Blogs

How XLA Compiles Deep Learning Models to GPU Kernels: Fusion, cuBLAS, Triton, and Where FlashAttention Fits

Published: September 2026

XLA compiler · XLA GPU · operation fusion · CUDA kernels · cuBLAS · cuDNN · Triton · HLO · StableHLO · FlashAttention · FlashInfer · LLM inference · GPU memory hierarchy

XLA is a compiler, not a CUDA kernel library. This is my engineering understanding of how the XLA compiler turns a high-level tensor graph into optimized GPU kernels through fusion, cuBLA...

Model Formats vs Inference Engines: Safetensors, GGUF, ONNX, vLLM, and llama.cpp Explained

Published: September 2026

Model Formats · Safetensors · GGUF · ONNX · vLLM · llama.cpp · LLM Inference

A clear mental model for LLM deployment — how Safetensors, GGUF, and ONNX differ as model formats, and how llama.cpp, vLLM, and ONNX Runtime actually execute them.

Reinforcement Learning for LLMs: RLHF, Reward Models, Reasoning RL, and Agentic RL

Published: August 2026

Reinforcement Learning for LLMs · RLHF · Reward Models · PPO · DPO · GRPO · Verifiable Rewards · RLVR · Reasoning Models · Agentic RL · LLM Post-Training · RL Systems Engineering

How reinforcement learning actually trains modern LLMs — RLHF, reward models, PPO, DPO, GRPO, verifiable rewards (RLVR), reasoning RL, and agentic RL — as one connected engineering story,...

Inside GenRec: How Netflix Turned an LLM into a Recommendation Ranker

Published: August 2026

GenRec · Netflix · Recommendation Systems · LLM Recommendation Ranker · Context Engineering · Decoder-Only Transformer · Prefill-Only Inference · RecSys · Ranking Head · LLM Systems

A technical teardown of Netflix's GenRec — how an LLM-backed ranker replaces feature engineering with context engineering, and scores the catalog in a single prefill pass instead of gener...

Prompt, Context, Loop, Graph: Why Context Engineering Is the Layer That Doesn't Go Away

Published: August 2026

Context Engineering · Prompt Engineering · Loop Engineering · Graph Engineering · Agentic Loop · Multi-Agent Orchestration · LangGraph · ReAct · Memory Engineering · AI Agents

Prompt, context, loop, and graph engineering aren't successive replacements. Here's why context engineering — and increasingly memory — stays the substrate that loops and multi-agent grap...

DSPy: Program, Don't Prompt — Programmatic Prompt Optimization for Production LLMs

Published: August 2026

DSPy · Prompt Engineering · Prompt Optimization · LLM · Production AI · GEPA · LiteLLM · ReAct · Program Optimization · AI Engineering

Why manual prompt engineering breaks in production, and how DSPy treats prompts as optimizable program components — Signatures, Modules, Metrics, and GEPA-driven optimization.

AI Security in Production: Lessons from Securing a Highly Sensitive AI Platform

Published: July 2026

AI Security · LLM Security · Production AI · Penetration Testing · Prompt Injection · LangGraph · GDPR · Application Security · Threat Modeling · Responsible AI

Lessons from securing a highly sensitive AI platform in production — AI-specific vulnerabilities, LLM pipeline security, and 5 HIGH severity findings uncovered by an AI-powered penetratio...

Deciphering Agent-to-Agent Communication using Google's A2A Protocol

Published: July 2026

A2A Protocol · Agent Communication · Multi-Agent Systems · AI Agents · Google · Interoperability · Agent Interoperability · LLM · Production AI

A deep dive into Google's Agent-to-Agent (A2A) protocol — how AI agents discover, connect, and collaborate securely across frameworks and vendors.

How I Productionised AI Agents for Long-Running Enterprise Workflows

Published: May 2026

A production walkthrough of how I turned a long-running legal drafting agent into a durable system with LangGraph, Lambda, SQS, DynamoDB, HITL checkpoints, and model-specific reliability ...

Context Engineering for AI Agents: Lessons from Production Legal Draft Generation

Published: May 2026

Over the last year, the conversation around AI systems has shifted from prompt engineering to something more fundamental: context engineering for AI agents.

Mastering LLM Inference Optimization: KV Caching, Attention, Parallelism, and the Memory Wall

Published: April 2026

Stacking transformer layers enables the creation of large language models with billions of parameters, driving higher accuracy, strong few-shot learning, and even near-human emergent capa...

How Transformer Models Are Evaluated: BLEU, Perplexity, F1, GLUE, and SuperGLUE Explained

Published: April 2026

“Attention Is All You Need” reports 28.4 BLEU on WMT 2014 English-to-German. That single number launched a thousand follow-up papers. But BLEU is only one instrument in the evaluation too...

What Training Actually Means for a Transformer: Batches, Steps, and Epochs Explained

Published: April 2026

For about three years I couldn’t precisely answer this question: what does training a transformer actually mean?

The Three Problems Transformers Solved That RNNs, LSTMs, and CNNs Couldn't

Published: April 2026

In 2017, the paper “Attention Is All You Need” reported 28.4 BLEU on WMT 2014 English-to-German translation — more than 2 points above the previous best result, including ensembles.

Engineering a Production-Grade RAG System for CV-Job Matching

Published: March 2026

Building CV-job matching in production is not a single-model problem. It is a distributed systems problem with strict data contracts, asynchronous processing, retrieval quality constraint...

Engineering a Scalable Resume Processing Pipeline: From EC2 Workers to Event-Driven Serverless Architecture

Published: March 2026

Large-scale document processing systems rarely fail because of machine learning models.

ColPali: Efficient RAG using Vision Language Models

Published: March 2026

Retrieval-Augmented Generation (RAG) systems power many modern AI search and question answering applications. However, traditional RAG pipelines struggle when dealing with visually rich d...

Budget Forcing in RAG Systems: Controlling Cost, Latency, and Runaway Reasoning

Published: February 2026

Large Language Models are no longer just text generators. In production systems, especially Retrieval-Augmented Generation (RAG) architectures, they act as reasoning engines.