Learning web development: Frontend frameworks
2ality.com·2d
🔗Concatenative Programming
The LLM models the user, and then it models itself
animassteward.substack.com·2d·
Discuss: Substack
🔢Denotational Semantics
Beginner here looking for a self-hosted notes app with sync + encryption + mobile
reddit.com·15h·
Discuss: r/homelab
🔌Offline-first Apps
OCR and RAG for Tables
mrm1001.github.io·1d·
Discuss: Hacker News, Hacker News
📄Document Digitization
espressif/esp-adf
github.com·5h
🎵Audio Codecs
Show HN: @Agent for Gitlab
github.com·1d·
Discuss: Hacker News
🌳Git Internals
Lovart-Al Nano Banana AI Image Generator
dev.to·23h·
Discuss: DEV
🔓Open Source Software
AWS Cloud Development Kit (CDK) Launches Refactor
aws.amazon.com·13h
🧱Immutable Infrastructure
Incidents Often Come in Pairs
dev.to·1d·
Discuss: DEV
🦀Rust Macros
Elixir + Rust = Endurance Stack? Curious if anyone here is exploring this combo
reddit.com·2d·
Discuss: r/rust
🦀Rust Borrowing
Autonomous Thermal Anomaly Detection via Multi-Modal Sensor Fusion and Dynamic Bayesian Networks
dev.to·1d·
Discuss: DEV
📊Homelab Monitoring
KRAFT: A Knowledge Graph-Based Framework for Automated Map Conflation
arxiv.org·3d
🔶Voronoi Diagrams
Software dev isn’t life or death (but it sure feels like it)
dev.to·15h·
Discuss: DEV
🔩Systems Programming
A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction
arxiv.org·1d
🧠Machine Learning
TubeForge - From Video to Knowledge: AI-Powered Notes, Blogs, and Quizzes Instantly
dev.to·7h·
Discuss: DEV
🌀Brotli Internals
Automated Anomaly Detection in Lunar Lander Telemetry Streams Using Adaptive Kalman Filtering
dev.to·3d·
Discuss: DEV
👁️Observatory Systems
Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
arxiv.org·3d
👤Search Personalization
Borrowed Brains: Are Pre-Trained Models a Developer's Best Friend... or Worst Nightmare? by Arvind Sundararajan
dev.to·1d·
Discuss: DEV
🎵Audio ML
Top Machine Learning Development Companies in the USA – 2025
dev.to·19h·
Discuss: DEV
🤖Lisp Machines
ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
arxiv.org·6d
🏺Compression Museums