Learning path in ML amid RAG and agentic AI hype

The Reddit post highlights a sentiment among some in the machine learning community: a sense that the field's focus has shifted heavily toward building RAG (retrieval-augmented generation) modules and agentic AI for large corporations. This focus can feel overwhelming to someone with a broader passion for ML, who wonders what foundational or other specialized knowledge remains relevant. The user's request for concrete topics, tech, and tools suggests they want a structured path amid the noise, but the article body does not provide any such list. Instead, the post serves as a reflection of a possible perception that practical, corporate-oriented applications are dominating the discourse, which might discourage those interested in other aspects of ML. Without additional context or responses, the article offers no specific guidance, so any implications about what to learn would be speculative.