Reinforcement Learning Course
Foundations of Reinforcement Learning
RL Part 1: Agents, environments, rewards, and why RL is different from supervised learning.
Avi Chawla
385 posts published
Classical ML and Deep Learning
Diffusion LLMs from the Ground Up: Training, Inference, and Practical Engineering
Diffusion LLMs Part 2: How dLLMs scale to 100B parameters, the inference stack that makes them fast, hands-on code, and when to actually use them.
Claude
10 Must-use Slash Commands in Claude Code
...explained with exact prompts and usage!
Agents
Build Agents That Never Forget
A first-principles walk through agent memory (open-source).
Classical ML and Deep Learning
Diffusion LLMs from the Ground Up: Theory, Math, and Why They Work
Diffusion LLMs Part 1: Understanding how diffusion language models work from first principles, the math behind masked diffusion, and why they represent a fundamentally different approach to text generation.
Agents
Advisor Strategy in Agents
Reduce token costs and improve performance...and how to use it with Claude!
Claude
The Anatomy of an Agent Harness
A deep dive into what Anthropic, OpenAI, Perplexity and LangChain are actually building.
MLOps/LLMOps Course
MLOps and LLMOps: Case Studies
An exploration of real-world MLOps and LLMOps case studies, examining the importance of reliable ML and AI engineering and their significance for business outcomes.
Claude
Anatomy of the .claude/ Folder
A complete guide to CLAUDE.md, custom commands, skills, agents, and permissions, and how to set them up properly.
MLOps/LLMOps Course
Concepts of LLM Serving
LLMOps Part 14: An overview of the fundamentals of LLM serving, including API-based access, inference with vLLM, and practical decisions.
MLOps/LLMOps Course
LLM Inference and Optimization: Fundamentals, Bottlenecks, and Techniques
LLMOps Part 13: Exploring the mechanics of LLM inference, from prefill and decode phases to KV caching, batching, and optimization techniques that improve latency and throughput.
MLOps/LLMOps Course
LLM Fine-tuning: Techniques for Adapting Language Models
LLMOps Part 12: Understanding LLM fine-tuning, parameter-efficient methods like LoRA and QLoRA, and alignment techniques such as RLHF, DPO, and GRPO.
LLMs
Paged Attention in LLMs
...explained visually!
LLMs
Prompt Caching in LLMs!
A case study on how Claude achieves 92% cache hit-rate.
MLOps/LLMOps Course
Evaluation: Multi-turn Conversations, Tool Use, Tracing, and Red Teaming
LLMOps Part 11: Understanding evaluation of conversational LLM systems, tool evaluations, tracing with Langfuse, and automated red teaming.
MLOps/LLMOps Course
Evaluation: Model Benchmarks and LLM Application Assessment
LLMOps Part 10: Understanding model benchmarks, LLM application evaluation, and tooling.
MLOps/LLMOps Course
Evaluation: Fundamentals
LLMOps Part 9: A foundational guide to the evaluation of LLM applications, covering challenges and a practical taxonomy of evaluation methods.
MLOps/LLMOps Course
Context Engineering: Memory and Temporal Context
LLMOps Part 8: A concise overview of memory, dynamic and temporal context in LLM systems, covering short and long-term memory, dynamic context injection, and some of the common context failure modes in agentic applications.
MLOps/LLMOps Course
Context Engineering: An Introduction to the Information Environment for LLMs
LLMOps Part 7: A conceptual overview of context engineering, covering context types, context construction principles, and retrieval-centric techniques for building high-signal inputs.
MLOps/LLMOps Course
Context Engineering: Prompt Management, Defense, and Control
LLMOps Part 6: Exploring prompt versioning, defensive prompting, and techniques such as verbalized sampling, role prompting and more.
MLOps/LLMOps Course
Context Engineering: Foundations, Categories, and Techniques of Prompt Engineering
LLMOps Part 5: An introduction to prompt engineering (a subset of context engineering), covering prompt types, the prompt development workflow, and key techniques in the field.
MLOps/LLMOps Course
Building Blocks of LLMs: Decoding, Generation Parameters, and the LLM Application Lifecycle
LLMOps Part 4: An exploration of key decoding strategies, sampling parameters, and the general lifecycle of LLM-based applications.
MLOps/LLMOps Course
Building Blocks of LLMs: Attention, Architectural Designs and Training
LLMOps Part 3: A focused look at the core ideas behind attention mechanism, transformer and mixture-of-experts architectures, and model pretraining and fine-tuning.
MCP Guidebook
Tools, Resources and Prompts
Tools, prompts and resources form the three core capabilities of the MCP framework. Capabilities are essentially the features or functions that the server makes available. * Tools: Executable actions or functions that the AI (host/client) can invoke (often with side effects or external API calls). * Resources: Read-only data sources that
MCP Guidebook
MCP Architecture Overview
At its heart, MCP follows a client-server architecture (much like the web or other network protocols). However, the terminology is tailored to the AI context. There are three main roles to understand: the Host, the Client, and the Server. Host The Host is the user-facing AI application, the environment where
MCP Guidebook
Why was MCP created?
Without MCP, adding a new tool or integrating a new model was a headache. If you had three AI applications and three external tools, you might end up writing nine different integration modules (each AI x each tool) because there was no common standard. This doesn’t scale. Developers of
MCP Guidebook
What is MCP?
Imagine you only know English. To get info from a person who only knows: * French, you must learn French. * German, you must learn German. * And so on. In this setup, learning even 5 languages will be a nightmare for you. But what if you add a translator that understands all