Crash Courses & Masterclasses
Hands-On Tool Tutorials & Masterclasses
Step-by-step textual crash courses and deep-dive masterclasses for modern AI tools, agentic systems, software architecture, and engineering workflows.
Learn Modern Engineering Step-by-Step with Hands-On Masterclasses
Our tool tutorials and masterclasses act as your step-by-step mentor. Instead of dry reference documentation, each course walks you through real-world setup, copyable code, under-the-hood explanations, and self-check verification checklists.
Master autonomous agent loops, CLI engineering, full-stack AI applications with FastAPI & Streamlit, scalable system design architectures, and technical interview algorithms.
Curriculum Tracks & Roadmaps
Autonomous Agents & CLI: Loop Engineering, Claude Code CLI, Codex Super App
Full-Stack AI Engineering: Python OOP, Gemini LLM SDK, FastAPI & Streamlit
Scale & Interviews: System Design Architectures, 9 Core DSA Cheat Sheets
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Beginner FAQs
Common questions about getting started with modern AI agent workflows, software architecture, and technical interviews.
What is Loop Engineering and why is it replacing manual prompt engineering?
Prompt engineering requires humans to manually inspect outputs and re-prompt when mistakes happen. Loop Engineering replaces manual supervision with autonomous, self-evaluating loops: the system continuously plans, executes, evaluates results against strict criteria, logs state to disk, and auto-stops when goals are met.
What are the 6 core components of a production agent loop?
Every resilient loop requires: (1) Automation & Trigger Schedule, (2) Rules & Constraints, (3) Action Execution, (4) Verifier / Critic Sub-Agent, (5) Memory Log & State Persistence, and (6) Explicit Stop Conditions.
What is the difference between Agent Engineering, Harness Engineering, and Loop Engineering?
Agent Engineering gives an LLM tools to take actions. Harness Engineering orchestrates multi-agent enterprise clusters (routing, security, rate limits). Loop Engineering constructs autonomous self-correcting feedback loops that run uninterrupted until work is verified and complete.
What is Claude Code and how does it differ from web chatbots?
Claude Code is Anthropic's agentic CLI tool that runs directly inside your local terminal or IDE. Unlike browser chatbots, it directly reads and edits your project code, executes bash commands, reviews diffs, runs tests, and connects to external MCP tools.
What actually is Codex in simple terms?
Codex is OpenAI's AI Agent Super App. It operates natively on your computer, manages local files, controls desktop applications with mouse and keyboard, automates browser testing, and runs scheduled background automations.
Which core data structures are most critical for technical coding interviews?
The 9 foundational data structures covered in this masterclass are Arrays, Stacks, Queues, Linked Lists, Hash Maps (Hash Tables), Binary Trees & BSTs, Heaps (Priority Queues), Graphs, and Tries (Prefix Trees). Mastering their memory layouts, Big-O operational complexities, and problem keyword triggers prepares you for over 90-95% of technical coding interview scenarios.
How should I structure a System Design interview response?
A proven 5-step framework: (1) Clarify Functional and Non-Functional Requirements (QPS, storage, latency targets), (2) Perform back-of-the-envelope capacity estimations, (3) Define core APIs and high-level architecture with single-server baseline, (4) Scale bottlenecks systematically (Blob storage, Load Balancers, Redis caching, Read Replicas, Sharding), and (5) Address failure modes, resilience, and monitoring.
Why should developers learn Python fundamentals in the age of AI coding assistants?
While AI assistants generate code quickly, developers remain legally and operationally responsible for production stability, security, and efficiency. Solid Python fundamentals allow engineers to orchestrate multi-agent pipelines, debug subtle model hallucinations, design scalable FastAPI/Streamlit microservices, and review AI-generated code authoritatively.
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