Object Packing
nms.ac.uk·3d·
Discuss: Hacker News
📦METS Packaging
Royal Philharmonic Orchestra/Hisaishi review – frothing strings and quacking brass as Studio Ghibli’s composer debuts
theguardian.com·43m
🎼Computational Musicology
Svelte, Markdown, and the Magic of Web Components
sh4jid.me·3d·
Discuss: Hacker News
🗣️Domain-Specific Languages
I talked to Sam Altman about the GPT-5 launch fiasco
theverge.com·11m
🖥️Terminal Renaissance
simonw/codespaces-llm
simonwillison.net·2d·
Discuss: Hacker News
λLambda Formalization
Euronews Culture's Film of the Week: 'Together' - The ultimate date movie?
euronews.com·12h
🏛️Philosophy
Nyctal: Zero dependency Golang Wayland compositor
github.com·3d·
Discuss: Hacker News
🔓Open Source Software
Accelerating exoplanet climate modelling: A machine learning approach to complement 3D GCM grid simulations
arxiv.org·8h
⚡Incremental Computation
Demystifying the (In)Security of QR Code-based Login in Real-world Deployments
usenix.org·2d
🛡️WASM Sandboxing
Two From Ridenour
irreal.org·2d
📟Terminal Typography
Inside gomarklint: Building a High-Performance Markdown Linter in Go
dev.to·6d·
Discuss: DEV
⟷Bidirectional Programming
Predictive Flood Mitigation via Dynamic Reservoir Allocation using Reinforcement Learning and Bayesian Optimization
dev.to·10h·
Discuss: DEV
🔲Cellular Automata
I'm Building an AI Agent to Write My Unit Tests
dev.to·19h·
Discuss: DEV
⚡Proof Automation
Software Engineering Laws - Risk & Security
leadership.garden·19h
🛡️CLI Security
SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
arxiv.org·4d
🔐Cryptographic Archaeology
SuperOptiX Memory: A Practical Guide for Building Agents That Remember
dev.to·3d·
Discuss: DEV
💾Persistent Memory
Go 1.25 is released
go.dev·3d·
Discuss: Hacker News
🦋Format Metamorphosis
SAEMark: Multi-bit LLM Watermarking with Inference-Time Scaling
arxiv.org·3d
⚙️Compression Benchmarking
Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
arxiv.org·3d
💻Local LLMs
Using LLMs to Capture Users' Temporal Context for Recommendation
arxiv.org·2d
⚖️Feed Ranking