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Proof-of-Skill Expert verified skill credentials for design

Hacker News

Proof-of-Skill Expert verified skill credentials for design

Hey HN! I’m excited to share Proof-of-Skill, a new way to evaluate and verify UI/UX design skills directly with the help of seasoned experts. We aim to streamline hiring by ensuring that candidates bring actual, expert-assessed skills right to your shortlist. The way we work is pretty straight forward: - Orgs select skills they're hiing for & submit a skill requirement - They invite their applicants to submit their proof-of-skill - Candidates submit their proof-of-skill if they already have it or submit real world assignments to prove their skills to design experts - Design Experts assess the candidate submissions and issue proof-of-skill to them - Orgs can shortlist candidates basis their proof-of-skill We’d love any feedback or thoughts on making hiring more trustworthy and efficient for design roles. Thanks, HN community!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% 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: new · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, io · Missing: https docs, just released, exist
29%29% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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