Vibe Coding Security Essentials for Top 1% Developers: 24+ Practical Prompts

It doesn’t stop at following along to create a sample game. Using ChatGPT- and Codex-based vibe coding, you’ll learn the entire process through a real launch project: from game planning → development → mobile builds → AdMob ad integration → publishing on ONE store, Google Play, and the App Store.

Do the results of vibe coding keep turning out differently from what you intended? Learn a workflow that uses specification-driven development (SDD) and Spec Kit to define goals, scope, business rules, and completion criteria, then shares the same specifications with Claude Code and Codex to reduce repetitive revisions.

We delve into how AI coding tools work internally based on 510,000 lines of leaked Claude Code source code. Understand context management, memory processing, and prompt delivery mechanisms, and learn vibe coding practices for applying them to real-world development workflows.

Everyone says agents are the trend, but why does your work stay the same? Have you been blocked by the "alien language" of APIs, stuck at tool installation, or found that what you learned doesn't translate to actual work? This integrated course connects the entire process into "one single path"—from the principles of API and communication structures to creating your own agent through Vibe Coding, and automating practical tasks like reports, Excel, and content with Claude Code. Whether you start from the basics or aim for professional application, you will complete the journey without interruption. (Windows recommended)

Have you ever regretted focusing only on notes instead of making eye contact, or failing to accurately record meeting details after an important client meeting? Don't worry anymore! In this lecture, we'll help you build your own CRM assistant using Vibe Coding to summarize key information with AI and neatly save meeting content. You'll learn how to drastically reduce cumbersome document creation time, maximize work productivity, and revolutionize customer management!

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

Non-Majors Welcome: Real-World Vibe Coding Projects Created Through Conversations with AI

I got answers from AI, but am I still doing the work myself? From the basics of using Claude Code to building apps, automating Instagram carousel posts, and researching competitors’ social media. Follow along while watching the screen and learn how to apply it to your own work. This includes 11 beginner-focused desktop app lessons, along with hands-on practice for real-world tasks.

This is Korea's first free lecture covering how to utilize 'On-Device Vibe Coding' agents that are 100% free, offer unlimited tokens, and provide perfect security. We will build an on-device development agent that integrates Codex + Ollama + Open Source LLMs to overcome the limitations of cloud-based vibe coding tools, such as frequent token limit interruptions, rising subscription fees, and concerns over source code leaks.

From learning to code from scratch to publishing your app on Google Play—all in one place?! An all-in-one app-building course that bridges the fundamentals and hands-on practice.

Even those who have never experienced even the basics of coding can create automation software with just a few clicks. I'm serious.

This course teaches the essential CS knowledge required for web development using AI. If you don't understand the principles of servers, databases, and deployment, "Vibe Coding" will inevitably hit a wall at some point. Through interactive textbooks, you will quickly learn essential concepts, enabling anyone to complete a web project using Vibe Coding.

Are you going to keep checking 2,500 stocks by hand every day? After the market closes, you search Naver Finance for stocks that have surged, read the news to decide whether it’s good or bad, check foreign and institutional investor flows one by one, open charts and analyze their patterns... Do you find yourself doing this every day? I did, too. I spent 2–3 hours after work analyzing stocks, and still missed more stocks than I could keep up with. There are over 2,500 stocks on the KOSPI and KOSDAQ combined. One person simply can’t review them all every day. --- So I built a system I built a system that automatically analyzes 2,500 stocks every day after the market closes. - It automatically collects prices, investor flows, and news - AI (Gemini) reads the news and determines whether it’s positive or negative - It scores each stock out of 15 using six factors - It selects only stocks that meet the criteria and calculates entry, stop-loss, and take-profit prices - It sends alerts through Telegram All I have to do is check my phone after work. Then, every Saturday, the system analyzes the previous week’s performance and automatically adjusts its stop-loss levels, take-profit levels, and holding period. It’s designed to learn and improve on its own. --- But I’m not a developer I didn’t write the code for this system myself. I built the whole thing by telling AI what to do. "Filter out stocks that rose more than 5% today and had over 50 billion won in trading value." "Send these three news articles to Gemini and have it determine whether they’re positive or negative." "Create a scheduler that runs automatically at 4 p.m. every day." When I say things like this, AI (Claude) writes the code for me. This is vibe coding. --- You’ll build this exact same system in this course Across 58 lessons, we’ll build from scratch the system I actually use every day. Starting with data collection, we’ll cover AI news analysis, a scoring engine, signal generation, a Flask API server, a Next.js web dashboard, automated Telegram alerts, and even a self-learning system. A Jupyter notebook isn’t the end product. The end product is a web dashboard that runs every day and sends Telegram alerts. You don’t need to know how to code. In each lesson, I’ll show you exactly what to tell Claude, and if you follow along, you’ll get the same results. --- Recommended for - Working investors who don’t have enough time to analyze stocks every day - Anyone who wants an automated system but doesn’t know how to code - Anyone curious about quant or systematic trading but unsure where to start - Anyone who wants to learn how to use AI in practice --- ⚠️ This course does not guarantee investment returns. It’s a programming course where you’ll build your own stock analysis tool. Each student is responsible for their own actual investment decisions.

