Practical Decision AI for AI Agents and LLMs: The Laya Decision Model and Human-in-the-Loop

Experience firsthand how conversations that once disappeared can become reusable assets again. With Hermes and Wiki, build a work system where decisions and their rationale are preserved!

Are you feeling stuck on how to apply what you've learned to real-world tasks even after mastering the basics? I will help you complete complex RAG and agent designs using my professional industry know-how.

This is a practical introductory course designed to help general office workers utilize generative AI as a business partner rather than just a simple tool. The objective is to develop the ability to use AI safely while complying with security and copyright standards, based on an accurate understanding of its operating principles and limitations. From the core principles of prompt engineering to streamlining text-based tasks such as planning, reporting, emailing, and minutes-taking, as well as Excel data analysis and the use of images and PDF documents, students will learn step-by-step. This helps drastically reduce time spent on repetitive tasks and secures the practical capability to handle multiple roles. Beyond individual use, the course covers how to build team-level work templates and standards. It is an action-oriented curriculum designed to ensure that participants possess work automation assets that can be applied immediately to their jobs after completion, leading to productivity improvements across the entire organization.

When you use a chat-optimized LLM for classification, routing, and guardrails, it returns a string that you then have to parse again. This comes with hallucinations, type drift, delays of several seconds, and output-token costs. This course follows the architecture of Jev, TypeSafe’s first System One model: provide a state, and it returns typed decisions within a schema along with calibrated probabilities. The model does not generate strings. It covers, through concepts and code, why parallel sampling and RLCD (Reinforcement Learning for Calibrated Decisions) make those decisions fast, how to combine Choice · Score · Noul in a single request, and a hybrid approach that leaves structurally defined judgments to Jev and open-ended descriptions to an LLM.

Let's create a service that improves itself even while you sleep! Learn step-by-step how to build a 24-hour vibe coding system using loop engineering with Codex.

Deterministically control AI agents through a 5-layer harness architecture and 4 workflow layers, and immediately apply a scalable project structure to real-world practice. (Based on Claude Code, but the same principles apply to other AI tools.)

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

This is an advanced course focused on designing and implementing Multi-LLM architectures and orchestration-centered Agent systems (Main/Sub, Tool, Task Runtime, Agent Registry) by strategically combining GPT, Gemini, and LLaMA (local) based on Spring AI and Spring Boot. Moving beyond simple LLM calls, the curriculum covers the implementation of scalable, stable, and continuously improving AI systems. This involves applying Agentic Workflow Patterns (Chain, Parallel, Routing, Orchestrator–Workers, Evaluator–Optimizer) and Multi-Agent structures, separating execution layers like RAG and external APIs/DBs via Tool/ToolRegistry, and utilizing DAG engines, YAML declarative workflows, and Validated DSL (validation immediately after loading). Furthermore, the course includes Circuit Breakers, Reactive Streams, Redis monitoring, parallel processing, and iterative evaluation loops. It expands from Thymeleaf (SSR) exercises to decoupled Front-end/Back-end architectures using React and REST, while integrating tool and agent runtimes via MCP (Model Context Protocol) to build AI architecture design capabilities at a production-ready level. The ultimate goal is to evolve beyond being a simple AI user who merely integrates APIs and writes prompts, becoming a developer capable of designing AI systems—explaining and balancing Multi-LLM, agents, workflows, declarations, and validation within a single execution architecture.

A selfish era where only those who know how to properly utilize AI can generate profit. Essential web development concepts for profitability and a realistic guide you must know to 'properly' direct AI coding agents. From the mindset of coding without writing a single line of code to realistic methods for building a product with zero budget, and even practical details on business registration and tax processing. This is an AI-driven profitable web programming guide filled with insights gained from the actual field.

A practical introductory course on Claude Code for Unity developers! From installing Claude Code to Unity MCP integration, managing project conventions using CLAUDE.md, custom slash commands, and collaboration workflows—we will teach you everything you need to know for real-world application. Finally, you will gain hands-on experience effectively applying Claude Code to a Unity project by building a Survivor-style game from scratch.

Simple tutorials alone make it difficult to apply in practice. I will clearly pass on my professional know-how, covering complex state management and multi-agent design methods.

: "From environment setup to 7 practical projects, a deep learning full-stack journey across data domains (35 lectures in total)" Go beyond simply following example code and learn how to cook various types of real-world data using deep learning. Starting from building CPU/GPU development environments, you will cover waste sorting classification (Image), stock price prediction (Time-series), ECG anomaly detection (AE), animation stylization (GAN), medical image segmentation (U-Net), and sound classification (Audio)! Systematically master core deep learning algorithms through hands-on projects.

Learn what JEV is in simple terms first. Then follow the process of checking the results in the TypeSafe AI Playground and creating a Mac app that recommends emojis matching a sentence by making verbal requests to Claude Code. From signing up and connecting your API key to making your code public on GitHub, learn together through five videos and learning materials that include screen captures and prompts you can copy.

AI is only as good as you are. Learn vibe coding properly in three steps: how to use AI → essential foundational knowledge → project development. For those who want to learn vibe coding the right way, we deliver the core concepts that remain unchanged regardless of which AI you use, broken down into bite-sized pieces.

Is your skill stagnating because you keep asking AI to 'just whip something up'? This course dissects the principles and philosophy of the 'Cocrates Harness,' a rigorous constraint system that fundamentally blocks the sloppy generation of LLMs. Defy the gravity of AI convenience and discover the cheat code to rapidly growing into a 'true top-tier architect' who commands and controls every design context!

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.

This course trains you to think in terms of frontend system design through real-world service cases such as Threads feeds, Amazon shopping carts, the Netflix streaming UI, Google Docs collaborative editing, Micro Frontends, Agentic UI, and Observability.

Build a Kiwoom Securities API-based automated stock trading system from scratch using the core features of Claude Code (MCP, Agents, Skills). It's perfectly fine for beginners. Get started with Claude Code right now!

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

Are you confused about which technology trends really matter among the flood of information? We've analyzed global reports from Gartner, Microsoft, and others to clearly explain 10 key technologies (multi-agent systems, physical AI, AI security, etc.) that will define 2026.