A commenting system that works via email

Hacker News

Hi everyone, I made a commenting system that accepts submissions via email, instead of requiring a login. The back story is I wanted some interactivity for my site/blog, but I felt like requiring a signup wouldn't be a good UX. I'm looking to get feedback on it from the HN community. Please feel free to ask questions and let me know your thoughts, especially what you don't like about it. If it's a decent UX then I would like to make it OSS, as I feel that it could potentially fill a void, especially for beautiful small websites. Here's the basic flow of data: 1. When the site is generated, mailto links embed information for where the future comment will go 2. When a user clicks on a "comment" or "reply" link, it opens a draft comment in their mail client. Instructions are pre-baked into the email body. 3. When a user hits send, the email is received by my software, which parses the email, validates it for tampering against a pre-computed hash, and then opens a pull request. The user gets an "auto-reply" email, informing them that the submission was successful, along with a link to preview it. The site moderator (so, in this case, me) gets an email, with links to the PR. When the comment is approved, the site is rebuilt and deployed. Other info and potential gotchas: * Emails are all hashed for privacy (with a secret "pepper") that's occasionally rotated * Comments are represented as individual files, so there are no merge conflicts * DKIM, DMARC, and SPF are all checked to help prevent spoofing There's a FAQ on the link above that has more information, and you can also see a demo on my personal website: https://spenc.es/writing/email-as-a-commenting-system/ Thanks for reading!

Visit product View on Hacker NewsDec 8, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, activity · Missing: mac, agents, macos

86%86% 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 · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: occasional · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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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