Agents
Grok Bot Masterclass
Everything you need to understand, set up, and get real work out of Grok Bot.
Avi Chawla
419 posts published
LLMOps
How a GPU Actually Works
The intuition an LLM engineer needs, without the hardware manual. By the end, techniques like quantization, speculative decoding, and continuous batching should stop looking like a list of tricks to memorize.
LLMOps
5 LLM Quantization Techniques
...explained visually.
Agents
Why Your Agent Remembers Everything and Understands Nothing
Building a pattern recognition layer for memory in production.
RAG Systems Course
Why RAG Latency Is a Prefill Problem, Not a Retrieval Problem
Part 12: Why prefill dominates RAG latency, and how to reuse KV caches that share no prefix, with implementations
LLMOps
6 LLM Deployment Formats in Production
...explained visually.
Agents
[Hands-on] Rebuilding Claude Code's Harness
...step-by-step, from scratch, using an open-source stack.
![[Hands-on] Rebuilding Claude Code's Harness](https://storage.ghost.io/c/3f/df/3fdf6ed2-17ac-4b12-a693-8078bd13e748/content/images/size/w400/2026/07/f3efbbc7-24b4-4c59-965d-6c3d3fc3c694_1200x1200.webp)
Reinforcement Learning Course
How do AI teams use RL in production?
RL Part 13: An exploration of real-world RL case studies.
Agents
Build Your Own 100% Local AI Second Brain
How the best builders in tech are all converging on AI second brains.
LLMs
Why Small Models Alone Don't Reduce Inference Costs
A complete guide to build production-grade SLM pipelines.
Reinforcement Learning
How to build an RL environment?
...explained step-by-step with code.
Reinforcement Learning Course
Agentic RL: Environments, Trajectories, and the Training
RL Part 12: From a single judged group to a full multi-step training loop with ART and RULER.
LLMOps
Rethinking KV Caching For Production Inference
A practitioner's guide to KV cache management in production.
Agents
How to Reduce LLM Costs by 50-60% Using Model Routing
...explained with code.
Reinforcement Learning Course
The Reward Signal Problem for Agents
RL Part 11: From verifiable rewards to LLM-as-a-judge.
Agents
[Hands-on] How to Build Your Own AI Company
100% local setup with open-source stack.
![[Hands-on] How to Build Your Own AI Company](https://storage.ghost.io/c/3f/df/3fdf6ed2-17ac-4b12-a693-8078bd13e748/content/images/size/w400/2026/08/8ac8dbbb-b5a8-4d2e-b87d-b7648d9afe47_1582x1126.webp)
Reinforcement Learning Course
Verifiable Rewards and GRPO
RL Part 10: Dropping two models from the four-model pipeline, and building rewards you can trust.
Agents
Loop Engineering, Clearly Explained!
Used by top products, including Anthropic, Google, etc.
Reinforcement Learning Course
RLHF: Aligning Language Models with Human Feedback
Part 9: From human preferences to a trained reward signal, and the four-model PPO pipeline.
Reinforcement Learning Course
Proximal Policy Optimization
RL Part 8: Trust regions, the clipped surrogate, and the workhorse of modern RL.
Reinforcement Learning Course
Policy Gradients: REINFORCE and Actor-Critic
RL Part 7: Learning the policy directly, from REINFORCE to actor-critic.
Reinforcement Learning Course
Introduction to Deep RL and DQN
RL Part 6: From linear features to neural networks, and the engineering choices that makes deep value-based RL possible.
Reinforcement Learning Course
Function Approximation
RL Part 5: From tables to parameterized value functions.
Memory
[Hands-on] Agent memory is only as good as its schema
A deep dive on building production-grade memory for Agents.
![[Hands-on] Agent memory is only as good as its schema](https://storage.ghost.io/c/3f/df/3fdf6ed2-17ac-4b12-a693-8078bd13e748/content/images/size/w400/2026/06/990fe7c9-7c72-49c9-8fb4-ea0b9cc8e01b.jpg)
Reinforcement Learning Course
Model-Free Learning
RL Part 4: Learning value functions and policies without a model. Monte Carlo methods, TD(0), SARSA, Q-learning, and the bias-variance bridge between them.
Agents
Hermes Agent Masterclass
Everything you need to understand and customize Hermes Agent.
LLMs
Speculative Decoding in LLMs
...explained with code and tradeoffs.
Reinforcement Learning Course
Bellman Equations and Dynamic Programming
RL Part 3: Bellman expectation and optimality equations, policy iteration, value iteration, and why dynamic programming needs a model.
Reinforcement Learning Course
Markov Decision Processes and Value Functions
RL Part 2: Markov decision processes, returns, policies, and value functions.
Reinforcement Learning
How to Beat GRPO Without Touching Model Weights
Berkeley beat GRPO by 10 points with 35× fewer rollouts and no GPU training,
