{"id":4285,"date":"2026-08-14T04:41:09","date_gmt":"2026-08-14T04:41:09","guid":{"rendered":"https:\/\/tucumandevelopers.com\/index.php\/2026\/08\/14\/hybrid-and-local-ai-course-at-deeplearning-ai\/"},"modified":"2026-08-14T04:41:09","modified_gmt":"2026-08-14T04:41:09","slug":"hybrid-and-local-ai-course-at-deeplearning-ai","status":"publish","type":"post","link":"https:\/\/tucumandevelopers.com\/index.php\/2026\/08\/14\/hybrid-and-local-ai-course-at-deeplearning-ai\/","title":{"rendered":"Hybrid and Local AI course at DeepLearning.AI"},"content":{"rendered":"
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Learning Courses<\/a> News<\/a><\/p>\n

Open weight models are having a moment, driven by control, choice, and cost. Hybrid and local AI are now getting serious looks, so JetBrains teamed up with DeepLearning.AI<\/a> on a free AI Coding Workflows: Hybrid to Local<\/a> course that covers the ideas and options.<\/p>\n

The course is now available and uses PyCharm<\/a> and its AI Chat. Here\u2019s a peek into the course.<\/p>\n

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Claude Code: Subagents and cheaper models<\/h2>\n

We start the course with, well, not-local. Instead, we use what you already know \u2013 Claude Code and its Anthropic models \u2013 to introduce some of the techniques and \u201clevers\u201d that help bring choice, control, and even cost reduction. (Yes, I wrote emdashes.)<\/p>\n

We did a previous course on Spec-Driven Development<\/a> (SDD) so of course, we wanted to start there. Smaller models struggle with big, open-ended \u201cvibe coding.\u201d Dividing and bounding the work keeps smaller models on track. Important note: this course\u2019s example app is really<\/em> basic. You might say \u201cthat\u2019s too easy.\u201d But that\u2019s part of the takeaway: big brain models can do the upfront work, forming right-sized steps for smaller models.<\/p>\n

We then illustrate this division with a Claude Code subagent<\/em>. The main chat prompt implements each roadmap phase in a fresh subagent, to better manage context. This then gives the payoff: a cheaper model for the implementer. Use a \u201cbig brain\u201d (Opus) for main conversation thinking and a \u201clittle brain\u201d (Haiku) for implementation.<\/p>\n

Each lesson finishes with metrics about the change in tokens, turns, cost, and estimated wall time. Which brings us to the main course goal: learning the ideas<\/em> instead of the specifics, which change weekly.<\/p>\n

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New agent, inference, and model<\/h2>\n

That covers the four levers:<\/p>\n