Thank you for your thoughtful comment! I completely agree.
It’s great to see someone emphasize the importance of mastering the fundamentals—like calibration, optics, and lighting—rather than just chasing trendy topics like LLM or deep learning. Your examples are a great reminder of the depth and diversity in machine vision.
Your clever remark highlights poor emotional intelligence and weak communication skills. Sarcasm might have its place in casual conversation, but in professional discussions, it signals insecurity and a lack of respect—neither of which contribute to meaningful dialogue.
Your disdain for LLMs is equally puzzling. Are you seriously suggesting I shouldn’t use tools to improve my grammar and delivery simply because they don’t align with your engineering view? Ironically, LLM-based tools likely support your own work—whether through coding assistance, debugging, or other tasks—even if you choose not to acknowledge it.
By the way, I used an LLM to craft this reply too—who doesn’t?
Most don't use LLMs, and I'm telling you, many people are going to be pissed if they figure out that you're writing to them through LLMs. Maybe you find this reaction strange, but it's at least good to know in advance and not be surprised.
You claim that 'most people' will be upset—are you their appointed spokesperson, or is this just your personal assumption? What I find strange is that I complimented and thanked you for your thoughts on machine vision, yet you responded with hostility. Is this how you communicate in real life too?
If 'most people' are upset about others using LLMs to improve their written communication, maybe they should reflect on why they hold such outdated views—or consider that the person replying might not be a native English speaker. Are platforms like Hacker News meant only for native English speakers?
Warning: The statement above was written by an LLM, so don’t be surprised—I’m letting you know in advance.
It’s great to see someone emphasize the importance of mastering the fundamentals—like calibration, optics, and lighting—rather than just chasing trendy topics like LLM or deep learning. Your examples are a great reminder of the depth and diversity in machine vision.