Start by installing the Claude desktop app and getting familiar with its interface. Then learn what’s changed with the all-in-one Claude and understand the difference between an AI you ask questions and an AI that does the work. PART 1 In “Giving Clear Instructions,” learn how to delegate work to Claude through a single chat window—from writing work requests and assigning files and research to getting finished documents, slides, and spreadsheets. PART 2 In “Connecting Claude to Your Work,” use memory and projects to teach Claude your rules, and connect it to your real work environment—including data analysis, email, Drive, Calendar, folders on your computer, and web-based work. PART 3 In “Handing Off the Whole Task,” delegate major tasks all at once and use skills, scheduled tasks, and work dashboards to automate recurring work—creating a workflow where people only need to approve the results. Each session centers on the instructor using Claude to handle work they’re actively doing, and includes examples of applying the same methods in corporate and school settings.

From tokens and context to RAG, agents, and guardrails, we examine how AI services work from a security perspective. We compare the concepts with the provided backend code and organize the areas to inspect across input, retrieval, and tool calls into a single attack-surface map.

If you design the tests first, AI handles the implementation. A practical development approach that captures both speed and quality!

Latest version as of October 2026. This is a 299-lesson course created by YouTube’s “AI Adjunct Professor Lee Jong-beom” (54,000 subscribers). It guides you step by step, starting with how to open a browser tab and progressing to creating an AI employee team with Claude Code. It includes approximately 290 practice worksheets and 1,000 credits.

