DeepLearning.AI and JetBrains Launch Hybrid and Local AI Course
New techniques for control, choice and cost advantages in AI development
1Course Core Objective
This free course, offered through collaboration between DeepLearning.AI and JetBrains, teaches developers techniques to achieve control, choice and cost advantages in hybrid and local AI solutions. Delivered via PyCharm IDE and AI Chat interface, it emphasizes strategy of planning with large models while implementing with smaller ones. The example application is kept simple to ensure clear understanding of concepts.
2Starting with Claude Code
The course begins with Anthropic's Claude models, demonstrating combination of 'big brain' (Opus) and 'small brain' (Haiku) models. Large models handle open-ended tasks while smaller ones execute well-defined subtasks. Subagent architecture enables context management and cost optimization. Each lesson concludes with metrics on token count, cost and duration for performance evaluation.
3Four Fundamental Control Levers
The course presents four key optimization strategies: Creating specifications tailored to model size, using specialist subagents for task division, employing cheaper models for routine work, and supporting decisions with performance metrics. These techniques enable both performance and cost optimization. Understanding limitations of smaller models emerges as crucial learning outcome from these exercises.
4OpenCode and OpenRouter Integration
Second phase introduces OpenCode agent and OpenRouter inference routing. PyCharm's open platform approach enables seamless transition from Claude to OpenCode. DeepSeek model (via OpenRouter) serves as new 'small brain'. Markdown-based implementer subagents create additional control mechanisms. Cost comparisons show clear advantages over Claude implementation.
5Local AI Setup
A highlight of the course teaches local AI setup using LM Studio inference server and Gemma 4 12B model, targeting 32GB RAM laptops. DeepSeek v4 Flash gets promoted to 'big brain' role in this phase. Fully local operation is tested with Qwen 3.5 27B model. Results demonstrate that with proper constraints, smaller models can outperform expectations significantly.
6Evidence-Based Approach with Metrics
A key emphasis of the course is tracking performance metrics at each step - token counts, conversation turns, cost and estimated wall time. This data supports model selection and architectural decisions. Pain points of smaller models and effectiveness of assistance techniques are demonstrated with concrete data. This evidence-based approach proves critical for professional implementations.
7Current Industry Developments
The course addresses timely topics like Sovereign AI, data privacy/security concerns and cost optimization. Rapid advancements in AI make understanding fundamentals increasingly important. JetBrains emphasizes human-in-the-loop approach. Course content will continue evolving alongside latest technological developments in the field.
8Course Significance and Future
This course stands among first comprehensive trainings demonstrating practical applications of hybrid and local approaches in AI development. It teaches techniques for flexible solutions while reducing costs. PyCharm team announced plans to expand their local AI initiatives. Offered free of charge, the course serves as valuable resource for both beginners and professionals in the field.
Original article: The JetBrains Blog — DeepLearning.AI ve JetBrains'ten Hibrit ve Yerel AI Kursu