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Arnie SMTP buffer server in 100 lines of async Python

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Arnie SMTP buffer server in 100 lines of async Python

Here's the 100 lines of code: https://github.com/bootrino/arniesmtpbufferserver/blob/master/arniesmtpbufferserver.py Here's the github repo: https://github.com/bootrino/arniesmtpbufferserver It's MIT licensed. Arnie is a server that has the single purpose of buffering outbound SMTP emails. I wrote Arnie because I got frustrated using complex queueing systems simply to buffer outbound SMTP emails. I'd rather spend time writing this than spend the same time debugging Celery configuration. A typical web SAAS needs to send emails such as signup/signin/forgot password etc. The web page code itself should not directly write this to an SMTP server. Instead they should be decoupled. There's a few reasons for this. One is, if there is an error in sending the email, then the whole thing simply falls over if that send was executed by the web page code - there's no chance to resend because the web request has completed. Also, execution of an SMTP request by a web page slows the response time down of that page, whilst the code goes through the process of connecting to the server and sending the email. So when you send SMTP email from your web application, the most performant and safest way to do it is to buffer them for sending. The buffering server will then queue them and send them and handle things like retries if the target SMTP server is down or throttled.

Visit productView on Hacker NewsOct 18, 2021
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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: email, single, using · Missing: mac, agents, macos
65%65% 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
62%62% 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
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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