I genuinely don't understand why some people are still bullish about LLMs

Write code to pull down a significant amount of public data using an open API. (That took about 30 seconds - I just gave it the swagger file and said “here’s what I want”)

Get the data (an hour or so), clean the data (barely any time, gave it some samples, it wrote the code), used the cleaned data to query another API, combined the data sources, pulled down a bunch of PDFs relating to the data, had the AI write code to use tesseract to extract data from the PDFs, and used that to build a dashboard. That’s a mini product for my users.

I also had a play with Mistral’s OCR and have tested a few things using that against the data. When I was out walking my dogs I thought about that more, and have come up with a nice workflow for a problem I had, which I’ll test in more detail next week.

That was all whole doing an entirely different series of tasks, on calls, in meetings. I literally checked the progress a few times and wrote a new prompt or copy/pasted some stuff in from dev tools.

For the calls I was on, I took the recording of those calls, passed them into my local instance whisper, fed the transcript into Claude with a prompt I use to extract action points, pasted those into a google doc, circulated them.

One of the calls was an interview with an expert. The transcript + another prompt has given me the basis for an article (bulleted narrative + key quotes) - I will refine that tomorrow, and write the article, using a detailed prompt based on my own writing style and tone.

I needed to gather data for a project I’m involved in, so had Claude write a handful of scrapers for me (HTML source > here is what I need).

I downloaded two podcasts I need to listen to - but only need to listen to five minutes of each - and fed them into whisper then found the exact bits I needed and read the extracts rather than listening to tedious podcast waffle.

I turned an article I’d written into an audio file using elevenlabs, as a test for something a client asked me about earlier this week.

I achieved about three times as much today as I would have done a year ago. And finished work at 3pm.

So yeah, I don’t understand why people are so bullish about LLMs. Who knows?

I use them almost daily in my job and get tremendous use out of them. I guess you could accuse me of lying, but what do I stand to gain from that?

I've also seem people claim that only people who don't know how to code or people doing super simple done a million times apps can get value out of LLMs. I don't believe that applies to my situation, but even if it did, so what? I do real work for a real company delivering real value, and the LLM delivers value to me. It's really as simple as that.

Annoying response of course. But I’d never used an LLM to debug before, so I figured I’d give it a try.

First: it regurgitated a bunch of documentation and basic debugging tips, which might have actually been helpful if I had just encountered this problem and had put no thought into debugging it yet. In reality, I had already spent hours on the problem. So not helpful

Second: I provided some further info on environment variables I thought might be the problem. It latched on to that. “Yes that’s your problem! These environment variables are (causing the problem) because (reasons that don’t make sense). Delete them and that should fix things.” I deleted them. It changed nothing.

Third: It hallucinated a magic numpy function that would solve my problem. I informed it this function did not exist, and it wrote me a flowery apology.

Clearly AI coding works great for some people, but this was purely an infuriating distraction. Not only did it not solve my problem, it wasted my time and energy, and threw tons of useless and irrelevant information at me. Bad experience.