PLEX: Perturbation-free Local Explanations for LLM-Based Text Classification
arxiv.org·6h
💻Local LLMs
maxsim-cpu: Maximising Maxsim Efficiency
mixedbread.com·1d·
Discuss: Hacker News
🌊Streaming Algorithms
Binary Search in a 2D Matrix
mmhaskell.com·2d
📐Error Bounds
Translation using deep neural networks – Transformer
aamster.github.io·1d·
Discuss: Hacker News
🧠Neural Codecs
Context-Aware Regularization with Markovian Integration for Attention-Based Nucleotide Analysis
arxiv.org·1d
🧠Machine Learning
What can we expect of LLMs as Software Engineers?
chelseatroy.com·1d
📝Text Parsing
Computer Architecture: A quantitative approach
ikerexxe.github.io·2d
🖥️Hardware Architecture
CLIP Model Overview : Unlocking the Power of Multimodal AI
towardsdatascience.com·1d
📊Learned Metrics
Students, here are 5 key things to know when learning how to train large language models
techradar.com·17h
💻Local LLMs
Compressed data structures for Heegaard splittings
arxiv.org·6h
🕳️Persistent Homology
News for June 2025
ptreview.sublinear.info·2d
🕸️Graph Algorithms
It’s Not What You Pay, It’s How Fast You Play: A History of MEV
hackernoon.com·19h
🖥️Terminal Renaissance
What are Matrix Operations?
dev.to·8h·
Discuss: DEV
📐Linear Algebra
Language Models for Adult Service Website Text Analysis
arxiv.org·6h
📝Text Parsing
Enabling Rapid Genomic Analysis with Illumina Dragen on Amazon EC2 F2 Instances
aws.amazon.com·12h·
Discuss: Hacker News
⚙️Compression Benchmarking
Types That Count: Journey across Qualitative and Quantitative Intersection Types
iris.unito.it·1d·
Discuss: Hacker News
🔍Type Inference
Show HN: Context Rot Technical Report – How Input Length Impacts LLM Performance
research.trychroma.com·1d·
Discuss: Lobsters, Hacker News
🔍Information Retrieval
Dynamic ClassFile Generation To Save 15.5 Nanoseconds
github.com·2d·
Discuss: r/programming
✅Format Verification
Adding lookbehinds to rust-lang/regex
systemf.epfl.ch·1d·
Discuss: Lobsters, Hacker News, r/programming
🔍RegEx Engines
🎯 Building Attention Mechanisms from Scratch: A Complete Guide to Understanding Transformers
dev.to·2d·
Discuss: DEV
📊Learned Metrics