Pragma Unroll
sandordargo.com·1d·
Discuss: Hacker News
🚀Compiler Optimization
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Website Breakdown Highlights Risks of Unmanaged Plugin Updates, Says Asaaju Peter of Phemight Technologies
dev.to·3h·
Discuss: DEV
🏗️Build Optimization
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Patterns for Defensive Programming in Rust
corrode.dev·4d·
Discuss: Hacker News, r/rust
🔬Static Analysis
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Optimizing filtered vector queries from tens of seconds to single-digit milliseconds in PostgreSQL
clarvo.ai·1d·
Discuss: Hacker News
🐕Ruff
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Understanding Value and Pointer Receivers in Go: Building a Crypto Tracker
dev.to·9h·
Discuss: DEV
🔄ONNX
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[Research] Cross-Stage Vulnerabilities in Large Language Model Architectures
reddit.com·1d·
Discuss: r/LocalLLaMA
🚀MLOps
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PhpStorm Plugins You Might Not Know
blog.jetbrains.com·1d
🐕Ruff
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OpenAI engineers don’t prompt like you do.
threadreaderapp.com·1h
🤖AI Coding Tools
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🚀 Unlocking Uniface: A Simple Guide to the $ude("exist") Function
dev.to·4d·
Discuss: DEV
💡LSP
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Beginner’s Guide to .NET Development
dev.to·1d·
Discuss: DEV
💡LSP
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Top Open Source Tools for Kubernetes ML: From Development to Production
dev.to·1d·
Discuss: DEV
🚀MLOps
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Cj: a tiny no-deps JIT in C for x86-64 and ARM64
reddit.com·15h·
Discuss: r/programming
⚙️JIT Compilation
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Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
arxiv.org·5h
📊Gradient Accumulation
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Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge
arxiv.org·5h
🎓Model Distillation
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Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG
arxiv.org·5h
🎓Model Distillation
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Enhanced Interoperability via Dynamic Semantic Alignment in Cross-Chain DeFi Protocols
dev.to·3h·
Discuss: DEV
🔄ONNX
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TypeScript Rewrote Itself in Go?! What That “10x Faster” Hype Really Means
dev.to·1d·
Discuss: DEV
🚀Compiler Optimization
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DPO-F+: Aligning Code Repair Feedback with Developers' Preferences
arxiv.org·2d
🐕Ruff
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Beating my head against the wall with this bizarre crash.
reddit.com·1d·
Discuss: r/skyrimmods
📈GPU Occupancy
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Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
arxiv.org·1d
🚀MLOps
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