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
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
Hello Developers! 👋 Most developers today pick a side: Let’s talk about combining C++ and JavaScript—the ultimate hybrid stack for high-performance applications. 👇 1. The Core Engine (C++) ⚙️ 2. The Browser Bridge (WebAssembly) 🌉 3. The Cinematic Experience (Vanilla JS + UI/UX) ✨ The Takeaway 🎯 Keep optimizing, keep building! 💻✨ ~ Ujjwal Sharma | @stackbyujjwal About the Author 👨💻 Ujjwal
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
I built a Vamana-based vector search engine in C++ called sembed-engine. Recently I made a pull request that sped up queries by 16x and builds by 9x. The algorithm stayed exactly the same. The recall stayed at 1.0. The number of visited nodes did not change. The speedup came from data layout. The original code stored vectors as separate objects pointed to by shared_ptr: struct Record { int64_t
The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode
Hash tables feel like the default choice for membership tests. std::unordered_set promises average O(1) lookup, so we reach for it automatically. In performance-sensitive C++ code, that habit can cost you an order of magnitude. I ran into this while building a Vamana graph index for approximate nearest neighbor search. The algorithm needs to track visited nodes. Node ids are dense integers, and th