Anurag Jha

Next topic: Validation and Transformation. Another article in the backend series: medium.com/@anuragdotdev/val… I wrote about what happens when data enters your backend and how to ensure your application handles it correctly. Input validation, data transformation, Zod, Express middleware, the 400 vs 500 problem, mass assignment, database constraints, testing, and more. I didn't want to write another article that just shows a Zod schema and calls it validation. I wanted to understand the whole thing properly. From untrusted client input to validated data reaching the service and database. It took me a while to put this together, but I wanted it to be something I'd actually want to read while learning backend development.

nerds ......... thank me later medium.com/@anuragdotdev/und…

INFERENCE ENGINEERING ROADMAP │ ├── 1. Programming & Computer Science Foundations │ ├── Python Programming │ │ ├── Advanced Python & OOP │ │ ├── Memory Management & Garbage Collection │ │ ├── Async Programming & Multiprocessing │ │ ├── Python Internals & GIL │ │ └── NumPy & Scientific Computing │ ├── C & C++ Programming │ │ ├── Pointers & Memory Management │ │ ├── References & Move Semantics │ │ ├── Templates & STL │ │ ├── Multithreading & Concurrency │ │ └── Modern C++ (C++17/20) │ ├── Data Structures & Algorithms │ ├── Operating Systems & Computer Architecture │ ├── Linux & Systems Programming │ ├── Networking Fundamentals │ ├── Git, Debugging & Build Systems │ └── Mathematics for Machine Learning │ ├── Linear Algebra │ ├── Probability & Statistics │ ├── Calculus │ ├── Numerical Methods │ └── Optimization Theory │ ├── 2. Machine Learning & Deep Learning │ ├── Machine Learning Fundamentals │ ├── Supervised & Unsupervised Learning │ ├── Neural Networks & Perceptrons │ ├── Forward & Backpropagation │ ├── Gradient Descent & Optimizers │ ├── Loss Functions & Regularization │ ├── Activation Functions │ ├── PyTorch & JAX │ ├── Tensors & Computational Graphs │ ├── Automatic Differentiation │ ├── Training vs Inference │ ├── Model Evaluation & Benchmarking │ └── Deep Learning Hardware Requirements │ ├── 3. LLM Architecture & Internals │ ├── Tokenization & Vocabulary │ ├── Embeddings & Embedding Spaces │ ├── Transformer Architecture │ ├── Encoder vs Decoder Architectures │ ├── Self-Attention & Multi-Head Attention │ ├── Query, Key & Value Projections │ ├── Scaled Dot-Product Attention │ ├── Positional Encoding & RoPE │ ├── Layer Normalization & RMSNorm │ ├── Residual Connections │ ├── Grouped Query Attention (GQA) │ ├── Multi-Query Attention (MQA) │ ├── Mixture of Experts (MoE) │ ├── Sparse vs Dense Models │ ├── Context Windows & Sequence Length │ ├── Llama, Qwen, DeepSeek & Mistral │ └── Reading & Understanding Model Configurations │ ├── 4. LLM Inference Fundamentals │ ├── What Happens During Inference │ ├── Model Loading & Initialization │ ├── Forward Pass Execution │ ├── Autoregressive Token Generation │ ├── Prefill Phase │ ├── Decode Phase │ ├── Token Sampling Strategies │ │ ├── Greedy Sampling │ │ ├── Temperature │ │ ├── Top-K & Top-P │ │ └── Repetition Penalty │ ├── Compute-Bound vs Memory-Bound Workloads │ ├── Arithmetic Intensity │ ├── Time to First Token (TTFT) │ ├── Time Per Output Token (TPOT) │ ├── Inter-Token Latency (ITL) │ └── Identifying Inference Bottlenecks │ ├── 5. GPU Architecture & Hardware Fundamentals │ ├── CPU vs GPU Architecture │ ├── GPU Execution Model │ ├── CUDA Cores & Tensor Cores │ ├── Streaming Multiprocessors (SMs) │ ├── Threads, Warps & Thread Blocks │ ├── GPU Scheduling & Occupancy │ ├── Memory Hierarchy │ │ ├── Registers │ │ ├── Shared Memory │ │ ├── L1 & L2 Cache │ │ └── Global Memory │ ├── HBM & VRAM │ ├── Memory Bandwidth & Latency │ ├── PCIe & NVLink │ ├── GPU Interconnect Topologies │ ├── NVIDIA GPU Architectures │ └── GPU Profiling & Hardware Monitoring │ ├── 6. CUDA & Low-Level GPU