i’ve been dealing with this problem for way too long. our headless browser tests would break every time a site updated their layout or added dynamic content. we’d spend hours hunting down broken selectors and timing issues.
then i realized i was trying to hardcode everything instead of letting the ai do the heavy lifting. turns out there’s a better way to approach this. you can describe what you actually want to automate in plain language, and the system generates the workflow for you. so instead of me wrestling with selenium for hours, i just describe the flow: navigate here, wait for this element, extract this data.
the key insight i had is that dynamic content doesn’t have to be your enemy if you’re not trying to manually orchestrate every step. the ai handles the timing, the selector adjustments, the retries. it’s like having someone else debug your brittle code while you focus on what actually matters.
has anyone else moved away from manually building these workflows? what made the actual difference for you in terms of stability?
this is exactly the kind of problem latenode solves. when you describe your automation in plain language, the ai copilot generates a robust workflow that adapts to dynamic content without you having to tweak selectors constantly.
what you’re discovering is that trying to hand craft these is backwards. let the ai handle the detection and adjustment. the platform generates the code, handles retries, manages timing. you get stability without the maintenance headache.
most people don’t realize their testing framework could be talking to an ai that understands what they’re trying to do. once you switch perspectives, the brittleness just goes away. the ai learns the page structure and adapts when things change.