Welcome to Bilwasiva's personal portfolio and blog. Explore my journey in AI, ML, Deep Learning, and more! https://bilwasiva.github.io/ From Models to Orchestration: The Quiet Evolution Defining the Future of GenAI Products The most important transformation happening in artificial intelligence right now is surprisingly easy to miss. It is not a new flagship model announcement. It is not a larger context window. It is not a benchmark-breaking score. Instead,... Sun, 25 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-25-coordination-layer-ML/ https://bilwasiva.github.io/2026-01-25-coordination-layer-ML/ When AI Starts Training on Itself: How GenAI Is Quietly Breaking Recommendation Systems For more than a decade, recommendation systems have been one of the most powerful forces shaping the internet. They decide what we watch, read, buy, listen to, and increasingly, what we believe. From social feeds to search... Thu, 15 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-15-genai-recommendation/ https://bilwasiva.github.io/2026-01-15-genai-recommendation/ The Invisible Failure Modes of Agentic AI Why Most Autonomous AI Systems Break in Production and How the Next Wave Will Fix Them Agentic AI has quickly become one of the most exciting ideas in modern artificial intelligence. Systems that can reason, plan, call tools, coordinate subtasks, and operate autonomously... Sat, 10 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-10-agentic-ai-1/ https://bilwasiva.github.io/2026-01-10-agentic-ai-1/ The Hidden Bottleneck in Modern LLMs Why Memory, Not Model Size, Is Quietly Defining the Future of Generative AI For the last few years, the story of generative AI has sounded almost monotonic. Bigger models. More parameters. Larger GPUs. Longer context windows. Each breakthrough seemed to come from scaling something... Fri, 09 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-09-kv-cache/ https://bilwasiva.github.io/2026-01-09-kv-cache/ Beyond Scaling: Why Liquid Neural Networks Signal the End of Brute-Force AI For more than a decade, progress in artificial intelligence has followed a simple rule: bigger is better. More data. More parameters. More GPUs. More energy. This logic gave us today’s large language models and spectacular capabilities, but it... Thu, 08 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-08-liquid-ai/ https://bilwasiva.github.io/2026-01-08-liquid-ai/ Agentic AI Is Not About Tools. It Is About Time. For the last year, the AI world has been obsessed with agents. Every new framework promises autonomy. Every demo shows an LLM calling tools. Every roadmap claims “agentic workflows” are the future. But almost all of this discussion is missing... Wed, 07 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-07-agentic-ai-dive-deep/ https://bilwasiva.github.io/2026-01-07-agentic-ai-dive-deep/ Beyond the Context Window The Rise of Inference Context Memory in 2026 Bilwasiva Basu Mallick 7 min read January 2026 Introduction The End of Stateless Artificial Intelligence For the first phase of the generative artificial intelligence revolution, nearly every large language model shared a hidden constraint. Intelligence was powerful, fluent,... Tue, 06 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-06-NVIDIA-Rubin/ https://bilwasiva.github.io/2026-01-06-NVIDIA-Rubin/ Beyond the Chatbox The Rise of Large World Models (LWM) and the Era of Predictive Physical Intelligence Author: Date: 2026 Category: AI Architecture, Agentic Systems, Robotics, Future of Intelligence Executive Summary The AI revolution of the early 2020s was powered by language. The AI revolution of the late 2020s will... Mon, 05 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-05-PPE/ https://bilwasiva.github.io/2026-01-05-PPE/ Coordination Latency as the Dominant Bottleneck in Agentic and Multimodal AI Systems Abstract Recent advances in large language models (LLMs), multimodal foundation models, and GPU acceleration have dramatically reduced the cost and latency of individual model inference. However, despite these improvements, many agentic and multimodal AI systems fail to scale... Sun, 04 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-04-agentic-ai/ https://bilwasiva.github.io/2026-01-04-agentic-ai/ The Reliability Revolution: Why Conformal Prediction Is the Future of Trustworthy AI Point predictions are a liability. Guarantees are the future. In 2026, the AI industry has reached a critical realization: high accuracy alone is not enough. If your model predicts “95% probability” but fails under distribution shift, rare events,... Sat, 03 