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The kinds of detailed (and excellent) plans Opus or Fable can generate on our large code base would take me maybe 1-2 days to work through and they do in 10-20 minutes.

Maybe I spent 2-4 hours reviewing it, checking things with colleagues etc.

Then I press "go" and maybe an hour later I have a tested system ready for manual review.

It's plans are at least as good as any I've seen. Their weakness is if there are unstated assumptions I have about how things need to be done, so most of my time is now getting those assumptions stated properly and then reviewing.

Why wouldn't I use this? It's the best tool I've used in my 30 years of professional programming.

I could do the planning but I don't, for the same reason that I could write the source code but I don't, for the same reason that I could write the machine code but I don't.

This is more or less what I do. Then again, I work on a small parts of the codebase at a time, so maybe the autonomous agent works better when you're doing larger refactors over large codebases.

Even in that situation, I think I would still only feel comfortable approaching the task as I would do it without AI, and using the AI to accelerate the parts that would be time-consuming. E.g. finding where/how feature X is implemented, how it would affect the overall system if I were to change it this way, etc.

whatever you delegated in the past probably also required planning by the engineer that went down and got it done, most planning done by agents is at this same level, agent explores the codebase, understands where to touch, tradeoffs, code-level architecture, and ask the user for more context or balance with assumptions and other patterns already present in code

People get defensive when you ask this, because the they think you’re saying they’re being lazy.

…but it’s than just that (in most cases; I am just lazy sometimes); but fundamentally there’s a limit to how much complexity people can comprehend.

We are good at working at high level abstractions, modules with clear apis that can be sprung to together into some kind of feature.

You don’t need to look inside the black box of the module if you trust the implementer; Ive never opened up the internals of a calendar be like “how does this work?”. I just don’t care. It’s a calendar. I use the api.

I think most people are using these tools in this way; very few people are having an agent write a plan, then a sub agent review it, no human in the loop. Those are for prototypes and are yolo cowboys using open claw and playing with the phones instead of working; we have a few at work, but their PRs are regularly rejected as slop.

…but, realistically; many people aren’t software architects. They may not even know coding patterns, forget architecture patterns.

Having an agent spit out generic software architecture is probably better than what they were producing before.

Writing a module / feature using generic architecture and planning is probably better than random code spaghetti right?

It’s easy to lament the loss of craft here, but at the end of the day, the models today do an ok job of this. The models of tomorrow will probably be better at it than many people.

Architecture is easy composed to actually implementing things. You just wave your hands from your ivory tower and say “more event sourcing”.

I find it hard to agree with the point that the content quality is low. Of course, that design does have some issues. But it is still valuable and worth reading.

The strengths are that the design forces Chain of Thought as a memory buffer and the TODO list in an FSM style. I think those are fine. The recovery strategy is also pretty good.

However, the problem is that the business logic does not run as Python code but lives inside the prompt. And it does not support parallel execution. But as a single run script, it is helpful enough for understanding the concept.

Of course, if I were to do the code properly, I would use a separate storage instead of in memory, and more carefully verify tool constraints and the actual scope limitations of the tools. But still, I think this is helpful enough.

I agree with 1, same for substack. bearblog seems cool tho

I don't think the content is low quality, though.

Seems odd that it would get upvoted to the front page then in the first place?

What a strange comment.

The original post is also available at the poster’s own blog [1], so the question is a very valid one. Clearly, “posting articles for free” is a hurdle already cleared by the author.

[1] https://www.ruxu.dev/articles/ai/build-an-ai-agent-planning/

Since the migration from setuptools -> poetry -> uv -> full Rust, I think my computer burns up less energy (not to mention all the CI/CD pipelines) from running slow tools over and over. So that's a win for Rust there.

The point they were making sarcastically is that this, doesn’t.

> great Rust is (which it is)

They just took undefined behaviour and called it unsafe. Theyve not really solved anything. Even their own std lib has security bugs in unsafe code.

And their only ever retort is "there are thousands of these bugs a day in c code"... Let's wait until rust gets used seriously in the systems and embedded space first, no point comparing c to minnows like rust when it comes to total cves.

Because it's more fun?

Being serious this is a silly line of reasoning. Maybe they are both bad? It's like asking why is it bad to light a forest fire when there is a forest fire already burning.

I take issue with the cognitive dissonance too though. HN became very hostile to Bitcoin but took no issue with people gaming on PCs and consoles that were consuming more and more electricity each year. Now everyone is silent on all fronts because LLMs make their job easy and gives them something new and interesting to play with.