2.3a · Find 10 People: Where to Look
Module 2 · Step 3a of 4 · From Idea to First Paying Customer
Input: a hypothesis you suspect is real (from Ch 1.1) + a sharpened Mom Test question list (built in Ch 2.1, polished in Ch 2.2)
Output: a 30-name list of specific people you can name because you read what they wrote, ready for the outreach templates in Ch 2.3b
TL;DR (Part 1 of 2): Paste your three-sentence hypothesis into Claude, get back the ICP (Ideal Customer Profile - the specific kind of person your hypothesis names; introduced in Ch 1.1) profile + exact communities + search strings. Read where your ICP is already complaining. Build a 30-name list. Part 2: What to Say covers the message templates, cadence, and follow-up sequence.
Most non-technical founders start with the same move: “I’ll just message my LinkedIn network.” Sixty polite DMs over a week tend to produce 3 calls - two old colleagues showing up to be nice, one real lead who ghosts on reschedule.
The technique below replaces that move with a different one: read where strangers are already complaining about your exact hypothesised problem, then write back to those specific complainers. Same hypothesis, same work hours, different place to look. The DM-the-network move books 2-3 polite calls. The read-where-they-already-complain move - half a day of reading threads, then 30 named outreach messages - produces a calendar of 10+ booked interviews.
The full journey, top to bottom:
- Translate the hypothesis into an ICP map - paste your three sentences and two competitor URLs into Claude or ChatGPT.
- Read where they’re already complaining - work through the channels the AI proposed. Note 30 sentences in their real words.
- Build a list of 30 specific people from those threads.
- Write to each one using the templates in Part 2.
- 10 interview calls on the calendar.
Calendar reality + smoke-test gate before you start. Full-time founder typically books 10 interviews across 2-4 calendar weeks; evening-only founder (2-4 hr/week) typically needs 6-8 calendar weeks - plan around the longer version. Your Ch 1.2-1.4 smoke test should have cleared roughly 6%+ email conversion (the “Promising” band) or 5%+ Stripe-click on the Ch 1.5 price-button variant. 3-6% is the “iterate the message” zone, not a green light. Below 3% means you have a demand-side problem - go back to Ch 1.1 and rewrite the weakest blank before booking interviews.
This page covers steps 1-3. Part 2 covers steps 4-5.
Before you start: write three sentences
Write three sentences in your own words before you open Reddit. Without them, every interview answer sounds encouraging and you can’t tell which ones confirm the bet and which ones kill it:
| Profile | What to write | Bad vs Good |
|---|---|---|
| Customer (one sentence) | Who is this person, in real-world detail? Role, company size, the moment in their week when the pain happens. | Bad: “small-business owners” Good: “a 12-person law-firm office manager on Friday afternoon trying to invoice ten clients before Quickbooks logs her out” |
| Business (one sentence) | What kind of business are you building? B2B SaaS, B2B services, B2C app, marketplace. Free or paid. Self-serve or sales-led. | Bad: “a SaaS tool” Good: “B2B SaaS, self-serve, $29-49/month annual billing” |
| Solution (one sentence) | Not a feature list - a sentence about the change. You won’t pitch this in calls, but you need it written down to know which conversations confirm or kill it. | Bad: “a tool that automates invoicing” Good: “I think a one-click invoice export to Stripe and Wave saves the office manager 90 minutes every Friday” |
If you can’t write all three on a single napkin, do that first. The deeper version of these three lines is the one-page Product Brief in Chapter 3.1.
How to find 10 people who actually have this problem
You can do every part of this with a Reddit account, a Gmail address, and short daily blocks. AI does the heavy lifting that used to need a researcher; the rest is reading and writing.
Translate the hypothesis into an ICP map
The 2026 shortcut: AI does the part that used to take a week of research. You hand it your three sentences plus two competitor URLs; it returns the ICP profile, the exact places those people post, and the search strings to find named individuals.
Paste this prompt into Claude or ChatGPT:
You are helping me find the first 10 customer interviews for a product I'm validating.
My hypothesis (3 sentences):
- Customer: [paste your customer sentence]
- Business: [paste your business sentence]
- Solution: [paste your solution sentence]
Two competitors or adjacent products serving a similar customer:
- [competitor 1 URL]
- [competitor 2 URL]
Return:
1. A sharper ICP profile (role, industry, company size, the moment in their week when the pain happens, one quote in their language).
