Ribbon API – create AI agents to interview anyone via a link

Hacker News

Ribbon is an AI interviewer platform for recruitment teams. Today we're opening up Ribbon for more than just recruitment. In just two API calls you can create an AI interview link for any use case: product interviews, surveys, recruitment, anything you can dream of. You can try a recruitment interview here: https://app.ribbon.ai/interview/08f5188d A bit more info on our API here: https://www.ribbon.ai/ribbon-api

Visit product View on Hacker NewsOct 28, 2024

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

62%62% 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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

21%21% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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