This should be the way. Have a tiny burner phone for maps and any apps that you absolutely can't use without google(it should be a tiny set of < 10 apps hopefully) until you can fully de-google
My current de-google project is categorizing all my pictures on my local NAS to create the memories feature (where it shows historic pics on multiple theme axes). You can get really far with just a few hours of work a month to de-google and some off the shelf image embeddings.
The hero project in this category — what one cannot do trivially as an indie dev — is creating a great fresh PoI dataset. This is tough to do on a planetary scale because its a societal cooperation problem.
The problem with this is gmaps. There is no alternative to it and by the nature of it knowing your location it removes anonymity. I would buy, or even pay a monthly fee, for something that is 75% as good as gmaps but respects your privacy but there is nothing out there I have found.
I've been using the planning framework from Matt Pocock on very typical brownfield code. I use a harness over claude code, this is so cheap that I would be tempted to mirror my initial prompt to it and compare their responses to the task.
The phrase going around the interwebs is "You can outsource your thinking but not your understanding". A phrase that can at times seem like this weird human<>llm endless loop; depending on what you think you understand and what the llm "thinks" to help you understand, it can seem like an LLM also understand. But it does not.
Its clear one can't really think about anything without building a basic understanding about it. Worth stating that these are distinct from learning. But, I would argue that it is important to know what you *have* to understand now and why is that important. An LLM can help you understand a great many things, you just need to know what you are looking for and that is something no artificial intelligence can really *do* for you. Trial and error, building a sense of self awareness, and talking to people is a better way to know what this is especially for fairly open ended problems.
Its already pretty easy to oneshot an extension aiding scraping and LI can do nothing about it. I've seen people build and install a local chrome extension in a couple of days and have an AI inject itself into devtools and scrape pretty much any website. And that was a few months ago. I don't think there is an easy way to defend against such things anymore. Its a matter of time that defensive programming measures like this become useless.
I use firefox with uBlock Origin's matrix turned on linked in and its cdn is explicitly black listed globally on it. I see links like ~`licdn` or some shit appear with a lot more frequency on webapps in the matrix now a days. I would recommend you all install it and block it actively.
About the "they asked us to view it and then fired us for it". Having worked in their RL division(I don't work at meta anymore) this story is quite weird for two reasons:
1. Meta AFAIR paid/compensated people — contractors or recruited via ads — to have them submit their data. There are strict privacy protocol and reviews in place to distinguish data use in these cases vs gen public. This is not to say the process is perfect, but if these users are gen public, I would be very shocked.
2. Hiring contractors to submit data is a more controlled environment VS recruitment of gen pub via ads to submit data, but the former has more well understood privacy disclosures than the latter. This means in practice asking contractors to wear glasses and "move around their surroundings naturally and do things" goes well with basically the privacy practice "the data your are submitting we can view and use all of it for purpose X and nothing but X". BUT this framing is with ad based recruited people — which are general users who willingly submit data — is much much harder. My suspicion is they are running ad based recruiting in general public and while those users may have signed a privacy statement it is very surprising that they did not tighten the privacy practices around the use of the data and who has access.
I can find no incentivized people or contractors collecting data in this story. Just normal users who had their data siphoned up and reviewed by the contractors in this report. I don't understand where your view of this story is coming from given the article.
To be clear we are — in the service of speed — trying to bake data into compiled program output binaries, which is ostensibly faster because code-pages in memory when executed by CPU as instructions already have it?
No you are baking data into the compiled program so that you can perform compile time operations on that data. These compile time operations are most often used to guard against erroneous runtime behaviors.
If all you want to do is bake data into a compiled program, there is the #embed feature added to C++26.
The point of the article and of define_static_array is to convert stuff like constexpr std::vector to constexpr std::array.
New (and delete) can be used in constexpr functions, however memory "allocated" like that cannot leave the constexpr "sandbox" so to speak, therefore std::vector cannot be generated at compile-time, but std::array may.
If you are working with fixed-size data like LUTs, just use std::array [1]
[1] Make sure not to use std::to_array when embedding 200KB+ files, as it's a mere constexpr function and not a language construct and will exceed constexpr limits; either specify the size or use a C-style array in this case
My current de-google project is categorizing all my pictures on my local NAS to create the memories feature (where it shows historic pics on multiple theme axes). You can get really far with just a few hours of work a month to de-google and some off the shelf image embeddings.
The hero project in this category — what one cannot do trivially as an indie dev — is creating a great fresh PoI dataset. This is tough to do on a planetary scale because its a societal cooperation problem.