[Deep Dive] How We Solved Poker: From Academic Bots to Superhuman AI (1998-2025)
💰TigerBeetle
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GitHub Copilot tutorial: How to build, test, review, and ship code faster (with real prompts)
github.blog·21h
🤖Automation
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Topographical sparse mapping: A training framework for deep learning models
👁️Computer Vision
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How do you find the right balance between using AI tools and actually learning
🤖Automation
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EDL - a JIT-compiled scripting language for certain performance critical workloads with high compatibility with Rust; written in Rust
🦀Rust Macros
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Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+
arxiv.org·3d
🌳Tree-sitter
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GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Models
arxiv.org·9h
💬Prompt Engineering
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MichaelAI vs. CogniFlow: A Developer's No-BS Guide to Enterprise AI Platforms
🎨Design Systems
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A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
arxiv.org·2d
⚙️JIT Compilation
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From Measurement to Expertise: Empathetic Expert Adapters for Context-Based Empathy in Conversational AI Agents
arxiv.org·9h
💬Natural Language Processing
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My dumb prompts that worked better
blog.nilenso.com·2d
💬Prompt Engineering
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