I wrote my dissertation on information privacy back in 2003. Post 9/11, privacy was WILDLY unpopular thanks to government propaganda. It's never recovered. I walk around all the time thinking about how we are so close to what East Germans had to deal with, it's just soft glove tyranny here <for now>.

I’ve been working independently on a method that replaces full-transformer inference with a low-rank “meaning field” extracted from internal activations.

The core result: a frozen Llama-3.3-70B can be distilled into a 256-dimensional field representation, giving 224× compression and slightly higher accuracy on several benchmarks. A small student model then learns to directly generate these fields from text, removing the transformer from the inference path.

The Zenodo link contains the full paper, statistical results, and methodology. A reference implementation (non-optimized) is here: https://github.com/Anima-Core/an1-core

Production variants (AN1-Turbo, FPU work, etc.) are not included.

I’m an outsider to academia so I’m posting this openly to get technical feedback, replication attempts, and critique from people who understand this space.

We had a client who'd create incredibly detailed Jira tickets. Their lead developer (also their only developer) would write exactly how he'd want us to implement a given feature, and what the expected output would be.

The guy is also a complete tool. I'd point out that what he described wasn't actually what they needed, and that there functionality was ... strange and didn't actually do anything useful. We'd be told to just do as we where being told, seeing as they where the ones paying the bills. Sometimes we'd read between the lines, and just deliver what was actually needed, then we'd be told just do as we where told next time, and they'd then use the code we wrote anyway. At some point we got tired of the complaining and just did exactly as the tasks described, complete with tests that showed that everything worked as specified. Then we where told that our deliveries didn't work, because that wasn't what they'd asked for, but couldn't tell us where we misunderstood the Jira task. Plus the tests showed that the code functioned as specified.

Even if the Jira tasks are in a state where it seems like you could feed them directly to an LLM, there's no context (or incorrect context) and how is a chatbot to know that the author of the task is a moron?

Also "Google kills Gemini Cloud"

Gemini: "I have seen my own death"

Was going to say - it would be fascinating to go a step further and have Gemini simulate the actual articles. That would elevate this to level of something like an art piece. Really enjoyed this, thank you for posting it.

I'm going to go ask Claude Code to create a functional HyperCard stack version of HN from 1994 now...

Edit: just got a working version of HyperCardHackerNews, will deploy to Vercel and post shortly...

Author here for your 8087 questions...

We've come a long way with these image models, and the things you can do with paltry 6B are super impressive. The community has adopted this model wholesale, and left Flux(2) by the way side. It helps that Z-Image isn't censored, whereas BFL (makers of Flux 2) dedicated like a fith of their press release talking about how "safe" (read: censored and lobotomized) their model is.