TalentLeads – Find the Best Talent for Your Startup

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Hey all, Just wanted to share my side project with you. I know this is not exactly HackerNews material, but still would love to hear your feedback, even if negative. Here is the link: https://gettalentleads.com This is a simple Django application that parses a few places online (including HN) where people that are looking for work share their profiles. I wrote some code to categorize people based on their location, tech stack, willingness to relocate and some other things to make it easy to search for the perfect candidate. I though that some companies might find this useful, hence why this is a paid product. What do you ladies and gents think? Thanks a ton in advance for your time and thoughts.

Visit product View on Hacker NewsMay 22, 2023

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

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best fitHighest predicted score across all platforms for this description.

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

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

50%50% 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 · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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

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