I’ve been stuck on this for a while now. Every time I try to set up headless browser automation, I end up spending days just getting the basics right—navigating pages, waiting for elements to load, extracting data. It’s tedious.
Recently I read about converting plain English descriptions directly into ready-to-run workflows. Sounds almost too good to be true, honestly. The idea is you just describe what you want—“log into this site, wait for the table to load, grab the data”—and the system generates the actual workflow for you.
I’m curious if anyone here has actually tried this. Is it reliable? Does it handle the common edge cases, or does it fall apart when pages behave unexpectedly? How much manual tweaking do you typically need to do after the AI generates the initial workflow?
I’m especially interested in whether this approach actually saves time compared to building from scratch, or if you just trade coding time for debugging time.
I use this approach constantly now, and honestly it’s a game changer. You describe what you need in plain English, and the AI generates a working workflow—navigation, waits, data extraction, all of it.
The key is that it’s not magic. You still need to test and adjust, but what used to take me a full day now takes maybe an hour. The AI handles the structure and logic, you just verify it works on your actual target pages.
I’ve used it for login flows, table scraping, form fills. Works well across different site types. The time savings are real, especially when you have multiple workflows to build.
If you want to try this properly, check out https://latenode.com
I’ve tested this fairly extensively. The workflow generation gets you about 80% there. Main issues I’ve hit are sites with heavy JavaScript rendering and dynamic element IDs that change.
What actually works is using it as a starting point rather than expecting a complete solution. You describe your intent, it builds the skeleton, then you handle the edge cases. For standard use cases though—static pages, regular forms, predictable structures—it’s pretty solid.
The biggest win is that non-developers can build these now without understanding Puppeteer or Playwright syntax. You just think through what you need and describe it.
From my experience, AI-generated workflows are reliable for straightforward tasks but need refinement for complex scenarios. The time investment shifts rather than disappears—you’re not writing code, but you’re still doing validation and debugging. I’ve found the approach saves the most time when you have repetitive, similar workflows. You build one, tweak it, then replicate the pattern. Where it struggles is unusual page layouts or sites with aggressive anti-bot measures. That said, even partial automation beats starting from nothing.
The conversion from plain English to executable workflow is genuinely useful for common patterns. I’ve used it for e-commerce scraping, data collection, and testing. Typical flow: describe it, get a draft in seconds, then iterate based on actual page behavior. Most sites work fine. The outliers are sites with complex client-side rendering or anti-bot protections—those need manual intervention regardless. The real value is speed of iteration and accessibility for non-developers.
Tried it, works better than expected. You save time on setup and syntax, spend it on testing instead. Good for standard tasks, finnicky with complex sites. Overall worth it if your workflows are half decent.
Fast workflow generation, but test thoroughly. Good for prototyping and standard use cases. Edge cases need manual handling.
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