Herdr in a Day: Multiplexer, Agent, and SSH Remote Access from Mac mini to Mobile Devices

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!

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.

From PC control to high-level research and app development A guide to building your own 'AI Work Assistant' ready for immediate real-world deployment

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.

"Why does my automated trading system collapse in a declining market?" I, too, have wrestled deeply with the same question. Most automated trading logic on the market may appear to generate profits during bull markets, but the moment market conditions change, it loses its ability to adapt. To solve this problem, I combined the expertise I developed through two years of hands-on research with the latest AI technology. In this course, you will learn how to design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and the movements of individual stocks, and how to automatically train and deploy it in a free GPU environment. In particular, by introducing sophisticated, hedge-fund-level data normalization techniques, you will build an 'intelligent comprehensive scoring system' that goes beyond simple indicators by incorporating short-selling balance and order-flow data. On top of that, an ‘AI safety gate’ infused with the insight of a Wall Street trader with 20 years of experience provides the final confirmation from the perspective of a human trader just before a purchase. Going beyond simply buying and selling, you will implement the entire process of an institution-grade algorithmic system with dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reporting—all with your own hands.

Deploy a Spring Boot SNS app to local Kubernetes, and automate deployments with GitHub Actions and Argo CD. Identify the causes of failures through metrics, logs, and traces, and review them based on AI analysis results.

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.

If you follow OpenClaw Parts 1 and 2, you eventually end up making a single agent do everything. As you do this, there comes a moment when prompts get tangled, memory gets mixed up, folders get messy, and you feel stuck, not knowing how to organize things anymore. This lecture is the "Introduction to Multi-Agents (Part 2.5)," designed to help you move one step further from that point. While maintaining a single bot, we will separate roles into two agents—planner and editor—and connect each agent to different workspaces and Telegram forum topics. You will personally build a structure where "different agents respond depending on which room you speak in." By following the hands-on practice, you will have the following in your environment: - A configuration file defining two agents: `content_planner` and `content_editor`, - A content folder structure where planning and drafting are separated, - A Telegram group chat where different agents are attached to specific forum topics, - And one actual draft of an Inflearn course introduction created through the collaboration of the two agents. In short, this is a practical class that helps you design the first step of upgrading your Telegram bot from a "single AI assistant" into a "small AI team with divided roles."

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!

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.

Instead of using more agents, experience firsthand how to entrust them with greater confidence. With Codex CLI, build your own harness so you can delegate work without worry!

This is a tutorial-style Claude Code development course that beginners and those new to development can easily follow. Based on experience teaching planners, designers, and non-developers in offline classes, the course covers everything in one continuous flow—from beginner examples not covered in books to intermediate development examples. Using Claude Code, you will experience the step-by-step development process, starting with Hello World and progressing to a lucky draw, a device-optimized café menu management web page integrating Claude Design with Excel data, and a SaaS web application that can be connected to a database and deployed to cloud services. Tutorials are provided at each stage—theory, practice, and projects. Through hands-on exercises across 6 levels, you will learn AI-collaborative development methodologies and experience the entire development cycle, including cloud deployment and operations.

The more you modify, the more anxious vibe coding can become. Now create save points you can return to anytime. You don’t need to know Git commands. Just tell the AI what to do, and it will handle saving, backups, and restoration.

Which AI tool should I learn first? Stop worrying now! - Rated 4.8 by 80 students ⭐️⭐️⭐️⭐️⭐️ - ChatGPT, Claude, Gemini, Genspark, NotebookLM, Gamma, Skywork, Manus, Antigravity, Claude Code, MiriCanvas, Opal, CapCut, and more - Learn the core features of over 15 tools very easily over 20 days. - Through weekly assignments, we provide a course you can "internalize" rather than just "passively listen to." - All-in-one coverage: Prompt Engineering > Image Generation > PPT & Video Creation > Vibe Coding - 4 hours of lecture videos + weekly study materials + assignment certification system

"How is a 1-trillion parameter model trained?" Discover the scaling strategy of Kimi K2.5, revealed directly on stage by Moonshot AI at GTC 2026, in Korean on Inflearn. 2x token efficiency · 1 million token context · 1,000 agent swarm The next step for open-source LLMs is here. * This lecture is released for free through an official collaboration between Moonshot AI, the creator of Kimi, and Inflearn.

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%.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

"Can I build an app without knowing a single line of code?" This course is a 6-day journey that answers that question with a resounding "Yes, you can." In the process of creating a random lunch menu picker app by conversing with AI, you will learn how to write prompts to order an app from Claude on the first day. From there, you will add features day by day—including adding/deleting items, slot machine animations, permanent data storage, statistical charts, and sharing functions—to create a high-quality app ready for the App Store. Each lesson offers three challenge courses tailored to your level, allowing both those unfamiliar with code and those with coding experience to grow at their own pace. What you truly learn in this challenge is not just code, but how to ask the right questions to AI—a skill that applies everywhere, not just in app development.

We're building an AI-powered video conferencing application with real-time AI agents, meeting summary features, and post-call functionality.

This course covers implementing combat, UI, data, and server functionality for a Unity idle game using CLI- and MCP-based AI tools. You’ll learn the entire process of applying Firebase, ads, in-app purchases, and Addressables to launch a mobile game on Google Play.