Automotive SW R&D for Job Seekers

It contains concepts necessary for practical use rather than academic concepts regarding CAN communication.

Learn the basic concepts of Autosar for automotive SW development.

Understand and learn about the Automotive International Standard Process Model ASPICE.

Learn about Vehicle Type Approval (VTA) and study the required documents and procedures. (UNECE R.155, R.156)

Based on learning fundamental concepts of programming and Python, we solve mathematical problems using Python. (Solutions and roots of equations, drawing graphs)

This is a lecture on Vehicle Cybersecurity Threat Analysis and Risk Assessment (TARA) based on UNECE R.155 and ISO 21434.

Learn about the ISO 15118-based electric vehicle charging protocol and the certificates used during the charging process.

Understand the basics of security algorithms and certificates, and learn about TLS based on this.

This lecture is about automotive ECU cyber security based on a basic understanding of cryptography.

This is a lecture on the software update processes for products released in the automotive industry, specifically Re-Programming and OTA (Over-the-Air).

Learn and understand the many services of ISO 14229-based UDS that are widely used in practice.

I am not a major and currently working as a developer. I also learned a lot from Infraon, so I organized it mainly based on the concepts that I had difficulty grasping at first. I hope it will be helpful to someone. Thank you.

Learn the basic concepts of Autosar for automotive SW development.

It contains concepts necessary for practical use rather than academic concepts regarding CAN communication.

We provide a detailed introduction to the process of updating software installed in a vehicle using diagnostic communication.

"AI will not replace people. People who master AI will replace those who rely on it." In the era of AI, where creating results has become easier than ever, human thinking and problem-solving skills are becoming even more critical. Jae-sung Kim, a former McKinsey consultant, will guide you step-by-step through advanced problem-solving skills using AI, powerful research strategies, and persuasion techniques to turn your results into reality.

This course is part of the 'Wrtn AI Literacy Voucher Program', a collaboration between Inflearn and Wrtn, and is available completely free for job seekers who are vulnerable to AI. Career expert Career D will teach you step-by-step how to apply AI to job searching and career transitions. From basics to hands-on practice, it's designed so that even AI beginners can follow along easily without any burden!

If you are curious about the various possibilities and future prospects of generative AI, including ChatGPT and Clova X, that can be used for data analysis, come to our seminar this May!

Daily repetitive tasks like organizing emails, writing reports, and summarizing data... Do humans really need to do these manually? Does ChatGPT feel like it's just for chatting, and does automation seem too difficult? But even without coding, you can build practical automations in just 1 hour. This is a practical course designed to help non-developers automate workflows and reduce repetitive daily tasks using n8n and AI. By following along, you will create automation workflows that can be applied at work immediately. We've cut out the fluff and included only what you truly need. You will experience a transformation in the way you work, starting today.

Gain insights from DataOven CEO Ha Yongho, a first-generation data analyst who has demonstrated numerous insights and successes. This is a meetup video for data professionals, held on July 18, 2024.

You'll gain a very clear understanding of what diagnostic communication is in automotive SW roles, and what a diagnostic communication practitioner does and how! This lecture provides content specific enough for practitioners to immediately start work with actual spec documents.

Understand the essence of the job market through economic and statistical concepts, and redesign employment and career-change strategies as data-driven probability problems rather than relying on intuition or "spec" (credentials) competition. Instead of simply accumulating more credentials, this strategy shifts the "playing field" to a rational recruitment market by using AI to directly collect and analyze job postings and corporate data to select markets and openings with a high probability of success. By utilizing AI as a tool built upon core economic and statistical concepts, we systematize document, interview, and application strategies, presenting a way for even non-majors to develop a data-driven employment mindset. Furthermore, we provide a method for non-majors to directly implement a 24-hour AI recruitment assistant—which can be used immediately as a portfolio piece—through AI-based "Vibe Coding."

No more outdated PPTs even after working all night! A veteran instructor with 25 years of experience has packed in only the essential core skills needed for practical work, from PowerPoint basics to the latest AI utilization methods. Even without design sense, we'll teach you 'everything about presentations' that cuts your work time in half and doubles your quality.

After using it for 14 days starting immediately upon release, here are the reasons why I recommend Claude Code (AI Coding) to everyone—from job seekers to industry professionals—as something they can apply right away.

The real story of an AI-native company run by GolbinHacker, the 1st place winner of Kakao AI TOP 100 and author of the bestseller "The Best Prompt Engineering Lecture." It explores how just two developers build and operate a service, along with the new competencies developers and organizations need in the AI era through real-world cases.

