The Dartboard Sequence
mathpages.com·2d·
Discuss: Hacker News
🎞️Tape Combinatorics
I Configure BorgBackup and Borgmatic
justus.pw·1d·
Discuss: Hacker News
🍎Apple Archive
Meta
engineering.fb.com·5h·
Discuss: Hacker News
🦀Rust Borrowing
Morty: Scaling Concurrency Control with Re-Execution
muratbuffalo.blogspot.com·3d·
Discuss: Hacker News
🌳Persistent Data Structures
Show HN: Crop Images Instantly – No Upload Required
cropimages.co·13h·
Discuss: Hacker News
🖼️JPEG XL
The Reanimated Renaissance, C-Speed Compression, and JSON-Loving LLMs
thereactnativerewind.com·3d·
Discuss: Hacker News
🎬WebCodecs
UniFi OS: Ubiquiti brings network operating system for its own server
heise.de·1d
🖥️Self-hosted Infrastructure
UCG-Fiber to Cisco 3850 problems
i.redd.it·19h·
Discuss: r/homelab
📡Network Protocol Design
OpenAI prepares to launch GPT-5, but big leaps are unlikely
the-decoder.com·1h
🔲Cellular Automata
Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning
arxiv.org·3d
🧠Learned Codecs
Mythbusting Large Language Models
medium.com·1d·
Discuss: Hacker News
💻Local LLMs
Tag & Track: Automating Attendance with RFID and ESP8266
hackster.io·1d
📼Cassette Hacking
Mojo: The Python-Compatible AI Language Taking 2025 by Storm
dev.to·15h·
Discuss: DEV
🔩Systems Programming
[RFC] Hardening mode for the compiler - Clang Frontend
discourse.llvm.org·15h·
Discuss: Hacker News, r/cpp
🏭Compiler Backends
2× A2000s + 128GB RAM vs 4090? I built a self-learning GPU/CPU allocator that makes the most of multi-GPU rigs
reddit.com·1d·
Discuss: r/homelab
⚡Homebrew CPUs
AI Helped Me Build a Redis-Powered IoT Anomaly Detector. In 1 Day.
dev.to·9h·
Discuss: DEV
⚡Redis Internals
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
arxiv.org·1d
📊Count-Min Sketch
Moravec's Paradox: Towards an Auditory Turing Test
arxiv.org·1d
🎼Audio Lambda Calculus
SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods
arxiv.org·3d
🎙️Whisper
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study
arxiv.org·4d
🧠Machine Learning