Architecting Future Intelligence.

From modular stacks to unified intelligence: How foundation models are reshaping AV and generalist robotics. Covers VLA models (GR00T, Pi0), Physical AI, and the 2026 embodied revolution.

A comprehensive guide to Python interview hacks, advanced patterns, tricky syntax, and gotchas that separate strong candidates from elite ones. Covers heapq, DP, bitwise operations, monotonic stacks, and more.

A modern, industry-standard approach to building robust RAG systems using OpenSearch as the core engine. Transition from simple vector retrieval to production-grade multimodal systems handling text, images, and video with advanced patterns like hybrid search, query rewriting, parent-document retrieval, and cross-encoder reranking.

A narrative-first walkthrough of reinforcement learning, starting with everyday intuition and ending with the math behind Q-learning and DQN.

A high level view on how modern vision-language models connect pixels and prose, from CLIP and BLIP to Flamingo, MiniGPT-4, Kosmos, and Gemini.

A clear introduction to diffusion and guided diffusion — how a simple physical process became a foundation for modern generative AI, from Stable Diffusion to robotics and protein design.

Why modern AI teams are handcrafting GPU kernels—from FlashAttention to Triton code—and how silicon-level tuning is the new frontier of MLOps.

From stateless inference to tool-augmented AI agents. Learn how the Model Context Protocol (MCP), secure sandboxes, and holistic versioning enable the next generation of AI systems.

A guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to 3D parallelism and fault tolerance.

A deep dive into how datasets and dataloaders power modern AI. Understanding the architectural shift from Python row-loops to C++ zero-copy data pumps.

Standard MLOps advice tells you to learn Git and Docker. But for the next generation of AI Engineers, that's just the baseline. This roadmap focuses on the Infrastructure Round—deep-diving into how data is structured for speed, how it's fed into models, how those models scale across clusters, and how we squeeze every drop of performance out of the silicon.

A comprehensive deep-dive into production inference optimization, tracing the path of a request through LLM and diffusion model serving systems. Understanding the bottlenecks from gateway to GPU kernel execution.

Pre-training gives models capability; post-training gives them value. A deep dive into LoRA, DoRA, DPO, and how we sculpt intelligence after the initial birth.

The unsung hero of modern data processing is how we structure data itself. Learn how Apache Parquet and Apache Arrow solve the fundamental trade-off between storage efficiency and compute speed.

How PagedAttention, Continuous Batching, Speculative Decoding, and Quantization unlock lightning-fast, reliable large language model serving.

Why L5 autonomy is harder than a moon landing. Understanding ODD, latency loops, compute constraints, and the modern Hybrid Architecture (Modular vs. End-to-End).

The raw senses of an autonomous vehicle: What data does each sensor provide? Covers cameras, radar, LiDAR, ultrasonics, and microphones—their physics, strengths, weaknesses, and why fusion is necessary.

How autonomous vehicles remember the world. Covers HD maps, lane graphs, offline vs. online mapping, MapTR, and the map-heavy vs. map-light debate.

From GPS to centimeter accuracy: How autonomous vehicles know their exact position. Covers GNSS, IMU, wheel odometry, scan matching, and Factor Graphs.

From pixels to objects: How autonomous vehicles understand their environment. Covers the full sensor suite, multi-modal fusion, 2D/3D detection, tracking, occupancy networks, and the 2025 "End-to-End" revolution.

The hardest problem in AV: predicting human irrationality. From physics-based Kalman Filters to Generative Motion Diffusion and LLM-based intent reasoning.

From perception to action: How autonomous vehicles make decisions. Covers cost functions, game-theoretic planning, MPC, and the "End-to-End" debate.

If you don't know where your eyes are relative to your feet, you trip. Covers intrinsics, extrinsics, SE(3) transforms, online vs. offline calibration, and time synchronization.

A deep dive into XGBoost — how second-order Taylor approximations and sophisticated regularization make it the dominant algorithm for structured data, bridging mathematical rigor with system engineering excellence.

How diffusion models predict action sequences instead of pixels. Covers Diffusion Policy, world models for robotics, and connecting diffusion to reinforcement learning for autonomous systems.

The evolution of image diffusion models from U-Net architectures to Diffusion Transformers (DiT). Covers latent diffusion, the DiT revolution, and the complete image generation pipeline.

Deep dive into state-of-the-art video generation models: Sora, Veo 3, and Open-Sora. Plus motion modeling techniques using optical flow, geometry, and diffusion fields.

How video diffusion models are built through pre-training and aligned through post-training. Covers the billion-frame training problem, DPO, RLHF, and the complete training pipeline.

How to accelerate diffusion sampling and control output quality. Covers DDIM, DPM-Solver, Classifier-Free Guidance (CFG), negative prompting, and inference optimization techniques.

Why video is harder than images, the DiT revolution for video, and how diffusion models learn temporal consistency. Covers V-DiT, AsymmDiT, and the mathematical foundations of video generation.

A deep dive into physics-aware video diffusion models: how researchers inject physical constraints into generative models, the three leading technical approaches, and their practical impact on robotics and scientific simulation.

An intuitive introduction to the Transformer architecture — from the attention mechanism to self-attention and cross-attention, using language translation as a concrete example.

Part 4 of a comprehensive guide to agentic AI design patterns. Covers common failure modes, safety mechanisms, verifiable pipelines, and how to build reliable production systems.

Part 3 of a comprehensive guide to agentic AI design patterns. Covers specialized patterns: embodied agents, 3D scene understanding, imagination loops, multi-agent societies, error recovery, and self-debugging.

Part 5 of a comprehensive guide to agentic AI design patterns. Covers 2025 trends, cost optimization, case studies, production checklist, and the state of the field.

Part 1 of a comprehensive guide to agentic AI design patterns. Covers the fundamentals: ReAct loops, planning, tool use, self-consistency, and graph-based reasoning.

Part 2 of a comprehensive guide to agentic AI design patterns. Covers production-ready patterns: memory management, supervisor/orchestrator, parallel tool execution, and hidden reasoning.

An exploration of modern agent systems, with math, analogies, and examples. From ReAct loops to multi-agent societies, discover the design patterns that make AI agents think, act, and fix themselves.

An intuitive introduction to Variational Autoencoders — how compressing data into probabilistic codes enables machines to generate realistic images, sounds, and structures.

Reflections on building production-grade behavior prediction systems for autonomous vehicles — and why closed-loop reasoning is the bridge between perception and planning.

My research journey from wireless communication foundations to solving the camera calibration bottleneck that enables autonomous vehicle vision.

How we used deep learning to automatically calibrate traffic cameras by observing vehicle motion—work that won Best Paper Award at ACM BuildSys 2017.