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It Started With a Bug When I was building VMMS — a voucher management system MySQL. Clean queries. Fast results. Then I deployed to a server running MariaDB. Half my charts broke. I had written date queries like this all over the codebase: // This breaks on MariaDB DB::table('voucher_transactions') ->selectRaw('MONTHNAME(created_at) as month, COUNT(*) as total') ->groupByRaw('MONTH(crea
Table of Contents Introduction Environment Requirements Core Features Core Design and Code Analysis Actual Execution Demo Architecture Overview How You Can Expand Future Plans & Conclusion What is this It is a basic debugger, running on Linux and implemented in C++, aiming to create a debugger that is easy to read and expand. In addition, Lavender's main function is to help users analyze the logic
If you are running production workloads, this is for you. Not side projects. Not early-stage experiments. Not a single-service app with low traffic. This is for teams shipping real systems. Systems with users, uptime expectations, and release pressure. Because at that stage, your deploy process is no longer a convenience. It is part of your product. And right now, for most teams, it is the weakest
If you've ever managed multiple GitHub accounts on the same machine — a personal account, a work account, maybe a freelance client account — you know the pain. You clone a repo, push some code, and then realize it went up under the wrong username. Or worse, you spend 20 minutes debugging why your SSH key isn't working, only to find out you're using the wrong identity file. I got tired of it. So I
I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions. It clicks quickly, which is exactly why it’s easy to walk away thinking you’ve “done RAG.” What bothered me was that the demo corpus is usually tiny and artificial. I write on DEV.to about things like NLP routing and CNN image classification. If I
Every developer has been here. Debugging why Puppeteer crashes in Docker but works on your machine And you still haven't built the actual feature you needed the PDF for. So I built Templar Describe your document Tell the AI what you want an invoice, a report, a contract, a receipt. It generates the HTML template for you. Call the API with your data POST /api/render { "templateName": "invoi
I wanted to add live chat to my WordPress sites without loading a 500KB third-party script. So I built my own. GhostChat is an open source embeddable Widget: Vanilla JS, no framework, ~10KB Backend: Cloudflare Workers + Durable Objects for persistent WebSocket connections Payments: Stripe for the hosted tier Self-hostable: Bring your own Cloudflare account Durable Objects give you stateful serve