Aurora, an extensible Python static site generator

Hacker News

Aurora is a fast, extensible Python static site generator. With Aurora, I can generate my personal website [1] (~1,700 files, with multiple layers of jinja2 templates for each page) in < 4 seconds. Aurora generated 292,884 pages from a Hacker News post dataset in 2m:20s. I appreciate being able to see changes I make as soon as possible, so Aurora comes with hot reloading and incremental static regeneration for development. Refreshes and reloads are ~200ms, and trigger automatically. You can see a demo at: https://github.com/capjamesg/aurora/assets/37276661/39f62bd8... Aurora comes with a "hooks" API that lets you control state before page generation and after site build. I use the former feature to read locally-cached link previews. There is also a feature to load data from JSON and CSV files. I would love feedback! [1] https://jamesg.blog [2] https://jamesg.blog/2024/06/16/aurora-isr/

Visit product View on Hacker NewsJun 18, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews, soon · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% 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

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

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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