đ§ Want to try out what AI can really do in programming? Give this course a go. Better than most paid ones.
đ§ Want to try out what AI can really do in programming? Give this course a go. Better than most paid ones. If you're interested in how you can use AI not just to supplement code but to truly programme as if you had a team of virtual developers, I recommend you try

đ§ Want to try out what AI can really do in programming? Give this course a go. Better than most paid ones.
If you're interested in how you can use AI not just to supplement code but to truly programme as if you had a team of virtual developers, I recommend you try the new free course Claude Code: A Highly Agentic Coding Assistant.
đ It was created by the team at DeepLearning.AI in collaboration with Anthropic (the creators of the Claude model). I was surprised by how practical and yet accessible the course is â its quality surpasses many expensive training sessions I've encountered before.
đ What will you learn?
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Working with agentic assistants â AI that can independently work on parts of your code for minutes to tens of minutes (sometimes even hours).
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Dividing tasks among multiple Claude agents â for example, one analyses data, another visualises it, and a third creates tests.
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Using Claude in real tools like GitHub, Jupyter, Figma, or Playwright â including automating pull requests or fixing UI bugs.
Translating a design from Figma into functional code â the AI agent can analyse the design in Figma and generate code for a real web application (e.g., in Next.js using React). This isn't about magical "one-click" solutions, but a guided process where the AI writes code for you.
đ§Ș A demonstration from my practice: Two agents calculating Ï To get a feel for it, I had two AI agents work in parallel on researching methods for calculating the number Ï. One was tasked with mapping traditional mathematical approaches, while the other sought creative and unconventional paths. Within minutes, they collaboratively created a comprehensive plan â from basic methods like Monte Carlo to the advanced Chudnovsky algorithm, and even proposed creative approaches using fractals or machine learning. The result of their collaboration, which I compiled based on their outputs, can be viewed here. The course practically demonstrates how to manage such collaboration â typically using scripts where the output of one agent serves as input for another. This is something you won't experience in standard "prompting." đhttps://alphai.cz/pi.html
â Why should you get involved? The course is free, but the API incurs costs (you have control over this). The video content itself is free. Practical experimentation and play require a paid API from Anthropic, but you can set a spending limit on your account, so you don't need to worry about unexpected bills. The total costs for the course are estimated at $12â20.
You're learning a skill that will save you time. Mastering these procedures can dramatically shorten the time needed for prototyping and solving routine tasks, freeing you up for more creative and complex work.
It's short (about 2â4 hours), but dense.
No powerful machines are required. You can run Claude in the cloud; all you need is a terminal or VS Code.
âïž What skills are beneficial? (Prerequisites) Basic Python: Most examples are in Python, so you can't do without a foundational knowledge.
Basic terminal (command line) skills: The course assumes you can execute basic commands.
Familiarity with Git and the Next.js framework is advantageous (but not essential) if you want to explore the generated code from Figma in more detail.
đŻ Who is this for?
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For those who enjoy trying out new AI tools and want to stay a step ahead.
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For developers who already use LLMs to write code and want to elevate AI from a helper to a full-fledged partner.
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For seniors dealing with integration into large projects: The course showcases procedures that work even in complex repositories where multiple people and agents collaborate. This isn't just about toys "on a green field." Moreover, the quality of the tool is underscored by speculation that it is also used for internal development at OpenAI.
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For anyone working on their own projects who wants to speed up development or gain a second (virtual) brain.
đ Link to the course: đ https://www.deeplearning.ai/short-courses/claude-code-a-highly-agentic-coding-assistant/
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