Optimize a Static Website with Jampack
Practical guide to optimizing a static website with Jampack, improving PageSpeed, and understanding why it outperforms LQIP for better FCP and LCP.
I spent far too many evenings trying to move my blog from “acceptable” to “actually fast” in Google PageSpeed. The frustrating part was that the site is static and should already be quick, but a few large images and render-blocking asset decisions kept dragging scores down, especially on mobile. After trying different image tricks, the biggest improvement came when I stopped tuning individual pages manually and moved optimization into a repeatable post-build step with Jampack.
🧠 What Jampack Does in a Static Site Pipeline
Jampack runs after your static site generator has finished and optimizes the generated output. That detail is important, because it means I do not have to mix optimization logic into Markdown content, template files, or custom Jekyll helpers. I let Jekyll generate _site, then Jampack takes that output and rewrites assets for production delivery.
In practice, this approach reduces payload size, improves asset delivery, and removes a lot of manual work. Instead of hand-converting images one by one and hoping I did not miss a page, I get a consistent optimization pass every time I build for production.
🚀 Why This Is Needed for Better Google PageSpeed Results
Google PageSpeed does not care how clean your source files are; it evaluates what users actually download and render in the browser. That is why static sites can still score poorly when media assets are too heavy. In my case, most score drops came from image-heavy posts where the browser had to fetch and decode too much before the page looked complete.
Once I switched to a Jampack-based output optimization flow, the site became more predictable under test conditions. The main win was not a single “magic” tweak, but that the final deployed assets became consistently smaller and faster to render. That consistency is what helps when you run PageSpeed multiple times and want stable, reproducible improvements.
📈 FCP and LCP in Plain Terms
When you spend hours with PageSpeed, two metrics quickly become unavoidable: FCP and LCP.
FCP (First Contentful Paint) is the moment users see the first real content instead of a blank page. If FCP is slow, visitors feel like nothing is happening and often leave early.
LCP (Largest Contentful Paint) measures when the biggest visible element is rendered, which is very often a hero or article image on blogs. If LCP is slow, users see partial content for too long and perceive the page as sluggish, even if interaction is technically possible.
For static blogs, image optimization has direct impact on both metrics. Smaller and better-delivered assets usually make first content appear sooner and reduce the time until the main visual element is fully rendered.
🆚 Why I Prefer Jampack over LQIP as the Baseline
I used LQIP techniques before, and they can improve perceived smoothness by showing a blurred placeholder while the full image loads. The problem is that this is mostly a presentation strategy, not a full delivery optimization strategy. The full-size image still needs to be transferred and decoded, and on slower mobile networks that cost is still visible in Core Web Vitals.
Jampack attacks the root issue by optimizing the final production assets themselves. That means the browser has less work to do from the start, and the improvement shows up in measurable timings, not only in visual tricks. LQIP can still be useful as an additional enhancement, but I no longer treat it as the foundation for performance work.
LQIP improves perceived loading, while Jampack improves real delivery cost. For PageSpeed-focused optimization, I get better results by starting with Jampack.
🛠️ A Practical Workflow That Stays Maintainable
The workflow that finally worked for me is simple: build the site, optimize the generated output, then deploy the optimized result. This keeps performance work inside CI/CD and prevents regressions when new posts are added with larger screenshots or diagrams.
The main benefit is maintainability. I no longer need to remember special per-post image handling rules, and I no longer depend on one-off local conversions done weeks before a release. The optimization step is part of the pipeline, so every deploy gets the same treatment.
🧠 Final Thoughts
After spending many hours fighting PageSpeed, my biggest lesson is that static sites still need a production-grade optimization step. Jampack makes that step practical because it works on the final build output instead of forcing complex content-level workarounds.
LQIP still has value as a visual polish technique, but it does not replace real asset optimization. If your goal is stronger FCP and LCP and consistently better PageSpeed results, a Jampack-first workflow is the approach that has given me the most reliable results.
Sources
- Jampack - https://github.com/divriots/jampack
- PageSpeed - https://pagespeed.web.dev
