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SkimAI – AI assistant triaging the email inbox

Hacker News

SkimAI – AI assistant triaging the email inbox

Hello HN community, My name is Matthieu and this is my first post on HackerNews, so I brace for impact. I want to share with you SkimAI, a product my cofounder and I developed to reduce the time knowledge workers spend on repetitive admin tasks. We start with tackling email overload, by leveraging AI to streamline your email experience. Currently SkimAI supports Gmail and offers the following features: - Inbox Management: it highlights unread emails removing the hassle of searching for important messages. - Email Summarization: it gives an instant overview of emails' content. Less time needed to reply to an inbound message. - Reply Drafting: it crafts responses ready for review, thus saving time and effort. This functionality can run automatically I want to improve the product and extend its capabilities. Your feedback is important. FAQs Q: Is SkimAI compatible with email clients other than Gmail? A: Currently, SkimAI supports Gmail. However, we have plans to extend its compatibility to other email services. Q: Can I connect SkimAI to my Mail app? A: You can connect SkimAI to your Gmail address. Once SkimAI creates a new draft, the draft will appear in your Mail app. Q: How you ensure privacy and security? A: We do not store any information in our cloud service. All messages and summaries are stored locally on your machine, and inside Gmail. Give it a try and let me know how it can improve. I want to hear from you.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, compatible · Missing: reddit linkedin, podcasting, created
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.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, inbox · Missing: agents, macos, agent
90%90% 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
55%55% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
14%14% 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.

Incorrect prediction on native model

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