Built a tool for founders to automate the grind of finding first users

Hacker News

Hey HN, We're all told to "do things that don't scale" to get our first users. For me, that meant spending 2-3 hours every day searching LinkedIn, Reddit and Twitter for anyone talking about a problem my software could solve. The lead quality was great, but it took so long that it was getting in the way of my productivity. I made a simple tool FeedPilot to fix this problem. It's a browser extension that runs in the background, scanning for keywords and conversations. It's not just a keyword alert; it uses a small AI model to find posts where people are actually asking for help or recommendations. This has been the main way I've got users, and it now takes me about 15 minutes a day instead of 3 hours. I thought other founders were having the same problems, so I improved it. There's a free version that's really useful. Please tell me what you think.

Visit product View on Hacker NewsOct 22, 2025

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

I built a tool finding potential users discussing problems47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I built a tool finding potential users discussing problems

Hacker News4

We built a tool that helps founders automate their LinkedIn28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We built a tool that helps founders automate their LinkedIn

Hacker News1

Growhtly64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Built by founders for funders

Indie Hackerscommitment-full-time

Golang binaries built for your users depending on their arch and system41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Golang binaries built for your users depending on their arch and system

Hacker News8

Finding puns computationally51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Finding puns computationally

Hacker News24

Finding Interesting Publications on PubMed44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Finding Interesting Publications on PubMed

Hacker News2

Recordo47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Photo finding tool

Indie Hackers1$50/moai

Recurflux31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The dunning tool RevenueCat users never had.

Product Hunt+6

Knocker, automate finding businesses without a website43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Knocker, automate finding businesses without a website

Hacker News6

Knocker, automate finding businesses without website43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Knocker, automate finding businesses without website

Hacker News4

Browse all products Analyze your launch