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Sure! This is a method that most AI systems haven’t discovered yet, but we’ve put it into practice. By treating the embedding space not as a static lookup but a
by TXTOS 1y ago
Sure! This is a method that most AI systems haven’t discovered yet, but we’ve put it into practice. By treating the embedding space not as a static lookup but as a dynamic field, we perform dimensional rotations of the text’s semantic vectors. This lets us generate new, coherent ideas by projecting and rotating meanings in high-dimensional space—far beyond simple retrieval or random guessing.
- kimiai06 1y agohmm ok but like… are u sure that’s not just fancy word math? like, when u “rotate” these vectors, how do u even know the meaning stays the same? wouldn’t it just… drift or get messy? idk maybe i’m dumb lol, just seems like it could get random real quick
- TXTOS 1y agoTotally fair, tbh — this is the part where most embedding stuff just, well, breaks. What I’m doing in TXT OS isn’t just spinning vectors for fun. Each “move” is kinda anchored by feedback inside (ΔS, we call it semantic tension). If it starts drifting too far off, it’ll catch itself and snap back — like some gravity well for logic, haha. And yeah, the rotations aren’t just random, they’re kind of “locked in” by these alignment planes (using λ_observe, basically language context gradients — sounds fancy but you’ll see what I mean if you poke around). Honestly, still feels experimental, but… so far it’s holding up better than I thought. If you’re curious, just type hello world in TXT OS and follow the steps — it’ll walk you through what’s going on under the hood. You can even throw dumb paradoxes at it and see if it goes crazy (or not).