Clean Amazon links
leancrew.com·6h
✅FLAC Verification
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I’ve gone down the rabbit hole. I need help before going any further.
i.redd.it·1d·
Discuss: r/homelab
💾Floppy Imaging
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Missed Connections and Markov Chains
nry.me·1d·
Discuss: Hacker News
🧮Kolmogorov Bounds
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Show HN: The Legal Embedding Benchmark (MLEB)
huggingface.co·1d·
Discuss: Hacker News
⚖️Legal Compliance
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The Science of Complexity
explorablescience.com·22h·
Discuss: Hacker News
⚛️Information Physics
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RAG Is Over: RL Agents Are the New Retrieval Stack
inference.net·1d·
Discuss: Hacker News
🤖AI Curation
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Beyond vibes: How to properly select the right LLM for the right task
aws.amazon.com·1d
📏Code Metrics
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Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain
arxiv.org·2d
🤖Grammar Induction
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Chimera: State Space Models Beyond Sequences
arxiv.org·3d
🚀SIMD Text Processing
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Schema for In-Context Learning
arxiv.org·1d
🤖Grammar Induction
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Is Python an interpreted or compiled language? How does Python code execution work?
dev.to·7h·
Discuss: DEV
💻Programming languages
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Invisible Languages of the LLM Universe
arxiv.org·4d
🛠Language Design
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Krish Naik: 5-Getting Started With Agentic RAG With Detailed Implementation Using LangGraph
dev.to·7h·
Discuss: DEV
📊Multi-vector RAG
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Volcano SDK, a TypeScript SDK for Multi-Provider AI Agents
github.com·1d·
Discuss: Hacker News
🔬Refinement Types
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The Anonymous Workers: Lambda Functions Explained
dev.to·5d·
Discuss: DEV
λLambda Formalization
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A contact solver for physics-based simulations
github.com·1d·
Discuss: Hacker News
🖥️Terminal Renaissance
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Securing MCP (Model Context Protocol) servers with OAuth 2.1: Architecture
dev.to·17h·
Discuss: DEV
⚡gRPC
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Extended Triangular Method: A Generalized Algorithm for Contradiction Separation Based Automated Deduction
arxiv.org·4d
🧠Computational Logic
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Personalized federated learning, Row-wise fusion regularization, Multivariate modeling, Sparse estimation
arxiv.org·1d
🧠Machine Learning
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Unveiling the Vulnerability of Graph-LLMs: An Interpretable Multi-Dimensional Adversarial Attack on TAGs
arxiv.org·3d
💻Local LLMs
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