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Candid: Automated Resume Screening – Try It Out (Series A $50m)

Hacker News

Candid: Automated Resume Screening – Try It Out (Series A $50m)

Ted here, maker of Candid. Candid helps companies resume screen applicants to find the best and filter out the rest. Is your resume good enough to pass a resume screening? See if you pass the test by trying our demo on our website by uploading your resume to get automated insights. Try our demo here to see what recruiters think of you based on your resume: https://candidresumes.com/ We're also live on ProductHunt! https://www.producthunt.com/posts/candid-resumes For the last few months, our team of Stanford engineers have been piloting our algorithms with our early customers(Google, BCG, FB, and more) on this exciting project. We've already helped them resume screen 500,000+ resumes and save thousands of hours. We know the growth pains of scaling companies and staffing recruiting teams to source and filter applicants. ATS systems like Greenhouse and Lever require tons of manual work to review all of the applicants. Plus, recruiters need to be well trained and experienced to prioritize the right applicants. Candid resolves this by automating the entire applicant filtering process with our resume grading system that companies can fine-tune to their own priorities. On average, our clients report 95% time savings for recruiting. Does it actually work? You can take our algorithms for a test run! Try the live demo on our website and upload your resume. ([app.candidresumes.com](http://app.candidresumes.com/)) Discover how big companies see you. Try our demo here to see what recruiters think of you based on your resume: https://candidresumes.com/ We're also live on ProductHunt! https://www.producthunt.com/posts/candid-resumes We couldn't have done this without the amazing support and funding from http://www.dystopia.vc/ - Ted Let us know what you think!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: google · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
45%45% 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 · Strong signals: growth · 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 · 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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