I spent weeks on my last SaaS project just setting up authentication, Stripe subscriptions, and three separate landing pages (waitlist, coming soon, and the actual app). By the time I got to building the actual product, I was already burned out.
Does this sound familiar?
That's why I built ValiPlate so you never have to waste time on the boring setup again.
I've been thinking about something. We've gotten really good at using AI to generate working code, but we're not treating it like production code in terms of documentation.
Traditional developers spend significant time documenting their code because they know future them (or their teammates) will need to understand, modify, or debug it later. But with AI-generated code, we often just copy-paste and move on.
You know that moment when AI writes perfect code for you, but six months later, you (or your teammate) have no idea how it works or why you built it that way?
The problem we're solving:
AI is amazing at writing code, terrible at explaining it for future you. You end up with working solutions but zero context about what they do, why they exist, or how to modify them.
Hey everyone, I m Michael I ve always hated the slow, manual grind of writing project documentation, especially after a long coding session. It s important, but it always ends up at the bottom of the to-do list.
That s why I built Andiku, an AI CLI tool that turns your code files into clean, structured, ready-to-use documentation in seconds.
I ve always loved building tools that remove friction, and one pain point I kept running into, as a developer, both in my own projects and watching others, was documentation.
If you live in the terminal, switching to a browser to write docs feels… wrong. Andiku lets you stay in flow, scan your project, pick files, and generate complete documentation in seconds. It’s AI-powered, code-aware, and works with your exact workflow.
AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?
Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).
AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?
Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).
