We

We made a CLI and API to automate inbound email

Hacker News

We made a CLI and API to automate inbound email

Hi HN! We're a 5 person team working on simplifying the creation of email apps. Right now, most email services focus on outbound emails (newsletters, transactional, delivery), but that doesn't solve the fact that we all hate receiving them. You can try it at https://mailscript.com Mailscript lets you create programmable email addresses. It works headless, via the CLI, API (we use Swagger to support most languages). You can even run it as a daemon on a server (to run functions when emails arrive). Once you get username@mailscript.com, you can create as many aliases as you'd like (including on the fly support for *@username.mailscript.com). Just like in Gmail, we support adding . or +. Every address gets an SMTP key as well, so you can use your favourite email client e.g. Gmail... We've been working hard on making the CLI and API easy to understand, and take a trigger/actions approach. We even have parsers for GitHub emails (so you can automate Issues, PRs, Actions). You can see what the code looks like in our templates: https://github.com/mailscript/cli/tree/main/packages/templates Example use cases: - send Zabbix alerts to SMS if 2 messages in one minute! - post Github releases to Discord - save pdf attachments to Google Drive - create shared email addresses You can even collaborate on your email infrastructure on GitHub by exporting it as a YAML file. If you'd like to learn more: Docs: https://docs.mailscript.com API playground: https://api.mailscript.com We'd really like to hear your feedback, especially the harsh criticism. If you'd like any help with building automations, we'll take the time to help you code them. Feel free to ask for help on our Discord (https://discord.gg/US24HAVYq2) or at contact@mailscript.com!

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, user · Missing: mac, agents, macos
88%88% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
51%51% 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
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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