> Speech recognition is useful.

Now try to mute a video on youtube and understand what's being said from the automatic subtitles.

If you do it in english, be aware that it's the best performing language and all others are even worse.

Just today, I received a note from a gas technician, part handwritten, for the life of me I couldn't make out what he was saying, I asked ChatGPT and it surprisingly understood, rereading the original note I'm very sure it was correct.

Sometimes speech-to-text machine learning models give very good results, however I think the key is that:

1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video.

2. When it fails, it tends to do so in ways that trigger human suspicion and oversight.

Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.

For some reason, YouTube is not using a very good STT system now. The lack of sentence punctuation is particularly annoying. Transcriptions by Whisper and Gemini 1.5 Pro are much better. From a couple of weeks ago:

https://news.ycombinator.com/item?id=41199567#41201773

I expect that YouTube will up their transcription game soon, too.

I've tried whisper too. I made this: https://codeberg.org/ltworf/srtgen

Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well.

I just spoke about youtube because it's more popular and easy to test.

> I don't find coding assistants to be very useful.

I've come to notice a correlation between contemporary AI optimism and having effectively made the jump to coding with AI assistants.

> effectively made the jump to coding with AI assistants

I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.

For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful.

But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective.

For other areas of software, they're not nearly as useful.

>I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants.

I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day.

I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions.

Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question.

There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.

As an "older dev" who doesn't like AI, the thing that annoys me most is the UX is horrible. It's like arguing in chat with an extremely overconfident junior dev who isn't capable of learning or improving with time and experience. That's just a miserable way to spend time. I'd rather spend that time thinking clearly about the problem, and then writing down the solution (clearly).

If this thing also conferred an actual productivity advantage that would be one thing, and it might motivate me to get past the horrible UX, but I haven't seen any evidence yet.

I fear the approach that maximises productivity is a literal one-shot approach: Give the LLM one or two shots at generating a somewhat passable first attempt (including all or at least most of the boilerplate) and then strictly fix up stuff yourself. I recently spend a day attempting to build a relatively simple GUI for a project which _maybe_ contains a couple of days of programming work. It got the gist of the GUI basically in one. And the next two or three prompts then added the buttons I wanted. Most of it even worked

But after that we ran into a kind of loop, where you put my feelings into much better words than I could. If I had stopped after iteration 3, I probably would have finished what I wanted to do in half a day