I've been frustrated with Copilot in this regard.

I work on a large C++ codebase, with large files. Human developers jump around between files with the Visual Studio fuzzy search, set breakpoints to trace execution in the Debugger, use the IDE's refactoring tools.

Microsoft's answer to this was to just ... expose none of this to their Agent Mode!? Replace the working semantic autocomplete with fucking lies!?

Maybe it's changed, I haven't been paying that much attention after bouncing off of this. I've gotten mild acceleration from using gptel-mode in emacs, manually adding references to context, and having models do various mechanical transformations on code. And I've even had some limited success writing tools for it to do LSP lookups.

I recently saw a Claude skill that used Claude, with no tools, as a spell checker.

I wanted to hurl my laptop out to the window.

Something something bitter lesson blah blah

I think the bitter lesson is severely misapplied in the current situation: If progress from "just add more resources" is very slow, and a huge amount of money is at stake, continous work on hand-engineering can give a continuous and very valuable competitive advantage.

The labs all seem to be going for AGI through bigger LLMs, and I am reasonably sure that it's not going to happen like that.

Hey man, speak for yourself.

It's never occurred to me to even try getting an LLM to design or layout a circuit for me.

Instead, I have dozens or hundreds of chats in my history where I debate the merits of different parts for different tasks and scenarios, the nuances of decoupling strategies (package size vs deregulation), work out resistor network ratios from the reels I have on hand.

Then being able to feed an LLM a datasheet and have it write a custom driver against the registers I need so that it does exactly what I want without the cognitive overhead of a buggy package with someone else's strong opinions about how a part should be used is amazing.

Frontier models are incredibly good at electronics, and it's got nothing to do with what happens inside the EDA.

A few days ago someone on HN commented that a teammate uses Claude to search for text in files on their own computer. Buddy... There's Command-line Tools Can Be 235x Faster Than Your Hadoop Cluster and then there's Command-line Tools Can Be ∞ Faster Than Your AI.

Way too much engineering effort to make something that might get leapfrogged by the next gen LLM.

It's a tantalizing thing, but far too treacherous to actually go for it, most of the time.

> nobody [wants to use AI] to augment already working solutions

Plenty of people do, but that only produces a blog post that will get you to the front page of HN. If you want VCs to drop $40M on your head, you need to pretend to reinvent the world.

Then, to further appease the rain gods, you need to sue the bloggers on the front page of HN who are challenging your world-changing narrative. Which will, heh, drop you on the front page of HN.

Our community is, literally, eating itself at this point. There was a time when we actually took "make something people want" literally. Now it's just part of the fiction.

If its any consolation: once we've burnt the last crumb of coal, the last drop of oil and last bit of natural gas to fuel the AI overlords, that particular problem will take care of itself.

> augment already working solutions

That's exactly how I use it, but I'm just a geezer on his own, writing free software for people that can't pay for it.

Annoying, but not surprising.

The future is using AI to do everything, and nobody gets funded saying they're taking a small step forward.