Asking AI to build scrapers should be easy right?

The same day, a post on reddit was about: "We built 3B and 8B models that rival GPT-5 at HTML extraction while costing 40-80x less - fully open source" [1].

Not fully equivalent to what is doing Skyvern, but still an interesting approach.

[1] https://www.reddit.com/r/LocalLLaMA/comments/1o8m0ti/we_buil...

Yes, it is easy. LLMs have reduced my maintenance work on scraping tasks I manage (lots of specialized high-traffic adfield sites) by 99%

What used to be a constant almost daily chore with them breaking all the time at random intervals is now a self-healing system that rarely ever fails.

This is exactly the direction I am seeing agent go. They should be able to write their own tools and we are soon launching something about that.

That being said...

LLMS are amazing for some coding tasks and fail miserably at others. My hypothesis is that there is some sort of practical limit to how many concepts an LLM can hold into account no matter the context window given the current model architectures.

For a long time I wanted to find some sort of litmus test to measure this and I think I found one that is an easy to understand programming problem, can be done in a single file, yet complex enough. I have not found a single LLM to be able to build a solution without careful guidance.

I wrote more about this here if you are interested: https://chatbotkit.com/reflections/where-ai-coding-agents-go...

I feel like this is how normal work is. When I have to figure out how to use a new app/api etc, I go through an initial period where I am just clicking around, shouting in the ether etc until I get the hang of it.

And then the third or fourth time its automatic. Its weird but sometimes I feel like the best way to make agents work is to metathink about how I myself work.

You gain experience getting interactions with other agencies optimised by dealing with them yourself. If the AI you rely on fails, you are dead in the water. And I'm speaking as a fairly resilient 50 year old with plenty of hands-on experience, but concerned for the next generation. I know generational concern has existed since the invention of writing, and the world hasn't fallen apart, so what do I know? :)

I misread this as 'sky scrapers'

We had a similar realization here at Thoughtful and pivoted towards code generation approaches as well.

I know the authors of Skyvern are around here sometimes -- How do you think about code generation with vision based approaches to agentic browser use like OpenAI's Operator, Claude Computer Use and Magnitude?

From my POV, I think the vision based approaches are superior, but they are less amenable to codegen IMO.

In AI First workshops. By now I tell them for the last exercise "no scrappers". the learning is to separate reasoning (AI) from data (that you have to bring.) and ai coded scrappers seem a logical, but always fail. scrapping is a scaling issue, not reasoning challenge. also the most interesting websites are not keen for new scrappers.

A point orthogonal to this; consider whether you need browser automation at all.

If a website isn't using Cloudflare or a JS-only design, it's generally better to skip playwright. All the major AIs understand beautifulsoup pretty well, and they're likely to write you a faster, less brittle scraper.

I'd be all over Skyvern if only they had enterprise compliance agreements available.

Over the past few days I've spent a lot of time dealing with terribly designed UIs. Some legitimate and desired use cases are impossible because poor logic excludes them.

Is AI capable of saying, "This website sucks, and doesn't work - file a complaint with the webmaster?"

I once had similar problems with the CIA's World Factbook. I shudder to think what an I would do there.