Deep technical breakdowns from projects where milliseconds, reliability, and maintainability matter.
How we reduced LLM failure rates by 65% through structured outputs, validation, and systematic testing.
Building production guardrails that keep language model features predictable and debuggable.
Lessons learned from deploying RAG systems for EdTech: multi-tenancy, security, and achieving >90% retrieval accuracy.
Designing a resilient retrieval-augmented generation platform for multi-tenant education workloads.
Deep dive into vectorization cost models and architecture-specific optimizations that achieved 3x performance improvements on NASA benchmarks.
Tuning LLVM vectorization and instruction selection to unlock 3× performance on real-world scientific workloads.
Technical insights from implementing AST bridging and name mangling compatibility in the Swift/Clang toolchain at Apple.
Extending the Swift compiler to seamlessly call modern C++ libraries without sacrificing safety or performance.
Every article distills battle-tested techniques from compilers, AI platforms, and reliability engineering. No fluff—just the patterns that saved releases and unlocked performance headroom.
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