Swishjam (YC W23) - simple open source front end performance monitoring

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Hey all - Collin here, co-founder of Swishjam ( https://www.swishjam.com ). Today we're excited to launch our simple, open-source ( https://www.github.com/swishjam/swishjam ) frontend performance monitoring solution to the world. Swishjam collects all of the 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. 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. 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. 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 dead 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. This is an early alpha version, we plan to continue to roll out new monitoring and optimization tools in the coming weeks. If you're interested in web performance and optimizing page speed we'd love to chat.

Visit product View on Hacker NewsMar 9, 2023

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

87%87% 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, new, visual · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, open source · Missing: https docs, just released, lua

69%69% 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

43%43% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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