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Finding Your Next Micro-SaaS Idea on Hacker News, Case Study
TimoCodes
July 29, 2025
In partnership with
Every founder faces the same paralyzing question: Is my idea a vitamin or a painkiller? We're told to build painkillers, solutions to urgent, painful problems that customers are desperate to solve.
But where do you find these problems?
You could spend months on customer interviews, or you could go where the pain is expressed daily, in public, by some of the world's most discerning tech users: communities like Hacker News.
The challenge is that Hacker News is a firehose of information. Manually sifting through thousands of comments for a glimmer of opportunity is a full-time job. But what if you could deploy an AI analyst to do the heavy lifting? By combining strategic searches with the power of modern AI, you can transform these noisy forums into a high-signal, validated pipeline of business ideas. It's not just about data mining; it's about strategic market intelligence to de-risk your next venture before you write a single line of code.
The Process: From Noise to Signal
For a recent project, I set out to test this exact methodology. My goal was simple: uncover validated, commercially viable business opportunities directly from the Hacker News community.
Instead of passively browsing, I deployed a targeted approach AI Agent to the job. It searched the archives for high-signal phrases that explicitly indicate user frustration or desire. These weren't random keywords; they were conversational triggers for problems:
"pain points""I wish there was""problem I have""how do you solve"
The raw output was a collection of hundreds of comments, threads, and "Ask HN" posts. This is where the second stage of AI comes in: synthesis. We fed this raw data into a large language model (LLM) tasked with acting as a market researcher. It categorized the findings, identified recurring themes, and validated the authenticity of each pain point.
The results were awesome. I surfaced acute problems like the "AI Tooling Production Gap," where developers love AI assistants in demos but can't trust them in production. I found a developer who earned over 70 paid customers in 90 days simply by building a SaaS boilerplate that solved common workflow frictions. I even saw a hardware project for a mosquito-killing drone born from a real-world frustration. These weren't guesses; they were validated needs.
Actionable Prompts for Your Own Research
You can replicate and adapt this process for your own niche. Here are copy-pasteable prompts designed for both high-level planning with an AI agent and deep-dive analysis with an LLM.
AI Agent Prompts (For Strategic Planning)
These prompts are designed for an AI agent capable of multi-step execution, like a custom GPT or a tool like Manus. or Flowith
The Data Collection Agent
This prompt automates the initial intelligence-gathering phase across multiple communities. (Add in your niche suitable Keywords etc.)
ROLE: Market Research Analyst
OBJECTIVE: Identify potential micro-SaaS opportunities by collecting user-expressed pain points from online tech communities over the last 30 days.
SCOPE:
1. Communities to Scan:
- Hacker News (news.ycombinator.com)
- Reddit: r/SaaS, r/Entrepreneur, r/programming
2. Search Keywords:
- "pain point" / "pain points"
- "I wish there was" / "I wish I had"
- "frustrating that" / "annoying that"
- "how do you handle" / "how do you solve"
- "the problem I have is"
3. Output Format:
- For each finding, create a structured JSON object containing:
{
"source_url": "[URL of the comment or post]",
"community": "[Name of the community, e.g., 'Hacker News']",
"quote": "[The direct quote expressing the pain point]",
"date": "[Date of the post]"
}
- Compile all findings into a single JSON array.
Here are some other subreddits you should look into
The Synthesis & Ideation Agent
This prompt takes the raw data from the first agent and transforms it into a strategic report.
ROLE: Product Strategist
OBJECTIVE: Analyze the provided JSON data of user pain points and synthesize it into a report identifying the top 3 most promising micro-SaaS opportunities.
INPUT: A JSON array of user-expressed pain points.
PROCESS:
1. Cluster & Theme: Group the raw quotes into recurring themes or problem categories (e.g., "Developer Tooling," "Project Management Friction," "Compliance Issues").
2. Prioritize: Rank these themes based on frequency, intensity of language (e.g., "nightmare," "hate," "impossible"), and implied willingness to pay.
3. Ideate Solutions: For the Top 3 themes, brainstorm a specific micro-SaaS solution for each.
4. Generate Report: Create a markdown report with the following structure for each of the Top 3 opportunities:
- ## Pain Point Theme: [Name of the theme]
- ### Summary of Problem: [A 2-3 sentence summary of the core problem.]
- ### Validating Quotes: [List up to 5 of the most powerful quotes from the input data.]
- ### Micro-SaaS Idea:
- Product Name: [A catchy, descriptive name.]
- Core Value Proposition: [A single sentence explaining the primary benefit.]
- Target Audience: [e.g., 'Solo developers,' 'Non-technical founders,' 'UX designers.']
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LLM Deep-Search Prompts (For Direct Analysis)
Use these prompts directly in a powerful LLM like Claude 4, Perplexity, or Grok4 to analyze specific threads or text.
Pain Point Extraction from Text
Paste the text of a comment thread below this prompt to pull out the gold.
You are a senior market researcher. Your task is to analyze the following text from an online discussion.
Extract every direct statement that indicates a problem, pain point, frustration, or unmet need. Ignore solutions or general chatter.
Present the output as a bulleted list of direct quotes.
[Paste the comment thread text here]
Solution Brainstorming
Take the bulleted list from the previous prompt and use it as input for this one.
You are an experienced startup founder. Based on the following list of validated user pain points, brainstorm 5 distinct micro-SaaS product ideas.
For each idea, provide:
1. A one-sentence value proposition.
2. The primary target user.
3. A key feature that directly solves one of the quoted pains.
Pain Points:
[Paste the bulleted list of pain points here]
Stop Guessing, Start Listening
The difference between a successful product and a failed one often comes down to solving a real, validated problem. Communities like Hacker News are a living focus group, constantly broadcasting the needs of the market. By leveraging AI as your research partner, you can move past guesswork and build with the confidence that comes from listening at scale.
The next great micro-SaaS idea isn’t locked in a brainstorming session. It’s already out there, written as a frustrated comment on a forum. Go find it.
P.S. Forward this to one smart, quiet builder who’s tired of being told to keep building Give them the clarity they’ve been missing out on!
I built a FREE cheatsheet to get anyone here started building their online business with AI! Ideas are FREE and that’s why I am giving this away. Use it as an inspiration or launch your million Dollar Empire. Just take action!
