Schlagbohrer

"Many places don’t respect privacy laws, in certain situations you may be forced to unencrypt your media, or worse, assumed to be guilty. A Veracrypt hidden volume is useful in the former situation, but not the latter. This is why I made Phantomdrive."

Can someone explain to me how this project is different than a Veracrypt hidden volume and also what he means that Veracrypt wouldn't help if you are assumed to be guilty? I thought Veracrypt hidden volumes aren't visible.

I have to point out that the united states absolutely does not, under any context, care about the health and wellbeing of children. If they did they would have good, easy to access free healthcare and support for families including parental leave, as well as a good public school system that served every single child adequately.

Really? You don't think astronauts and engineers get not only inspired by their work to make art, but inspired by art when they are working?

I'm like that. I see art exhibits which energize me and inspire me and give me ideas for technical things I want to build.

What should people do about this?

I see with qwen3.6-35B running in Pi that it gets stuck in loops when the 262k context gets more than 30% full.

We are not going to know we have crossed the line until it's been crossed and we can look back and say "Oops. We should have done something back then."

In this scenario OpenAI would have to have Huggingface's full cooperation in the deception, right? That seems challenging to achieve.

What is is about MSFT and OpenAI that the AI's they train and produce in-house always come out so warped?

Anthropic landed on a winning recipe with Claude's personality.

US Law doesn't actually encode the little workaround that "If you're really rich, none of this applies to you", it's hidden somewhere in the metadata of society

Wonderful passage, thank you for sharing.

My pedantic side wants to ask- Why not both? Luxurious space exploration AND meditative, poetic examinations of the human soul as well? I'd love to read Vonnegut's book someday while sitting in a nice research outpost on Titan, admiring great Saturn's crown out my window with my own eyes.

This is obvious marketing / PR bullshit. AI isn't inevitable, but we are told it is by the people who profit from building and using it and integrating it into everything.

I worry that there could be real DMCA style weight put behind it. People would still be able to pirate open weights models perhaps, but big penalties for ever getting caught with one, and an end to public discussion about them. That would kill development for anyone not in a big firm, for example if Reddit and Hacker News are legally forced to ban discussions or link sharing on these topics. This is where so many of us learn about these topics and keep apace of it.

Is this just IRC

Which LLM do you think they used to generate the keywords

Have you used this method yourself for long workflows and with contexts approaching 1 million tokens? It doesn't work very well. LLM context is nearly half unusable.

The director James Cameron stands out because he has only produced huge box office hits. Extremely hard to do like you said, and most artists and auteurs would be happy to have one great work they can be remembered for, even if it's amidst a dozen forgettable pieces.

They probably just vibe coded it with ChatGPT

The twin trends of AI slop being churned out at tremendous rate and deployed in everything, plus frontier AIs becoming really good at exploiting many small security holes to pwn boxen, is going to collide in an amazing cyberstorm that was predicted by every cyberpunk novel from the 80s X-D and we are gonna have front row seats 8-D

Amazing test. I wish I could spend $2000 of compute on having my own swarm build some weird piece of software with 1000 commits per second.

But it is now possible for a very non technical user to vibe code Grand Theft Auto II for fun. In two years they'll be able to vibe code GTA V for fun.

Likewise with many other things - people who don't know how to code can produce large custom programs which do what they needed. They're probably not amazingly well written nor maintainable by a human with limited time on their hands but that also doesn't matter at all for nearly all use cases.

Yes but what are LoRAs being tested for / trying to do in this case? I couldn't find any information there about what the actual output goal is for the LoRAs. Speed doing what?

I have been using the docling software for PDF and XLSX reading/viewing/comprehension by my local qwen3.6. Claude was telling me that docling includes within it a very small LLM model just to assist with what is basically "super OCR". We are definitely in this era of ultra-mega-huge frontier models and super-tiny-micro models, all finding their uses.

On an aside I tested docling by giving it the Ronin TTRPG rulebook PDF and it did an astounding job of converting it all into .txt and .md. Given the wild graphic design of the book that's pretty astounding. Next we'll see if it can read the other Borg books like Mörk Borg and Cy Borg, which are also famous for their messy information.

Becoming the person who helps set up local AI at firms

How much could we accomplish if Space Force's budget ($71B) were instead spent on astronomy and astrophysics?

The Xbox 360 was called that instead of the Xbox 2, because it was gonna sit on shelves next to the Playstation 3 and MSFT didn't want consumers to look at "2" next to "3".

There is tremendous investment and work being done in creating new high quality data sets. Some of this is happening in-house at various firms, such as Meta making their employees use AI for their workflow to generate high quality training data.

Also, AI companies get huge amounts of human input when people use their cloud models, including thumbs-up or thumbs-down on millions of outputs. So the usage of these cloud models is itself producing new, high quality datasets.

To extend this metaphor or the "million monkeys typing at a million keyboards", it occurs to me that if you can just slightly increase the intelligence of each sub-agent by a tiny amount, the overall end product can shoot up in quality tremendously.

If I can make my small fast AI model just a tiny, tiny bit more capable, and still run 100 of them or 1000 and run an evaluation model on top of that, the overall system capability will scale quickly with tiny increases in base model intelligence.

This is a fun idea but they'll be outdated in 6 months and it would take 12 months to develop, build and ship them assuming they can design and manufacture these at superhuman speeds.

Geoffrey Hinton pointed out that exponential growth can be hard to see in the near term (2-3 years) because that part is a bit within predictions, but, it's the exponential part which means 5+ years out is totally unpredictable.

I cannot imagine how much context window it needs for this. I am struggling with local AI in Pi to even get small (500-1000 lines of code) programs written without overrunning a 220k context. Doing all this EDA design and testing must surely require millions of tokens of context window right? Maybe they just do a far better job of architecting the work and sub-tasking it to sub-agents, each with their own context, to keep the primary context window from overfilling.