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Jampack – Optimizes static websites as a post-processing step

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Jampack – Optimizes static websites as a post-processing step

Hi! Jampack is a post-processing tool that takes the output of your Static Site Generator (aka SSG) and optimizes it for best user experience and best Core Web Vitals scores. As of today it can: - Optimize local images, CDN images or external images - Optimize above-the-fold vs below-the-fold - Limit images max width - Inline critical CSS - Prefetch links on scroll - Improve browser compatibility - Auto-fixes HTML issues - Warn for HTML accessibility issues - Compress all assets in the end It processes directly the static output so it's compatible with any SSG or framework. We are intensively using it as a post-processing step to our Astro websites for example. With Jampack, we end-up focusing more on how simple, readable and maintainable our code is, throw images of any size, and let it optimize for maximum performance. We hope this can be helpful to lot of people! Cheers, Georges and the ‹div›RIOTS team!

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% 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
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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