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Oboxly – tell headhunters what you want

Hacker News

Oboxly – tell headhunters what you want

As you, I get a lot of messages from headhunters and recruiters via email, LinkedIn or XING. Most of them are of no interest to me, though I am always open to really interesting or life changing things. So I scratched this itch with some code last weekend. On Oboxly you (I :-) can tell headhunters and recruiters what would make me change my job and what makes me unique. It also enables me - you - to send the link to Oboxly in a reply without being searchable by your employer (a reason not to be candid on LinkedIn) http://www.oboxly.com Feel free to critizize, I'm open for everything and please assume I know nothing.

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Actual performance

3points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, code, open · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% 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
26%26% 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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