AI recruiter for pre-screening candidates

Hacker News

For the people who don't have time to spend hours looking through CVs and interviewing candidates, we have created a tool that handles the initial pre-screening conversations. I used to spend hours pre-screening CVs, scheduling interviews and often wasting a lot of time on interviews after rejecting the candidate in my head after 5 minutes. And sometimes we just have some many applicants that it's not humanly possible to reach all the candidates in time. Now we have AI to handle the pre-screening chats with the candidates. It's like a mini recruiter, chats with the candidate and creates a report about the candidate. We have already tested it with more than 2k candidates, so far we get 8.8 out of 10 for the candidate experience. If anyone has an open position and doesn't want to spend any more time pre-screening candidates - let me know

Visit product View on Hacker NewsJan 30, 2024

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Launch Intel predictions

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

83%83% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

34%34% 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 · Missing: mobile apps, ios, personal

28%28% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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