Functional correctness -- Haskell-ing your way to reliable code (hackover2024)
cdn.media.ccc.de·9h
⚡functional programming
The Inverse Matrix problem
talesfrom.dev·1d
🚂Error Propagation
Culture, Language, and Generative Language Models
cacm.acm.org·17h
🧠Semantic Parsing
Java Annotated Monthly – October 2025
blog.jetbrains.com·22h
💬Smalltalk VMs
Building a earning app like Duolingo with React + Java backend – SEO & Routing advice?
reddit.com·22h·
Discuss: r/reactjs
🎮Language Ergonomics
The Invisible Hand: LLM influence is the new SEO
getspore.com·11h·
Discuss: Hacker News
📈Query Optimization
The Danger of High (or Small) Numbers In Your Computer And ML Models
pub.towardsai.net·8h
🎯Bit Vectors
valuetier.org (and some thoughts on LLMs)
ericphanson.com·1d·
Discuss: Lobsters, Hacker News
📚Self-Documenting Code
Adventures on the AI Coding side of things
medium.com·22h·
Discuss: Hacker News
🎭Program Synthesis
I Spent 6 Hours Per Blog Post Until I Built This AI Content Platform
terradium.io·1d·
Discuss: DEV
⚡Incremental Parsing
A Primer on Memory Consistency and Cache Coherence, Second Edition
link.springer.com·1d·
Discuss: r/programming
🧠Memory Models
Training Dynamics of Parametric and In-Context Knowledge Utilization in Language Models
arxiv.org·1d
🪜Recursive Descent
PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
arxiv.org·1d
✅Type Checking
Batch Processing with Spring Batch and Multiple Data Sources
dev.to·1d·
Discuss: DEV
🔧Error Recovery
A Fixed Point Framework for the Existence of EFX Allocations
arxiv.org·2h
📏Linear Logic
Vector Trifference
arxiv.org·2h
🎯Bit Vectors
From Moments to Models: Graphon Mixture-Aware Mixup and Contrastive Learning
arxiv.org·2h
🔍ML Language
NEURODNAAI: Neural pipeline approaches for the advancing dna-based information storage as a sustainable digital medium using deep learning framework
arxiv.org·1d
📋JSON Parsing
Learning to Predict Chaos: Curriculum-Driven Training for Robust Forecasting of Chaotic Dynamics
arxiv.org·2h
🌱Minimal ML
Unlock Peak Model Performance: MINERVA's Surprisingly Simple Feature Selection by Arvind Sundararajan
dev.to·20h·
Discuss: DEV
🗺️Region Inference