Google’s Distributed System Multi AI Agent Architecture [ A2A Protocol ]

We present ways to improve RAG performance based on Cognitive Load Theory. To overcome the limitations of existing RAG enhancement techniques, we synthesize various methods by applying the conceptual framework of cognitive load.

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

You know about AI agent frameworks, but understanding how they work and applying them in practice can be difficult, right? Drawing on firsthand experience from Silicon Valley, we’ll guide you from the core principles to practical applications.

We will summarize the core theories spanning from Machine Learning and Deep Learning to Generative AI in one go. And by implementing various Generative AI applications with easy Python code, you can take your first step as an AI developer.

This course is not about collecting a large number of prompts. It is about learning an AI directing framework that can be applied immediately to any role or task. Even if you download prompts for specific tasks, in practice you still need to find the prompt you need, adapt it to your situation, and spend time revising the output. Through the M·A·G·I·C prompt framework, you will learn how to design the purpose, context, examples, completion criteria, and review criteria all at once. From reports, proposals, and marketing execution plans to meeting summaries, work emails, and repetitive task automation, you can connect everything with a single framework, reduce unnecessary revision conversations with AI, and build practical AI skills that will change the speed of your work starting tomorrow.

A Practical AI Automation Series That Directly Transforms Your Work If you’ve learned about AI but still feel unsure about how to apply it to your work, what you need now is not a “feature guide,” but practical ways to use AI that immediately transform your work. The SMART WORK ORDER series is a hands-on AI automation series designed to reduce repetitive tasks across marketing, planning, HR and general affairs, sales, customer support, data analysis, and team leader and manager responsibilities—and turn them into tangible work deliverables. From content drafts, meeting minutes, proposals, and consultation scripts to FAQs, VOC reports, data analysis reports, and team prompt libraries. Each course is designed not as a "lecture you watch and understand," but as a hands-on experience where you follow along and create real deliverables. The prompts and templates you learn today can be applied to your work immediately tomorrow, expanding into practical assets that improve not only individual work speed but also team productivity. AI-powered work transformation that reduces repetitive tasks while improving the quality of deliverables. SMART WORK ORDER is a job-specific practical AI training series that changes the way you work—making it faster, more accurate, and smarter.

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%.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

Innovation in Work Efficiency and Creativity – Agentic AI AI is now evolving beyond simple automation into a new stage where it can judge and execute on its own. Agentic AI is at the center of this change, serving as a core technology that helps businesses and organizations operate smarter. In this course, we have organized the content clearly and simply so you can understand how Agentic AI connects decision-making with execution and how it can be applied to practical work. Discover the essential knowledge for every professional preparing for the future right now.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

Build a Saju (Four Pillars of Destiny) analysis AI agent that integrates Manseryeok, Astrology, Zi Wei Dou Shu, Onomancy (Name Study), and MBTI using n8n workflows and Python algorithms. We will complete a production-level service system from start to finish where 13 specialized agents collaborate to generate professional-grade Saju reports.

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

"I'd like to publish a book at least once in my lifetime, but it seems difficult. Maybe I could do it easily if it's a picture book..." "My child (grandchild) would be so happy if I made a picture book about a theme they love..." "I want to learn ChatGPT and Midjourney, but studying them normally is boring. I want to learn while having fun!" This course is for people like you.

Linear Algebra in One Go. Learn linear algebra concepts in detail—concepts that, once learned, will serve as the building blocks for your thinking for a lifetime. Going beyond simple explanations, we kindly guide you through practical exercises to show you how to apply them in real-world situations.

If you keep getting errors after asking AI to do it, the problem isn’t the AI—it’s that you lack the foundational knowledge to guide it. In this video, we’ll cover the seven foundational concepts that people who get stuck while vibe coding were missing, and explain what each one is and why it’s necessary. 👾 This is explained for people on day zero of coding who have never opened a terminal before.

[Problem-Solving Q&A with McKinsey Alum Jaesung Kim: We'll Solve Your Problems Together] Required! Prerequisite Course: Problem-Solving & Research Masterclass with AI by McKinsey Alum Jaesung Kim https://inf.run/LmvrD 'Forget theory-only lectures!' A magical solution video that solves real practical problems instantly on the spot! This is a video series where Jaesung Kim, a McKinsey alum and AI expert, meets with individuals one-on-one in a Q&A format to structure and solve your problems. From career paths and minor concerns to massive topics like AI that's shaking the world and improvement directions for entire industries! Videos will be uploaded in a format where each episode can be purchased individually. Paid course students will have the opportunity to participate through quarterly drawings of selected participants! (The more diverse courses by Jaesung Kim you take, the higher your chances of winning!) [Concept] This is a one-on-one Q&A format where an industry expert and strategy expert meet to approach the answer together. Participants have been extremely satisfied. (You can see and hear this in their testimonials!) The key to this video series is that strategy expert Jaesung Kim doesn't receive the topic in advance, but through strategic questioning and answering, they get closer to the solution together. Without relying on intuition and experience, but based on strategy and structuring, experience the magical solution that naturally makes you go "This actually works?" hahaha The free version (~10 minutes) will be available to everyone, and the full version will be available to Inflearn course students or YouTube channel [Jaesung Kim's Problem Solving] 'Premium Membership' subscribers. If you'd like to participate, please send an email to jaesung_kim@jcurvepartners.ai with the subject line [Coffee Chat Application] In the email, please include your name, age, contact information (mobile number), occupation and years of experience (students are welcome), detailed reasons for applying, and attach a screenshot of your enrollment in Jaesung Kim's Inflearn courses. Winners will be contacted. (Personal information of non-winners will be immediately discarded after 3 months) When appearing in the video, using a pseudonym or not revealing your company name is possible, but if you wish for face blurring, voice modulation, or other modifications, participation is not possible. By consenting to video recording, you are considered to have agreed that the video may be reprocessed and used on Inflearn and Jaesung Kim's YouTube channel.

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.

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.

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.