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.
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'd just say "market irrationality" and call it a day. TSMC is far closer to a monopoly than NVIDIA is, and they win no matter which fabless company is buying their capacity.
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.
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?
huggingface has plenty of openai and antrophic user to assistant chains, beware there are dragons (hallucinations), but good enough for instruction training. I actually recommend distilling kimi k2 instead for instruction following capabilities.
The training data is contained inside main.rs; it looks like about 50 statements about general facts. Probably to keep training time down. If you go off-script things fall apart pretty quickly:
Enter prompt: hello
Model output: What are eclipses ? Assistant : Eclipses occur when one celestial body moves into the shadow of another </s>
Enter prompt: what are facts
Model output: eclipses Assistant : Eclipses Eclipses What What was when What through you ? through you meet through using of What was What tall ? of What thank help you explain using , and do you a patterns you you a patterns through air in tall you help metal metal </s>
Enter prompt: how are mountains formed?
Model output: I ? ' I ' : Of : in happy Hi wind in yeast altering it it </s>
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.