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A College Project That Planted a Seed Years ago I was on a university team trying to build a Go AI. We explored monte carlo simulation for lookahead search, basic neural networks for pattern recognition, and expert systems for encoding domain knowledge. None of them worked well enough on their own. Go's branching factor is enormous, so brute-force search fails quickly. Neural networks without th
This section is the map for the rest of the book. The five stages introduced in the 1.1 chapter overview (parse, analyze/rewrite, plan, portal, execute) are traced here through the actual code: which functions implement each stage, and in what order they get called. The mechanics of each of the five stages are unpacked in later chapters. Here, only the skeleton matters: how a backend starts up, ho
Vibe coding is a good starting point, but it is not where serious AI-assisted development ends. The next step is agentic engineering: using AI coding agents inside a controlled engineering workflow, with context, tests, review and clear boundaries. Vibe coding often focuses on the generated output: Ask for feature -> get code -> run it -> ask for fixes Agentic engineering focuses on the system ar
PostgreSQL Internals · Chapter 1 Query Processing Suppose a client sends SELECT * FROM users WHERE id = 1. The path that single line travels before coming back as a result row is longer than you might expect. Inside the PostgreSQL backend, that SQL goes through a five-stage pipeline. Backend entry and dispatch. The backend receives the message from the client and decides which processing path it s
This article is an AI-assisted translation of a Japanese technical article. In April 2026, Amazon Bedrock AgentCore added a new capability called Optimization, which takes real agent traces and proposes prompt improvements based on them. https://aws.amazon.com/about-aws/whats-new/2026/05/bedrock-agentcore-optimization-preview/ In this article, I apply AgentCore Optimization to a Strands Agents-as-
A Haystack pipeline can be perfectly wired and still unsafe. The retriever returns documents. Every component did its job. But if untrusted text moved through the pipeline as ordinary context, the trust boundary was lost. That is the problem this post is about. Not bad Python. A valid component connection only says: this value fits the next component It does not say: this value is safe to influen
Abstract: This article walks through configuring both Claude Code (terminal CLI) and Claude Desktop (Cowork) to use Amazon Bedrock as the inference backend — no Anthropic API key or subscription required. Claude Code needs two environment variables in ~/.claude/settings.json. Claude Desktop needs a few fields in the built-in Setup UI. Both share the same AWS credentials and Bedrock model access. T