An autonomous WhatsApp recruiter that introduces candidates <> founders

Hacker News

I’ve been experimenting with an idea over the last few months - a fully autonomous recruiter that works entirely over WhatsApp. No forms. No job board. No ATS. Just a conversation -> an intro. Here’s how it works today - For candidates: 1. You message the agent on WhatsApp 2. It asks a few questions about what you're looking for 3. It does a quick 5-6 min call to clarify your background 4. It matches you to relevant founders and introduces you directly You don’t browse roles; the agent finds them For founders: 1. You send your open roles over WhatsApp 2. The agent screens candidates via chat/call 3. You only receive a few relevant intros You don’t sift through applicants or profiles It’s simple, but it seems to work. Right now ~700 candidates and ~30 founders are using it, and the agent handles everything (screening, matching, intros, follow-ups). This is not a chatbot bolted onto a job portal. It’s closer to a “recruiter brain” running through WhatsApp. Some things I’m trying to understand: 1. Is WhatsApp a viable long-term UI for hiring? 2. Does removing interfaces (CVs, job boards, dashboards) actually help signal? 3. How much autonomy is too much for an agent in hiring flows? 4. What breaks if this scales? If you’re curious or want to try it: Candidates: https://shorturl.at/oxUJt Founders hiring: https://shorturl.at/oxUJt I’d love feedback, critiques, or failure modes to think about.

Visit product View on Hacker NewsNov 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, using, open · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency

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

Correct prediction on native model

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