I have been working on a .NET assertion library called Axiom Assertions. It started as a way to learn how assertion libraries work, then grew into an experiment around deterministic output, batching, analyzers, and AI-focused test assertions. The repo is here: https://github.com/spearzy/Axiom-Assertions This did not start as a plan to overthrow every assertion library in .NET. That would be a bit
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The Problem: We Were Flying Blind At Refer, we're on a mission to enable talented individuals to fulfill their professional potential by helping them pursue their ideal job. Behind the scenes, that means a lot of microservices, and recently we decided to consolidate everything into a mono-repository. If you've ever migrated dozens of microservices into a monorepo, you know the drill: contracts b
I shipped mcp-probe — a CLI that points at any MCP server, enumerates every tool, resource, and prompt, calls each with auto-generated arguments, validates against declared schemas, prints a pass/fail scorecard, and exits 0/1 for CI. The plan for launch week: run it against the official Node MCP servers and post results. The first run made me look like I'd broken half the ecosystem. The second, af
The Problem If you are building AI applications with LLMs, you know the pain: raw HTML is useless for training data. You need clean, structured Markdown. Most solutions like Firecrawl or Crawl4AI require setup, dependencies, and often paid plans. You could write your own parser: import re import urllib.request def html_to_markdown(url): html = urllib.request.urlopen(url).read().decode()
Hi, we are back again. Previously, I created a simple Google Cloud VPC and then improved the configuration by introducing variables. This time, I want to continue with another Terraform concept: outputs. But, we will not be using the previous code, because adding outputs for one vpc is too simple. So, I made the lab slightly more practical. In this lab, I will create: a custom VPC network a subnet
In Part 1 of this series, I enumerated a few obstacles for engineers taking vibe coding from side projects to production. Part 2 looked at AI usage from the manager's perspective: measuring adoption, understanding the gap, coaching to fill the gap. Both of those were "Day 1" problems: getting started, getting people on board, figuring out the tools. This article focuses on what comes next: the vib
This technical post walks through the design and implementation of Secure Playground: a local web app that simulates prompt-injection attacks against large language models and demonstrates simple defenses. Provide a minimal, reproducible environment to test payloads and defensive strategies. Make it easy to add new providers and run mutation-based red-team experiments. Offer a leaderboard and scor