Brent Yorgey: Competitive programming in Haskell: range queries, classified
byorgey.github.io·4d
λLambda Encodings
Diving deep into Binius M3 arithmetization using Merkle tree inclusion as an example
blog.lambdaclass.com·3d
🌳Archive Merkle Trees
Introducing Northguard and Xinfra: Scalable log storage at Lin...
linkedin.com·2d·
Discuss: Lobsters
🌊Streaming Systems
Study Finds LLM Users Have Weaker Understanding After Research
slashdot.org·16h
🧠Intelligence Compression
Scaling Pinterest ML Infrastructure with Ray: From Training to End-to-End ML Pipelines
medium.com·2d·
Discuss: Hacker News
🧮Z3 Applications
Why Your Next LLM Might Not Have A Tokenizer
towardsdatascience.com·2d
🤖Grammar Induction
Learning to Be a Transformer to Pinpoint Anomalies
arxiv.org·3h
🤖Grammar Induction
Hong Mong 5 Development Treasure Case Sharing Buried Point Development Real Battle Guide
dev.to·1d·
Discuss: DEV
🔗Data Provenance
Introduction to Algorithms: What They Are and Why They Matter
dev.to·2d·
Discuss: DEV
🧮Kolmogorov Complexity
Development of MR spectral analysis method robust against static magnetic field inhomogeneity
arxiv.org·3h
🧲Magnetic Resonance
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework
arxiv.org·3h
💻Local LLMs
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks
arxiv.org·1d
🌀Brotli Internals
Computing Betti tables and minimal presentations of zero-dimensional persistent homology
arxiv.org·3d
🕸️Algebraic Topology
Open-Source AI Stacks for E-Commerce (2025 Guide)
dev.to·2d·
Discuss: DEV
🌀Brotli Internals
How Decision Trees Work: Real Demo and Simple Explanation
dev.to·17h·
Discuss: DEV
🌳Huffman Trees
ColumnTransformer and Pipelines in Scikit-Learn: Clean, Scalable, and Powerful Preprocessing
dev.to·12h·
Discuss: DEV
🌊Streaming Compression
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
arxiv.org·1d
🔍Information Retrieval
Machine Learning Fundamentals: active learning with python
dev.to·1d·
Discuss: DEV
🧠Machine Learning
A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior
arxiv.org·1d
📊Learned Metrics
DRIFT: Data Reduction via Informative Feature Transformation- Generalization Begins Before Deep Learning starts
arxiv.org·2d
🧠Machine Learning