one year of studying (it was a mistake)

Summary

Key Points

• The speaker spent a year self-studying math, computer science, and AI, ultimately concluding it was not the most effective use of his time due to a lack of practical application. 0:00
• His learning journey began out of boredom with web development and an interest in AI, leading him to discover significant gaps in his foundational knowledge, particularly in mathematics. 0:31
• He dedicated four months to foundational math, realizing that while generally useful, it's often not directly applied in typical software engineering or AI model *usage* scenarios. 3:25
• He explored various computer science topics, including low-level hardware and algorithms, but found that passive learning without practical exercises led to poor knowledge retention. 5:00
• A more effective method for learning algorithms was found in structured roadmaps like NeetCode, which build skills incrementally through problem-solving, making the process engaging. 5:26
• Data engineering was the most directly applicable area of study, as much of the knowledge gained, including understanding data flow and tooling, was immediately useful in his day job. 7:00
• The primary mistake was focusing heavily on consuming information (learning) rather than producing or building something (doing), which hindered deep understanding and practical application. 9:27
• His scattered approach across multiple, disconnected career tracks (e.g., Cuda coding, AI research, data pipelines) prevented him from gaining deep expertise in any single area. 11:02
• Moving forward, he plans to adopt a highly project-focused learning strategy, aiming to complete one challenging project per month to solidify knowledge through practical application and focused learning. 11:31