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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
Why this list is different The "best" email API depends entirely on what you're building. A side project optimizing for the free tier needs different things than a Series B SaaS sending two million transactional emails a month. This post grades eight providers against the criteria that actually move the needle in production, and tells you which one to pick for which use case. Most roundups in th
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
If you want to Automate GitHub PRs, the real goal is not just adding another bot comment to a pull request. The goal is to give reviewers the context they usually have to gather manually: who owns the service, whether it is deployed, whether basic repository standards are in place, and whether the change looks safe to merge. A useful AI pull request workflow can do exactly that. When a PR opens, i
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