Do Not Want, an extension to help you protest website anti-features

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Hello HN! A few days ago I saw a comment on reddit which proposed a service that allowed the user to spam a website's server logs with messages objecting to the presence of various anti-features like popups, noisy ads, and autoplaying content. The idea tickled my fancy, so I've put together a Chrome extension which detects if a page is using Google Analytics, and if so it injects a small script which sends bunk utm (urchin tracking module) data to the GA backend. It sets the traffic source to "DO NOT WANT!", and the campaign name to various things like "I do NOT want noisy ads!" Here's the extension on the Chrome Store: https://chrome.google.com/webstore/detail/do-not-want/pkejgbaojajbaimhneomoklkcejanpfd For screenshots, more info, and if you want to have a good laugh at my shitty code, here's the repo: https://github.com/sdmtr/donotwant Note: before you jump all over me, yes, I realise this isn't going to make a huge impact on anyone and it's unlikely that whoever's monitoring the site's analytics will even notice the appearance of a new campaign with virtually no traffic. It's just a bit of fun, although if you check the README in the repo you'll see a list of future ideas that I'd like to implement at some point which might actually make this extension useful to some degree.

Visit product View on Hacker NewsJan 28, 2017

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

12%12% 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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