Comment by zachwills | Hacker News Reader

Comment by zachwills | Hacker News Reader

Hey HN, author here.

I spent last week in a deep-dive experiment to see how far I could push modern agentic workflows on a greenfield project. I wanted to move past simple code generation and see if I could build a system where I was orchestrating a team of agents to build a full application.

The results were pretty wild (~800 commits, 100+ PRs, and a functioning app we use internally at my company), but the most interesting part was the playbook of rules I had to develop to make it work. The post covers the 8 rules I learned, from managing the AI's context window with sub-agents and manual checkpoints, to creating autonomous test loops, to why I had to become ruthless about restarting failed runs.

A few quick notes to preempt questions:

Tech Stack: The core of this was Claude Code, a custom parallelization script, and open-source MCPs like Serena.

Cost: The token cost was significant (~$6k). This was an experiment to push the limits, not to optimize for cost efficiency... yet.

Effort: This was not a standard 40-hour week. It was an intense, "in the hole" sprint with a very high cognitive load.

I’m convinced the role of an engineer is shifting from a hands-on coder to an architect of these intelligent systems. I’m curious to hear how others are approaching this. What workflows or tools for managing agents have you found to be effective?

> The results were pretty wild (~800 commits, 100+ PRs, and a functioning app we use internally at my company)

I'd be very interested to hear less about the process, and more about the app itself.

If a week "in the hole" resulted in a Slackbot that greets people when they enter a Slack channel, I'd be significantly less impressed than if you'd built, say, a CI pipeline, or something that automatically creates Jira tickets based on outages, or automatically handles subscription renewals or something.

Plus, the number of commits & PRs is absolutely not a useful metric. How well is the app running? How many bugs are you finding day to day? How much functionality is missing, how easy is it to add new functionality based on user feedback, etc? Monitoring?

Great points -- thanks for contributing to the discussion. It's an internal app with auth, ties to LLMs for analysis, ties to 3rd party data sources for aggregation of data, etc. It's not a Slackbot! It's actually something that we can use instead of paying ~$30k/yr (what we were quoted for similar products by vendors). I'd consider it an alpha release in it's current form.

> Great points -- thanks for contributing to the discussion.

Again, like my other comment, I'm not being snarky - but it feels weird and inauthentic to be thanked for contributing to the discussion, and sounds like you ran my comment through ChatGPT as well and asked it to post a response.

But the end result indeed sounds like more than just a trivial app, so I am genuinely impressed it's working well. (I'd love to see how long it would take another team of engineers, or another single engineer I guess, to re-create with the same functionality.)

Haha, I know what you mean. I just believe in being civil. Some of the comments on here are just rude; so I am grateful when people are constructive or generally active in the discussion vs. random hate or bashing.

I'm not entirely convinced that this entire thing isn't an experiment at throwing a complete dev / release cycle at some AI engine in some sort of weird marketing ploy.

"See, we built, announced and marketed an entire piece of software from our CLI prompt!"

Good gravy.

Meh, maybe if I was selling something. I have nothing to sell. Just sharing my thoughts from going heads-down for a week and trying a new workflow.

> I’m convinced the role of an engineer is shifting from a hands-on coder to an architect of these intelligent systems.

I think this is what a lot of people do and don't understand. The AI is only as good as your ability to architect the software. I think I'd be okay if someone wrote AI generated code if they hand wrote all unit testing to ensure that the AI isn't producing garbage, I think this is a reasonable trade.

Agreed 100%!

It's cool to hear about the process, but I'd be much more interested to see it in action. You mentioned this was for a work project, so I doubt you can record yourself live working on it, but is there an adequately innocent proxy scenario you could gin up to demonstrate working on this for a shareable live demo? (Or even just screenshots)

I think I will build something open source or shareable to get around this. I understand now folks really want to / need to see the thing to get the most out of the learnings I share.

$6K/week is a senior engineer's salary. How long would it have taken a crack coder to do the work?

Good question, potentially they could have done the same. I'm not an IC day-to-day, so this was me getting back in the saddle. I imagine someone who is currently a high-level IC using the approaches I did could have accomplished even more.

So where is the app?

From another comment: "It's an internal app with auth, ties to LLMs for analysis, ties to 3rd party data sources for aggregation of data, etc. It's not a Slackbot! It's actually something that we can use instead of paying ~$30k/yr (what we were quoted for similar products by vendors). I'd consider it an alpha release in it's current form."

Ask yourself what happens when Claude releases Claude Code 2.0, which just uses AI to self-manage the swarms from a simpler prompt.

You would do the same thing with less work. Progress! If only, though; I find it easy to stump even the best model on brownfield code bases.

Yea, generally I think humans just move farther to the right. Higher leverage. Work on the stuff only you can work on.

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