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Joined 2 years ago
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Cake day: July 4th, 2023

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  • All of these people who don’t apply the things they learn in school just don’t really think that much in my opinion.

    When I was in the military in a leadership class, we had to use a protractor to calculate angles and distances on the map given a bunch of coordinates. I realized these were all right triangles, said fuck the protractor, and used trigonometry to get exact answers. I earned distinguished honor graduate, ie top of the class, despite my lab nerd POG ass being mixed in with a ton of infantry and ranger battalion guys.

    I use dimensional analysis on a near daily basis because it’s just so damn handy. You can convert anything to nearly anything else as long as you have some numbers with the appropriate units in between.




  • It’s definitely the patterns. What I’m saying is the patterns are not any different for cannabis versus any other photoperiod plant. Cannabis isn’t the only thing you veg under 18/6 and flower under 12/12 light cycles. It’s not illegal to grow plants indoors and if I spend 5kW doing it every day that’s nobody’s business but my own. I’ll use my joules however I feel.

    That said, it seems like the way to defeat this type of analysis would be to invest in batteries so you can always have a constant 24h drain rather than 5kW turning on every day at the same time for 18 or 12 hours.













  • I’m not pretending to understand how homomorphic encryption works or how it fits into this system, but here’s something from the article.

    With some server optimization metadata and the help of Apple’s private nearest neighbor search (PNNS), the relevant Apple server shard receives a homomorphically-encrypted embedding from the device, and performs the aforementioned encrypted computations on that data to find a landmark match from a database and return the result to the client device without providing identifying information to Apple nor its OHTTP partner Cloudflare.

    There’s a more technical write up here. It appears the final match is happening on device, not on the server.

    The client decrypts the reply to its PNNS query, which may contain multiple candidate landmarks. A specialized, lightweight on-device reranking model then predicts the best candidate by using high-level multimodal feature descriptors, including visual similarity scores; locally stored geo-signals; popularity; and index coverage of landmarks (to debias candidate overweighting). When the model has identified the match, the photo’s local metadata is updated with the landmark label, and the user can easily find the photo when searching their device for the landmark’s name.


  • It’s not data harvesting if it works as claimed. The data is sent encrypted and not decrypted by the remote system performing the analysis.

    From the link:

    Put simply: You take a photo; your Mac or iThing locally outlines what it thinks is a landmark or place of interest in the snap; it homomorphically encrypts a representation of that portion of the image in a way that can be analyzed without being decrypted; it sends the encrypted data to a remote server to do that analysis, so that the landmark can be identified from a big database of places; and it receives the suggested location again in encrypted form that it alone can decipher.

    If it all works as claimed, and there are no side-channels or other leaks, Apple can’t see what’s in your photos, neither the image data nor the looked-up label.