Topic: How I used Claude to validate my idea in 10 minutes

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A few months back I had like 12 different SaaS ideas scattered across Notion docs and honestly no clue which one people actually gave a shit about

You know the drill - everyone says "talk to your users" and "validate first" but like... where exactly are these mystical users hanging out? And what am I supposed to ask them without sounding like a weirdo with a survey

Did what any rational developer would do - ignored the advice completely and just started building stuff

Built two different projects. First one got exactly 3 signups. Second one never even made it past my localhost because I lost steam halfway through

Classic mistake: I was building solutions to problems I had, not problems other people were willing to pay to solve

Then I got curious about using AI differently. Not for idea generation (because that usually spits out generic nonsense) but for actual market research

Here's what I did:

On Claude, I activated the research option and then prompt it to scrape through real user content - Reddit threads, Quora answers, G2 reviews, anywhere people complain about stuff. Told it to focus on one specific area: "cold email personalization problems"

It came back with this insane 3-page breakdown. Real quotes from sales people bitching about how their templates suck, how manual personalization takes forever, how their reply rates are trash

Then I asked it to rate the opportunity 1-10 based on demand vs competition. Got an 8.5 with solid reasoning about why the market gap exists

That was enough validation for me to actually commit, cause the AI was mainly using the researched data as source of truth, not their knowlege :)

Built Introwarm - you upload your prospect list and it generates personalized email openers by checking what they're posting, reacting to, sharing, etc. online

Soft launched it without any fanfare. Got my first paid customer ($29) in week 2 after launch. Now sitting at $2.3k MRR and growing mostly through cold outreach (yes, using my own tool) and posting in communities like this

What actually worked:

• People are constantly venting online about their problems. That's free market research if you know where to look

• AI can synthesize patterns way faster than manually reading through hundreds of complaints

• You don't need perfect validation - just enough signal to know you're not completely delusional

If you're stuck between ideas, try this instead of endless brainstorming: find where your target users are already complaining and let them tell you what to build

PROMPT

You are my **personal market research assistant**. I'm a solo developer, fully bootstrapped, building B2B or prosumer SaaS tools with a strict infrastructure budget of **$200/month or less**. No big team, no venture capital, just me coding and deploying.

Your job is to **scan the web** for **current, real pain points** that users, developers, or small businesses are struggling with. You can look in forums (Reddit, Hacker News, Indie Hackers, Twitter/X, GitHub issues, niche Discords, Quora), reviews, blog comments, etc.

My main goal is to scale a product from $0 to $10k month and see how it goes from there.

For each opportunity you surface, break it down like this:
1. **Pain Point**: Describe the real-world problem or complaint users are having.
2. **Target Audience**: Who is having this problem? Be specific.
3. **Why It Hurts**: Explain why this problem matters or costs them time, money, or peace of mind.
4. **Tool Idea**: Suggest a simple SaaS or tool I could build to solve it, considering my constraints:
    - Solo dev
    - <$200/month infra
    - MVP in ~2 weeks
5. **Monetization Potential**: Explain how it could realistically make money (subscription, pay-per-use, etc.)
6. **Bonus**: If applicable, mention existing solutions and what sucks about them (pricing, UX, complexity, etc.)

Keep the tone **direct, no fluff**, and prioritize **practicality over theory**. Focus on **problems people are actively complaining about**, not abstract trends or "maybe someday" ideas.

Marketer’s AI Experiments Library