Xiao Lin

[Xiao Lin @much_science]

@SteakandChickn "Just run everything on a GPU" is the new "just have an NPU"

[Xiao Lin @much_science]

@TheUnjournaling quantum too

[Xiao Lin @much_science]

@realmemes6 @BullTheoryio You get a free leveraged stock without the 1% fund management fee.

[Xiao Lin @much_science]

@AnalysisFact Too much for calculus but might be a good topic for machine learning.

[Xiao Lin @much_science]

@xyz3c7a @__tinygrad__ @tenstorrent That came with a cost of constant vllm and kernel updates for supporting new LLMs. Won't make sense at low market share. Performance being too sensitive to optimization tweaks is the issue.

[Xiao Lin @much_science]

@pmddomingos with verifiable rewards

[Xiao Lin @much_science]

@t0kenl1mit Distributed LLM inference using x3d cache

[Xiao Lin @much_science]

@Giuliano_Mana Now, hopfield networks

[Xiao Lin @much_science]

@AlexanderKalian @PlasmoLab Transformer was the graph architecture. It has permutation equivariance for graph nodes and positional encoding for adjacency. Works well on knowledge graphs and even ARC-AGI.

[Xiao Lin @much_science]

@littmath What else is out there that can be falsified so quickly

[Xiao Lin @much_science]

@geohotarchive Much of the confusion is not on the pie size but on the design. There could be a self-propelled robotics industry that occupies, mines, powers, builds more robots and is well defended, but no human jobs and no human-relevant work.

[Xiao Lin @much_science]

@cosminnegruseri I read this 10 years ago so memory is blurry. Not exactly logN, but it's more like adding a dial between memory and recomputation.

[Xiao Lin @much_science]

@cosminnegruseri https://t.co/ZgD0KPO9oB

[Xiao Lin @much_science]

@cosminnegruseri Google pulished log N gradient checkpointing

[Xiao Lin @much_science]

@ziv_ravid Reasoning can be hardware intensive too even at few parameters. Think PDE solvers, larger machines are still needed.

[Xiao Lin @much_science]

@andrewgwils Did electromagnetism came out of
A. more physics experimental data,
B. more computing or
C. our genes through billions of years of evolution?

[Xiao Lin @much_science]

@AnActualSparrow That'd fit 4x John Nash's entire thesis.

[Xiao Lin @much_science]

@ryanlpeterman TIL it took him 3 years to publish it.

[Xiao Lin @much_science]

@t0kenl1mit Home distributed inference is hard capped by 10gb eth and 1ms latency though. Might also need some work on model design and hardware.