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Self hosting and version control for your 3P JavaScript

Hacker News

Self hosting and version control for your 3P JavaScript

Hey all - we decided to build an open-source ( https://www.github.com/swishjam/tagsafe ) tool to self-host and version control all of your third party javascript on your website's frontend. What's the problem with third party javascript? Honestly, there's quite a few issues. Here are some of the most relevant ones: 1. You don't have control over what the 3P JS does on your site or when updates are made to it. With each 3P tag added to your site, you give the ability for someone else to push changes to your site without your knowledge or control. 2. There are lots of performance tradeoffs with adding a third party tag to your site. Over the years, the web has become flooded with 3P JS, it's quite common for there to be more 3P JS than first party. The browser has to go through a bunch of additional work for each new domain it has to download content from. 3. Are your 3P JS providers using best practices? Most people today simply trust their 3P JS is using efficient caching, minified code, and global CDNs. We built a simple tool to help alleviate some of these pains that gives you control of your third party JS. We do this by: 1. Tagsafe captures and hosts the content of each 3P tag you specify and hosts them behind a single CDN, consolidating all of your third party JS to a single domain. 2. We then monitor the "origin" for changes. When a changes is made, we will capture a new version of that tag and host it. This gives you the ability to view the changes made by the tag provider through a git-diff like view, and rollback capabilities so that you control what version is live on your site. 3. We have a small, inlined JS snippet that you place at the top of your web pages. The JS will intercept 3P script tags added to your page and re-point them the Tagsafe CDN version. This is an early version, and we have lots of ideas of where this could go. We'd love any thoughts, feedback, or contributions as we continue to build this out.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
78%78% 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
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host, efficient · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
13%13% 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.

Correct prediction on native model

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