A common problem with a familiar shape: a process can dial outbound to the internet, but nothing on the internet can dial it back. Your dev server on a laptop. A service in a private VPC. A homelab app behind your router. A container in a pod with no ingress. Same shape every time — outbound works, inbound doesn't. rift is a small Go binary I built to solve that. Run it as a server on a VPS you ow
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
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
Posted by the RagLeap team — building RagLeap, a private-server AI business platform When we started building RagLeap, the easiest path was obvious: spin up an API, connect to OpenAI, store everything in a managed cloud database, and ship fast. The Problem Nobody Talks About You upload your documents, customer data, order history It works. But ask yourself: where is your data right now? What Our U
I like servers. Not in a "let me spend Saturday hand-tuning nginx" way. More in a "this $6 VPS is sitting right here and could probably run half my side projects" way. The weird part is that deploying to one still feels more complicated than it should. For a lot of small and medium web apps, the app itself is not the hard part. The annoying part is everything around it: building the app getting it