Tokens a resource and should be managed as such.
Hang on, that sounds like common corporate SaaS apps.
The landing page doesn't advertise it yet, but essentially, I give agents a small set of tools to explore apps' surfaces, and then an API over common macOS functions, especially those related to accessibility.
The agent explores the app, then writes a repeatable workflow for it. Then it can run that workflow through CLI: `invoke chrome pinTab`
Why accessibility? Well, turns out that it's just a good DOM in general. It's structure for apps. Not all apps implement it perfectly, but enough do to make it wildly useful.
[1] https://getinvoke.com - note that the landing page is targeted towards creatives right now and doesn't talk about this use case yet
I think this is very fertile ground - big labs need to use approaches that can work on multiple platforms and arbitrary workflows, and full-page vision is the lowest common denominator. Platform-specific approaches are a really exciting open space!
One thing I am curious about is a hybrid approach where LLMs work in conjunction with vision models (and probes which can query/manipulate the DOM) to generate Playwright code which wraps browser access to the site in a local, programmable API. Then you'd have agents use that API to access the site rather than going through the vision agents for everything.
https://playwright.dev/docs/getting-started-mcp#accessibilit...
I've mentioned several times and gotten snarky remarks about how rewriting your code so it fits in your head, and in the LLM's context helps the LLM code better, to which people complain about rewriting code just for an LLM, not realizing that the suggestion is to follow better coding principles to let the LLM code better, which has the net benefit of letting humans code better! Well looks like, if you support accessibility in your web apps correctly, Playwright MCP will work correctly for you.
Amazing.
Harder to scale if it's doing a lot of them, I suppose.
and now the fact that interfaces need to be accessible to agents, not just humans, ironically increases it for humans in return
i so far haven't found any application that doesn't.
all you're able to get out, as far as i can tell, is the length of the entered password.
invoke rather has overlap with Claude's and Codex' computer-use, except the steps are stored/scripted.
webmcp is bottom-up. computer-use & invoke are top-down
in the context of this blog post, the conclusion looks similar though!
"use the whole web like it's an API"
works much better than
"figure out similar or identical tasks from a clean slate every single time you do them"
i tend to think of invoke as "an API over macOS apps" tho...
doesn't `invoke finder shareAndCopyLink` read very nicely? :P
If one agent just explores the UI, maybe in a test environment, and outputs a somewhat-structured description of the various UI elements and their behavior, then another agent was given that description, would the other agent perform better that an agent that both explores the UI and tries to accomplish the given task at the same time?
With an example UI I made up, the description (API-like interface definition) could be something like:
Get all reviews:
To get all the reviews you need to go to each page and click "show full review" for every review summary in that page.
Go to each page:
Start at page 1 (the default when in the Reviews tab). Continue by clicking the "next" button until the "next" button is no longer available (as you've reached the last page).
Or am I misunderstanding the article completely? Probably. But it's interesting nonetheless. Sorry if it makes no sense.
If I think an LLM is good for something I create well defined, very deterministic "middleware" for that purpose on top of Openrouter.
Anthropic even says, that an agent based solution should only be your last resort and that most problems are well served with a one-shot.
https://www.anthropic.com/engineering/building-effective-age...
I'm much more agreeable with that type of LLM workflow. Running "agents" with monolithic "harness" for long time horizon tasks seems wasteful, unecessary but probably super appealing to lazy people.
Agent use can be used to improve quality and maintainability
Apps written now will have mcp servers / AI compatibility when relevant
The issue that still needs solving is how to make llms interact with everything we already have and use (efficiently, not with screenshot, read, screenshot, ...)
Most of the time that means reverse engineering, either the app itself or the APIs it uses
From github (not my projects):
https://github.com/SimoneAvogadro/android-reverse-engineerin... => reverse engineer android app APIs from APKs
https://github.com/HKUDS/CLI-Anything => convert ooen-source GUI apps to clis
https://github.com/kalil0321/reverse-api-engineer => API reverse engineering from traffic (claude skills)
My take at the same issue (very young project):
Also api reverse engineering from traffic captures, with a focus on mobile app, safety & community mcp generation
IMO, this is the argument for doing work in the first place.
