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Screenr – Block Recruiter Spam Effortlessly

Hacker News

Screenr – Block Recruiter Spam Effortlessly

Screenr blocks job spam and hides your email address. It’s perfect for everywhere you used to list your email, like “Who Wants To Be Hired?” You can also add your own custom questions that you want companies to answer upfront. Too often companies avoid giving concrete answers about job offerings or double back on vague promises later. Screenr eliminates this ambiguity — if you want to know what the minimum and maximum salary range is, just add it as a custom question to your Screenr. You can use Screenr on resumes, Hacker News profiles, or anywhere you would otherwise share your email. If you want to add a brief bio and your full name you can do that too. You can use Screenr to fight back against vague LinkedIn messages, or even use it as a first response to cold emails. Try out the product here: https://www.screenr.app/ Here’s a sample Screenr profile: https://www.screenr.app/?id=mkcMfL Please try out the product and let me know if there’s anything you’d like added. You don’t even need an account to use the product and making your own Screenr takes less than 60 seconds. Responses to your Screenr’s will get emailed directly to you (small note, I am working on getting SES sending out of sandbox but Amazon is being slow so responses are getting forwarded from a previously production approved email blocktimize.io from a previous side project), I’ll move it over to info@screenr.app once that’s approved but the sending email really doesn’t matter.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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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, email · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
61%61% 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 · Strong signals: answers · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

Incorrect prediction on native model

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