Programming │ ├── CUDA Programming Model │ ├── CUDA Kernels & Kernel Launches │ ├── Thread Hierarchy & Indexing │ ├── Device & Host Memory │ ├── Memory Allocation & Transfers │ ├── CUDA Streams & Events │ ├── Thread Synchronization │ ├── Atomic Operations │ ├── Memory Coalescing │ ├── Shared Memory Optimization │ ├── Register Pressure │ ├── Warp-Level Primitives │ ├── Kernel Launch Overhead │ ├── CUDA Graphs │ ├── Asynchronous Execution │ └── CUDA Error Handling & Debugging │ ├── 7. GPU Kernel Engineering │ ├── GPU Kernel Design Principles │ ├── Matrix Multiplication (GEMM) │ ├── Tiled Matrix Multiplication │ ├── Reduction Operations │ ├── Softmax & Layer Normalization │ ├── RMSNorm Kernels │ ├── Attention Kernels │ ├── Fused Attention │ ├── Kernel Fusion & Operator Fusion │ ├── Memory-Bound Kernel Optimization │ ├── Compute-Bound Kernel Optimization │ ├── CUDA C++ Kernel Development │ ├── Triton Programming │ ├── FlashAttention & FlashAttention-2/3 │ ├── CUTLASS & cuBLAS │ ├── Kernel Auto-Tuning │ └── Kernel Benchmarking & Profiling │ ├── 8. Inference Memory Management │ ├── Model Weight Memory │ ├── Activation Memory │ ├── KV Cache Architecture │ ├── Key & Value Tensor Storage │ ├── KV Cache Allocation Strategies │ ├── PagedAttention │ ├── Prefix & Prompt Caching │ ├── KV Cache Reuse │ ├── KV Cache Quantization │ ├── Sliding Window Attention │ ├── Memory Pooling & Allocation │ ├── GPU Memory Fragmentation │ ├── Cache Eviction Policies │ ├── CPU-GPU Memory Offloading │ └── Memory-Efficient Long-Context Inference │ ├── 9. Quantization & Numerical Precision │ ├── Floating-Point Representation │ ├── FP32, FP16 & BF16 │ ├── FP8 & FP4 │ ├── INT8 & INT4 Quantization │ ├── Symmetric vs Asymmetric Quantization │ ├── Per-Tensor & Per-Channel Quantization │ ├── Weight-Only Quantization │ ├── Activation Quantization │ ├── Post-Training Quantization (PTQ) │ ├── Quantization-Aware Training (QAT) │ ├── GPTQ & AWQ │ ├── GGUF & GGML │ ├── SmoothQuant │ ├── Marlin & ExLlama Kernels │ ├── Mixed-Precision Inference │ └── Accuracy, Memory & Throughput Trade-offs │ ├── 10. Advanced Inference Optimization │ ├── FlashAttention Optimization │ ├── Continuous Batching │ ├── Dynamic & Static Batching │ ├── Request Scheduling │ ├── Chunked Prefill │ ├── Speculative Decoding │ ├── Medusa & Multi-Token Prediction │ ├── Prefix Caching │ ├── Prompt Caching │ ├── Disaggregated Prefill & Decode │ ├── Asynchronous Inference Execution │ ├── CUDA Graph-Based Optimization │ ├── Adaptive Batch Sizing │ ├── Head-of-Line Blocking │ ├── Workload-Aware Scheduling │ └── Latency vs Throughput Optimization │ ├── 11. LLM Inference Engines & Runtime Internals │ ├── PyTorch Inference │ ├── Hugging Face Transformers │ ├── Hugging Face Accelerate │ ├── Hugging Face Optimum │ ├── vLLM │ │ ├── PagedAttention Internals │ │ ├── Continuous Batching │ │ └── Scheduler Architecture │ ├── SGLang │ │ ├── RadixAttention │ │ ├── Radix Cache │ │ └── Structured Generation │ ├── TensorRT-LLM │ ├── NVIDIA Triton Inference Server │ ├── llama.cpp │ ├── ONNX Runtime │ ├── OpenVINO │ ├── MLX │ ├── Runtime Graph Optimization │ ├── Model Execution Pipelines │ └── Comparing Inference Engine Architectures │ ├── 12. Distributed LLM Inference │ ├── Distributed Computing Fundamentals │ ├── Data Parallelism │ ├── Tensor Parallelism │ ├── Pipeline Parallelism │ ├── Expert Parallelism │ ├── Context Parallelism │ ├── Sequence Parallelism │ ├── Hybrid Parallelism │ ├── NCCL Communication │ ├── All-Reduce & All-Gather │ ├── Reduce-Scatter │ ├── GPU-to-GPU Communication │ ├── NVLink & InfiniBand │ ├── Multi-GPU Scheduling │ ├── KV Cache Transfer │ ├── Disaggregated Serving │ ├── Fault Tolerance │ └── Multi-Node Inference Clusters │ ├── 13. Production Inference Infrastructure │ ├── Model Serving Architecture │ ├── FastAPI & Async APIs │ ├── OpenAI-Compatible APIs │ ├── Streaming Responses & SSE │ ├── Request Queuing │ ├── Rate Limiting & Backpressure │ ├── Authentication & API Management │ ├── Docker & Containerization │ ├── Kubernetes & GPU Scheduling │ ├── GPU Orchestration │ ├── Load Balancing & Routing │ ├── Autoscaling & Capacity Planning │ ├── Prometheus & Grafana │ ├── OpenTelemetry & Distributed Tracing │ ├── Logging & Error Monitoring │ ├── Health Checks & Readiness Probes │ └── Reliability & High Availability │ ├── 14. Performance Engineering & Cost Optimization │ ├── Performance Modeling │ ├── Roofline Model │ ├── TTFT & TPOT Optimization │ ├── Tokens Per Second │ ├── Requests Per Second │ ├── P50, P95 & P99 Latency │ ├── GPU Utilization & Occupancy │ ├── Memory Bandwidth Utilization │ ├── FLOPs & Arithmetic Intensity │ ├── Load Testing & Stress Testing │ ├── Benchmark Design & Reproducibility │ ├── Nsight Systems & Nsight Compute │ ├── PyTorch Profiler │ ├── Bottleneck Identification │ ├── GPU Capacity Planning │ ├── Cost Per Million Tokens │ ├── GPU Cost Per Request │ └── Performance vs Cost Trade-offs │ ├── 15. Advanced Inference Systems │ ├── Mixture of Experts Inference │ ├── Expert Routing & Load Balancing │ ├── Long-Context Inference │ ├── Multimodal Model Inference │ │ ├── Vision-Language Models │ │ ├── Audio & Speech Models │ │ └── Video Inference │ ├── Retrieval-Augmented Generation Serving │ ├── Structured Output & Constrained Decoding │ ├── Grammar-Based Sampling │ ├── Edge & On-Device Inference │ ├── CPU & Mobile Inference │ ├── Advanced KV Cache Architectures │ ├── Multi-Tenant Model Serving │ ├── Model Routing & Cascading │ ├── ML Compilers │ │ ├── TorchInductor │ │ ├── TVM │ │ ├── XLA │ │ └── MLIR │ ├── GPU Kernel Auto-Tuning │ └── Hardware-Software Co-Design │ ├── 16. Research Papers & Engineering Practices │ ├── Attention Is All You Need │ ├── FlashAttention Papers │ ├── PagedAttention / vLLM Paper │ ├── Efficient Memory Management Papers │ ├── SmoothQuant & AWQ Papers │ ├── Speculative Decoding Research │ ├── DeepSeek-V2 & V3 Architecture │ ├── Sarathi-Serve │ ├── Splitwise │ ├── DistServe │ ├── Reading GPU Architecture Papers │ ├── Reproducing Research Results │ ├── Designing Controlled Experiments │ └── Writing Technical Engineering Reports │ ├── 17. Projects & Open Source Contributions │ ├── Build a Transformer From Scratch │ ├── Implement Attention Using PyTorch │ ├── Implement Matrix Multiplication in CUDA │ ├── Build Custom Softmax & RMSNorm Kernels │ ├── Implement FlashAttention Concepts │ ├── Build a KV Cache Manager │ ├── Implement PagedAttention │ ├── Build a Quantized LLM Runtime │ ├── Implement Continuous Batching │ ├── Build a Mini vLLM │ ├── Build a Speculative Decoding Engine │ ├── Deploy an OpenAI-Compatible Inference API │ ├── Build a Multi-GPU Inference System │ ├── Benchmark Different GPU Configurations │ ├── Optimize an Existing Inference Engine │ └── Contribute to vLLM, SGLang, Triton & llama.cpp │ └── 18. Career Preparation & Specialization ├── Master Python, C++ & CUDA ├── Develop Strong Systems Programming Skills ├── Understand GPU Microarchitecture ├── Master PyTorch & Triton ├── Study Inference Engine Internals ├── Read & Reproduce Research Papers ├── Build Production-Grade Inference Projects ├── Publish Reproducible Performance Benchmarks ├── Maintain a Strong GitHub Portfolio ├── Contribute to High-Impact Open Source Projects ├── Study ML Systems & Distributed Systems ├── Prepare for GPU Programming Interviews ├── Prepare for ML Systems Interviews ├── Prepare for Performance Engineering Interviews └── Target Roles ├── LLM Inference Engineer ├── ML Systems Engineer ├── GPU Kernel Engineer ├── AI Infrastructure Engineer └── Distributed Systems Engineer FINAL GOAL: Become an Expert Inference Engineer Capable of Designing, Building, Optimizing & Scaling High-Performance LLM Inference Systems Across GPUs, Distributed Clusters & Production Environments.

