Mental Models and Potemkin Understanding in LLMs
alangrow.com·10h
🔲Cellular Automata
AI swallows misinformation and spits it back at us: ‘It’s optimized to give useful information, but not for it to be correct’
english.elpais.com·1d
🎛️Feed Filtering
New technique rapidly identifies high-performing enzymes for sustainable biomanufacturing
phys.org·2d
🌈Spectroscopy
Google/Videoprism
huggingface.co·4d·
Discuss: Hacker News
📊Learned Metrics
Claude on Rails
obie.medium.com·1d·
Discuss: Hacker News
👑Isabelle
Releasing 0.5.0 of lfqueue - Lock-free MPMC queues
reddit.com·8h·
Discuss: r/rust
🔩Systems Programming
Creativity & Technological Evolution
techdirt.com·1d
🌍Cultural Computing
VMware Fundamentals: Photon Packer Templates
dev.to·6h·
Discuss: DEV
🔐HSM Integration
Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings
arxiv.org·5d
🗂️Vector Databases
How AI Helped Me Build a CRM Integration 60% Faster: A Real Vue.js + CouchDB Case Study
dev.to·2d·
Discuss: DEV
🌀Brotli Internals
MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification
arxiv.org·4d
🌀Riemannian Computing
Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales
arxiv.org·4d
👁️Perceptual Coding
ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation
arxiv.org·4d
📊Learned Metrics
Middleware Magic Advanced Request Processing（1751149077660400）
dev.to·12h·
Discuss: DEV
🌊Stream Processing
Development of a Multiprocessing Interface Genetic Algorithm for Optimising a Multilayer Perceptron for Disease Prediction
arxiv.org·6d
🧠Machine Learning
RAG vs Fine-tuning vs Prompt Engineering: The Complete Enterprise Guide
dev.to·1d·
Discuss: DEV
🌊Stream Processing
Fully lifted \emph{blirp} interpolation -- a large deviation view
arxiv.org·4d
🌀Fractal Compression
Machine Learning Fundamentals: adam optimizer
dev.to·2d·
Discuss: DEV
📊Quantization
Variational Learning of Disentangled Representations
arxiv.org·6d
🧠Machine Learning
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
arxiv.org·3d
🧠Machine Learning