Browse Comments — Clean (de-noised)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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Great
Real talk — most AI "learning resources" are just theory dressed up in hype. This is different. Production-ready apps + actual implementation = the gap most courses never fill. Thanks for sharing this 👏
Very interesting 🔥🔥🔥
Keep going ✨
27k impression ..
Imran Gul My comment was genuine but if you still want to learn then .. padh lo hamse..hamari fees bhi kisi se jyada ya kisi se kam hi hai...ham bhi mast padhate hai. .www.evolvance.solutions.
Liked for the shirt
Great!
Oooh, thanks for sharing Abhishek Veeramalla
Thanks for sharing this.
Shane Marotical you
Good to see a full stack resource that focuses on end to end systems instead of isolated demos. Most people get stuck at tutorials, so having production level examples with real architectures is actually where the learning starts to become useful.
This is a strong collection of production level examples that helps connect theory with actual implementation across different AI use cases. Having complete workflows, notebooks, and agent systems in one place makes it easier to understand how these architectures are applied in real projects.
#CFBR
These are really current SOTA projects and notebooks, such a useful resource. Thank sharing 🙏
Plz send it to tpo@dypiemr@ac.in
Very much informative 👏
Thanks for sharing 👍🏻
Great 👍
Abhishek Veeramalla pls respond