Your personal insult aside, resonance is a fundamental term in physics and harmonic oscillators are fundamental to quantum field theory and modern physics. Music was a metaphor- this isn’t Nature.
I’m sharing not arguing. This is the comment section of a website. I sold my autonomy for a wage doing other things, but I happily accept my affliction of contemplating the universe. Maybe it will spark something in the imagination of someone. Amateurs thinking is what led humanity to this point. I clearly stated my lack of domain expertise- but I reserve my right to unprofessionally question foundations and reject treating silence about first principles as intellectual virtue. I also accept, with grumbling, the downvotes.
Waves don’t come from pressure. Pressure comes from constrained waves… constraints prevent oscillatory relations from freely satisfying their phases. Pressure is a local manifestation of the same idea behind gravity. When many interacting modes lock into a persistent configuration, they impose constraints on nearby modes. To us on the inside it looks like curvature and attraction. But the comment section on HN is a bloodsport…
No pip freeze to lock the doom,
No tangled trees in darkened bloom,
No maintainers tricked by phishing spree —
In C I hold the memory key.
Through buffer, pointer, syscall roar,
I own the land, I own the shore;
Let Python’s spiders weave their scheme,
I’ll keep my ship rock-steady in C-stream.
You could be right. But it could also be due to things like: automatic 401k injections into the market, easy retail investing, and general speculative attitudes.
Isn’t NVIDIA fabless? I imagine (I jump to conclusions) that design is less of a challenge than manufacturing. EUV lithography is incredibly difficult- almost implausible. Perhaps one day a clever scientist will come up with a new, seemingly implausible, yet less difficult way, using “fractal chemical” doping techniques.
I’m curious where you got your training data? I will look myself, but saw this and thought I’d ask. I have a CPU-first, no-backprop architecture that works very well on classification datasets. It can do single‑example incremental updates which might be useful for continuous learning. I made a toy demo to train on tiny.txt and it can predict next characters, but I’ve never tried to make an LLM before. I think my architecture might work well as an on-device assistant or for on-premises needs, but I want to work with it more before I embarrass myself. Any open-source LLM training datasets you would recommend?