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Mail Organizer for Gmail

Hacker News

Mail Organizer for Gmail

This Google Apps Script labels emails accoding to the address the email was sent to. For example, you own the domain name "example.com", and you set up an email forwarding service such as Forward Email or ImprovMX to relay all emails sent to "*@relay.example.com" to your Gmail account. The script will label your emails according to a set of rules. For example: - "hello@relay.example.com" will be labeled "hello" - "hello-world@relay.example.com" will be labeled "hello" and "world" - "hello.world@relay.example.com" will be labeled "hello/world" ("world" as a sublabel of "hello") - "hello.world-yo.wassup@relay.example.com" will be labeled "hello/world" and "yo/wassup"

Visit productView on Hacker NewsAug 28, 2023
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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, email · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
45%45% 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, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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