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emacs-ssg file-based routing site generation with and without Emacs

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emacs-ssg file-based routing site generation with and without Emacs

Emacs' org-mode is great to write documents (.org files, similar to .md) and export them to HTML. It is also practical to evaluate and export "code blocks", like "notebooks" would do (observable framework, jupyter, mdx etc.). With emacs-ssg, we don't have to use emacs to write the document, but in the background it is used to evaluate the files and produce the HTML result in a `./public` folder. With `npm create @sctlib/emacs-ssg` we get the textual list of the few steps it takes to install and publish a project with content. This project is mainly a wrapper around emacs and org-mode, to allow usage in the npm ecosystem and ways of publishing websites and pages. It tries to add nothing but a formal way to organize content in `/content` for each pages and `/assets/{js,css,…}/` for the static assets.

Visit productView on Hacker NewsFeb 23, 2024
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3points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, code · Missing: agents, macos, agent
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 · 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: lua, io · Missing: https docs, excited, just released
52%52% 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
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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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