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After years of failed startup ideas, I tried something different. Instead of building on a hunch, I let AI run a full market analysis to find a validated problem worth solving. Here's what I learned and how you might do the same. #buildinpublic

First, a caveat: ChatGPT won't cut it for this. You need an AI that can independently research the web, pull real sources, and come back with evidence -- not just confident-sounding hallucinations. I used Manus AI and gladly paid the $40/month for this.

My initial prompt was simple: "I'm a Ruby developer who wants to build a B2B SaaS. Analyze potential opportunities, validate market need, and recommend ideas with real pain points." Then I pushed it to go deeper on each recommendation.

The AI came back with 7 initial ideas. But I didn't just pick one. I asked it to validate each with real evidence: Reddit threads, G2 reviews, market data. It provided actual links -- not vague claims. I could verify everything myself.

That was actually reassuring. It wasn't just telling me what I wanted to hear. It was giving it to me straight. And that's the benefit of this approach -- no ego, no confirmation bias, just cold, hard research.

One idea kept surfacing: runbook discovery for on-call engineers. The AI found Reddit threads like "Senior SRE who knew all our incident procedures just left -- now we're screwed" and G2 reviews complaining about scattered documentation.

But I didn't stop there. I asked: "Is this even technically feasible?" The AI researched GitHub, Notion, and Confluence APIs -- rate limits, webhooks, authentication. It came back with a full technical feasibility report. Answer: Yes, very feasible.