> Do you hear that noise in the distance? It’s me sigh-ing. (...) Yes, maintaining good documents for specific tasks is a good idea. I keep a big list of useful docs in a docs folder as markdown.
I'm not that familiar with Claude Code Plugins, but it looks like it allows integrations with Hooks, which is a lot more powerful than just giving more context. Context is one thing, but Hooks let you codify guardrails. For example where I work we have a setup for Claude Code that guides it through common processes, like how to work with Terraform, Git or manage dependencies and the whitelisting or recommendation towards dependencies. You can't guarantee this just by slapping on more context. With Hooks you can both auto-approve or auto-deny _and_ give back guidance when doing so, for me this is a killer feature of Claude Code that lets it act more intelligently without having to rely on it following context or polluting the context window.
Cursor recently added a feature much like Claude Code's hooks, I hope to see it in Codex too.
LLMs struggle with simplicity in my experience, so they struggle with the first step. They also lack the sort of intelligence required to understand (let alone evolve) the system's design, so they will struggle with the second step as well.
So maybe what's meant here is not refactoring in the original meaning, but rather "cleanup". You can do it in the original way with LLMs, but that means you'll have to be incredibly micro manage-y, in my experience. Any sort of vibe coding doesn't lead to anything I'd call refactoring.
I think a lot of this is because people (and thus LLMs) use verbosity as a signal for effort. It's a very bad signal, especially for software, but its a very popular signal. Most writing is much longer than it needs to be, everything from SEO website recipes, consulting reports, and non-fiction books. Both the author and the readers are often fooled into thinking lots of words are good.
It's probably hard to train that out of an LLM, especially if they see how that verbosity impressess the people making the purchasing decisions.
If the point is that you can't solve with AI what you messed up with AI, but with human intelligence spending a bit more time on the problem does indeed tend to help, you need to explain why his technique with the AI won't work either.
Plus he's adding human input to it every time, so I see no reason to default to "it wouldn't work".
For example, I have some code which is a series of integrations with APIs and some data entry and web UI controls. AI does a great job, it's all pretty shallow. The more known the APIs, the better able AI is to fly through that stuff.
I have other code which is well factored and a single class does a single thing and AI can make changes just fine.
I have another chunk of code, a query language, with a tokenizer, parser, syntax tree, some optimizations, and it eventually constructs SQL. Making changes requires a lot of thought from multiple angles and I could not safely give a vague prompt and expect good results. Common patterns need to fall into optimized paths, and new constructs need consideration about how they're going to perform, and how their syntax is going to interact with other syntax. You need awareness not just of the language but also the schema and how the database optimizes based on the data distribution. AI can tinker around the edges but I can't trust it to make any interesting changes.
300k LOC is not particularly large, and this person’s writing and thinking (and stated workflow) is so scattered that I’m basically 100% certain that it’s a mess. I’m using all of the same models, the same tools, etc., and (importantly) reading all of the code, and I have 0% faith in any of these models to operate autonomously. Also, my opinion on the quality of GPT-5 vs Claude vs other models is wildly different.
There’s a huge disconnect between my own experience and what this person claims to be doing, and I strongly suspect that the difference is that I’m paying attention and routinely disgusted by what I see.
I think it's good to keep up with what early adopters are doing, but I'm not too fussed about missing something. The plugins is a good example: A few weeks ago there was a post on HN where someone said they are using 18 or 25 or whatever plugins and it's the future, now this person says they are using none. I'm still waiting for the dust to settle, I'm not in a rush.
The trick is to create deterministic hurdles the LLM has to jump over. Tests, linting, benchmarks, etc. You can even do this with diff size to enforce simpler code, tell an agent to develop a feature and keep the character count of the diff below some threshold, and it'll iterate on pruning the solution.
Here's how the article starts: "Agentic engineering has become so good that it now writes pretty much 100% of my code. And yet I see so many folks trying to solve issues and generating these elaborated charades instead of getting sh*t done."
Here's how it continues:
- I run between 3-8 in parallel
- My agents do git atomic commits, I iterated a lot on the agents file: https://gist.github.com/steipete/d3b9db3fa8eb1d1a692b7656217...
- I currently have 4 OpenAI subs and 1 Anthropic sub, so my overall costs are around 1k/month for basically unlimited tokens.
- My current approach is usually that I start a discussion with codex, I paste in some websites, some ideas, ask it to read code, and we flesh out a new feature together.
- If you do a bigger refactor, codex often stops with a mid-work reply. Queue up continue messages if you wanna go away and just see it done
- When things get hard, prompting and adding some trigger words like “take your time” “comprehensive” “read all code that could be related” “create possible hypothesis” makes codex solve even the trickiest problems.
- My Agent file is currently ~800 lines long and feels like a collection of organizational scar tissue. I didn’t write it, codex did.
