Title TBA
usenix.org·1d
📄PostScript
Alexander Fehrmann: How To Analyze Impression Evidence In Amped FIVE
forensicfocus.com·2d
🔍Binary Forensics
Cactus Language • Semantics 3
inquiryintoinquiry.com·1d
🔢Denotational Semantics
Fighting Email Spam on Your Mail Server with LLMs — Privately
cybercarnet.eu·14h·
Discuss: Hacker News, Hacker News, r/LocalLLaMA, r/selfhosted
⚡gRPC
BQN "Macros" with •Decompose (2023)
saltysylvi.github.io·1d·
Discuss: Hacker News
🦀Rust Macros
AI Is Just Making Everyone Faster at Being Boring
nvnt.substack.com·6h·
Discuss: Substack
🎛️Feed Filtering
Social Science PhD Tech Stack
kevinbryanecon.com·2d·
Discuss: Hacker News
🔄Archive Workflows
Laion, the dataset behind Stable Diffusion (2023)
deeplearning.ai·22h·
Discuss: Hacker News
🎓Academic Torrents
A Proof of the Riemann Hypothesis
dev.to·3d·
Discuss: DEV
⚔️Lean Theorem
What is a Large Language Model (LLM)
dev.to·1d·
Discuss: DEV
💻Local LLMs
Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects
arxiv.org·1d
🧠Machine Learning
ParsTranslit: Truly Versatile Tajik-Farsi Transliteration
arxiv.org·1d
📜Digital Philology
Quantum Agents: The Algorithmic Alchemists Reshaping Discovery
dev.to·17h·
Discuss: DEV
⚛️Quantum Algorithms
The Hidden Oracle Inside Your AI: Unveiling Data Density with Latent Space Magic by Arvind Sundararajan
dev.to·3d·
Discuss: DEV
🧠Machine Learning
Bridging the Digital Divide: Automated Accessibility Audit & Remediation via Hyperdimensional Semantic Analysis
dev.to·4d·
Discuss: DEV
🔤Character Classification
MARC: Memory-Augmented RL Token Compression for Efficient Video Understanding
arxiv.org·1d
🧠Learned Codecs
AI Fixed Coding, but Not the Bottleneck: Why Lisp, FP Still Matters
github.com·4d·
Discuss: Hacker News, r/neovim
🔗Lisp
ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
arxiv.org·1d
🌀Riemannian Computing
#14 Logarithm ....History and Etymology of Math Terms
pballew.blogspot.com·1d·
Discuss: www.blogger.com
📊APL Heritage
TRIM: Token-wise Attention-Derived Saliency for Data-Efficient Instruction Tuning
arxiv.org·2d
🔨Compilers