Collect Referrals with a Link

Hacker News

tl/dr: A full pipeline of referrals is the best way to hire. We made a free tool to help collect referrals on linkedin without the back and forth. Hey HN, At Hedgy, we're helping our talented tech friends figure out their next gigs and helping companies hire with referrals. We recently worked on a little tool to help hiring managers collect referrals with a link and review/group them. We’ve been going deep with pilot customers, learning about referral motions and hiring strategies in the current market. I wanted to share what we’ve learned and a free tool we built. Hiring managers are getting crushed by cold applications. A combination of market conditions and gen AI tools are causing low-quality candidates to apply to 1000 companies at once. Some managers are skipping the inbound applications altogether and only looking at referral candidates. You’ve probably written investor emails, slacked employees, and made LinkedIn posts asking for referrals for an open role. Hedgy referral links are easy to share and capture referral info in a structured way. You can then triage and manage outreach to these referrals in our dashboard or export them for another tool. We just launched a beta and are looking for founders and hiring managers to give it a try. Signup now to get it free forever. Try it here: https://www.hedgy.works/r

Visit product View on Hacker NewsFeb 19, 2025

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

75%75% 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: slack, email, using · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, 000, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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