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I built a tool to stop cold email spam (Unspam.io)

Hacker News

I built a tool to stop cold email spam (Unspam.io)

Hi HN! I'm Louw, founder of the enterprise insurtech scale-up Root[0]. Over the last 8 years, my inbox has been flooded with unsolicited emails, especially since early 2023. Recruitment services, outsourced software development agencies, and a crazy amount of lead generation services (thanks, Apollo!) drowned my inbox. To solve this, I created Unspam. Initially, it was a simple tool for myself and fellow founders to reduce noise. And it worked — about 10% of my emails were swiftly and accurately categorized as unsolicited messages. Given its success, I've decided to package and share Unspam with the broader community, evolving it into Unspam.io. How it works: 1. Hook up your Gmail 2. Unspam monitors received mail in the background 3. Unsolicited emails are labeled and archived Please try it out and let me know your feedback. What would you improve? https://unspam.io --- [0] https://rootplatform.com [1] PS: I value privacy intensely. Unspam doesn’t record, store, or train on your emails. No need to.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
93%93% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: inbox, email · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% 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
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.

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