"In the era of AI, what should developers do?" To answer this question, former Toss Payments Technical Director Jaemin Kim and Inflab CTO Hyangro share their insights. Beyond flashy technologies, discover the most practical answers on 'how to grow steadily within your given environment'!

Recommended for these students! Frozen~! Coco! I watched it too~ But how was it made? I want to major in animation too, but is this really what I want to do? Can I learn everything once I enter a university program? What should I prepare for employment? Should I just watch a lot of animations? That animation looks like it has a different style - I want to know how it's produced AI has advanced too, can't I make it on my own? I want to get a job at an animation company - any tips? In this class, you'll learn: ✔ Animation genres, fields, concepts, and all the production processes needed for creation! ✔ What are the actual working departments in animation companies? Which departments play what roles as the gears turn in the production process? ✔ Concepts, production, management, and even hands-on practice! Once you complete all of this, you'll become an animation master who understands the concepts even better than animation majors!

[Understanding AI Foundation Models and Their Operating Principles: Engineering Control and System Architecture, Practical Methodologies for Resolving AI Uncertainty and Engineering Assetization] 1. Introduction: The Necessity of Engineering Control of Intelligence (Engineering Control vs. Systemic Chaos) A core conclusion derived from long-term practical insights in industrial fields is that power that is not properly controlled acts as a potential liability rather than an asset. Even a high-performance engine is nothing more than an unstable physical mass if it lacks sophisticated combustion logic and microsecond-level control systems. The organizational chaos currently appearing in the process of adopting Generative AI is judged to stem from a lack of understanding of these control principles and a blind faith in technical "black boxes." This masterclass redefines Artificial Intelligence not as a mysterious stochastic phenomenon, but from the perspective of Model-Based Engineering (MBE). By transforming the uncertain domain of intelligence into a predictable and reliable engineering framework, we aim to present a strategic methodology that allows organizations to secure strong leadership across the entire system without being dependent on technical trends. 2. The 4 Pillars for Solving Core Challenges ① Epistemological Paradigm Shift: Visualizing the Black Box and Assetizing Technical Debt Many companies are facing "technical debt"—characterized by exposure to security vulnerabilities and exponential increases in maintenance costs—by adopting AI models without a clear understanding of their internal structures. This course assetizes this through the following approaches: Deconstruction of Mechanisms: We engineeringly deconstruct the Self-Attention mechanism, the core of the Transformer architecture, from the perspective of numerical weight analysis. By understanding the numerical mechanisms that determine information priority, we visualize the basis for the model's judgment. Analysis of ID Formation: We transparently track the process by which a series of pipelines—leading from Pre-training to Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)—forms the model's technical identity and ethical guidelines. This converts invisible threats into controllable system parameters. ② Securing Deterministic Reliability: Hallucination Control Strategies to Overcome Probabilistic Limits Large Language Models (LLMs) are not systems that reason for truth, but systems that generate the next most probable token. The "hallucination" phenomenon resulting from this inherent characteristic becomes a fatal flaw in engineering fields where reliability is vital. Constraints of Retrieval-Augmented Generation (RAG): We move away from closed structures that rely solely on the model's internal fixed memory (Internal Weights). We establish an "open-book strategy" that provides clear grounding for generated results by allowing real-time reference to trusted external knowledge bases. Hybrid Model Architecture: We design a redundancy strategy that achieves both accuracy and operational efficiency by deploying large models for areas requiring enterprise-wide knowledge and optimized Small Language Models (SLMs) for specific domains where security and real-time response are essential. ③ Computing Architecture Optimization: Overcoming Physical Bottlenecks (Memory Wall) While intelligence is implemented in software, its performance and economic sustainability are defined by the physical limits of hardware. Physical Constraint Analysis: We diagnose the "Memory Wall" problem, where data transfer speeds cannot keep up with the processing speeds of computing units, and heat generation issues resulting from high-density computation from an engineering perspective. Infrastructure Design Capability: We precisely analyze the physical impact of High Bandwidth Memory (HBM) stacking structures and 2.5D/3D advanced packaging technologies on inference efficiency. We cultivate design capabilities to optimize Total Cost of Ownership (TCO) through full-stack integrated insights that complement hardware limitations with software architecture. ④ Acceleration of Functional Expansion: Transitioning from Passive Tools to Autonomous Agent Systems Current AI remains at the level of simple Q&A, failing to create added value for practical business automation. This course evolves AI into an active subject that judges and executes on its own. Decomposition: We learn techniques for decomposing complex goals into achievable sub-tasks and logically organizing the execution sequence upon receiving them. Digital Workforce Deployment: We define the process of applying "Active Agent" systems to the field, which autonomously call internal ERPs, browsers, and external APIs to complete actual business logic and accept feedback on results. 