The Rabbit R1 has had quite the journey through its highs and lows over the course of this year — starting strong on a ground swell of hype at CES 2024, and eventually launching to a lot of negative reviews for not doing what it promised (including from us). On top of that, there was just the key running issue in our testing that many of the features we were told about didn’t work as intended (bugs aplenty).
In fairness to the Rabbit team, they have worked hard in the background and delivered 16 over-the-air updates to bring new features, fix bugs and speed up pre-existing features. But the real heartbreaker at launch was that the promise of this Large Action Model (LAM) agentic AI never truly came to pass.
As Founder and CEO Jesse Lyu said while talking to me in a two-hour interview (got a lot to share this weekend), the pre-existing system was based on a smaller set of “recordings from people.” This means there would be some things it could do, but was rather limited in scope from the big promise made at the beginning.
Well, Rabbit is back with a next generation LAM — launching in beta as LAM playground on October 1 — and I got the chance to try it and see what it’s like. I can’t show you what it looks like, but I can talk about it.
How the new Rabbit LAM playground works
This is what Rabbit is calling a Generic Website Agent — something that is capable of doing stuff for you, either through a text prompt within Rabbit Hole or (the one you were all waiting for) making the request with natural language to your Rabbit R1.
So to begin with, it all works via the virtual machine-esque system that Rabbit users are probably used to interacting with when logging into their respective accounts. From here, if you make a request for something on the internet — Jesse’s example was to add a 12-pack of Diet Coke to his Amazon shopping cart — the LAM gets to work.
You can watch it happen in real-time, as a vision model observes what’s going on by taking screenshots, analyzing and directing a script to interact in a particular way based on its understanding of what’s happening in the browser window. This includes closing cookies prompts, and can be resilient to UI changes in the future.
What I liked
Well, there is really only one big thing I liked about it, and that's that Rabbit has followed through with what it promised all those months ago. In the tasks I saw and threw at the LAM playground — from visiting a property website and finding all the homes under £500,000 in South London, to playing a Wordle-esque game for three rounds, you watch it fulfill your request in real-time.
Nobody spoke about purchasing things like what was claimed on stage at CES, but it is possible if you give the Rabbit R1 the right instructions and have yourself logged into the retail site’s respective account. One big thing that we did get an answer on was the agentic approach to travel — giving you a plan and actually taking action to book the flights and experiences.
“We didn’t know that in the US, we require a travel agency license to be able to handle booking. That was our fault,” Jesse quickly commented when I asked him about this feature. But now, with the LAM built in the way it is — a way that you can see it working and have the ability to directly interact and interrupt what it's doing too, this absolves them of needing said license.
Another big feature of this playground is that the LAM has been trained on a desktop OS too — namely Linux. This is still all very early days, but what Lyu managed to show me was a request to open this OS’ equivalent to Microsoft Word and write a poem. What this means is with more development, it could fundamentally take over and create the work you may not be so bothered to do, like create a presentation deck.
Watching the LAM once again do this work in the background is a sign that Rabbit is moving in the right direction. The tool that has been fundamentally built here and shown working to me is what was talked about all the months ago. It’s been quite the catch-up effort, but it’s the first sign of the AI agent I was looking for. As the old adage goes, better late than never.
Room for improvement
There was clear transparency on the issues of this beta right now (emphasis on beta) because I stumbled across a bunch of problems in its running. The first thing I noticed is that it is slow. On average, a new instruction is given every 5-6 seconds after a screenshot is taken and analyzed.
And when I say task, I mean going down to every single instruction — opening a website, then accepting the cookies prompt, clicking the text box, entering text in the text box, and hitting enter. All of these take that amount of time each. Rabbit is aware that there’s a lot to work through here in terms of making it faster and reducing the latency.
Second, as you’d expect from any beta, there are bugs. For example, with that poem the LAM opened up a word document to type in, the model hallucinated and gave us roughly four pages of garbled letters and numbers. Again, Lyu made it clear these things will happen and the beta test is specifically to find these bugs.
Finally, let’s cast an eye on the future here for a second. One obstacle that every AI company has been coming up against is whether the very companies that its models interact with actually agree to play ball.
In Rabbit’s case, the user-friendliness of a generic AI agent working across the entire web could be huge, but it’s just as dependent on these websites allowing this bot to visit and navigate the website. What happens if the big players say “no?"
And Lyu is aware of the task ahead of him in securing these permissions. He talked about using Linux for its OS part of LAM, and how it could work with Windows or macOS, but that would require working on an extensive license agreement to do so.
Just like OpenAI, I can imagine a scenario where money would have to change hands for permissions to visit certain big brand sites. It’s an interesting mountain that isn’t here just yet for Rabbit to climb, but is certainly on the horizon.
Don’t call it a comeback?
It’s been a while since the launch in May, but from what I’ve heard and tested, I’m quietly confident that Rabbit is on the right track for its development into the LAM we all imagined after Jesse went big with the CES announcement.
Will it wash the bad taste out of the mouths for those who felt jilted by the expectations vs reality of picking up the R1 without these claimed features in the first place? That remains to be seen. But taking an actions over words approach to the initial response is definitely a smarter approach, and I’m keen to see this speed up and grow into something cross-platform and really quite clever.
Other things that Rabbit are bringing are more natural language interactions with the R1 (this went public most recently), and currently an Alpha test of Teach Mode is running. From my time using this, it almost feels like a slicker version of Apple’s Shortcuts app — relying instead on a natural language prompt over needing to manually enter each step of the process.
The biggest question that Rabbit hasn't satisfactorily answered is whether a smarter version of its AI gadget will make sense in an age of powerful AI phones like the iPhone 16, Galaxy S24 and Pixel 9.
Lyu told me that his future vision expands beyond these phones by having the capabilities of being cross platform — bringing agentic AI to fulfill any request regardless of the OS needed to do it, rather than being limited to Android or iOS like these phones. But does the regular user actually care about this? That's the big one, which is up for debate — especially since it's more likely than not that while Rabbit is first out of the gate with a beta, you may see phones get a similar feature set soon.
There is a lot of work to do to get to the purported public release of this in roughly six months time, according to Lyu, but based on achieving an OTA update every week to squash bugs and improve the experience, there’s the capability to possibly pull it off.
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