From Prototype to Production: How to Engineer Reliable LLM Systems
⚡Tokenizer Optimization
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Building Clean, Maintainable vLLM Modifications Using the Plugin System
blog.vllm.ai·3d
🔄Incremental Compilation
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Uncertainty Architecture: A Modern Approach to Designing LLM Applications
pub.towardsai.net·15h
🎮Language Ergonomics
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Trying Out C++26 Executors
🔮Speculative Execution
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What To Know Before Building Fluent Bit Plugins With Go
thenewstack.io·1d
🤖Embedded Go
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It's LIT! Reliability-Optimized LLMs with Inspectable Tools
arxiv.org·3d
🪜Recursive Descent
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Modern X86 Assembly Language Programming • Daniel Kusswurm & Matt Godbolt • GOTO 2025
youtube.com·2d
🔧Assembly DSLs
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How LLM Inference Works
arpitbhayani.me·1d
🚀Tokenizer Performance
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10000
jro.sg·17h
📦Executable Size
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The Engineering Guide to Efficient LLM Inference: Metrics, Memory, and Mathematics
pub.towardsai.net·2d
🗺️Region Inference
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October 2024 Progress in Guaranteed Safe AI
lesswrong.com·2d
📈Monotonic Logic
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Learning AI From Scratch: Streaming Output, the Secret Sauce Behind Real-Time LLMs
🌊Streaming Lexers
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A Function Inliner for Wasmtime and Cranelift
📥Inlining
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No Deus ex Machina
📚Forth Compilers
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How pairing SAST with AI dramatically reduces false positives in code security
infoworld.com·2d
🧪Parser Testing
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