Model Checking, Theorem Proving, Specification Languages, Correctness, Proof Assistants, Correctness Guarantees, Logic Systems, Specification, Proof Assistants, Coq, Lean, Program Correctness, Agda, TLA+, Model Checking, Safety Properties, Specifications

Explicit Lossless Vertex Expanders!
gilkalai.wordpress.comยท1d
๐Ÿ’ŽInformation Crystallography
Vulnerability Management โ€“ Requirements, Scoping & Target Setting
blog.nviso.euยท2d
๐Ÿ”Capability Systems
Joy & Curiosity #57
registerspill.thorstenball.comยท38m
๐ŸบZIP Archaeology
Practical Guide to Production-Grade Observability in the JS ecosystem
medium.comยท14h
๐Ÿ‘๏ธSystem Observability
Understanding conflict resolution and avoidance in PostgreSQL: a complete guide
pgedge.comยท1dยท
๐Ÿ›ก๏ธByzantine Fault Tolerance
The optimistic case for protein foundation model companies
owlposting.comยท13hยท
Discuss: Hacker News
๐Ÿ”“Open Source Software
Can AI Co-Design Distributed Systems? Scaling from 1 GPU to 1k
harvard-edge.github.ioยท1dยท
Discuss: Hacker News
๐ŸŽฏPerformance Proofs
AI can help your DevSecOps pipeline
spiceworks.comยท1dยท
Discuss: Hacker News
๐Ÿ Homelab Pentesting
Show HN: An open-source starter kit for implementing OWASP ASVS 5.0
github.comยท1dยท
Discuss: Hacker News
๐Ÿ”“Open Source Software
Which Heads Matter for Reasoning? RL-Guided KV Cache Compression
arxiv.orgยท2d
๐Ÿ“ผCassette Combinators
From Documents to Dialogue: A step-by-step RAG Journey
dev.toยท1dยท
Discuss: DEV
๐Ÿ“ŠMulti-vector RAG
How To Build Effective Technical Guardrails for AI Applications
towardsdatascience.comยท5d
๐Ÿ”—Constraint Handling
AI Renaissance: Bridging the Gap Between Intuition and Logic
dev.toยท1dยท
Discuss: DEV
๐Ÿค–Paleographic AI
Enhanced Predictive Maintenance of Geothermal Heat Exchangers via Hybrid Bayesian Optimization and LSTM
dev.toยท1dยท
Discuss: DEV
๐Ÿ’ปLocal LLMs
Krish Naik: Complete RAG Crash Course With Langchain In 2 Hours
dev.toยท8hยท
Discuss: DEV
๐Ÿ“ŠMulti-vector RAG
Tech With Tim: How to Build AI Agents in Python
dev.toยท26mยท
Discuss: DEV
๐Ÿค–AI Curation
Why The Future of Code Is More Human Than Ever
dev.toยท1dยท
Discuss: DEV
๐Ÿ“Code Metrics
AsyncSpade: Efficient Test-Time Scaling with Asynchronous Sparse Decoding
arxiv.orgยท2d
โš™๏ธCompression Benchmarking
Aligning Large Language Models via Fully Self-Synthetic Data
arxiv.orgยท3d
๐Ÿ”—Monadic Parsing