Open-source Page speed tooling. Real user monitoring and Audits

Hacker News

Hey all - Swishjam is open-source (<a href=" https://www.github.com/swishjam/swishjam">https://www.github... ) frontend page speed tooling. Swishjam collects all of the page speed/performance related metrics from each of your real users' experiences as they visit your site (such as Core Web Vital metrics, resource load times, and more), and then makes sense of it for you by giving you intuitive visualizations and actionable insights on where performance bottlenecks exist on your site. Swishjam’s goal is to simplify performance and ultimately automatically apply performance improvements to your site on your behalf such as auto-image compression, CDN hosting, and strategically loading your page resources. The web today is littered with massive javascript files, unoptimized images, and poor resource loading strategies, resulting in slow web sites and poor experiences for end users. We initially set out to solve for the third party javascript that is slowing down sites, however after talking with many users it became clear that there was a larger need helping users improve the overall performance of their entire frontend. While these users agree performance is of utmost importance to their web experience, most simply don’t know where to start to tackle the issue. The existing tools on the market today are complicated, and don’t provide actionable insights as next steps. While there are other options that exist to collect performance metrics (ie: CrUX data - https://developer.chrome.com/docs/crux/ ), we want to provide a simple, open-source way of collecting and visualizing your performance metrics, and give you actual actionable next steps to improve performance rather than leave it up to you to figure it out.

Visit product View on Hacker NewsApr 17, 2023

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Actual performance

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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

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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