so i’ve been wrestling with puppeteer for a while now, and every time a website redesigns or loads content dynamically, the whole thing breaks. i end up rewriting selectors, adding retries, handling edge cases manually. it’s exhausting.
i heard about using ai to generate workflows from plain english descriptions. sounds almost too good to be true, but i’m curious if it actually works in practice. like, can you really describe what you want—“log in to this site, wait for the data to load, extract table rows, handle dynamic content”—and get something that actually runs without weeks of debugging?
the idea is that ai could handle the brittleness by building in retries and adapting to dynamic sites automatically. but i’m skeptical. in my experience, automation is all about the edge cases. does ai-generated code account for that stuff, or do you still end up doing 80% of the work yourself?
has anyone actually used this approach and found it saved real time? or is it one of those things that looks impressive in a demo but falls apart when you hit real-world sites?
i’ve actually tested this exact workflow with Latenode’s ai copilot, and it surprised me. i described a login-to-scrape flow in plain text, and the copilot generated a puppeteer workflow that handled page waits and dynamic content out of the box.
what made the difference was that it didn’t just generate the code once and call it done. the ai built in retry logic and element selectors that were more flexible than what i typically hand-write. when the site changed their form layout, the workflow adapted instead of crashing immediately.
the real win? the copilot also generated explanations for each step, so i could understand what it did and tweak it if needed. that’s huge because you’re not locked into black-box code.
i’m not saying zero tweaking needed, but we’re talking maybe 20% cleanup rather than 80% rebuilding. the ai handled the structural brittleness better than i expected.
if you want to test this yourself, check out https://latenode.com
i actually tried generating a scraping workflow from a description a few months back. i was skeptical too, but here’s what i learned.
the ai generated about 70% of what i needed right away. the login logic, page navigation, basic extraction—all there. but then i hit real data. the site had modal popups, lazy-loaded images, cookie handling edge cases. the generated code didn’t anticipate those.
what helped was that the code was readable and structured well. i could add my edge case handling without starting from scratch. the framework was already there. that’s different from hand-writing everything.
the dynamic content part actually worked better than expected because the ai included waits for elements and fallback selectors. not perfect, but smarter than just hardcoding timeouts.
i’d say use it as a starting point, not as a finished solution. but starting with 70% working code instead of 0% is a real time saver.
from what i’ve seen, plain english generation works best when your task is relatively standard—login flows, table scraping, form submission. those are patterns the ai has been trained on extensively. but the moment you need something custom, you’re back to debugging.
the sweet spot is using it as a foundation. generate the base workflow, then layer your edge case handling on top. that approach cuts development time significantly compared to building everything manually.
the retry and dynamic content handling is actually decent because ai models understand that websites are fragile. they know to add waits and fallbacks. that alone saves a lot of page.waitForNavigation() and selector frustration.
i’d test it on something non-critical first to see how the generated code handles your specific sites. but if it saves you even a few hours of debugging, it’s worth it.
plain english generation leverages the ai’s understanding of common automation patterns. the quality depends heavily on how precisely you describe the task. vague descriptions produce vague code. specific, step-by-step descriptions produce usable results.
where it excels is handling retries and waits automatically. ai models understand that web pages are inconsistent, so they build in fault tolerance that you might skip if you’re writing quickly.
the brittleness issue you mentioned is real, but ai-generated code actually manages it better than average hand-written code because the code follows defensive patterns by default. that’s counterintuitive but demonstrable.
start with a clear description: “click login button, fill email field with value x, fill password with value y, wait for dashboard to load, extract all rows from the results table.” you’ll get better results than “make it work.”
generate a clear, step-by-step description. ai handles retries and waits automatically. good foundation, not a finished solution.
This topic was automatically closed 6 hours after the last reply. New replies are no longer allowed.