I don't think many realize how could the cheap, alternative models are becoming. I prefer SOTA models for key work, but I can also spend 10X as many tokens on an open model hosted by a non-VC subsidized provider (who is selling at a profit) for tasks that can tolerate slightly less quality.
The situation is only getting better as models improve and data centers get built out.
I think everyone making claims that inference is getting more expensive are unaware that there are more LLM providers than Google, Anthropic, and OpenAI.
Try the exhorbitant expenses and ballooning waste of generated electricity and usable water.
The problem is that not everything from the 'past' can be accessed via APIs. It would be a fun time - remember Prism [1] - I would just run that and get all the API calls in a nice format and then replay them over and over to do things in succession.
In the new world, we have access to OpenAPI.json and whatnot, but in the world where things were built in the days pre-OpenAPI and pre-specs and best practices...I am not so sure! (and a lot of world lives then)
Alas, this works for a good chunk of things but not everything. Which is why the other technnology exists.
In particular, the vision-based approach used in the evaluation has clear limitations with regard to efficiency due to its nature (small observation window, heterogeneous modality)
At Smooth we use an hybrid DOM/vision approach and we index very strongly on small models. An interesting fact is that UIs are generally designed to minimize ambiguity and supply all and only the necessary context as token-efficient as possible, and the UX is cabled up to abstract the APIs in well-understood interface patterns e.g. dropdowns or autocompletes. This makes navigation easier and that's why small models can do it, which is another dimension that must be considered
We typically recommend using APIs/MCP where available and well designed, but it's genuinely surprising how token-efficient agentic browser navigation can actually be
The only reason you wouldn’t choose an API is if it wasn’t viable.
To me the browser is a translation layer. Working on the browser directly while hard enables big advantages on compatibility. The only thing I miss as of now which is on the todo is ocr of the images in the browser into text out. But an api would need to do that anyways to work.
The main loss in my view of pure API based is, where do you get the data? We won't replicate human work without seeing that done. Humans work in the UI that's it. Computer use to me is the promise of being able to replicate end to end actions a human does. API can do that in theory but the data to do that is also near impossible to collect properly.
I think OpenAI designing their own phone is the next logical step. I hope they succeed which should bring major competition to Apple and Android.
Yes, in an ideal world, that'd be great for both humans and LLMs, but we are about as far from that ideal world as we could be. You can't even do some of the "advanced actions" as a human with human-level reflexes without encountering a captcha, but sure, all of a sudden, everyone will just decide to make their bread and butter that is data easier to explore via an LLM.
Why on earth would they offer convenient hooks for AI chatbots?
Watch how fast Meta adds this if a new hot shot social media app succeeds by designing for AI agents controlled by users.
This is the exact opposite of what will happen (and in fact what has happened). Reddit is suing Perplexity right now for scraping.
Meta will not serve content to some other app for free - for what benefit? They will not see advertising data.
There are no shortcuts in life and its just expensive text autocomplete.
"Lets spin up $750k in GPUs full throttle to scrape a web page with my $200.00 CC subscription."
Everyone is delusional.
That's only another step in the path I experienced since the 80s, when I had to type every single character because there was no auto complete, no command line history, very few libraries. I was very good at writing trees, hash tables, linked lists and so was everybody else. Nobody would hire me if I were that slow at writing code today.
This is not going to happen, or if it does it will just be Android (like Samsung reskins/modifies it) and it will certainly use Google Play Services.
The good ideas and the bad ideas don't signal success in a bubble, nor does making money or not. Its random and any notion of "this was a good business model and that was bad" is post-hoc rationalization. The number of people who make fun of pets.com but order from chewy.com is a prime example of this.
I could imagine an AI future where agentic shopping companies who promise me the best deal are pitted against Walmart and Amazon, trying to algorithmically squeeze me for $2 more- just two bots playing a cat and mouse game to save me a few bucks.
For some reason a lot of tech ends up in these antagonistic monopolies- Apple wants to sell privacy aware devices as a product feature, Google wants give you mail and maps, but sell your data. Despite any appearances neither give a shit about you, even if you benefit from the dynamic.
So, like a Unix system?
I imagine the AIs will get a lot better at intercepting things at an intermediate level - API calls under the hood, etc. Probably much better (and cheaper) vision abilities, and perhaps even deeper integration into the machine code itself. It's really hard to anticipate what an advanced model will be capable of 5 years from now.