2. 8 subreddits, Slack/Discord communities, and forums where this person posts. For each, give the community's topic focus, typical post frequency (e.g., "20 new posts/day" or "2-3 per week"), and 2-3 short keyword phrases that come up most often. Do NOT generate URLs - you cannot browse the web. I will verify the community myself with these inputs.
3. 5 Google + LinkedIn search strings I can paste in today to find named people complaining about this problem (use `site:`, quotes, and `intext:` where helpful).
4. 5 second-degree adjacent search terms I might miss (workarounds they use, related complaints, tool names they'd mention while frustrated).
If you cannot describe a real community for any item, respond with "NOT FOUND - [item]" rather than guessing.
No competitor URLs yet? If you ran the naive Claude/ChatGPT prompt in Chapter 1.1 with the follow-up “name 3-5 competitors,” you already have them. Otherwise: Google your problem in plain words plus
toolorsoftware, grab the top 2 results that aren’t blog posts.
What you get back: the channels you’ll read next and the search strings you’ll use to build the list. If a community the AI proposes turns out to be dead or off-topic, drop it and ask: Suggest 3 alternatives more focused on [vertical].
If your hypothesis is consumer-facing, swap “Slack/Discord” for “TikTok hashtags, Instagram comments, YouTube comment threads, and product subreddits.”
Read where they’re already complaining
Read before you write a single message. You’re looking for the exact words people use when their problem flares up - those words become your subject lines when you write the cold messages in Part 2.
The simplest way:
- Open one of the channels the AI proposed in the ICP map.
- In the search bar, paste the exact problem phrase in quotes (e.g.
"invoicing takes forever"). - Sort by Top → Past Month. Read the top 30 results.
- Open a Google Doc. Each time a complaint matches your hypothesis, copy the sentence verbatim - with the username and URL.
- Repeat for two more channels.
When you’re done you should have 30 real sentences and 30 named people. Don’t paraphrase. The exact wording is the point.
Where to search (the AI gave you specifics; here are the common starting points):
- Reddit - subreddits in your vertical. Sort by Top → Past Month. The 1% willing to complain in public are usually willing to take a 20-minute call. Free tool Keyworddit surfaces the keywords a given subreddit is currently using, so you can search those phrases back into Reddit and find the named complainers.
- LinkedIn - paste the problem in quotes into search, filter to Posts → Past Week.
- Industry Slack and Discord - Indie Hackers, Lovable, No Code Founders, and the vertical-specific communities your AI map named.
- G2 and Capterra reviews - pull every 2-star and 3-star review of the closest competitor. Pain a stranger typed for free, organized by feature.
- Twitter/X - the 280-character constraint forces complaints to be precise.
- Personal network referrals - text 10 people you know:
Do you know anyone who [painful task] regularly? Research call, not sales.Warm referrals book at 70%+ show rates.
One Reddit rule: don’t blast a launch post on day one. Read the sub for a week, leave three real comments, then post a research question. The self-promotion on Reddit guide covers the karma floor and the unwritten rules.
Write down 30 specific sentences in their language with the username next to each. That bank is your raw material when you write the cold messages. Don’t paraphrase.
Build a list of 30 specific people
Turn the 30 sentences into 30 names. Open each thread you saved while reading, click each useful username, and copy four things into a spreadsheet:
- Name (theirs, not their company)
- Role + company (one cell)
- The post you’ll reference (paste the URL)
- One specific line they wrote (the phrase you’ll quote back when you write to them)
Aim for 30 hand-picked people in one focused sitting.
This is the most important step in the chapter. A list of 30 individuals you can name - because you read what they wrote - replies at 3-5× the rate of a list of 30 strangers a tool exported for you.
If you run out of named posters before you hit 30, Apollo’s free tier (credit-based: roughly 100 email credits + 10 export credits per month, no credit card) lets you filter on role + industry + company size and export the rest (at 10 exports/month, this fills the gap over several weeks, not one sitting). Treat it as backfill, not the source - the hand-picked names always perform better.
Save the Apollo filter and whatever contacts your monthly export credits cover (roughly 10 per month on the free tier) to a tab named “Module 5 cold seed” in your outreach spreadsheet. You will reuse this exact filter in Ch 5.5 cold outbound.
