Uploaded August 2026 | Updated September 2026, 2 weeks ago
Learn More: bit.ly/4q6zSEc
AI coding agents like Claude Code and Codex give you a ready-made way to work. That convenience comes at the cost of control: over which models run, what you spend, and what leaves your machine.
In our new short course, AI Coding Workflows: From Cloud to Local, built in partnership with JetBrains and taught by Paul Everitt, Developer Advocate at JetBrains, you'll take that control back one layer at a time.
You'll rebuild the same Python app across setups: first from a Claude Code baseline, then with subagents that break the job into focused tasks, then with a cheaper model on the routine work. From there, you'll switch to an open-source coding agent, connect it to different inference providers, and finally run models on your own machine. At each step, you'll see how the change shifts cost, speed, and usage.
In this course, you'll learn how to:
- Split work across specialized subagents with focused context and clear specs
- Assign different models to different tasks, keeping frontier models for the hard parts
- Swap your coding agent and connect it to different inference providers
- Run models locally and see how far a fully local setup can take you
By the end, every layer of your setup is a choice you've made.
Enroll Now: bit.ly/4q6zSEc
Learn More: bit.ly/4q6zSEc
AI coding agents like Claude Code and Codex give you a ready-made way to work. That convenience comes at the cost of control: over which models run, what you spend, and what leaves your machine.
In our new short course, AI Coding Workflows: From Cloud to Local, built in partnership with JetBrains and taught by Paul Everitt, Developer Advocate at JetBrains, you'll take that control back one layer at a time.
You'll rebuild the same Python app across setups: first from a Claude Code baseline, then with subagents that break the job into focused tasks, then with a cheaper model on the routine work. From there, you'll switch to an open-source coding agent, connect it to different inference providers, and finally run models on your own machine. At each step, you'll see how the change shifts cost, speed, and usage.
In this course, you'll learn how to:
- Split work across specialized subagents with focused context and clear specs
- Assign different models to different tasks, keeping frontier models for the hard parts
- Swap your coding agent and connect it to different inference providers
- Run models locally and see how far a fully local setup can take you
By the end, every layer of your setup is a choice you've made.
Enroll Now: bit.ly/4q6zSEc
