I got into 30 days in a row of product releases using AI agents. Here are some honest insights.

For some context. It is my side project, I built a Cursor for product management, solo. I spent about ~2.5 hours per day. Also, I have a rich development background, and before the challenge started, I already had the basic app (which I would call MVP). So my goal was to get closer to my vision of how the product should look in production.

Feature creep gets real, as always.

Every new feature brings edge cases – obvious, sure. But when AI helps you ship features like a factory, the mess accumulates faster than you'd expect. These aren't isolated tools; they affect each other in weird ways. UX consistency breaks. Things that worked last week don't anymore.

Here's something I didn't expect: You get stuck, not because the LLM is bad or because you've run out of ideas. You get stuck because the product became complex, and now you actually have to think about it. The further you go, the more thinking is required. What a surprise (not really).

AI agents are not so autonomous.

LLMs have improved in terms of hallucinations. They rarely make stuff up nowadays. However, they easily wander off in the wrong direction or get stuck in infinite reasoning loops. When building a product, not a demo, trade-offs pile up. Those require a human touch.

You can't vibe code if you care.

If reliability and maintainability matter to you, or if you ever want to scale, tech debt will sneak in, even with a solid initial architecture. Yes, there are ways to combat it: rules, declarations, specific agents, and skills. But, without engineering instincts, keeping all of this under control becomes a job in itself—and not a fun one.

The quality definition is still yours.

This one's simple, but not easy. You can't hand off the definition of quality to an LLM. What constitutes quality depends on your product's unique features, user context, and trade-offs. You can't delegate that part.

What surprised me?

• Test writing is almost excellent. You can plan and generate tests with Playwright (with MCP), and finally start applying TDD if you like it, with almost no costs.
• Quick automations with embedded tooling are great (hmm… in the past days we used to call these macros). You can ask an LLM to build skills for repetitive, complex tasks, and it will. So, your productivity increases day by day, continuously.
• UI generation has become genuinely cool. You still iterate as you did with designers, but the speed is unmatched. You can generate near-production-ready UIs in a couple of hours to collaborate around, get new ideas, and agree on the final look. This part has really changed.

The "software development is cooked" fantasy

If you buy into this narrative, great! You just have to think about architecture, infrastructure, QA, scaling, maintenance, bug fixes, UI/UX, security, and integrations. And don't forget vision, strategy, marketing, and legal matters.

Can AI answer all these questions and build it for you? Sure, but this isn't copycat work like boring boilerplate code. Your product has specifics. All of these decisions require the full context. You must think about it and decide exactly what you need.

Imagine being the CEO, CPO, and CTO all at once. Easy job? (Spoiler: No.)

AI tooling differs more than it looks.

I tried several tools this month - Cursor, Codex, Claude Code, and Kilo Code. I think it’s better to write another post about it, but I can say that there is no magic; they can do a lot, but with each one, you have to master its usage.

The Monthly Arc

The daily contribution is fun for the first week. It gets hard after the second week—consistency drains you, and the product becomes more complex. By the end of the month, you're left with a pile of questions about how to maintain reliability.

However, there's an upside: since it's like an evolutionary game in real time, you experience scaling pitfalls in weeks instead of months or years. This allows you to develop ideas faster about how to create a sustainable product. Whether that's worth the chaos is still up for debate.

Marketing unlocks differently.

When you truly take ownership of your product with this AI exoskeleton, you can experiment with proposals more quickly. You have real estimates now, so you know the implementation cost in days or hours instead of hearing, "We'll see in a few sprints." The feedback loop closes much faster.

"Fake it till you make it" can work better because the faking part is eliminated. You can responsibly make promises and deliver value while the new customer is still going through onboarding. That's something.

So, can you build a product by yourself in a month?

Yes.

But will this product survive the next month or year?