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I made CV screening, where each CV battles for Elo score

Hacker News

I made CV screening, where each CV battles for Elo score

Hey HN, I’ve been on both sides of hiring — as a job seeker and as someone trying to build a team. And honestly, the current process is messy. Recruiters drown in resumes. Great candidates get lost. And keyword-based filtering doesn’t really work — especially for technical roles. So I started building vkncy, with a big vision: an AI agent that can handle everything from screening to interview scheduling. Right now, I’ve shipped the first part — intelligent CV ranking, powered by a dynamic Elo system. Here’s how it works: 1. Every new CV is parsed and given an initial Elo score by AI. 2. Existing applicants are split into three Elo tiers: high, middle, and low. 3. From each tier, we randomly select one CV (3 total). 4. The new applicant is compared to each of those in a “battle,” where an AI model evaluates and picks a winner. 5. Based on the results, we update the Elo scores using the standard formula. This happens for every new applicant — which creates a constantly evolving, self-adjusting ranking of candidates for that specific job. Why this matters: instead of relying on keyword matching or rigid filters, this system adapts. It introduces relative performance, lets different types of candidates shine in their own context, and reduces bias from manual screening. Right now, you can: - Create a job post - See how the system ranks candidates over time Next steps: - Adding depth control: so recruiters can define how many comparisons to make - Building toward an AI agent that also handles voice screening and interview scheduling Would love any thoughts, feedback, or pushback.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, new · Missing: mac, agents, macos
92%92% 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: started · Missing: supports, reddit linkedin, podcasting
91%91% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
32%32% 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
26%26% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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