[Meetup VOD] "How to Survive as a Developer in the AI Era" by a 31-Year Veteran Developer

Facing the AI Era

31-year veteran developer Toby's
honest story

"If we don't need to write code,
what should developers do in the future?"

No developer is free from the message that AI will replace our jobs. Everyone from newcomers preparing for employment to veterans with decades of experience is asking the same question. I too cannot be certain about how developers should live in the AI era. However, I want to honestly share the experiences I've had directly in the midst of this change.

What is clear is that developers are no longer a fixed profession, but a role that is constantly being redefined alongside AI. Therefore, what we need might be the ability to accept and adapt to change, rather than the technology itself.

Adding thinking power on top of AI
Augmented Developer

In this course, we aim to explore together what capabilities developers should build and in what direction they should grow amidst these changes.

Stories to Share

Chapter 1. 6 Stages of AI Coding Spectrum

AI coding has been by our side for a long time. It has gradually permeated and changed the way we develop, even without us realizing it. In this chapter, we follow the '6 Stages of AI Coding Spectrum' to envision the past, present, and future landscape of AI coding that will unfold.

Chapter 2. Collaboration Between Humans and AI

Development is no longer a stage for humans alone, nor for AI alone. Beyond the simple choice of human-led versus AI-led, various collaboration models exist that suit different situations and task characteristics. In the second chapter, we explore how humans and AI can work in sync and sometimes switch roles while working together.

Chapter 3. Architecture Design for AI Collaboration

Currently, it's still effective for human developers to make high-level architecture and design decisions and maintain control. This chapter covers architectural design methods to maximize collaboration with AI. Well-designed architecture and clean code provide clear guidelines not only for humans but also for AI, and can actually become even more crucial standards for AI.

Chapter 4. The Renaissance of Test-Driven Development

TDD is a core workflow for safely and productively utilizing AI, and it's the most preferred approach by AI-leading companies like Anthropic and OpenAI. This is not just a simple development technique, but has established itself as a core principle for handling code steadily in the AI era. In this chapter, we'll explore the power of TDD.

Chapter 5. Using AI as a Learning Catalyst

These days, people only focus on "how quickly we can develop with AI." However, if we pursue only productivity, a developer's growth may stagnate or even regress. Now we need a perspective that utilizes AI within the balance of two axes: productivity and learning.

Chapter 6. Toby's Current AI Usage Methods

AI is not simply a tool that writes code for you. In this chapter, we share various real-world examples of how to utilize AI in development and learning processes. From analyzing existing code together with AI, learning derived knowledge through that process, to learning conversationally with AI based on reference documents... We show you the vivid ways real developers grow together with AI.

Chapter 7. AI Era Survival Strategy: Exploratory AI Usage Methods

We must not become developers who simply "just click." If we want to continue growing, we need a process of reading, reviewing, understanding, and sometimes critically evaluating AI-generated code while actively learning from it. In this chapter, we present a new survival strategy called "exploratory AI usage." We'll discuss how to use AI as a tool for deliberate practice instead of passively consuming it.