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The key point for me is that AI performance is not only about model size or raw capability. It is about whether the architecture preserves the context that matters for the task. Voice is a good example. A transcript captures words, but not always meaning. That is where more specialised or ensemble-based approaches could become very important.

How many testing hours and benchmarking has been done with real world scenarios. Let’s just accept today creating something isn’t hard. But validation is the difficult part. After years of working with OpenAi models, I think their models are NOT production ready.

Neeraj Tiwari

This is exactly what's missing in most AI learning journeys practical, production-ready examples. Saving this immediately. Thanks for sharing.

Really compelling direction, moving from monolithic models to orchestrated ensembles feels like a practical path past current scaling limits, especially for richer modalities like voice. Excited to see where this goes 👏

Thank you for this Gem )

Git hub is really nice 👌 Recently I’m using git but in past i used SVN repo, compare that git is really good 👍 🙌

Thanks for sharing 👍🏻

Love this approach- how many times has a team gone in the wrong direction because a metric was misinterpreted.

Repo repo kerte kerte aap ne Tesla le li

i was very surprised to see my preferred email@ prefix in the doc. i guess i cant talk with you guys sadly. name collisions are serious biz ess :)

Martin Pirrie we were talking about becoming bosses in AI

I agree, a repository become powerful when it doesn't just teach tools it teaches enginnering thinking Abhishek Veeramalla

Anwar Ahmed PMP®

Very Helpful , Keep going

Shreelakshmi Balachandran(She/Her)

Let's go..

B*c