Strict alternation of data and behaviour in Smalltalk
eighty-twenty.org·19h
📞Session Types
Prompts vs. Context
dbreunig.com·3h
⚡Proof Automation
My AI Workflow for Understanding Any Codebase
steipete.me·18h
⚡Proof Automation
Announcing crabstep: A pure Rust, cross-platform, zero-dependency Apple/NeXTSTEP typedstream deserializer
github.com·11h
🔒Linear Types
Simulating a neural operating system with Gemini 2.5 Flash-Lite
developers.googleblog.com·9h
🎯Emulator Accuracy
Programming Entry Level: how to interpreter
dev.to·1d·
Discuss: DEV
🔗Lisp
The Internal Inconsistency of Large Language Models
blog.kortlepel.com·1d·
Discuss: Hacker News
💻Local LLMs
Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges
arxiv.org·35m
🧮Theorem Proving
Language Workbenches: The Killer-App for Domain Specific Languages? (2005)
martinfowler.com·4d·
Discuss: Hacker News
🗣️Domain-Specific Languages
LASA: Enhancing SoC Security Verification with LLM-Aided Property Generation
arxiv.org·2d
🔍FPGA Verification
LOGICPO: Efficient Translation of NL-based Logical Problems to FOL using LLMs and Preference Optimization
arxiv.org·2d
🔗Monadic Parsing
Text2Struct: A Machine Learning Pipeline for Mining Structured Data from Text
arxiv.org·2d
🔤Character Classification
On Union-Closedness of Language Generation
arxiv.org·2d
🔗Monadic Parsing
The Power of Strong Linearizability: the Difficulty of Consistent Refereeing
arxiv.org·2d
🎯Performance Proofs
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
arxiv.org·1d
💻Local LLMs
AI’s ‘Neutral Voice’ Is a Structural Illusion
hackernoon.com·21h
🤖Grammar Induction
Building Scalable Web Applications with Advanced JavaScript Patterns
dev.to·8h·
Discuss: DEV
🛠Language Design
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
arxiv.org·35m
🔍BitFunnel
EvoLM: In Search of Lost Language Model Training Dynamics
arxiv.org·3d
💻Local LLMs
Programming by Backprop: LLMs Acquire Reusable Algorithmic Abstractions During Code Training
arxiv.org·2d
💻Local LLMs