Claude Code's 19 cent Parser
blogger.com·3d
🔧Binary Parsers
No, Your Domains and Bounded Contexts Don’t Map 1 on 1
verraes.net·17h
🔢Denotational Semantics
Do the simplest thing that could possibly work
seangoedecke.com·17h·
Discuss: Hacker News
💾Persistence Strategies
Learning Perl in one day and the importance of building strong foundations
guilhermenl.dev·1d·
Discuss: Hacker News
🔗Concatenative Programming
On Continuous Test Improvement
dsyme.net·9h·
Discuss: Hacker News
📏Code Metrics
Generative Interfaces for Language Models
arxiv.org·1d
⚙️Parser Generators
Even Heads Fix Odd Errors: Mechanistic Discovery and Surgical Repair in Transformer Attention
arxiv.org·13h
✨Effect Handlers
Bridging Big Data and Machine Learning Ecosystems: A Cloud Native Approac... Johnu George & Shiv Jha
youtube.com·3h
🏠Homelab Orchestration
Go’s unsafe: Unlocking Performance Hacks with a Risk
dev.to·3d·
Discuss: DEV
🔒Type Safety
nodeWSNsec: A hybrid metaheuristic approach for reliable security and node deployment in WSNs
arxiv.org·2d
🕸️Network Topology
The Performance Spectrum
mropert.github.io·1d·
Discuss: Hacker News
🔩Systems Programming
Secure by Design: A Shift-Left Approach with Testcontainers, Docker Scout, and Hardened Images
docker.com·4h
📦Container Security
Popular nx packages compromised on NPM
aikido.dev·1d·
Discuss: Hacker News
💣ZIP Vulnerabilities
8 Tool Tech Stack to Build an Enterprise-Grade RAG System (Without the Headaches)
dev.to·1d·
Discuss: DEV
🌀Brotli Internals
Marisa Trie – Static memory-efficient Trie-like structure
github.com·16h·
Discuss: Hacker News
🌳Trie Structures
Automated Cognitive Bias Mitigation in Genomic Data Analysis via Multi-Modal Fusion & Bayesian Calibration
dev.to·21h·
Discuss: DEV
✓Automated Theorem Proving
Generalizing Monocular 3D Object Detection
arxiv.org·13h
🕳️Persistent Homology
Future Research in XP Modeling: A Call for Self-Learning Models
hackernoon.com·1d
⚙️Operating System Design
Evaluating Language Model Reasoning about Confidential Information
arxiv.org·13h
💻Local LLMs
PyTorch vs TensorFlow: Which to Choose, When, and Why?
dev.to·2h·
Discuss: DEV
📊Learned Metrics