Dogukan Tuna

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hi!! I usually go by the "dthinky!" alias, though my name is Doğukan.

>> doing research.

>>> energy hardware, multi-physics solvers & RL/CUDA kernels.

building two things:

Mythein Labs, a neo-lab building the design systems for compact energy hardware — simulators, agents and RL that find what nobody would draw.
Manuel AI, superhuman HVAC & energy reasoning.

excited about flourishing in a world with MAS at 100 trillion params, together.

staring at nature · thinking about thinking machines · prob. rn making fun with fable 5

Research

A running log of posts, experiments, and longer notes from the work. View all

Neural Networks & New Kinds

Compression is how I think about learning. The tighter a model can compress its inputs, the more structure it has actually found. Kolmogorov complexity makes this precise. It measures the length of the shortest program that produces a given output, which turns out to be the theoretical floor for any compressor.

The Ultimate Compressor

K(X) = length of the shortest program that outputs X. For any computable compressor C and all strings X:

K(X) ≤ |C(X)| + K(C) + O(1)

via the simulation argument: run C inside a universal machine

The Catch

K(X) is uncomputable. You can never know the true shortest program. But a deep network is a finite parallel computer that approximates it with bounded resources.

Why Neural Nets are Compressors

Neural nets can simulate arbitrary programs.
They are small computers, circuits wired by data.
SGD searches over the space of programs they can express.

Micro-Kolmogorov Complexity

Fix an architecture, then fit a network with SGD. The bit-length of the resulting weights is a practical proxy for description length:

min_{f ∈ F} [ loss(f) + λ · micro-K(f) ]

micro-K(f) ≈ bit-length of weights in a fixed architecture

Shorter description length → better generalization.

I'm deeply invested in methods that make learning systems compress harder and generalize further.