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neomHNSource author
Business & incomeExperience excerpt · English

For fun last month I decided to see if i could build a fully functional business of agents

For fun last month I decided to see if i could build a fully functional business of agents. It's 175 python files (employees) build up of internal roles within those files (tasks). So what I have is 175 employees who are able to pass output around each other, understand the work, complete the work, understand where to send the output. The whole system has the ability to do around 275 base processes (same as a business at > 100MM arr) I started on a Friday afternoon and slept a little bit and finished on Monday afternoon. After I had it running I sent it to a VC friend to show them and they sent back the deck of a startup that is in stealth with $25MM doing it the exact same way. With 1 month and a designer and an engineer, I could have it mvp functional for anyone to use ($40k?). Times are changing. Here is kinda how it looks: https://s.h4x.club/9ZuO4XQR / https://s.h4x.club/jkuB8ZED (I've evolved it a little since this, and if you're an engineer and look at my files and think, this guy is a moron: I know!:))

Original discussionSurprisingly fast AI-generated kernels we didn't mean to publish yet217 source comments · at import
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MI
miki123211HN

I think how the authors of this post think about "AI agents" is really interesting. Most people think of agents like they think of human employees. They set up a limited number of agents to run in parallel (often just one), with each agent running in a loop and doing one task at a time. They're still in a world where you have a fixed (on the timescale of hours or days) number of employees, each employee can only do one thing at a time, and transferring tasks between employees is slow and costly. LLMs don't really work like that. You effectively have an infinite number of agents that you can conjure out of thin air at any time. There's no cost advantage to performing LLM requests in series rather than in parallel. If you realize this, the pattern of each agent fanning out and forking itself into as many sub-agents as are needed to fulfill the task becomes obvious. This is exactly what the…

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LI
literalAardvarkHN

Engineers who would judge someone's frontier MVP like that are not even worth worrying about. This is epic work. Would love to see more of it but I guess you're gonna take it the startup route since you have connections. Best of luck.

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WS
wslhHN

Your message doesn't make it clear what those 175 employees can realistically accomplish on their own. For instance, you might have an SEO expert on the team, but that alone won't guarantee top search engine rankings. There are countless SEO professionals and tools (human or AI-powered), and even having the best one doesn't eliminate the underlying challenge: business competition. LLMs, like any other tool, don’t solve that fundamental problem.

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YU
yusinaHN

> understand the work LLMs don't understand . It's mind-boggling to me that large parts of the tech industry think that. Don't ascribe to them what they don't have. They are fantastic at faking understanding. Don't get me wrong, for many tasks, that's good enough. But there is a fundamental limit to what all this can do. Don't get fooled into believing there isn't.

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IM
immibisHN

Does this experiment do anything useful or does it just soak up investor money? Not that there's anything wrong with the latter.

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AC
acchowHN

> The whole system has the ability to do around 275 base processes It’s incredibly easy to get LLMs to do a lot of stuff that seems convincing. They are literally trained for plausibility.

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RO
robbomacraeHN

Is anyone else annoyed that VC's are out there sharing decks of startups in stealth with potential competitors? How often does this happen?

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IA
iammrpaymentsHN

Sounds really interesting but I have no idea what’s the purpose of having 175 “employees” here? Maybe it is a smart way to sell the idea you’re going to replace 175 people if you buy the product? Could just buy chatgpt instead I guess, but a chatbot doesn’t sound as cool as 175 employees.

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JP
jprokay13HN

I’ve been floating around a similar set of ideas and it’s been very fun (if not all that useful yet) to build Did you try taking it one step further where a “recruiter” has to hire the engineers after a screening process? I wonder if this could get you even better AI engineers

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