LightfieldAI-native CRM that builds itself and does work for you
Promoted
vibeCleaners
From vibe-coded demo to launch-ready product
7 followers
From vibe-coded demo to launch-ready product
7 followers
I built vibeCleaners because AI makes it easy to ship something that works, but much harder to know if it is ready. This helps builders find the risky gaps before users do.
How does it actually flag the risky gaps, does it run static analysis on the code or some kind of behavioral test on the running app?
@nazlbornovxsfy It’s not fully automated.
I use tools where they help, but the core value is a hands-on production-readiness review based on 15 years of building and operating real systems.
That means looking at the app/code for things like auth/session behavior, data safety, logging, error handling, deployment/config gaps, rollback paths, and where the product may break once real users show up.
Static analysis can catch some things. This is more about experienced judgment on whether “it works” is actually close to “it’s production ready.”
Curious how you actually surface the risky gaps in practice, do you run static analysis on the code or is it more of a prompting checklist the builder walks through?
@turukmen29080 It’s not fully automated.
I use tools where they help, but the core value is a hands-on production-readiness review based on 15 years of building and operating real systems.
That means looking at the app/code for things like auth/session behavior, data safety, logging, error handling, deployment/config gaps, rollback paths, and where the product may break once real users show up.
Static analysis can catch some things. This is more about experienced judgment on whether “it works” is actually close to “it’s production ready.”
Tried it on a small side project and it flagged a couple of edge cases I had completely glossed over, like race conditions in my auth flow. Really useful gut check before pushing updates.