Ex

Exorbitant Email Addresses

Hacker News

Exorbitant Email Addresses

I posted this a couple of months ago, but "anon" user gatherhunter (or something), jumped straight to name calling it a "joke" and "creepy" and "illegal" and killed the thread. The main criticism was against the terms. I checked the terms of GMail, FB, and so on and see they all include provisions for inspecting all data in the system to comply with the law, and for the operation of the service. It's actually pretty normal, and illegal to not check if content complies with the law, just as I thought. Also, as the purchase is so huge, verifying identity is part of KYC AML regs. So again, it seems this anon user's hysterical name calling was a high pitched shriek that the tribe responded to without thinking, but actually goes against the law, while pretending to be for it. How does this sort of counterfactual, abusive comment get through the HN hivemind / filters, again? Anyway, reposting now to get some love and affection for this beauty of an idea. https://unobtainium.email/?ukyli=udiyd

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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: user, email · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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 · Missing: web3, chat, crypto
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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