LLMs to Blaze a Trail
blogger.com·2d
💾Persistence Strategies
Best Way to Recover Stolen Crypto
tracehackerscryptorecovery.com·1d·
Discuss: Hacker News
🔍Binary Forensics
Decoding UTF-8. Part III: Determining Sequence Length – A Lookup Table
nemanjatrifunovic.substack.com·1d·
Discuss: Substack
🔤Character Encoding
Pretraining a LLM with less than $50 budget which outperforms Google BERT
medium.com·2d·
Discuss: Hacker News, r/LLM, r/LocalLLaMA
🌀Brotli Internals
Show HN: A vibe port of security library libinjection from C to Rust
github.com·1d·
Discuss: Hacker News
🦀Rust Verification
IL-SLAM: Intelligent Line-assisted SLAM Based on Feature Awareness for Dynamic Environments
arxiv.org·11h
🏺Computational Archaeology
Explainability-Driven Dimensionality Reduction for Hyperspectral Imaging
arxiv.org·1d
📐Vector Dimensionality
Algorithmic Randomness and Probabilistic Laws
arxiv.org·1d
🧮Kolmogorov Complexity
MILO: A Lightweight Perceptual Quality Metric for Image and Latent-Space Optimization
arxiv.org·1d
📊Rate-Distortion Theory
🔥Embeddings: The Hidden Power Behind AI & Search
dev.to·1d·
Discuss: DEV
🧮Vector Embeddings
Secure Password Generator Based on Secure Pseudo-Random Number Generator
arxiv.org·11h
🔓Password Cracking
A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
arxiv.org·1d
🎧Learned Audio
Automated Prior Art Landscape Analysis via Semantic Graph Traversal and HyperScore Prioritization
dev.to·17h·
Discuss: DEV
🧭Content Discovery
Python Data Analytics for Deep Cleaning Service Insights
dev.to·1d·
Discuss: DEV
🌊Stream Processing
Graphical Quadratic Algebra
arxiv.org·11h
🔄Graph Rewriting
Solving Polynomial Systems with Gr\"obner Bases: An Introduction to F4 and FGLM
arxiv.org·11h
🧮SMT Solvers
TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering
arxiv.org·1d
🔗Parser Combinators
On the Alignment of Large Language Models with Global Human Opinion
arxiv.org·1d
🚀SIMD Text Processing
Benchmarking compression programs
maskray.me·4d
📦Deflate
Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
arxiv.org·1d
🧠Machine Learning