Testing Unnatural Prompt Engineering Across Five Large Language Models
blog.codeminer42.com·4d
⚡Proof Automation
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Scala vs. F#
💧Liquid Types
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When Your Hash Becomes a String: Hunting Ruby's Million-to-One Memory Bug
🦀Rust Borrowing
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Data Observability in Analytics: Tools, Techniques, and Why It Matters
kdnuggets.com·20h
👁️System Observability
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AI Models Write Code with Security Flaws 18–50% of the Time, New Study Finds
📏Code Metrics
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How I Use Every Claude Code Feature
⚡Proof Automation
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Feature-Guided Analysis of Neural Networks: A Replication Study
arxiv.org·1d
🧠Machine Learning
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Reflections on Trusting Trust (1984)
📜Proof Carrying Code
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Finding Non-Redundant Simpson's Paradox from Multidimensional Data
arxiv.org·1d
📐Interval Trees
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Building an AI-Powered Recipe Assistant with Agentic Postgres: A Deliciously Data-Driven Adventure 🍳🤖
🧮Algorithmic Cooking
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Algorithmic Assistance with Recommendation-Dependent Preferences
arxiv.org·1d
🔍Information Retrieval
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The Curvature Rate {\lambda}: A Scalar Measure of Input-Space Sharpness in Neural Networks
arxiv.org·1d
🌀Riemannian Computing
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Knowledge Elicitation with Large Language Models for Interpretable Cancer Stage Identification from Pathology Reports
arxiv.org·1d
🔍Information Retrieval
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Hybrid Retrieval-Augmented Generation Agent for Trustworthy Legal Question Answering in Judicial Forensics
arxiv.org·1d
🔍Information Retrieval
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ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation
arxiv.org·1d
📊Learned Metrics
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DEER: Disentangled Mixture of Experts with Instance-Adaptive Routing for Generalizable Machine-Generated Text Detection
arxiv.org·1d
📝Text Parsing
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Variational Geometric Information Bottleneck: Learning the Shape of Understanding
arxiv.org·4h
⧗Information Bottleneck
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