Insights on AI development, RAG systems, and production-ready software architecture
Reflections on founding and scaling an AI startup. Technical challenges, team building, and the journey to Forbes 30 Under 30.
A deep dive into the challenges and solutions when building RAG systems for production. From vector database selection to multi-tenant architecture and confidence scoring.
How to build robust MLOps pipelines that can handle the complexity of modern AI systems. Covering monitoring, deployment, and maintenance strategies.
Key principles for designing AI systems that can scale from prototype to enterprise. Real-world examples and architectural patterns that work.
Techniques for deploying AI models on resource-constrained devices. From quantization to custom architectures and performance optimization.
How we achieved 100x inference speed improvements in clir.ai's speech enhancement models. Custom convolution layers, caching strategies, and optimization techniques.