I worked for 2 years in a co-working space full of founders next to ETH Zurich. The most consistent worker? The cleaning lady. Every morning at 6 am, she did not miss a single day.
I grew up in a small village in Germany. 500 people, 5000 cows. Only farmers and a cheese factory. In the factory, we worked on Christmas, Easter, and New Year's Eve every morning at 5 am. Farmers don't take days off because cows don't take days off.
Maybe it's not the most healthy way of life. I don't think it physically requires us to take time.
I would love to fix my docs with this.
I have them in the main browser-use repo.
What do you recommend that the agent does never push to main browser-use, but only to its own branch?
One option could be for the main apps like WhatsApp to have defined custom actions, which are almost like an API to the service. I think the interplay between LLM and automation scripts will succeed here:
Agent call 1:
Send WhatsApp message (to=Magnus, text=hi)
Inside, you open WhatsApp and search for Magnus (without LLM)
Agent call 2:
Select contact from all possible Magnus contacts
Script 3: Type the message and click send
So in total, 2 calls - with Gemini, you could already achieve this in 10-15 seconds.
We see people replacing UIs and using browser-use to fill out the real UI. So there could be a world where everyone has their own UI, and you could have that filter option.
Furthermore, valid point: if Pepsi spends $1M on ads, why don't you get a piece of it if they pitch to you?
It could be useful to run a prompt/test once, get the xPaths, and rerun it deterministically.
When it breaks, you know something is wrong, and the LLM could be used as a fallback to fix the script.
On most pages, we don't need vision, and the DOM alone is sufficient. We have not worked with the accessibility tree yet, but it's a great idea to include that. Do you have any great resources on where to get started?
What do you think is the main problem it solves there?
The cool thing is that we can extract xPaths from the agent runs and re-run these scripts deterministically. I think that's a big advantage over pure vision-based systems like Operator.
Yes! People love Deepseek-Chat / R1 and the new Qwen versions. It works with ChatOllama. However, Llama itself does not work very well with our tool calling and is often confused by the structured output format.
If you run it locally, you can connect it to your real browser and user profile where you are already logged in. This works for me for LinkedIn automation, e.g., to send friend requests or answer messages.
A bigger problem on LinkedIn for us is all the nested UI elements and different scrolling elements. With some configuration in our extraction layer in buildDomTree.js and some custom actions, I believe someone could build a really cool LinkedIn agent.
Yes, some hosting services have experienced a 100%-1000% increase in hosting costs.
On most platforms, browser use only requires the interactive elements, which we extract, and does not need images or videos. We have not yet implemented this optimization, but it will reduce costs for both parties.
Our goal is to abstract backend functionality from webpages. We could cache this, and only update the cache if eTags change.
Websites that really don't want us will come up with audio captchas and new creative methods.
Agents are different from bots. Agents are intended as a direct user clone and could also bring revenue to websites.
https://browser-use.com/posts/two-ways-to-sandbox-agents