Dependency thoughts
macwright.com·2d
🔗Dependency Resolution
FreeBSD PKGBASE pkgbasify(8) Tool Explained
discoverbsd.com·1h
🔌Operating system internals
Unlocking high software engineering pace: Build less, ship frequently, learn faster
dev.jimgrey.net·23h
🔄Reproducible Builds
We benchmarked Cyberpunk 2077 on Mac M1 to M4 — the numbers don’t lie
tomsguide.com·1d·
Discuss: Hacker News, r/hardware
🖥️Vintage Macintosh
Fast and Simple Rust Interner
matklad.github.io·4d
🦀Rust Verification
Show HN: I just published my practical guide to building a database server
leanpub.com·2d·
Discuss: Hacker News
🗄️Database Internals
Luckfox Lyra Pi – A Raspberry Pi-like SBC with Rockchip RK3506B triple-core SoC
cnx-software.com·3h
🔌Single Board PC
How I Built an AI-Powered Face Recognition App from Scratch
dev.to·1d·
Discuss: DEV
🌊Streaming Algorithms
How the Right QMS Can Improve Agent Performance and Customer Experience
dev.to·1h·
Discuss: DEV
🌊Stream Processing
Different way to look at LLMs, think word embeddings going to college
dev.to·23h·
Discuss: DEV
🧮Vector Embeddings
Weekly Challenge: Clearly the Title
dev.to·1d·
Discuss: DEV
🌳Incremental Parsing
Building a BLE-Powered Air Quality Dashboard with Grafana
hackster.io·22h
🏠Home Automation
Title: Open vs. Closed Models: Balancing Trade-offs for Enterprise AI Adoption
dev.to·1d·
Discuss: DEV
🔒WASM Capabilities
Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference
arxiv.org·6h
🧠Machine Learning
🚀 The Complete Guide to Prompt Engineering: From Zero-Shot to AI Agents
dev.to·2d·
Discuss: DEV
⚡Proof Automation
Learning Pluralistic User Preferences through Reinforcement Learning Fine-tuned Summaries
arxiv.org·1d
👤Search Personalization
Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts
arxiv.org·5d
🕸️Algebraic Topology
AI Agent Protocols Every Developer Should Know in 2025
dev.to·16h·
Discuss: DEV
🔌Interface Evolution
Machine Learning Fundamentals: dropout tutorial
dev.to·3d·
Discuss: DEV
👁️Observatory Systems
Mastering Dimensionality Reduction: A Comprehensive Guide to PCA, t-SNE, UMAP, and Autoencoders
dev.to·5d·
Discuss: DEV
📐Vector Dimensionality