or RAD is still here, but now it’s called low- or no-code.

i'm not sure when you tried it, but if you've had copilot disabled it might be worth giving it another go. in my totally anecdotal experience, over the last few months it's gotten significantly better at shutting up when it can't provide anything useful.

I’ve recently written a message queue <> database connector in Go using Claude Code, checkpointing, recovery, all that stuff built in.

I’d say it made me around 2x as productive.

I don’t think the cynicism of HN is justified, but I think what people forget is that it takes several months of really investing a lot of time into learning how to use AI well. If I see some of the prompts people give, and expect it to work, yeah no wonder that only works for React-like apps.

The thing is cryptocurrency and metaverse stuff was obvious bullshit from day one while even GPT-3 was clearly a marvel from day one. It's a false pattern match.

The original context of this sub-thread was Karpathy saying how AI coding tools were pretty useless for him when working on this particular project.

One of my hobby projects is an esoteric game engine oriented towards expressing simulation mechanics. I simply do not use agentic tools when editing the core code for this project (mostly rust and wgsl). It always stumbles, and leaves code that I need to fix up manually, and even then feel unsure about. I've tried a few different agents, including the current top of the line. The power is just not there yet.

At the same time, these tools have helped me reduce the development time on this project by orders of magnitude. There are two prominent examples.

--- Example 1:

The first relates to internal tooling. I was debugging a gnarly problem in an interpreter. At some point I had written code to do a step-by-step dump of the entire machine state to file (in json) and I was looking through it to figure out what was going wrong.

In a flash of insight, I asked my AI service (I'll leave names out since I'm not trying to promote one over another) to build a react UI for this information. Over the course of a single day, I (definitely not a frontend dev by history) worked with it to build out a beautiful, functional, easy to use interface for browsing step-data for my VM, with all sorts of creature comforts (like if you hover over a memory cell, and the memory cell's value happens to be a valid address to another memory cell, the target memory cell gets automatically highlighted).

This single tool has reduced my debugging time from hours or days to minutes. I never would have built the tool without AI support, because I'm simply not experienced enough in frontend stuff to build a functional UI quickly.. and this thing built an advanced UI for me based on a conversation. I was truly impressed.

--- Example 2:

As part of verifying correctness for my project, I wanted to generate a set of tests that validated the runtime behaviour. The task here consists of writing a large set of reference programs, and verifying that their behaviour was identical between a reference implementation and the real implementation.

Half decent coverage meant at least a hundred or so tests were required.

Here I was able to use agentic AI to reduce the testcase construction time from a month to about a week. I asked the AI to come up with a coverage plan and write the test case ideas to a markdown file in an organized, categorized way. Then I went through each category in the test case markdown and had the AI generate the test cases and integrate them into the code.

---

I was and remain a strong skeptic of the hype around this tech. It's not the singularity, it's not "thinking". It's all pattern matching and pattern extension, but in ways so sophisticated that it feels like magic sometimes.

But while the skeptical perspective is something I value, I can't deny that there is core utility in this tech that has a massive potential to contribute to efficiency of software development.

This is a tool that we as industry are still figuring out the shape of. In that landscape you have all sorts of people trying to evangelize these tools along their particular biases and perspectives. Some of them clearly read more into the tech than is there. Others seem to be allergically reacting to the hype and going in the other direction.

I can see that there is both noise, and fundamental value. It's worth it to try to figure out how to filter the noise out but still develop a decent sense of what the shape of that fundamental value is. It's a de-facto truth that these tools are in the future of every mainstream developer.

That's exactly why I said he would cringe at it. Seeing someone look at him saying "it's not able to make a good GPT clone" and going "yeah it's useless for anything besides React todo list demos" would definitely evoke some kind of reaction. He understands AI coding agents are neither geniuses nor worthless CRUD monkeys.