71–80 of 112 posts
> We tested AdFlush on a dataset of 10,000 real-world websites, achieving an F1 score of 0.98, thereby outperforming AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84). Additionally, AdFlush significantly reduces computational overhead, requiring 56% less CPU and 80% less memory than AdGraph. We also assessed AdFlush's robustness against adversarial manipulations, demonstrating superio…
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If Google's goal is to thwart adblockers by creating limitations on what browser extensions can do, then creating a browser extension that blocks ads within the current set of limitations is a temporary solution at best.
Google doesn't control the browser, user does.
Unless you’re running that 20%, Google controls it, and they basically write the standards anymore.
Without comparison to the accuracy of crowed sourced blocklists it's not that valuable. Maybe there is a group of hopelessly overworked blocklist maintainers/contributors, that I'm not aware of. If so, their cries for help don't seem to make the HN front page. From a user perspective, blocking banner ads feels like a basically solved problem. I think the real pain point here is that for large chunks of the web, there…
There will never be a solution to native ads. It's part of the content you choose to consume, that someone produced. The only way to avoid native ads is to stop consuming content that relies on ads.
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I guess that's why uBO Lite exists :) I started using it a couple of months ago instead of Ublock Origin, and still haven't seen any ads since. https://github.com/uBlockOrigin/uBOL-home
I think eventually there is nothing that can stop certain adds on Chrome once specific API's are removed, even using manifest 3. Maybe someone could chime in on this as its really confusing now since Google keeps pushing back the date to remove manifest 2. (This might be outdated info)
AdFlush (F1 Score: 0.98) seems to do better than some other adblockers: AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84), but it begs the question: why not compare to the most popular adblockers: uBlock Origin, Adblock Plus etc. I think the authors want to compare apples with apples, so they only compare their algorithm to other adblockers that use algorithms, as opposed to those whi…
The filter based adblockers are at risk though, with Google's new extension thingy that - at least a few years ago, I haven't heard from it since - limited the amount of rules. If there's a non-rule based system that is 98% effective then that would circumvent the arbitrary rule limits that Google set.
Always a joy to see efforts in the ongoing battle against advertisements. There are few things I feel radical about, and Ads are one of them. I believe they are a drain in several ways: They waste computational resources and electricity on both ends. They compromise the visual design and layout of webpages. They distract and take mental energy away from the user. They make the internet (and anywhere ads exist) more "…
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This is true. Extensions currently (manifest v2) are able to evaluate net requests dynamically, and are able to modify requests according to a dynamic ruleset that the extension can retrieve from some filter list published on the internet. Under manifest v3, extensions are not able to dynamically inspect requests, instead, they may only apply rules to net requests. Even worse, there is a limitation of only 5000 rules…
Chrome allows at least 30000 static rules + 30000 dynamic rules[1]. [1] https://developer.chrome.com/docs/extensions/reference/api/d...
What's fascinating here is AdFlush is a classical feature engineering approach: define a bunch of features on the data manually, and then use ML to figure out the most useful / impactful ones. This is not the "throw terabytes of data and see what happens" approach we see with LLMs. It's a bit funny to even point this out because I don't recall the last time a feature-engineered ML project made it to the HN front page…
The superior score was an F1 of 0.86 vs 0.84 for AdFlush vs uBlock Origin, and it's not clear to me that this is a statistically significant difference. They do not claim it is.
What's fascinating here is AdFlush is a classical feature engineering approach: define a bunch of features on the data manually, and then use ML to figure out the most useful / impactful ones. This is not the "throw terabytes of data and see what happens" approach we see with LLMs. It's a bit funny to even point this out because I don't recall the last time a feature-engineered ML project made it to the HN front page…
More specifically, page load time was 2.7 seconds without adblocker, decreased to 2.1 with uBlock Origin, but increased by 250% to 6.6 seconds with AdFlush, or increased to 3.4 seconds with AdFlush retaining prior predictions. The superior score was an F1 of 0.86 vs 0.84 for AdFlush vs uBlock Origin, and it's not clear to me that this is a statistically significant difference. They do not claim it is.
What's fascinating here is AdFlush is a classical feature engineering approach: define a bunch of features on the data manually, and then use ML to figure out the most useful / impactful ones. This is not the "throw terabytes of data and see what happens" approach we see with LLMs. It's a bit funny to even point this out because I don't recall the last time a feature-engineered ML project made it to the HN front page…
For Brave browser users, you can see what hardcoded lists you're using at brave://adblock .
As for the whole cat and mouse game, how to detect an "ad" if it's served with the content fully sever-side? Now _that_ needs some serious ML to decipher.