I built a Platform to transform hiring into a AI-driven process

Hacker News

Hey Hacker News community, I'm Leon, and alongside my business partner Arun, we've dedicated the past couple of months to crafting the ultimate AI-driven solutions for recruiting. Allow me to introduce Phase 1 of Hiringtools AI – our comprehensive platform engineered to revolutionize the hiring process. With years of experience in the recruitment field, I've always questioned the rush to fully automate recruitment. While AI holds immense promise, recruiting remains inherently human-centric. Nevertheless, we recognize the potential for AI to enhance recruiting efforts for both professionals and entrepreneurs alike. Hiringtools AI marks our initial step towards establishing the premier AI recruiting platform and community. Our vision is to provide a centralized hub for all things hiring, supported by cutting-edge AI tools, an upcoming AI Prompt Library, curated job board databases, and other resources aimed at simplifying and optimizing the recruiting process. Explore Hiringtools AI: https://hiringtools.org We're eager to hear your thoughts and suggestions on how we can further improve our platform. Best regards, Leon

Visit product View on Hacker NewsMay 26, 2024

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

92%92% 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.

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

57%57% 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: platform · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, 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 · Strong signals: introduce · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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