161–170 of 420 posts
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 m…
So does everyone just run with giving full permissions on Claude code these days? It seems like I’m constantly coming back to CC to validate that it’s not running some bash that’s going to nuke my system. I would love to be able to fully step away but it feels like I can’t.
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?
As long as we're on the same page that what he's describing is itself a miracle .
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> Meanwhile AI can't be trusted to give me a recipe for potato soup. Because there isn’t a canonical recipe for potato soup.
That is not the issue, any potato soup recipe would be fine, the issue is that it might fetch values from different recipes and give you an abomination.
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"When was the last time you reviewed the machine code produced by a compiler?" Compilers will produce working output given working input literally 100% of my time in my career. I've never personally found a compiler bug. Meanwhile AI can't be trusted to give me a recipe for potato soup. That is to say, I would under no circumstances blindly follow the output of an LLM I asked to make soup. While I have, every day of…
This is obviously besides the point but I did blindly follow a wiener schnitzel recipe ChatGPT made me and cooked for a whole crew. It turned out great. I think I got lucky though, the next day I absolutely massacred the pancakes.
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This is actually an aspect of using AI tools I really enjoy: Forming an educated intuition about what the tool is good at, and tastefully framing and scoping the tasks I give it to get better results. It cognitively feels very similar to other classic programming activities, like modularization at any level from architecture to code units/functions, thoughtfully choosing how to lay out and chunk things. It's always b…
"Become better at intuiting the behavior of this non-deterministic black box oracle maintained by a third party" just isn't a strong professional development sell for me, personally. If the future of writing software is chasing what a model trainer has done with no ability to actually change that myself I don't think that's going to be interesting to nearly as many people.
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Whether it's interesting or not is irrelevant to whether it produces usable output that could be economically valuable.
Yeah, still waiting for something to ship before I form a judgement on that
I've been building systems like what the OP is using since gpt3 came out. This is the honeymoon phase. You're learning the ins and outs of the specific model you're using and becoming more productive. It's magical. Nothing can stop you. Then you might not be improving as fast as you did at the start, but things are getting better every day. Or maybe every week. But it's heaps better than doing it by hand because you…
First off, appreciate you sharing your perspective. I just have a few questions. > I've gone back to managing the context window in Emacs because I can't be bothered to learn how to deal with another model family that will be thrown out in six months. Can you expand more on what you mean by that? I'm a bit of a noob on llm enabled dev work. Do you mean that you will kick off new sessions and provide a context that yo…
While I also use LLMs in other ways, this is my core workflow. I quickly get frustrated when I can't _quickly_ modify the context.
If you have some mastery over your editor, you can just run commands and post relevant output and make suggested changes to get an agent like experience, at a speed not too different from having the agent call tools. But you retain 100% control over the context, and use a tiny fraction of the tokens OpenCode and other agents systems would use.
It's not the only or best way to use LLMs, but I find it incredibly powerful, and it certainly has it's place.
A very nice positive effect I noticed personally is that as opposed to using agents, I actually retain an understanding of the code automatically, I don't have to go in and review the work, I review and adjust on the fly.
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"Become better at intuiting the behavior of this non-deterministic black box oracle maintained by a third party" just isn't a strong professional development sell for me, personally. If the future of writing software is chasing what a model trainer has done with no ability to actually change that myself I don't think that's going to be interesting to nearly as many people.
It sounds like you're talking more about "vibe coding" i.e. just using LLMs without inspecting the output. That's neither what the article nor the people to whom you're replying are saying. You can (and should) heavily review and edit LLM generated code. You have the full ability to change it yourself, because the code is just there and can be edited!