Blogs

Mistral's Sovereign Appeal: Mistral Large 4 "le chonk"

Published: October 2026

Mistral AI · Mistral Large 4 · Sovereign AI · Open-Weight Models · AI Cybersecurity · Large Language Models

Mistral Large 4, nicknamed le chonk: a 1-trillion-parameter open-weight model trained in Europe, and why its sovereign AI pitch matters for security teams.

Being Mean to Claude Can Get You Banned: A Deeper Dive Into What It Actually Means

Published: October 2026

Claude AI · Anthropic · AI Safety · AI Policies · Large Language Models

Can being mean to Claude get you banned? What Anthropic's new rule actually covers, how Claude's constitution shapes training, and why insults don't feed RLHF.

Agent SRE: Building an AI Incident Detection and Response System with JEV

Published: October 2026

Agent SRE · AI Incident Response · LLM Observability · JEV · Langfuse · AWS

An Agent SRE design that separates detection from reasoning: CloudWatch and Langfuse signals flow through EventBridge to JEV, which decides what is a real incident before Claude Sonnet 5....

JEV Decoded: What It Is and What Are the Use Cases?

Published: October 2026

JEV · JEV Use Cases · JEV Inference · AI Inference · LLM Inference

What is JEV? JEV decoded for engineers: what JEV is, how it fits into AI inference, and the practical JEV use cases that matter for production systems.

Inside TPU and GPU Clusters: The Anatomy of Collective Communication

Published: September 2026

Collective Communication · All-Reduce · All-Gather · Reduce-Scatter · All-to-All · TPU · Torus Topology · ICI · NVLink · NVSwitch · InfiniBand · Fat Tree · NCCL · SHARP · Bisection Bandwidth · Distributed Training

A ground-up walkthrough of collective communication in TPU and GPU clusters: torus topologies, NVLink and InfiniBand fat trees, and cost models for All-Gather, Reduce-Scatter, All-Reduce ...

Inside NVIDIA GPUs: How High-Performance Matmul Kernels Actually Work

Published: September 2026

NVIDIA GPU · Matmul Kernels · Hopper H100 · Tensor Cores · wgmma · TMA · Warp Tiling · Shared Memory · Bank Conflicts · PTX · SASS · Swizzling · CUDA · GEMM · Roofline Model · Arithmetic Intensity

A ground-up walkthrough of high-performance NVIDIA GPU matmul kernels: the Hopper H100 memory hierarchy, PTX/SASS, warp-tiling, TMA swizzling, and asynchronous tensor core pipelines.

Exploring SIMA: Generalist Instructable Agents Across Simulated Worlds

Published: September 2026 · Exploratory Research

SIMA · SIMA 2 · Google DeepMind · Gemini · Embodied AI · Physical AI · Generalist Agents · Instructable Agents · Simulated Worlds

An exploratory study of SIMA — DeepMind's generalist, instructable, multiworld embodied agent — and what it means for Physical AI.

Reinforcement Learning for LLMs: RLHF, RLAIF, RLVF, RLEF & SWiRL Post-Training Explained

Published: September 2026

Reinforcement Learning for LLMs · RLHF · RLAIF · RLVF · RLEF · SWiRL · DPO · PPO · GRPO · Reward Models · Verifiable Rewards · Execution Feedback · LLM Post-Training · Preference Optimization

A deep technical guide to reinforcement learning for LLMs: how RLHF, RLAIF, RLVF, RLEF, and SWiRL differ by feedback source, and where PPO, GRPO, and DPO fit in modern post-training.

Megatron-LM Explained: Tensor, Pipeline, and Data Parallelism for Training Large Language Models

Published: September 2026

Megatron-LM · Tensor Parallelism · Pipeline Parallelism · Data Parallelism · 3D Parallelism · Distributed Training · GPU Systems · Model Parallelism · Collective Communication · MoE · Sequence Parallelism · NVLink

Megatron-LM makes trillion-parameter LLMs trainable by combining tensor, pipeline, and data parallelism. Here's how 3D parallelism and its GPU communication patterns actually work.

Why We Need Distributed Training

Published: September 2026

Distributed Training · Distributed Inference · GPU Systems · Model Parallelism · Data Parallelism · Scaling · Communication Overhead · KV Cache · LLM Inference · Distributed Systems

Why a single GPU is no longer enough for modern AI, and how distributed training turns into a distributed systems problem. An engineering-first look at the parallelism axes, DDP, ring all...

Maximal Update Parameterization (μP): Hyperparameter Transfer Across Model Width — and Why It Isn't Free

Published: September 2026

Maximal Update Parameterization · muP · Hyperparameter Transfer · Learning Rate Scaling · Model Width Scaling · Feature Learning · Adam · Cosine Annealing · Scaling Laws · LLM Pretraining · Training Dynamics

Maximal Update Parameterization (μP) lets you tune hyperparameters on a small model and transfer them to a much wider one. But μP is not a learning-rate trick — it's a parameterization th...

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.

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...

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,...

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...

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...

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.

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.

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.