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-03-conformal-prediction/ https://bilwasiva.github.io/2026-01-03-conformal-prediction/ The Future of AI Design: Unlocking Neural Architecture Search (NAS) for Optimized Deep Learning Models In the realm of Deep Learning, one of the most challenging tasks for data scientists and engineers is designing optimal neural network architectures. For years, the process of manually designing models has been time-consuming, requiring... Fri, 02 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-02-Neural-Architecture-Search/ https://bilwasiva.github.io/2026-01-02-Neural-Architecture-Search/ Your Model Shouldn’t Stop Learning at Deployment: A Deep Dive into Test-Time Training For more than a decade, the standard deep learning lifecycle has been remarkably consistent: Curate a large labeled dataset Train a model offline Freeze the weights Deploy for inference Repeat when performance decays This paradigm implicitly assumes... Thu, 01 Jan 2026 00:00:00 +0000 https://bilwasiva.github.io/2026-01-01-A-Deep-Dive-into-Test-Time-Training/ https://bilwasiva.github.io/2026-01-01-A-Deep-Dive-into-Test-Time-Training/ Introduction In the realm of Artificial Intelligence, Large Language Models (LLMs) have emerged as a game-changer. These models, trained to understand and generate human language, have the ability to mimic human-like speech and writing, making them a buzzword topic in the field of Natural Language Processing (NLP), Natural Language Understanding... Sat, 28 Jan 2023 00:00:00 +0000 https://bilwasiva.github.io/2023-01-28-understanding-power-of-language-large-language-models/ https://bilwasiva.github.io/2023-01-28-understanding-power-of-language-large-language-models/ 80% of the Fortune 100 companies use Apache Kafka for some use case or other. Let’s see how it works Apache Kafka is a Distributed, Replicated Messaging Queue. It functions like a commit log. It is completely open source and you can download it directly. But to use it, you... Mon, 10 Oct 2022 00:00:00 +0000 https://bilwasiva.github.io/2022-10-10-kafka-benefits/ https://bilwasiva.github.io/2022-10-10-kafka-benefits/ Bitcoin block explorer service! - Blockchain technology is most simply defined as a decentralized, distributed ledger that records the provenance of a digital asset. From Wikipedia: A blockchain,[1][2][3] originally block chain,[4][5] is a growing list of records, called blocks, that are linked using cryptography.[1][6] Each block contains a cryptographic hash of the previous block,[6] a... Sun, 19 Jan 2020 00:00:00 +0000 https://bilwasiva.github.io/2020-01-19-Blockchain-Technology/ https://bilwasiva.github.io/2020-01-19-Blockchain-Technology/ Well. Finally got around to putting this old website together. Neat thing about it - powered by Jekyll and I can use Markdown to author my posts. It actually is a lot easier than I thought it was going to be. Wed, 17 Jul 2019 00:00:00 +0000 https://bilwasiva.github.io/2019-07-17-bilwasiva-site-launched/ https://bilwasiva.github.io/2019-07-17-bilwasiva-site-launched/ It is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. Deep learning is an AI function... Sun, 27 Jan 2019 00:00:00 +0000 https://bilwasiva.github.io/2019-01-27-deep-dive-into-Deep-Learning/ https://bilwasiva.github.io/2019-01-27-deep-dive-into-Deep-Learning/ C++ is the most preferred language for competitive programming mainly because of its STL. Short for Standard Template Library, the STL is a collection of C++ templates to help programmers quickly tackle basic data structures and functions such as lists, stacks, arrays, etc. Tue, 15 Jan 2019 00:00:00 +0000 https://bilwasiva.github.io/2019-01-15-Competitive-programming/ https://bilwasiva.github.io/2019-01-15-Competitive-programming/ How to train a Machine Learning model & achieve high accuracy - The training dataset is used to prepare a model, to train it. We pretend the test dataset is new data where the output values are withheld from the algorithm. We gather predictions from the trained model on the inputs from the test dataset and compare them to the withheld output... Fri, 22 Dec 2017 00:00:00 +0000 https://bilwasiva.github.io/2017-12-22-training-model/ https://bilwasiva.github.io/2017-12-22-training-model/ Introduction to the realm of Machine Learning - You can write cool Python codes for representing your learning model with the right ML Algorith,WIKI. Machine learning algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy... Sat, 24 Sep 2016 00:00:00 +0000 https://bilwasiva.github.io/2016-09-24-tryst-with-AI-ML-DL/ https://bilwasiva.github.io/2016-09-24-tryst-with-AI-ML-DL/