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It's amusing how everyone seems to be going through the same journey.

I do run multiple models at once now. On different parts of the code base.

I focus solely on the less boring tasks for myself and outsource all of the slam dunk and then review. Often use another model to validate the previous models work while doing so myself.

I do git reset still quite often but I find more ways to not get to that point by knowing the tools better and better.

Autocompleting our brains! What a crazy time.

> Break down sessions into separate clear, actionable tasks. Don't try to "draw the owl" in one mega session.

This is the key one I think. At one extreme you can tell an agent "write a for loop that iterates over the variable `numbers` and computes the sum" and they'll do this successfully, but the scope is so small there's not much point in using an LLM. On the other extreme you can tell an agent "make me an app that's Facebook for dogs" and it'll make so many assumptions about the architecture, code and product that there's no chance it produces anything useful beyond a cool prototype to show mom and dad.

A lot of successful LLM adoption for code is finding this sweet spot. Overly specific instructions don't make you feel productive, and overly broad instructions you end up redoing too much of the work.

This matches my experience, especially "don’t draw the owl" and the harness-engineering idea.

The failure mode I kept hitting wasn’t just "it makes mistakes", it was drift: it can stay locally plausible while slowly walking away from the real constraints of the repo. The output still sounds confident, so you don’t notice until you run into reality (tests, runtime behaviour, perf, ops, UX).

What ended up working for me was treating chat as where I shape the plan (tradeoffs, invariants, failure modes) and treating the agent as something that does narrow, reviewable diffs against that plan. The human job stays very boring: run it, verify it, and decide what’s actually acceptable. That separation is what made it click for me.

Once I got that loop stable, it stopped being a toy and started being a lever. I’ve shipped real features this way across a few projects (a git like tool for heavy media projects, a ticketing/payment flow with real users, a local-first genealogy tool, and a small CMS/publishing pipeline). The common thread is the same: small diffs, fast verification, and continuously tightening the harness so the agent can’t drift unnoticed.

Much more pragmatic and less performative than other posts hitting frontpage. Good article.

> At a bare minimum, the agent must have the ability to: read files, execute programs, and make HTTP requests.

That's one very short step removed from Simon Willison's lethal trifecta.

Nice writeup!

For those using Emacs, is there a Magit-like interface for interacting with agents? I'd be keen on experimenting with something like that.

For those wondering how that looks in practice, here's one of OP's past blog posts describing a coding session to implement a non-trivial feature: https://mitchellh.com/writing/non-trivial-vibing (covered on HN here: https://news.ycombinator.com/item?id=45549434)

This seems like a pretty reasonable approach that charts a course between skepticism and "it's a miracle".

I wonder how much all this costs on a monthly basis?

> I'm not [yet?] running multiple agents, and currently don't really want to

This is the main reason to use AI agents, though: multitasking. If I'm working on some Terraform changes and I fire off an agent loop, I know it's going to take a while for it to produce something working. In the meantime I'm waiting for it to come back and pretend it's finished (really I'll have to fix it), so I start another agent on something else. I flip back and forth between the finished runs as they notify me. At the end of the day I have 5 things finished rather than two.

The "agent" doesn't have to be anything special either. Anything you can run in a VM or container (vscode w/copilot chat, any cli tool, etc) so you can enable YOLO mode.

I recently also reflected on the evolution of my use of ai in programming. Same evolution, other path. If anyone is interested: https://www.asfaload.com/blog/ai_use/

I'd be interested to know what agents you're using. You mentioned Claude and GPT in passing, but don't actually talk about which you're using or for which tasks.

I find it interesting that this thread is full of pragmatic posts that seem to honestly reflect the real limits of current Gen-Ai.

Versus other threads (here on HN, and especially on places like LinkedIn) where it's "I set up a pipeline and some agents and now I type two sentences and amazing technology comes out in 5 minutes that would have taken 3 devs 6 months to do".

Good article! I especially liked the approach to replicate manual commits with the agent. I did not do that when learning but I suspect I'd have been much better off if I had.

> a period of inefficiency

I think this is something people ignore, and is significant. The only way to get good at coding with LLMs is actually trying to do it. Even if it's inefficient or slower at first. It's just another skill to develop [0].

And it's not really about using all the plugins and features available. In fact, many plugins and features are counter-productive. Just learn how to prompt and steer the LLM better.

[0]: https://ricardoanderegg.com/posts/getting-better-coding-llms...

Thanks for sharing your experiences :)

You mentioned "harness engineering". How do you approach building "actual programmed tools" (like screenshot scripts) specifically for an LLM's consumption rather than a human's? Are there specific output formats or constraints you’ve found most effective?

There are so many stories about how people use agentic AI but they rarely post how much they spend. Before I can even consider it, I need to know how it will cost me per month. I'm currently using one pro subscription and it's already quite expensive for me. What are people doing, burning hundreds of dollars per month? Do they also evaluate how much value they get out of it?

> babysitting my kind of stupid and yet mysteriously productive robot friend

LOL, been there, done that. It is much less frustrating and demoralizing than babysitting your kind of stupid colleague though. (Thankfully, I don't have any of those anymore. But at previous big companies? Oh man, if only their commits were ONLY as bad as a bad AI commit.)

For the AI skeptics reading this, there is an overwhelming probability that Mitchell is a better developer than you. If he gets value out of these tools you should think about why you can't.

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