Sequential Programming Models, Linear Processing, Magnetic Computation, Storage Abstractions

LLM Optimization Notes: Memory, Compute and Inference Techniques
gaurigupta19.github.io·1d·
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💻Local LLMs
The Chip That Spoke Lisp
jxself.org·13h
🤖Lisp Machines
Implicit Models: Expressive Power Scales with Test-Time Compute
arxiv.org·21h
🎯Performance Proofs
Efficiently Updatable Neural Network (NNUE)
beuke.org·1d
📊Quantization
Toy Binary Decision Diagrams
philipzucker.com·2d
🧮Algebraic Datatypes
Latency vs. Accuracy for LLM Apps — How to Choose and How a Memory Layer Lets You Win Both
dev.to·13h·
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Performance Mythology
Fast Matrix Multiply on an Apple GPU
percisely.xyz·4h·
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SIMD Vectorization
Property-based testing of batch-invariant operations
mmaaz.ca·2d·
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🧪Property-Based Testing
The Next Computing Revolution: Bringing Processing Inside Memory
computer.org·1d·
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Hardware Transactional Memory
Making performant Python bindings to compiled code
kylebarron.dev·10h·
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🦀Rust Macros
Beginner's guide to making AI useful in production
medium.com·13h·
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Proof Automation
Walrus, A 1M ops/sec, 1 GB/s Write Ahead Log in Rust
nubskr.com·9h·
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💿ZFS Internals
A Primer on Memory Consistency and Cache Coherence, Second Edition
link.springer.com·2d·
Discuss: r/programming
Cache Coherence
LLM-Guided Evolutionary Program Synthesis for Quasi-Monte Carlo Design
arxiv.org·21h
🧮Kolmogorov Bounds
Rigorous Evaluation of Microarchitectural Side-Channels with Statistical Model Checking
arxiv.org·1d
🔧Hardware Verification
Beating the L1 cache with value speculation (2021)
mazzo.li·1d·
CPU Microarchitecture
A Solution to the Paperclip Problem
link.springer.com·22h·
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🔲Cellular Automata
SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
github.com·6h·
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Modern Compression
What You Didn't Learn in Berkeley CS 188: Intro to RL
neelsomaniblog.com·21h·
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🔲Cellular Automata