Hybrid and Local AI course at DeepLearning.AI - The JetBrains Blog

JetBrains and DeepLearning.AI have launched a free course, AI Coding Workflows: Hybrid to Local, using PyCharm and AI Chat to explore how developers can gain greater control, model choice, privacy, and cost efficiency through hybrid and local AI workflows. The course begins with Claude Code, demonstrating how specifications, specialist subagents, context isolation, smaller implementation models, and performance metrics can make coding tasks more manageable and less expensive. It then moves to OpenCode, OpenRouter, and DeepSeek models to show how changing agents, inference providers, and models expands workflow flexibility, including configurable implementer subagents. Later lessons examine hybrid setups using LM Studio and Gemma 4 12B on a 32 GB laptop, followed by a fully local workflow with Qwen 3.5 27B, with results suggesting that structured guardrails can help smaller or local models perform effectively. Throughout, the course emphasizes measuring tokens, cost, time, and outcomes to evaluate tradeoffs, while positioning hybrid and local AI as increasingly relevant for sovereign AI, security, privacy, and human-in-the-loop development.

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