Commandry
The AI CTO that tells you what to run, fix, and ship.
44 followers
The AI CTO that tells you what to run, fix, and ship.
44 followers
Commandry.dev is your AI CTO for vibe coders. Paste confusing errors like “Module not found” or blank screens and get exact terminal commands, structured prompts for Cursor/Replit, and clear validation steps you can run instantly. Run Ship Ready checks to test your app, verify environment setup, and catch issues before launch. Other AI tools explain. Commandry executes. Built for non-technical founders, vibe founders, and indie hackers—fix faster, stay in flow, and ship with confidence. 🚀
Triforce Todos
@abod_rehman That “blank screen after deploy” is probably one of the most frustrating ones 😅
Commandry won’t magically fix every case instantly, but it’s designed to break that problem down properly — for example:
checking if data is loading
verifying API responses
spotting env/config issues
identifying frontend rendering failures
Instead of guessing, you get a clear path to isolate what’s actually causing the blank screen, which is usually the hardest part.
What’s the difference between this and just using Copilot + StackOverflow?
@leonhard1 Copilot helps you write code.
StackOverflow helps you search for answers.
Commandry tells you:
what broke → why → exactly what to run to fix it
It’s more like a debugging system than a coding assistant.
Commandry could really help non technical founders ship faster. Does it also guide architecture decisions early on?
@ahmed_al_mansoori Yes — early guidance is actually one of the most important parts.
Commandry helps decide stack, structure, and tools before you even start building — like a CTO would
@riskycactus Environment issues are honestly the worst part of deploying. Does this handle multi-env setups well?
@callum_bennett 100% — env issues are a nightmare.
Commandry checks things like missing vars, mismatches between dev/staging/prod, and config gaps. Multi-env support is a big focus.
@brandon_ellis2 Yes — that’s the goal.
Outputs adapt based on the project context (stack, logs, environment).
Eventually it’ll learn per project so fixes stay consistent and don’t drift.