Tw

Twitter accounts from your product waitlist

Hacker News

Twitter accounts from your product waitlist

Hi HN! I’m Nikita. For the past few days I've been working on a TweetHub enrich (https://enrich.tweethub.io) which is a tool that connects email accounts with Twitter profiles. The idea is to target high-followed accounts on your waitlist so that you can target them accordingly. The way it works is by taking each email on your list and scraping the web for and links between that email and a Twitter account. Since everyone doesn't have a Twitter account this can be really unpredictable, but we've been able to see hit rates of over 50%! The App is still in its early stages but I would love to hear any feedback that you have! Your first 50 searches are free! Give it a try: https://enrich.tweethub.io

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: 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: ide, io · Missing: https docs, excited, just released
52%52% 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: way · Missing: mobile apps, ios, personal
29%29% 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
27%27% 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
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
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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