🛠 Language Design

Type Systems, Compiler Theory, Syntax Evolution, Memory Models, Syntax Creation, Parser Design, Domain Languages, Programming Paradigms

polarizing parsers
flak.tedunangst.com·16h
🛣️BGP Hijacking
2025-07-25: Feature Engineering with Shallow Features and Methods
ws-dl.blogspot.com·1d·
Discuss: ws-dl.blogspot.com
🧠Machine Learning
You might not need a Python class
adamgrant.micro.blog·1d·
Discuss: Hacker News, r/programming
⬆️Lambda Lifting
The Best Hardware Store in the World
willdennis.com·20h·
Discuss: Hacker News
🖥️Modern Terminals
Performance of Threads vs. Processes
medium.com·15h·
Discuss: Hacker News
⚡Performance Tools
Machine Learning. Literally.
trivialorwrong.com·4d·
Discuss: Hacker News
⚡CPU Microarchitecture
SceneLoom: Communicating Data with Scene Context
arxiv.org·3d
🏛Digital humanities
RAG Isn’t Enough. Context Engineering is how real AI gets built
dev.to·1d·
Discuss: DEV
🌀Brotli Internals
Machine learning - the code that feels
dev.to·2d·
Discuss: DEV
🧠Machine Learning
Improving Academic Writing Style with AI Rewrites & Feedback
dev.to·1d·
Discuss: DEV
🔬Academic Search
GASPnet: Global Agreement to Synchronize Phases
arxiv.org·3d
📝ABNF Parsing
The Context Illusion: Why LLMs Don't Know Your Code Like You Think They Do
dev.to·3d·
Discuss: DEV
✨Effect Handlers
The Context Illusion: Why LLMs Don't Know Your Code Like You Think They Do
dev.to·3d·
Discuss: DEV
✨Effect Handlers
Project-connex Decompositions and Tractability of Aggregate Group-by Conjunctive Queries
arxiv.org·5d
🎯Performance Proofs
Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN
arxiv.org·1d
⧗Information Bottleneck
Understood: Real-Time Communication Support for Adults with ADHD Using Mixed Reality
arxiv.org·1d
🏛Digital humanities
How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs
arxiv.org·4d
🧠Intelligence Compression
Rethinking Memorization Measures and their Implications in Large Language Models
arxiv.org·4d
💻Local LLMs
Mastering Multi-Head Attention in Transformers: An In-Depth Guide
dev.to·23h·
Discuss: DEV
🧮Vector Embeddings
Multi-Modal Content Processing with strands agent and FAISS Memory
dev.to·1d·
Discuss: DEV
🤖Archive Automation