I made a CMS for Astro content collections

Hacker News

I like writing content in Markdown and I enjoy Astro very much. When I tried out Astro's content collections feature, I couldn't stop wondering how would a nice packaging look for it, which would smooth out the rough edges around Markdown in static websites. So I made the app that I can enjoy when writing posts on my Astro-powered blog. It's been saving me a ton of time already. I don't need to: 1. Edit in iA writer then paste into VS Code. 2. Google YAML date format. 3. Generate Unix timestamps to prefix new files with. 4. Copy paste images and write down their URLs manually. 5. Peek at collection schema to remember which frontmatter fields am I supposed to fill out. Because it's exclusively made for Astro, it can integrate deeply with it and support Astro-specific features and conventions, like where images need to be stored or how to reference entries from other collections in the frontmatter. Also, this app has the coolest name of all my projects - Darkmatter. If you own or work with an Astro website, Darkmatter can be the content editing tool that makes you want to write again.

Visit product View on Hacker NewsSep 19, 2023

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6points

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

77%77% 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

51%51% predicted probability of success on Indie Hackers, 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

48%48% 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 · Strong signals: google · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · Missing: plus, platform, intuitive

38%38% 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

15%15% 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.

Correct prediction on native model

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