I build mdedit.io — a no-account Markdown editor with live preview, collaboration and AI assistance I’m looking for feedback on the public beta of mdedit.io: https://mdedit.io Repository: https://github.com/MatthiasHertel21/mdedit mdedit.io is a browser-based Markdown editor focused on writing, structuring, previewing, sharing and exporting longer Markdown documents. It does not require an accou
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
The drift problem Every project that ships a translated README has the same lifecycle: Someone writes README.md in English. A contributor opens a PR with README.zh.md. Great. Three months later, English has six new sections. Chinese has the original. A second translator opens README.es.md. Spanish gets translated from… which version? The current README.md? Or README.zh.md, by accident, because t
§0 — Hook The work-pool schema that runs the paragraf project names three work types: spec, package, and issue-bucket. Only two of the three have a defined The first article introduced a methodology that produced a working library — Two parallel improvements happened in the one week that followed. The first was The second improvement was a sprint. Two new color-related packages shipped under The
The previous three posts covered how events flow from the SDK to the UI, how the timeline renders, and how tool cards visualize. This final post looks at SwiftWork's infrastructure — how data is stored, how state is restored, how Markdown is rendered, how code is highlighted, and how API keys are managed. These components are independent, but all essential to making the app usable. SwiftWork uses
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about