Just as I predicted, one of the four candidates I mentioned has now been selected as Nvidia's new CEO I also mentioned Colette Kress as a strong contender Even Grok agreed in the replies to my post, calling her the strongest candidate for interim CEO. Check out the quoted post below.

My friend with a $10 bank balance is worried about Jensen Huang

Elon , I want to see this Collab very soon

Breaking: Nvidia CEO just resigned Board of Directors announced today that Jensen Huang has resigned as President, Chief Executive Officer, and a member of the Board, effective immediately L decision by NVIDIA

Everyone talks about prompt engineering but there's something more important when you're building AI agents and that is Context Engineering now think about it You can give an LLM the perfect instruction, but if it doesn't have the right information, tools, memory, or conversation history, it can still fail. Context engineering is about deciding: - What information should the model see? - What should be retrieved when needed? - What should be removed? - What should be remembered for later? It's not about giving an AI more information. It's about giving it the right information at the right time that's the foundation of reliable AI agents.

Anthropic is going public. The company has revealed its IPO plans under the ticker $ANT on Nasdaq. Some eye-catching numbers: • $17B revenue • 12x+ YoY revenue growth • 11,900+ customers spending $100K+ annually • 15% adjusted operating margin • $23.3B reported net loss The AI IPO race is getting interesting.

When she sends 500 parallel requests to an API without connection pooling, retries failed payments indefinitely, processes the same Kafka message multiple times without idempotency, and handles duplicate orders by asking customer support to refund them, but you lwk see the beauty in her ways so you suit up one last time to fix her

Harry Potter if he were an engineer 😭

Unpopular opinion: Most AI engineers today are just API integrators with a fancy title. Knowing how to connect GPT to a vector database, write prompts, and build a chatbot doesn't make you an AI engineer. Understand inference, evaluation, context management, retrieval, failure handling, and distributed systems The real engineering starts when the demo breaks.

@grok Who do you think is most likely to succeed Jensen Huang as the next CEO of NVIDIA?

most probably opus

What if he hire him , start another company and sell ram and gpu at cheap prices 💀

Jensen huang with both of his girlfriends

Sir wakeup Jensen had another affair with $NVDA executive