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🎚️ Audio Engineering

Signal Processing, Studio Techniques, Sound Design, Acoustic Analysis

Fantastic Collaboration!
asymptotia.com·1d
⭐Awesome lists
About the Cryptopocalypse
ritter.vg·1d
🌳Archive Merkle Trees
Boolean SHA256 with TFHE-rs (2023)
zama.ai·1d·
Discuss: Hacker News
🔢Bitwise Algorithms
Uncertain⟨T⟩
nshipster.com·1d·
Discuss: Hacker News
🧪CBOR Fuzzing
Haptic Feedback in Apple Touchpads
xray.greyb.com·1d·
Discuss: Hacker News
✋Tactile Computing
The Transformative Power of Inspiration
cacm.acm.org·3d
🏴󠁧󠁢󠁳󠁣󠁴󠁿Scottish Computing
Hidden black and white feathers found to intensify blue and yellow bird plumage
phys.org·15h·
Discuss: Hacker News
🌈Color Science
The Missing Guide to Prompt Engineering
appetals.com·5d·
Discuss: Hacker News
🧠Intelligence Compression
Lobsters Interview with Technomancy
lobste.rs·1d·
Discuss: Lobsters
🦀Rust Macros
We Built An Entire Newsroom with LLMs – Then We Showed The Print To the Real One
medium.com·4d·
Discuss: Hacker News
📋Document Layout
I tested this mini PC - and GMKtec chose a 2019 AMD processor over the Intel N100 in its G10 for a good reason
techradar.com·1d
🖥️Modern Terminals
Threadripper 9000 CPUs will revive the HEDT market on July 31, starting at $1,499 for 24 cores — the flagship 64-core chip will set you back $4,999
tomshardware.com·14h
🖥️Terminal Renaissance
Thought To Be Impossible – Scientists Rewrite a Material’s “Magnetic DNA”
scitechdaily.com·1d
⏰Time Crystals
8 Advanced Python List Concepts with Real Use Cases
dev.to·18h·
Discuss: DEV
📝Concrete Syntax
need to restructure: please help beginner plan NAS and give hardware suggestions
reddit.com·2d·
Discuss: r/homelab
🏠HomeLab
From DevOps to MLOps: A Practical Guide to Shifting Your Career
dev.to·2d·
Discuss: DEV
❄️Nix Adoption
Exhausting Hot Air?
reddit.com·1d·
Discuss: r/homelab
🌡️Preservation Physics
SPE Acts As An All-In-One Portfolio Strategy But May Not Be Optimal
seekingalpha.com·22h
🔍BitFunnel
AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
arxiv.org·2d
🌳Context free grammars
Hierarchical Dimensionless Learning (Hi-{\pi}): A physics-data hybrid-driven approach for discovering dimensionless parameter combinations
arxiv.org·2d
🧠Machine Learning
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