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Foundry: a Markdown-first CMS written in Go

Hacker News

Foundry: a Markdown-first CMS written in Go

Hi HN! I've been building a CMS called Foundry, brought together from multiple smaller private projects as well as greenfield code. The short version is: it's a CMS written in Go with a focus on markdown content, a simple hook-based plugin model, themes, archetypes, preview flows, and a clean authoring/developer experience. I started working on it because I wanted something that was more powerful than Hugo for a few of my websites, without having to resort to dangling onto a database. What seems different about it, at least to me, is that I'm trying to keep the system small in concept: local content, explicit behavior, compile-time plugin registration, and an admin/editor layer that is meant to stay close to how the content actually lives on disk. The goal is not to make "yet another website builder", but to make a CMS that is easy to use and quick to onramp onto, but has powerful advanced features and extensibility. Still early, but usable enough that I wanted to put it in front of people here and get feedback. Please don't castigate me on the UI look - I'm not a designer, and the themes are basically clones of each other. Happy to answer technical questions, architecture questions, or hear where this seems useful versus where it does not.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, code · Missing: mac, agents, macos
87%87% 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: started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
58%58% 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
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · 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
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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