In the fast-paced world of continuous integration and deployment (CI/CD), managing sensitive information like API keys, tokens, and credentials—collectively known as secrets—is not just a best practice; it's a critical foundation for security and efficiency. GitHub Actions provides a robust framework for automating workflows, but a common friction point for many development teams, particularly tho
The Challenge of Scalable Secrets Management in GitHub Actions For development teams scaling beyond a handful of repositories, managing environment-specific variables and secrets in GitHub Actions can quickly become a significant bottleneck. The manual duplication of configurations across multiple repos, especially when dealing with distinct environments like development, staging, and production
I got tired of the same three-step content publish loop: write draft → open CMS → paste, format, re-paste, fight the rich-text editor, click publish. Repeat for every environment — staging, then production. For one article, fine. For a team publishing 20+ pieces a month? That workflow is a quiet tax on everyone's time. So I wired up a pipeline that cuts the loop entirely. You commit a .md file to
The task at hand is drawing the circuit schematics for a robot I'm working on. I had already written down the components and the connections, all that's left is to draw it in KiCad. I had already started doing that, but then... I got sidetracked forcing Gemini to create the circuit using KiCad. I would have made progress if I had continued doing it by hand. I spent yesterday trying to generate an
Most teams I have worked with have one auth test in their suite. It looks like this: test('valid token verifies', () => { const token = signSync({ sub: 'user-1', aud: 'api://backend' }, secret); const result = verify(token, options); expect(result.valid).toBe(true); }); That test is fine. It is also a smoke test, not a regression suite. It catches the case where verification is completely b
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This is part three of a series on display consistency in embedded systems. The first two parts were technical. This one is about why the technical parts worked. The picture: ATtiny85 thermometer. Neural network inference. QUAD7SHIFT display. Built from datasheets. He had datasheets. No Stack Overflow. No libraries to install. No AI to generate boilerplate. No tutorials that abstracted away the in
If you've ever used a bottleneck calculator, you've probably seen a simple percentage telling you whether your CPU or GPU is holding your system back. But here’s the truth most people don’t realize: Bottlenecks are not fixed numbers — they are dynamic, workload-dependent behaviors. In this post, we’ll go beyond basic tools and break down how CPU and GPU bottlenecks actually work in real-world scen