Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Implement associative memory networks for AI agents that link related memories together, using association graphs, link strength, spreading activation, and pattern-based retrieval.
Learn how to use AsyncOpenAI with Python's asyncio to make concurrent API calls, implement connection pooling, and build high-throughput AI pipelines.
Build an audio analysis agent in Python that classifies music genres, identifies speakers through diarization, and detects sound events. Covers audio feature extraction, classification models, and structured audio understanding.
Build a complete audio preprocessing pipeline for voice AI agents — covering noise reduction, echo cancellation, gain normalization, and both client-side Web Audio API and server-side Python implementations.
Implement dynamic auto-scaling for AI agent worker pods using KEDA, custom Prometheus metrics, queue-depth-based scaling, scale-to-zero for cost savings, and warmup strategies that prevent cold-start latency spikes.
Explore Microsoft's AutoGen framework for building multi-agent systems using conversable agents, group chat orchestration, and integrated code execution for collaborative problem solving.
Understand the current capabilities and limitations of autonomous coding agents like Devin, SWE-Agent, and Claude Code. Learn how these tools are reshaping developer workflows and what the future holds for AI-augmented software engineering.
Explore the complete AV technology stack from perception to planning. Learn how modern self-driving systems combine sensors, AI, and end-to-end architectures.
Implement background processing for AI agent workloads using FastAPI BackgroundTasks, Celery integration, and custom task queues. Learn task status tracking, webhook notifications, and long-running agent job management.
