Overall, I am satisfied with the content. The course provides detailed instruction on how to carry out projects using Codex and how to collect repetitive data using loops. However, I wish the lectures had been better prepared. The instructor’s way of speaking, using expressions like “~~gu” and “~~de,” does not inspire confidence in the lectures. In addition, there are many parts during the course where the screen is scrolled up and down without any explanation. The instructor also checks the values output by Codex during the lecture. It would be easier to follow the course if the content were reviewed in advance and then explained. Although I have listed some complaints, the course was still very helpful to me.

For those who have been developing for a long time or are already familiar with using AI, this may feel somewhat basic. However, it consists of various hands-on projects using Codex, including a Funding Fee dashboard for a cryptocurrency exchange, so it would be a good course for those interested in this field. Rather than focusing on technical aspects, it seems to cover only the essentials of vibe coding, with the key being how you apply and incorporate the instructor’s know-how. If you are already familiar with the subject, it may be a good idea to skip the earlier sections and start with the hands-on exercises. I felt this course would be especially suitable for those who are encountering practice environments such as Codex App, Codex Cloud, and Codex CLI for the first time and want to become familiar with them through hands-on work. It also includes practical sections on automating office tasks and using Codex App Server, so I would recommend it to those who are just starting to learn programming and want to develop a feel for it through practice.

At first, the theory behind Loop Engineering was explained concisely while highlighting the key points, which made me look forward to the hands-on section that followed. However, once I entered the practical section, it was conducted in a live-coding format, making it difficult to fully concentrate on the lecture content. So I let go of trying to follow along with every step as I had during the theory section and listened more casually. In doing so, I came to feel that this lecture was less about teaching a technology step by step and more about showcasing practical use cases. If you are interested in topics such as disclosure calendars, web service revenue dashboards, or notable-stock analysis, or if you work in a related field, this lecture would be a good opportunity to learn how to use the Codex tool to handle actual work more efficiently. On the other hand, if you are studying AI concepts one by one from a technical perspective, it may be best to approach this lecture as a chance to glimpse how the concepts you have learned can be applied in practice.

Thank you for the excellent lecture.