Boulder uses 9 Strands agents on Bedrock AgentCore to generate, deploy, and maintain full-stack apps on AWS Amplify — with self-healing builds and self-improving prompts.
Anthropic just dropped a model that autonomously finds and exploits zero-days in every major OS and browser. Then they built an industry coalition to use it defensively. Here's why this changes everything.
Weekly roundup of AWS announcements: AI Scholars program, Agent Plugin for serverless, Aurora Express setup, Lambda upgrades, Polly streaming, and more.
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.
How Amazon Connect's native AI stack replaces fragmented CCaaS platforms with a unified, pay-per-use contact center backbone for global enterprises.
A deep architectural comparison of four open-source frameworks that turn messaging apps into AI assistant interfaces — from a 349-file TypeScript monolith to a 10MB Go binary that runs on a $10 board.
A 5-component framework for writing effective system prompts for any AI agent — Bedrock Agents, Claude Code, LangChain, Strands, or custom builds. With a practical Claude Code implementation.
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.
Turn Claude Code into a persistent executive assistant with morning briefings, auto-logging, context-aware reminders, complex skills, and a memory that compounds over time — using only markdown files.
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).
A curated summary of the most important AWS announcements from February 2026 — from Bedrock AgentCore deep dives to new EC2 instances and the European Sovereign Cloud.
A hands-on walkthrough of deploying OpenClaw on AWS using AgentCore Runtime for serverless agent execution, Graviton ARM instances, and multi-model Bedrock access — from CloudFormation template to customizing the agent's personality.
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.
Unitree ships a humanoid robot with 43 degrees of freedom, a full AI training pipeline on GitHub, and Apple Vision Pro teleoperation — for $13.5K. Here's what the developer ecosystem looks like.
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.
AI platform teams need governance before scaling. Learn how to use Amazon Bedrock inference profiles, AWS Budgets, and a proactive cost control pattern to track, allocate, and cap AI spending per team.
Discover AI-DLC (AI Development Lifecycle), a structured framework for AI-assisted software development. Learn how I used it to build this blog from scratch and how it enables continuous iteration.
A practical guide to building generative AI applications with Amazon Bedrock