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Shivon AI – Practice job interviews with AI and get a shareable report

Hacker News

Shivon AI – Practice job interviews with AI and get a shareable report

Hi HN, Over the past several months we’ve been building Shivon AI, a platform aimed at candidates getting ready for interviews. The core idea: let you simulate real interviews with an AI (any domain, any role), generate a detailed performance report, and then share that report as part of your job-application portfolio. Key features: A chat-based AI interviewer that asks theory, practical usage and case-study style questions. After each simulation you receive a breakdown of your performance across metrics (e.g., communication, problem solving, confidence) with actionable suggestions. You can track improvements over time and share your interview-report as tangible evidence of your prep. We just launched the beta at https://candidate.shivonai.com (no paywall; demo mode available). Why we built this: We’ve seen how difficult it can be for candidates to highlight their real skills beyond a résumé or LinkedIn profile. Interview preparation often happens in isolation, with no way to measure improvement or share results. Our goal is to make this process measurable, transparent, and empowering for every job seeker. We’re looking for feedback from the HN community especially around: How realistic/interview-like the AI questions feel (domain-agnostic). Whether the performance report adds value and how it could be improved. Thoughts on sharing such a report as part of a portfolio — does it meaningfully affect job odds, or feel gimmicky? Happy to answer questions on the architecture, data pipeline, UI/UX, even business model if you’re interested. Thanks for checking it out — looking forward to the discussion. Vraj Patel, Shivon AI

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
80%80% 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: model · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way, para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
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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