Deep dives into artificial intelligence, large language models, transformers, embeddings, and the core concepts powering modern AI systems.
18 articles in this seriesAWS is deploying Cerebras CS-3 systems in its data centers, pairing Trainium for prefill with the Wafer Scale Engine 3 for decode. Why disaggregated inference is the right architecture, and what makes a 4-trillion-transistor chip the right tool for the decode problem.
Moonshot AI shipped a 2.8-trillion-parameter open-weight model with a 1M-token context, native vision, and a new attention stack. What's actually new in the architecture, what the benchmarks say, and why it matters for builders.
Amazon built two radically different approaches to predicting the future — a proprietary supply chain optimization pipeline (SCOT) and an open-source time series foundation model (Chronos). This post compares their architectures, trade-offs, and when each philosophy applies.
A decision framework for choosing between Amazon's Chronos-2 foundation model and custom XGBoost many-models pipelines for demand forecasting. Based on real patterns from SKU-level supply chain work.
A discovery call with a global specialty chemicals company revealed that the real AI bottleneck isn't models — it's data. Here's what enterprise chemistry teams actually need versus what the hype promises.
Most RAG tutorials stop at 'put vectors in a database.' This post covers what actually determines quality: how you chunk documents, which vector search engine to pick, and how to measure and iterate on retrieval performance using Bedrock Knowledge Bases and LLM-as-judge evaluation.
A deep dive into World Monitor — an open-source intelligence dashboard that aggregates 150+ feeds, 40+ geospatial layers, and AI-powered analysis into a real-time situational awareness platform. What OSINT is, how these platforms work under the hood, and why it matters now more than ever.
A beginner-friendly walkthrough of how an LLM actually works end-to-end: from typing a prompt to receiving a response — covering tokenization, embeddings, Transformer layers, KV cache, the training loop, embeddings for search, and why decoder-only models won.
The 5 key concepts every cloud architect should know about LLM serving: PagedAttention, KV cache mechanics, continuous batching, MoE trade-offs, and real production numbers.
Two strategies to shrink LLMs — one compresses weights, the other transfers knowledge. A practical guide to distillation and quantization: when to use each, how to implement them with Hugging Face, and why the real answer is both.
A practical walkthrough of two paths to working with Mistral — the managed API for fast prototyping and self-hosted deployment for full control — with real code covering prompting, model selection, function calling, RAG, and INT8 quantization.
Everything a cloud/AWS engineer needs to know about Python, the Hugging Face Transformers framework, SageMaker integration, quantization, CUDA, and AWS Inferentia — without being a data scientist.
What TFLOPS actually measures, why FP16 matters for LLMs, and why the most important GPU bottleneck for inference isn't compute at all.
A deep dive into the Transformer architecture — how attention connects tokens and why the Feed-Forward Network is the real brain of the model. Plus the key to understanding Mixture of Experts (MoE).
End-to-end guide: fine-tune Mistral models with LoRA using Hugging Face Transformers, then deploy at scale with vLLM on AWS — from training to production serving on SageMaker, ECS, or Bedrock.
A practical walkthrough of how large language models are aligned with human values — from collecting feedback to PPO optimization and the reward hacking pitfalls.
Understand how LLM fine-tuning works, when to use it, and how to choose between full fine-tuning, LoRA, soft prompts, and other PEFT methods.
Building a production forecasting application without the complexity of traditional ML model training and feature engineering.
Best practices and deep dives into cloud security — from zero trust architectures to WAF configurations and penetration testing.
19 articlesPatterns, services, and architectural decisions for building on AWS — including Bedrock, SageMaker, Transfer Family, and more.
16 articlesExploring the frontier of autonomous AI agents — multi-agent systems, RAG pipelines, and agentic frameworks like Strands.
16 articles