Postgres.email – Readable Mailing Lists

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This is a small POC that I've been developing to transform the Postgres mailing lists[0] more readable I've linked to a thread "COPY TO (FREEZE)?" in the Show HN link, and the equivalent on the Postgres mailing list site is here: https://www.postgresql.org/message-id/20220802.133046.194197... The idea of postgres.email is to make all responses threaded, like the comments here on HN or Reddit. I created this by using an imap Foreign Data Wrapper to ingest the messages into a Postgres database. I have only ingested 1000 messages for the POC. It also doesn't display attachments. The code can bew found here: https://github.com/kiwicopple/postgres.email The technologies used are: - Gmail to receive the email - Steampipe for the FDW: https://hub.steampipe.io/plugins/turbot/imap - Supabase for the Postgres database / APIs: https://supabase.com - Remix for the frontend: https://remix.run/ Given the size of the mailing lists, I doubt the current approach will scale much further than the POC, so I'll need to re-think the architecture. I'll probably keep the emails in gmail and and leverage the FDW for older messages. Some things I'd like to do next: - explore alternative architectures - show attachments - add a REST API - add search - the size of the mailing lists might make this difficult - allow readers to toggle on a "markdown" view. Often authors use markdown syntax in their emails - add a light mode [0] Mailing lists: https://www.postgresql.org/list/

Visit product View on Hacker NewsAug 21, 2022

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

82%82% 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: created · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, 000 · 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.

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

44%44% 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

36%36% predicted probability of success on AppSumo, 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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