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AI can now help implement features across an entire project.
Agentic engineering tools can inspect a repository, plan work across multiple files, implement changes, run tests, and iterate when something breaks. That matters because you learn and work through complete engineering workflows, shifting your role toward defining the goal, reviewing the output, and guiding the system instead of writing every line by hand.
Topics to know
A coding-focused model designed to work across larger engineering tasks and multi-file repositories.
A terminal-first coding agent that turns high-level requests into working implementations and can run commands, modify files, and iterate on fixes.
A lightweight open-source agent harness that allows different models to act like repository-aware coding agents.
Instead of asking one system to do everything, some setups divide work between multiple specialized agents.
AI agents and workflows split complex tasks into roles such as planning, coding, testing, and review, with a lead system coordinating the handoffs between steps. That structure makes larger automations more reliable, repeatable, and easier to understand because each part of the workflow stays explicit instead of being hidden inside one big prompt.
Topics to know
A visual workflow platform that allows you to connect multiple steps into a repeatable automation pipeline.
An open-source automation system that coordinates agents and allows them to interact with tools such as browsers, APIs, and local applications.
Modern AI systems can interact with files, tools, and external services. This makes infrastructure and security an important part of building reliable workflows.
As AI systems move beyond text generation and start reading files, running commands, and calling tools, infrastructure and security become part of the product. Running agents in controlled environments, limiting permissions, and understanding risks like prompt injection helps you build safer, more dependable workflows without losing the practical benefits of local and hybrid setups.
Topics to know
A standard that allows systems to securely connect to tools, services, and data sources.
A local runtime that lets developers run models on their own machine for experimentation and private workflows.
A secure networking tool often used to safely connect local machines, servers, and services.
The world is evolving and we help you keep up.
Explore AI Courses
Direct AI systems to plan, build, test, and ship meaningful work.
Outcome: AI as a force multiplier, not just a chatbot.
Build engineering discipline that keeps AI-generated code maintainable.
Outcome: Ship faster without accumulating tech debt.
Build the technical foundation and judgment that AI still can't replace.
Outcome: Catch mistakes, make trade-offs, own your code.
Flexibility
Pause, rewind and learn on your schedule. No deadlines, no pressure.
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Founder & Lead Instructor
Starting out at the age of 13, Max has been coding for over two decades and became the world's largest coding teacher. With a Master’s degree in Business Administration and deep expertise across web development, cloud infrastructure and AI-powered development, he creates courses that bridge fundamentals with cutting-edge AI workflows, teaching over 3 million students worldwide.
Co-Founder & Instructor
Manuel is an experienced developer and entrepreneur who started Academind together with Max in 2017. With degrees in Business Administration and Finance, he has always been passionate about Tech and teaches students how to leverage modern AI tools alongside rock-solid fundamentals to build better software, faster.