Filter the final list on six dimensions:
- Buyer OR user - not both
- Company size in your sweet spot (50-500 for most B2B SaaS)
- One industry first - vertical depth beats horizontal spread
- One timezone - so the calls are actually bookable
- The tool you replace or integrate with - filters out the “different problem” lookalikes
- A recent funding or hiring signal - movement = budget = openness
Drop anyone outside the band. You want signal, not volume.
Consumer founders - skip the database backfill. Your buyer is on Reddit, Discord, TikTok comments, and Instagram. The hand-picked path is the only one that works for you.
What to do next
| Step | Action | Output |
|---|---|---|
| 1 | Write the three sentences: customer, business, solution. One napkin. | Three sentences locked |
| 2 | Run the AI ICP map prompt with your three sentences + 2 competitor URLs. | ICP profile + 8 communities + 5 search strings |
| 3 | Read the top channels. Copy 30 verbatim complaint sentences with usernames and URLs. | Google Doc with 30 sentences |
| 4 | Build the 30-name spreadsheet: name, role+company, post URL, one quoted line. | 30-name list ready |
| 5 | Move to Part 2: What to Say for the message templates and send cadence. | Next chapter |
Optional upgrades
These are skip-by-default. The main chapter works without any of them.
Upgrade the AI ICP map prompt with a deep-research tool. The Claude/ChatGPT version above is fast and free; the trade-off is the AI synthesizes text without source links. For a verifiable evidence trail, swap in Perplexity Pro ($20/mo) or Gemini Deep Research ($20/mo Advanced) with the same prompt - both return real-source citations for every claim. Spot-check that each proposed community is alive and on-topic before you invest reading time, and grab verbatim quote snippets you can reuse as cold-message subject lines later.
Offline-heavy verticals - paid panel as Plan A. If your ICP lives in trades, nursing, in-store retail, elderly users, or regulated B2B, the Reddit / LinkedIn / G2 flow returns nothing useful. Use a paid panel instead. UserInterviews and Respondent have screened participants across these verticals; cost is $30-$100 per interview. Decision rule: if your ICP description names an offline trade, an over-60 user, or a regulated profession, budget for a paid panel as Plan A.
Monitoring tools that cut the manual reading load. Keyworddit (free, no signup) surfaces the high-frequency keywords inside any subreddit. F5Bot (free) sends email alerts when your keywords appear on Reddit, Hacker News, or Lobste.rs. Reddinbox / Pushshift (free) searches Reddit’s full archive for high-commercial-intent phrases like “how to automate X” or “sick of doing Y manually.” These tools surface the threads faster - you still read them yourself.
Further reading
- Rob Fitzpatrick, The Mom Test (book site) - the past-behavior interview technique you’ll run on every call this chapter’s list books.
- Y Combinator, Talking to Users (Startup Library) - the canonical YC essay on why this conversation has to happen.
- Apollo - contact database for filtering by role + industry + company size when the hand-picked list runs thin.
- Clay - list enrichment with email verification, useful once you’re past 5 paying customers.
- User Interviews and Respondent - research panels for ICPs that cannot be reached cold.
Done when: 30-name list is built in your spreadsheet with name, role+company, post URL, and one quoted line per row. Next click: 2.3b · Find 10 People: What to Say - the message templates, cadence, and follow-up sequence. If blocked: If the AI returned “NOT FOUND” for every community, your hypothesis is too vague. Go back to Ch 1.1 and rewrite the customer sentence with a specific role, company size, and the moment in their week when the pain happens.
Stuck? Most first-timers stall here: your name list stops at 3 people. Fix: search a related keyword - “boarding costs” instead of “pet sitter,” “claim denial appeal” instead of “medical billing.” The second-degree search surfaces people with the same problem but different vocabulary. 30 minutes of keyword variation turns 3 names into 12. Not “License Apollo Pro.”
Case Study: Tomas & Mia
Tomas: AI ICP map identifies r/Accounting (300K members), Controller-specific LinkedIn groups, AICPA conference attendees. Builds a 30-name list of controllers who posted about “manual reconciliation” or “month-end close pain” on LinkedIn in the last 90 days.
Mia: AI ICP map identifies Facebook parent groups (ADHD Parent Support, Dyslexia Moms Unite), r/ParentingADHD, local school district special-ed coordinators. Builds a 30-name list of parents who posted about “can’t find a tutor” or “tutoring waitlist” in the last 60 days.
Built by JetThoughts as part of the From Idea to First Paying Customer curriculum.