Unless there's a significant sense of what people are working on, and how LLMs are helping -- there's no point engaging -- there's no detail here.
Sure, if your job is to turn out tweaks to a wordpress theme, presumably that's now 10x faster. If its to work on a new in-house electric motor in C for some machine, presumably that's almost entirely unaffected.
No doubt junior web programmers working on a task backlog, specifically designed for being easy for juniors, are loving LLMs.
I use LLMs all the time, but each non-trivial programming project that has to move out of draft-stage needs rewriting. In several cases, to such a degree that the LLM was a net impediment.
I've never been able to get it to work reliably myself either.
The internet just tells me to prompt harder. Lots of "grind-set" mentality energy around AI if you ask me. Very little substance.
You list what look like quite greenfield projects, very self-contained, and very data science oriented. These are quite significantly uncharacteristic of software engineering in the large. They have nothing to do with interacting systems each with 100,000s lines of code.
Software engineers working on large systems (eg., many micro-services, data integration layers, etc.) are working on very different problems. Debugging a microservice system isn't something an LLM can do -- it has no ability, e.g., to trace a request through various apis from, eg., a front-end into a backend layer, into some db, to be transfered to some other db etc.
This was all common enough stuff for software engineers 20 years ago, and was part of some of my first jobs.
A very large amount of this pollyanna-LLM view, which isnt by jnr software engineers, is by data scientists who are extremely unfamiliar with software engineering.
Every codebase I listed was over 10 years old and had millions of lines of code. Instagram is probably the world's largest and most used python codebase, and the camera software I worked on was 13 years old and had millions of lines of c++ and Java. I haven't worked on many self contained things in my career.
LLMs can help with these things if you know how to use them.
That's more a function of your tooling more than of your LLM. If you provide your LLM with tool use facilities to do that querying, i don't see the reason why it can't go off and perform that investigation - but i haven't tried it yet, off the back of this comment though, it's now high on my todo list. I'm curious.
TFA covers a similar case:
>> But I’ve been first responder on an incident and fed 4o — not o4-mini, 4o — log transcripts, and watched it in seconds spot LVM metadata corruption issues on a host we’ve been complaining about for months. Am I better than an LLM agent at interrogating OpenSearch logs and Honeycomb traces? No. No, I am not.
I spent ~4 months using Copilot last year for hobby projects, and it was a pretty disappointing experience. At its best, it was IntelliSense but slower. At its worst, it was trying to inject 30 lines of useless BS.
I only realized there was an "agent" in VS Code because they hijacked my ctrl+i shortcut in a recent update. You can't point it at a private API without doing some GitHub org-level nonsense. As far as my job is concerned, it's a non-feature until you can point it your own API without jumping through hoops.
I absolutely don't, and I'd love if you could highlight a spot where I suggested I was. As I said, the problem isn't that I don't want to try using an agent, the problem is that I can't because one incredibly basic feature is missing from VS Code's agent thing.
I'll occasionally use chatbots, mostly for spitballing non-professional stuff. They seem to do well with ideation questions like "I'm making X, what are some approaches I could take to do Y?" In other words, I've found that they're good at bullshitting and making lists. I like R1-1776, but that's only because Perplexity Playground seems less restricted than some of the other chatbots.
It's also nice for generating some boilerplate bash stuff, when I need that kind of thing. I don't need that very often, though.
What's the implication here? That Thai food was invented 1 year after Chinese food?
