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SQRL – Anti-spam rules language Twitter acquired in 2018, open-sourced

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SQRL – Anti-spam rules language Twitter acquired in 2018, open-sourced

Hey all, author here! This is a demo of the rules engine we built to fight spam/abuse on the internet. It was built based on learnings from Facebook & Google, while trying to make a language that makes it simple for anti-spam analysts to quickly (and safely) deploy rules to production. Unfortunately it looks like the Twitter event feed is temporarily down (they're blocking it, possibly as part of shutting down the API on Thursday). I have a cache of events from a little earlier that I'm going to try play through the stream.

Visit productView on Hacker NewsFeb 6, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: google, open · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
74%74% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
33%33% 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
23%23% 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
20%20% 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.

Correct prediction on native model

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