Peerdiem — Discover and discuss one painting a day

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Hi everyone, I made Peerdiem (a portmanteau word between Peer and Per Diem, which means Per Day in latin). The idea is very simple, a new painting or artwork to discover and discuss with your peers every day. Content is currently only fetched from Chicago Art Institute Free API [1] but I'm planning to add more sources in the short future. It was built with a couple of technologies I wanted to try for some time. Frontend is made with Preact and styled with Tailwindcss. Backend consists of an FastAPI app deployed in a Docker container. [1] https://www.artic.edu/open-access/public-api

Visit product View on Hacker NewsJul 27, 2022

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Product HuntUnlikely to reach the leaderboard · Strong signals: dock, new, open · Missing: mac, agents, macos

47%47% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

32%32% 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

19%19% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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