My Courses
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Building A Multi-Agent AI Marketing Assistant with AWS
Generate powerful ad copies with AI! Learn to build a Streamlit app using LlamaIndex & Gemini, then deploy it on AWS EC2 with Docker.
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A Practitioner’s Guide to Agent Communication Protocol (ACP)
IBM’s Agent Communication Protocol (ACP) is an open standard for seamless agent-to-agent communication.
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Mastering Data Compression with LLMs via LMCompress
LMCompress uses large language models to achieve state of the art, lossless compression across text, image, audio, and video by approximating Solomonoff induction.
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Mastering Scientific and Algorithmic Discovery with AlphaEvolve
AlphaEvolve by DeepMind evolves and optimizes code using LLMs and evolutionary algorithms, enabling breakthroughs in science and engineering.
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A Deep Dive into J1’s Innovative Reinforcement Learning
J1 by Meta AI is a reasoning-focused LLM judge trained with synthetic data and verifiable rewards to deliver unbiased, accurate evaluations—without human labels.
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A Deep Dive into Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Absolute Zero enables language models to teach themselves complex reasoning through self-play—no human-labeled data required. Discover how AZR learns coding and logic tasks using autonomous task creation, verification, and reinforcement.
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A Deep Dive into Continuous Thought Machines
Explore the Continuous Thought Machine (CTM), a neural network architecture that integrates neuron-level timing and synchronization to bridge the gap between AI and biological intelligence.
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Mastering AI Code Execution in Secure Sandboxes with E2B
Explore how E2B provides secure, isolated sandboxes for running AI-generated code with LLaMA-3 on Together AI—ideal for building safe, intelligent data workflows and autonomous agents.
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How L&D Leaders Can Drive AI Readiness Across the Enterprise?
A strategic guide to AI Readiness helping L&D leaders align talent, tools, and training for the agentic future.
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A Deep Dive into Federated Learning of LLMs
Federated Learning (FL) enables privacy-preserving training of Large Language Models (LLMs) across decentralized data sources, offering an ethical alternative to centralized model training.