201–210 of 294 posts
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I have 26tb hardrives, 50gb doesnt scare me. Or should I be?
I think you'd want things in RAM for performance reasons but would love to be corrected by people with more knowledge/experience on the subject
Hacking officially stopped being non-political in EU.
https://artificialintelligenceact.eu/
Enjoy understanding this here: https://artificialintelligenceact.eu/article/3/
Measures of Innovations rank at... Article 57! https://artificialintelligenceact.eu/ai-act-explorer/
I bet that soon, anyone involved with sophisticated AI systems will be system-checked and require a license.
God bless you all out there and have phun!
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Sure but you're also constrained to only one platform. It's like the C++ vs Python argument in ML, yes writing everything in low level high speed highly optimized native code would be perfect, but ain't (almost) nobody got fucking time or skill for that.
Cross-platform toolkits are (still) a thing.
Qt is such a pain to work with it's almost like it's intentional that people should avoid it.
Any way to install this via just a container? Similar to a `docker compose up -d` that a lot of projects offer. Just download the docker-compose.yml file into a folder, run the command, and you're running. If you want to delete everything, just `docker compose down` and delete the folder, and the container and everything is gone. Anything similar to that? I don't want to run a random install.sh on my machine that doe…
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Huh? Bleeding edge laptops can last a lot more on battery. M3 16'' mbp lasts definitely enough for a full office day of coding. Twice that if just browsing and not doing cpu intensive stuff.
Even the M4 Max is not "bleeding edge". Apple is doing impressive stuff with energy efficient compute, but you can't get top of the line raw compute for any amount of financial of energy budget from them.
This is fantastic work. The focus on a local, sandboxed execution layer is a huge piece of the puzzle for a private AI workspace. The `coderunner` tool looks incredibly useful. A complementary challenge is the knowledge layer: making the AI aware of your personal data (emails, notes, files) via RAG. As soon as you try this on a large scale, storage becomes a massive bottleneck. A vector database for years of emails c…
Having locally distributed similar grounds is one thing. Push everyone to much in its own information bubble, is an other orthogonal topic.
When someone mind recall about that email from years before, having the option to find it again in a few instants can interesting. But when the device is starting to funnel you through past traces, then it doesn't matter much whether it the solution is in local or remote: the spontaneous thought flow is hijacked.
In mindset dystopia, the device prompts you.
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Everyone benefits from focusing on efficiency and finding better ways of doing things. Those people with 4TB+ of fast storage can now do more than they could before as can the "bottom tier." It's a breath of fresh air anytime someone finds a way to do more with less rather than just wait for things to get faster and cheaper.
Of course. And I am not arguing against that at all. Just like if someone makes an inference runtime that is 4% faster, I'll take that win. But would it be the decisive factor in my choice? Only if that was my bottleneck, my true constraint. All I tried to convey was that for most of the people in the presented scenario (personal emails etc.) , a 50 or even 500GB storage requirement is not going to be that primary co…
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Good point! Maybe indexing is a bad term here, and it's more like feature extraction (and since embeddings are high dimensional we extract a lot of features). From that point of view it makes sense that "the index" takes more space than the original data.
Why would the embeddings be higher dimensionally than the data? I imagine the embeddings would contain relatively higher entropy (and thus lower redundancy) than many types of source data.