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I Built the First AI Candidate Classifier App

Hacker News

I Built the First AI Candidate Classifier App

For the past two years, I've been diving into AI studies and work, but I craved something more meaningful. Looking around, I noticed a common problem: job seekers feeling overwhelmed and bored with the repetitive task of filling out CV templates that seem to have been used by everyone, only to face rejection. On the flip side, recruiters were spending excessive time on sorting through applications. So, I came up with a solution for both parties. I created Xandidate, an AI-powered application that streamlines the recruitment process. Recruiters simply need to create a form, input the job description, and share the form link where candidates can apply. That's it. The AI then takes over, analyzing each applicant based on the job description, assigning a score, and providing a detailed evaluation. For candidates, Xandidate eliminates the need for traditional CV submissions. Instead, they can express themselves genuinely, describing their skills and experiences in their own words. No more CV hassles; that's a thing of the past. This app is built using Next.js and is hosted on Vercel.

Visit productView on Hacker NewsApr 25, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
87%87% 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 HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
28%28% 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
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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
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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