Architecting the future
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Latest Posts
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Attention Residuals: When Residuals Start Attending To Themselves
Deep-Learning Transformers
Residual connections made Transformers scalable, but increasingly inflexible as models grow deeper. Attention Residuals rethink this hidden backbone, allowing layers to selectively retrieve earlier computations rather...
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Manifold-Constrained Hyper-Connections - Rethinking Residual Connections
Deep-Learning Transformers
Training deep neural networks should be straightforward: stack more layers, get better results. Reality? Gradients vanish or explode, making depth your enemy. This post traces the evolution from ResNet's breakthrough ...
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Introduction to Mixture-of-Experts
GenAI Transformers
Modern AI models are getting bigger, but what if you could use trillions of parameters without the computational cost? This post explores Mixture-of-Experts (MoE) architectures and how selective expert activation is r...
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From Hype to Reality: The Rise of GenAI and the Need for GenAIOps in Azure
GenAI Azure GenAIOps LLMOps
GenAI is transforming the tech world, but how do you operationalize it in Azure? This post introduces GenAIOps and the building blocks for scalable, production-ready GenAI applications.
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