
NanoBanana: Building Agentic Workflows
Building a multi-agent system with NanoBanana — architecture patterns, tool-calling orchestration, and lessons from deploying agentic workflows in production.
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Building a multi-agent system with NanoBanana — architecture patterns, tool-calling orchestration, and lessons from deploying agentic workflows in production.

A TypeScript-native multi-agent framework that decomposes goals into runtime task DAGs — runs fully offline on quantized models via Ollama with just 4-8GB VRAM.

An infrastructure-as-code orchestration runtime for multi-agent AI systems — treating agents as YAML resources with manifests, routing, and lifecycle.

Real-time web retrieval, citation-grounded generation, and the architecture behind sub-second answers — building a Perplexity-style search engine.

A Pydantic-powered agent framework leveraging Pydantic's validation and schema capabilities for type-safe, structured AI agent outputs.

Microsoft's lightweight SDK for integrating AI into applications — with plugins, planners, and memory for building intelligent apps.

Benchmarking quality, cost per minute, and the 5 patterns that produced watchable results — experiments with OpenAI Sora 2 for technical explainer videos.

Prompt engineering patterns, temporal consistency techniques, and how we integrated Google Veo 3 into our documentation pipeline for product demo videos.

AI pair programming in your terminal — Aider edits code in any git repo using any LLM (GPT, Claude, or local), with automatic commits and file mapping.