We invite you to become a ‘Top-Tier Enterprise AI Systems Architect’ Do not remain a micro-level technician (Prompt Writer) who merely assembles libraries created by others or polishes prompt sentences. From mathematical first principles to runtime control-loop kernels and autonomous recovery infrastructure — through 40 years of practical engineering insight that has driven industrial operations in South Korea, I will firmly guide you to become a top-level ‘Enterprise AI Systems Architect’ who protects your company’s business and digital sovereignty. Now, begin the great journey of transforming uncontrollable AI into a perfect industrial core. “Why does an AI that ran perfectly in a demo start spouting strange nonsense or bring the pipeline to a halt as soon as it is deployed to production?” “Why do AI projects that so many companies launch with great enthusiasm ultimately fail to escape the ‘PoC (Proof of Concept) swamp’ and get abandoned?” These are the most desperate questions that countless developers and systems architects I have met in the field have asked me. Hello. I am Hongjip Kim, who spent more than 40 years in the powertrain R&D division of the South Korean automotive industry (including 17 years as an executive in Hyundai Motor Group R&D), led the export of engine and drivetrain technologies worth 130 billion won, and commanded operations as the CEO/CTO of a listed mid-sized manufacturing company. From the perspective of an engineer who has spent decades in mechanical engineering and precision-control environments, a ‘risk of fatal uncertainty’ lurks behind today’s frenzy to adopt generative AI. In mission-critical industrial environments where even a mere 0.1% error or mistake can directly result in the destruction of physical equipment worth hundreds of millions of won and an enterprise-wide shutdown, integrating a probabilistic chatbot that simply relies on probability to generate plausible text directly into a service is like carrying a ticking time bomb into a fire. This course was designed to impart the “practical engineering methodology for training uncontrollable probabilistic AI into the most robust industrial control core, one that allows not even 0.000% of syntax errors.” 1. The 3 Major Fatal Bottlenecks You Face in the Field (Problem) Many engineers attempt to challenge production environments using an approach limited to one-off prompt tuning (Vibe Prompting), only to become frustrated when they encounter the following 3 structural defects: Finite Context and ‘Lost in the Middle’: As conversations or multistep executions grow longer, critical system instructions are pushed aside and the context window is exhausted, causing the system to lose its memory and amplifying hallucinations. Nondeterministic Outputs and Cascading Pipeline Collapse: Tiny probabilistic fluctuations, such as one missing closing bracket in JSON or an incorrect type, can paralyze legacy ERP/CRM API calls and destroy data integrity. Untrusted Code Execution and Infrastructure Security Bankruptcy: When scripts autonomously generated by an agent run in a non-isolated host OS environment, they create risks such as kernel escapes and sensitive-data leakage. As such, AI malfunctions in industrial environments are not merely quantitative accuracy problems; they become a massive financial disaster in the form of asymmetric business loss (Asymmetric Business Loss). 2. ‘How’ to Solve These Problems Perfectly (Solution) This course differs fundamentally from commercial courses that teach only how to use fragmented off-the-shelf tools. By combining 40 years of hands-on field expertise with computer systems engineering, it delivers 3 hardcore architectures for vertically integrated, system-level control of probabilistic AI: ① Move Beyond Prompting to ‘LLM-OS & Context Orchestration’ Following Andrej Karpathy’s paradigm, redefine the LLM not as a conversational respondent but as the central computational core (CPU) of a computer. Systematically organize the context window as ultra-low-latency volatile RAM, the internal vector DB as Disk, and external APIs as an I/O bus. Through MemGPT hierarchical virtual-memory paging, preserve critical state without saturation even during infinite conversations, while scaling a dynamic agent network through MCP-based Liquid Swarm MAS, minimizing token costs. ② ‘Deterministic Harness Engineering’ That Constrains Probabilistic Hallucinations to 0.000% Leave the language model’s creative flexibility open only during the Reasoning phase, while firmly confining the physical output layer that interacts with external systems using mathematical Boolean algebra and compilers. Apply EBNF / XGrammar-based CUDA logit masking to forcibly block the generation probability of invalid tokens at P = 0.000%. Form a physical security isolation ring so that code generated by agents runs only once inside a Firecracker MicroVM micro-virtualization sandbox with 5ms snapshot restoration capability. ③ ‘High-Reliability MLOps·LLMOps Autonomous Recovery Infrastructure’ Without Wasting GPU Resources Build vLLM PagedAttention and S-LoRA hot-swapping to suppress idle VRAM waste to below 4%. When a multi-agent API fails, restore data consistency 100% through SagaLLM reverse compensation transactions. Through an OpenTelemetry/Langfuse distributed observability network, defend system availability by executing an unmanned automatic canary rollback whenever a decline in the RAGAS faithfulness score (Data Drift) is detected. 3. Practical Value That Drives Spiral Growth (Pedagogy & ROI) This course is systematically designed according to the 5 levels of Bloom's Taxonomy. Starting from simple theoretical recall (Remember), learners directly code and validate a 5-stage debugging pipeline (analyzing deceptive baseline trap source code ➔ deconstructing adversarial engineering ➔ establishing a Robust integrity pipeline ➔ calculating the TEI ROI formula ➔ SOTIF/ISO 42001 governance). Going beyond the intuitive satisfaction of simply “working well,” I will apply the Forrester TEI (Total Economic Impact) financial model to visualize, from an executive perspective, how much the AI system you build reduces your company’s rework cost (Rework Cost), avoids failure risks worth hundreds of millions of won, and proves an ROI of more than 200%.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

Before installing the caveman skill that has earned over 110,000 stars with its “65% token reduction” claim, I tested it myself. I ran the same questions 156 times in Claude Code under four conditions—default, “Answer concisely,” caveman, and ultracave—and used a script to count output tokens and correct answers. In under 10 minutes, I’ll show you how to measure fairly and share a free measurement kit you can use with your own questions.