News for June 2025
ptreview.sublinear.info·2d
🕸️Graph Algorithms
A PhD's Apology
cappadokes.github.io·15h·
Discuss: Hacker News
🧮Theoretical Computer Science
What people said about an OpenZFS bug
despairlabs.com·12h·
Discuss: Lobsters, Hacker News
🔒Type Safety
Solving the Context Rot Problem for Coding Agents
install.md·8h·
Discuss: Hacker News
🌳Incremental Parsing
Show HN: Context Rot Technical Report – How Input Length Impacts LLM Performance
research.trychroma.com·2d·
Discuss: Lobsters, Hacker News
🔍Information Retrieval
Inheritance over composition, sometimes
death.andgravity.com·1d
🦀Rust Macros
Benchmarking AWS Nova on Log Data: How It Compares to ChatGPT-3.5
bronto.io·1d·
Discuss: Hacker News
📝Log Parsing
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
arxiv.org·42m
🌀Riemannian Computing
Finding Order-Preserving Subgraphs
arxiv.org·1d
🕸️Graph Algorithms
Fridge to Feast AI: Your Kitchen's AI Sous Chef
dev.to·2h·
Discuss: DEV
🧮Algorithmic Cooking
Machine Learning Fundamentals: data preprocessing with python
dev.to·2d·
Discuss: DEV
⚙️Batch Processing
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
arxiv.org·1d
🌀Differential Geometry
Using AMD MI300X for High-Throughput, Low-Cost LLM Inference
herdora.com·4d·
Discuss: Hacker News
⚡Performance Mythology
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air
arxiv.org·1d
💻Local LLMs
Understanding the Basics: Linear Equations and Matrices
dev.to·3h·
Discuss: DEV
📐Linear Algebra
The hidden dangers of AI-generated code in modern web frameworks
dev.to·2h·
Discuss: DEV
📏Code Metrics
Sparse Identification of Nonlinear Dynamics with Conformal Prediction
arxiv.org·42m
🧮Kolmogorov Bounds
Google I/O Connect 2025 Recap: Gemini, Open Models, and AI in Action
dev.to·16h·
Discuss: DEV
🌀Brotli Internals
Know Your Playground: The Essential Guide to Algorand for MEV Enthusiasts
hackernoon.com·1d
🔗Hash Chains
Class-Proportional Coreset Selection for Difficulty-Separable Data
arxiv.org·1d
🧠Machine Learning