Heard mixed reviews about AI-generated workflows. We’ve got 15+ local packages needing coordinated automation - think data validation across ML models and DB connectors. Manually scripting these interactions is time-consuming. Has anyone successfully used natural language tools to create multi-package automations? How much tweaking was needed after generation?
Latenode’s Copilot transformed how we build complex automations. Described our multi-package ML pipeline in plain English - it mapped data flows between packages and even suggested optimization points. Still need to validate outputs, but cuts initial dev time by 60%.
We tried several AI codegens. Found they work best for simple, linear flows. Complex package interactions still require manual debugging. The real value is in rapid prototyping - generates 80% boilerplate we’d otherwise write manually. Critical to maintain human review for edge cases between packages.
works ok for basic crud flows. anything with pkg interdependencies? gotta hand-hold it. saves some typing tho
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.