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🧲 Magnetic Resonance

Tape Physics, Signal Processing, Media Degradation, Storage Forensics

When your formulation IP is beyond compare (T 0722/24)
ipkitten.blogspot.com·9h·
Discuss: ipkitten.blogspot.com
⚖️Emulation Ethics
A Coup Unfolding in Slow Motion
synystron.substack.com·1h·
Discuss: Substack
📼Cassette Hacking
Episode 163 - Ever Heard of the PDP-11?
adventofcomputing.libsyn.com·18h
💾vintage computing
From Transcripts to Descriptions: Using Consumer AI Tools for Audio Collection Metadata
saaers.wordpress.com·5d
🔄Archival Workflows
Google's Liquid Cooling at Hot Chips 2025
chipsandcheese.com·23h·
Discuss: Hacker News
🖥️Modern Terminals
How to load test PostgreSQL database and not miss anything
habr.com·3d·
Discuss: Hacker News
🗄️PostgreSQL WAL
God I Love Zram Swap
reddit.com·2d·
Discuss: r/linux
🛠️Greaseweazle
Dissonance: A journey through musical possibility space
aatishb.com·2d·
Discuss: Hacker News
🌈Spectral Audio
Computed Tomography
fab.cba.mit.edu·4d·
Discuss: Hacker News
🌀Fractal Compression
Sharing Is Scaring: Why Is Cloud File-Sharing Hard?
blog.brownplt.org·1h·
Discuss: Lobsters, Hacker News
💧Liquid Types
A chatbot that builds Rails apps
kodykendall.com·27m·
Discuss: Hacker News
🦋Format Metamorphosis
86Box 5.0
86box.net·1d·
Discuss: Hacker News
🕸️WASM Archives
I tested Synology's BeeStation Plus - and this subscription-free NAS could replace your cloud storage
techradar.com·1d
🏠Homelab Preservation
Contributions to Label-Efficient Learning in Computer Vision and Remote Sensing
arxiv.org·14h
📐Vector Similarity
Attention Mechanism in Randomized Time Warping
arxiv.org·14h
📊Learned Metrics
Scalable Scientific Interest Profiling Using Large Language Models
arxiv.org·14h
🏛Digital humanities
Physics-Based Explainable AI for ECG Segmentation: A Lightweight Model
arxiv.org·14h
🧠Machine Learning
CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation
arxiv.org·14h
🧮Kolmogorov Complexity
Automate the Testing of Your LLM Prompts
dev.to·18h·
Discuss: DEV
⚡Proof Automation
Understanding Convolutions: The Sliding Window of Insight
dev.to·16h·
Discuss: DEV
🧠Machine Learning
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