Sorry for the late reply, I missed the notification earlier.
What I'm really curious about is how modern AI systems work end-to-end in production. I can code and understand the basics, but I want to get into the real-world implementation details. Like, how do companies actually structure their training pipelines? What's the data collection process like at scale? How do they handle data quality issues? What are the actual trade-offs teams face when choosing between different approaches?
I'm also interested in where these systems break down in practice and what that means for developers building with them.
I started a small blog about tech and marketing. Honestly, I got into this thinking about marketing, but I realized quickly that if you don't have an audience that trusts you, marketing doesn't work. The only way to build that is by creating genuinely valuable content. So now I'm focused on learning properly and sharing what I figure out.
Since I just wrote about ambient AI, I want to go deeper into how these systems actually get built in practice. I know "how AI works" is massive, so I'm thinking of starting with the data pipeline how training data gets collected, cleaned, and prepared at that scale. Feels like that's foundational but doesn't get nearly as much attention as model architectures.
Your question really helped me think about this more clearly. I honestly wasn't expecting anyone to respond, so I really appreciate you taking the time. Thank you sir!
What complex systems would you be interested in learning about, decomposing, and then writing to teach others about?
https://en.wikipedia.org/wiki/Learning_by_teaching
Sorry for the late reply, I missed the notification earlier. What I'm really curious about is how modern AI systems work end-to-end in production. I can code and understand the basics, but I want to get into the real-world implementation details. Like, how do companies actually structure their training pipelines? What's the data collection process like at scale? How do they handle data quality issues? What are the actual trade-offs teams face when choosing between different approaches? I'm also interested in where these systems break down in practice and what that means for developers building with them.
I started a small blog about tech and marketing. Honestly, I got into this thinking about marketing, but I realized quickly that if you don't have an audience that trusts you, marketing doesn't work. The only way to build that is by creating genuinely valuable content. So now I'm focused on learning properly and sharing what I figure out.
Since I just wrote about ambient AI, I want to go deeper into how these systems actually get built in practice. I know "how AI works" is massive, so I'm thinking of starting with the data pipeline how training data gets collected, cleaned, and prepared at that scale. Feels like that's foundational but doesn't get nearly as much attention as model architectures.
Your question really helped me think about this more clearly. I honestly wasn't expecting anyone to respond, so I really appreciate you taking the time. Thank you sir!
Thank you for your kind words, happy to help, best wishes. Looking forward to reading what you write next.