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Tickerstar – Instant SEC and company earnings summaries
Hacker NewsTickerstar – Instant SEC and company earnings summaries
I created an alert system that scans and extracts SEC and company files and sends key takeaways (earnings, 10-K, other material events) within minutes of the release. I was also able to have it listen to earnings calls following the event and email incremental takeaways. It requires an email for you to add tickers and receive alerts. Signup is quick. Looking forward to any feedback!
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released39%39% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto21%21% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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