It's the same magical incantations and elaborated charades as everyone does. The "the no-bs Way of Agentic Engineering" is full of bs and has nothing concrete except a single link to a bunch of incantations for agents. No idea what his actual "website + tauri app + mobile app" is that he build 100% with AI, but depending on actual functionality, after burning $1000 a month on tokens you may actually have a fully functioning app in React + Typescript with little human supervision.
Yeah at this point you could hire a software developer.
Though I'm aligned that I don't (yet) believe in this "AI writes all my code for me" statements.
I'll give codex a try later to compare.
Recently I’ve been using Claude for code gen and codex for review.
I keep trying to use Gemini as it is so fast, but it is far inferior in every other way in my experience.
If you're going to use AI like that, it's not a clear win over writing the code yourself (unless you're a mid programmer). The whole point of AI is to automate shit, but you've planted a flag on the minimal level of automation you're comfortable with and proclaimed a pareto frontier that doesn't exist.
And does it require auth? How is that spec’d out and validated? What about RBAC or anything? How would you even get the LLM to constantly follow rules for that?
Don’t get me wrong these tools are pretty cool but the old adage “if it sounds too good to be true, it probably is” always applies.
1. Note the discussion of plan-driven development in the claude code sections (think: plan = granular task list, including goals & validation criteria, that the agent loops over and self-modifies). Plans are typically AI generated: I ask it to do initial steps of researching current patterns for x+y+z and include those in the steps and validations, and even have it re-audit a plan. Codex internally works the same, and multiple people are reporting it automates more of this plan flow.
2. Working with database for tasks like migrations is normal and even better. My two UIs are now the agent CLI (basically streaming AI chat for task list monitoring & editing) and GitHub PR viewer: if it wasn't smart enough to add and test migrations and you didn't put that into the plan, you see it in the PR review and tell it to fix that. Writing migrations is easy, but testing them is annoying, and I've found AI helping write mocks, integration tests, etc to be wonderful.
A simple task that would have taken literally no more than 2 minutes in Claude Code is, as of now, 9m+ and still "inspecting specific directory", with an ever increasing list of read files, not a single line of code written.
I might be holding it wrong.
With one hour of experience of Codex CLI, every single prompt - even the most simple ones - are 5+ minutes of investigation before anything gets done. Unbearable and totally unnecessary.
But. Sometimes when I see someone talking about cranking out hundreds of thousands of lines of vibe coded apps, I go watch their YouTube videos, or checkout their dozens of unconnected, half finished repos.
Every single time I get a serious manic vibe.
I dunno. People say these tools trigger the gambling part of your brain. I think there is a lot of merit to that. When these tools work (which they absolutely do) it’s incredible and your brain gets a nice hit of dopamine but holy cow can these tools fail. But if you just keep pulling that lever, keep adding “the right” context and keep casting the right “spells” the AI will perform its magic again and you’ll get your next fix. Just keep at it. Eventually you’ll get it.
Surely somebody somewhere is doing brain imagery when using these tools. I wouldn’t be surprised to see the same parts of the brain light up as when you play something like Candy Crush. Dig deep into the sunk cost fallacy, pepper with an illusion of control and that glorious “I’m on a roll” feeling (how many agents did this dude have active at once?) and boom…
I mean read the post. The dude spends $1000/mo plugging tokens into a grid of 8 parallel agents. They have a term for this in the gaming industry. It’s a whale.
I am in a mood where I find it excessively funny that, all that talk about AI, agents, billions of dollars, tera-watts/-hours spent, and people still manage to publish posts with the "its/it's" mistake.
(I am not a native English speaker, so I notice it at a higher rate than people who learned English "by ear".)
Maybe you don't care or you find it annoying to have it pointed out, but it says something about fundamentals. You know, "The way you do one thing is the way you do all things".
But OP isn't native either. He's Austrian.
In the picture right at the top of the article, the top of the bell curve is using 8 agents in parallel, and yada yada yada.
And then they go on to talk about how they're using 9 agents in parallel at a cost of 1000 dollars a month for a 300k line (personal?) project?
I dunno, this just feels like as much effort as actually learning how to write the code yourself and then just doing it, except, at the end... all you have is skills for tuning models that constantly change under you.
And it costs you 1000 dollars a month for this experience?
squirrel•8h ago
throw-10-13•6h ago
That and testing/reviewing the insane amounts of ai slop this method generates.
XenophileJKO•6h ago
So I might tell one to look back in the git history to when something was removed and add it back into a class. So it will figure out what commit added it, what removed it, and then add the code back in.
While that terminal is doing that, on another I can kick off another agent to make some fixes for something else that I need to knock out in another project.
I just ping pong back to the first window to look at the code and tell it to add a new unit test for the new possible state inside the class it modified and I'm done.
I may also periodically while working have a question about a best practice or something that I'll kick off in browser and leave it running to read later.
This is not draining, and I keep a flow because I'm not sitting and waiting on something, they are waiting on me to context switch back.