3. Core Architecture: Closed-loop Control System The way an AI agent manifests intelligence and performs complex tasks is theoretically identical in logical structure to the closed-loop control system performed by an ECU (Electronic Control Unit), the core brain of a car. This course analyzes this in detail from the perspective of the ReAct (Reasoning and Acting) framework. First, the system begins at the Input stage, receiving ambiguous and complex requests from the user. This plays the same role as a sensor in control engineering collecting physical data from the external environment and delivering it to the system, serving as the standard for defining the initial state of the task at hand. Second, based on the received data, the Thought stage proceeds, where plans are established through logical reasoning within the LLM architecture. This is in line with the process where control algorithms in an ECU calculate optimal control values by processing input sensor data. At this stage, the agent sets the optimal path to achieve the goal and secures the logical rigor of the system. Third, the Action stage follows, where tasks are completed by calling external tools or APIs according to the established plan. This logically matches the mechanism where the calculation results of a control system are converted into physical power through an actuator to execute commands. Through this, intelligence exerts actual physical and digital influence beyond abstraction. Finally, the Observation stage is performed, analyzing the execution results and correcting errors relative to the initial goal. This is identical to the core principle of control engineering, which reduces system deviation through a feedback loop. The agent self-verifies whether the execution results meet the goal and continuously upgrades performance by reflecting occurred errors into the next action plan. AI equipped with such a closed-loop structure is no longer an incomplete system dependent on probability. By securing engineering rigor that self-verifies execution results and corrects errors, it functions as a trust-based partner capable of performing business-critical tasks. 4. Practical Application and Expansion: Software-Defined Vehicles (SDV) and Physical AI The final destination of AI architecture lies in the cross-industry proliferation of Software-Defined Vehicles (SDV) and Physical AI, which overcome and evolve physical constraints through software intelligence. This is the standard model for future System Integration (SI) across manufacturing and service industries. Securing Edge Intelligence and Data Sovereignty: Small models (SLMs) mounted on-device in vehicles or facilities learn real-time field data immediately. This minimizes cloud dependency, perfectly protecting data sovereignty—a core asset of the company—and enables precision services based on ultra-low latency. Hardware Optimization and Lightweight Engineering: To implement the best intelligence within limited power and computational resources, we actively introduce model compression technologies such as Quantization, Pruning, and Knowledge Distillation. Model deployment considering hardware bandwidth becomes a core competency that determines system response speed and user experience. Hybrid Orchestration: We design an integrated architecture that organically connects "Cloud LLMs" possessing broad general knowledge with "Edge SLMs" specialized for specific physical control and security. Integration from a full-stack perspective, penetrating from silicon chipsets to software stacks, provides a powerful competitive advantage that evolves the entire system through software updates alone. 5. Conclusion: The Role and Vision of the AI Architect The ultimate goal of this masterclass is to elevate students from the position of a "User" who passively relies on technology and hopes for luck, to a professional "AI Architect" who perfectly controls and tunes everything from the physical limits of the system to the depths of the software architecture. While the phenomenon of intelligence manifests from software logic, it is silicon (hardware) that defines the physical limits of that intelligence, and only sophisticated engineering can overcome those limits to complete actual business value. "Intelligence may reside in the realm of probability, but the vessel that contains that intelligence and makes it operate according to purpose must belong solely to the realm of rigorous and sophisticated engineering."

Learn the core concepts and operating principles of Physical AI, which moves beyond the screen to be implemented in robots, autonomous vehicles, and drones. Understand the sensing-perception-action mechanism, digital twin-based learning, and the importance of embodied data, while exploring the global technological competition and Korea's strategic direction through real-world cases in industries such as manufacturing, mobility, and agriculture.

The AI era is bringing many changes to the gaming industry. Let's explore how AI-based content generation is impacting the game industry.

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!

Employment is possible even with a lack of credentials. This lecture is a practical guide that reveals the structure of those who actually get hired, focusing on "strategy" and "the ability to utilize experience" rather than GPA or certifications. ✔ Common strategies of people who get hired without credentials ✔ How to make experiences look like achievements ✔ STAR+I based self-introduction structure ✔ How to write self-introductions and portfolios using AI We focus not on "what more to build," but on how to showcase what you already have.