Engineering Notes
Engineering Notes
Internal playbooks on shipping production AI systems—performance, reliability, and the tradeoffs we make while building AI MVPs.
CUDA & AI Performance
Why performance notes matter for MVPs
Most MVPs don’t need low-level optimization. When an AI product hits latency, cost, or throughput limits, the engineering tradeoffs become the product.
Practical GuideCUDA6 engineering notes•~1 hour total•Updated regularly
CUDA Performance Engineering
A production playbook for profiling, memory behavior, and execution-model tradeoffs in real AI workloads.
Used when: latency, cost, or throughput becomes a bottleneck.
Included chapters
- 1CUDA C++ Tutorial: Getting Started
- 2CUDA Programming Model and Memory Management Explained
- 3Thread Hierarchy, Indexing & Kernels
- 4CUDA C++ Tutorial: Learning Through Coding Examples
Written from production experience shipping AI MVPs.
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Occasional updates for founders and engineers shipping AI MVPs—performance wins, reliability patterns, and hard-earned tradeoffs.
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