Isn’t the whole ‘promise’ of AI that it doesn’t need any of those things?
Humans would be the second-class users of said OS, which can generate UIs on demand as needed.
I've thought about this quite a bit. Started implementing as a side project, but I have too many side projects at the moment...
Perfect.
It’s why we did this benchmark :) - reflex team member
The modern javascript ecosystem is a perfect example of what happens when everyone tries to rebuild from scratch and it's a nightmare.
Recently, I asked Claude to log into my local grocery store chain's website and add all of the items from my shopping list to a cart. It was hilariously slow, but it did get the job done.
Unless I missed it, the article doesn't explictly mention speed in the copy, but the results do show a 17 minute (!!!) total time for the vision agent vs. 0.5s - 2.8s for the API approach.
A big part of the challenge with vision is that to manipulate the DOM, you first have to be sure the entire (current) DOM is loaded. In my experience this ends up in adding a lot of artificial waits for certain elements to exist on the page.
idk.. not really thought out too much, but has to be better
> To make the comparison apples-to-apples, we rewrote the vision prompt as an explicit UI walkthrough, naming the sidebar items, tabs, and form fields the agent should interact with at each step. Fourteen numbered instructions covering the navigation the agent had failed to figure out on its own.
This is a model problem, though. Because the model failed to understand it could scroll, you forced it to consume multiples of the tokens. Could you come up with an alternative here?
Do you know what the vision model was trained on? Because often people see “vision model” and think “human-level GUI navigator” when afaik the latter has yet to be built.
The models frequently failed for many reasons on earlier runs, and the browser-use prompt ended up being pretty granular. I'll add a couple of runs that include a scroll instruction to the repo today and see how that compares
Pretty hard to guess what Anthropic trained sonnet on, but general multimodals are what people are using to drive similar tools today, whether GUI-trained or not, so the comparison still holds, for now
I can see the appeal in pixel route given universality but wow that seems ugly on efficiency
Not possible on wayland, maybe on X11 protocol?
or even one based on PDF like OSX: https://en.wikipedia.org/wiki/Quartz_2D
There are usecases where the vision agent is the more obvious, or only choice though, e.g. prorprietary/locked-down desktop apps that lack an automation layer.
I don't think any new app should ever be specifically designed for AI to interact with them through computer use
I embedded a Google Calendar widget on my Book a demo page, I don't know the API and Google doesn't expose/maintain one either.
What we are doing at Retriever AI is to instead reverse engineer the website APIs on the fly and call them directly from within the webpage so that auth/session tokens propoagate for free: https://www.rtrvr.ai/blog/ai-subroutines-zero-token-determin...
The problem is, all the tasks are essentially: a) things agents probably just can't do, and b) things that absolutely cannot afford to be hallucinated or otherwise fucked up. So far the tasks I've thought of:
- Taxes. So it needs a lot of sensitive information to get W2's. Since I have to look up a lot of this stuff in the physical world anyway, it's not like I can just let it run wild.
- Background check for a new job. It took me 3 hrs to fill out one of them (mostly because the website was THAT bad). Being myself, I already was making mistakes just forgetting things like move in dates from 10 years ago, and having to do a lot of searching in my email for random documents. No way I'm trusting an agent with this.
- Setting up an LLC. Nope nope nope. There's a lot of annoying work involved with this, but I'm not trusting an LLM to do this.
Anyway, I guess my point is that even if an LLM was good at using my computer (so far, it seems like it wouldn't be), the kind of things I'd want an agent for are things that an LLM can't be trusted with.
1. things you wouldn’t otherwise bother doing
2. things where it otherwise would get stuck iterating on hacky workarounds doomed to fail
“Reverse engineer this app/site so we can do $common_task in one click”, “by the way, I’m logged in to $developer_portal, so try @Browser Use if you’re stuck”, etc.
I just had Codex pull user flows out of a site I’m working on and organize them on a single page. It found 116. I went in and annotated where I wanted changes, and now it’s crunching away fixing them all. Then it’ll give me an updated contact sheet and I can do a second pass.
I’d never do this sort of quality pass manually and instead would’ve just fixed issues as they came up, but this just runs in the background and requires 15 minutes of my time for a lot of polish.
Electron uses 10x more RAM than regular apps. But it's so convenient.
Python is 100x slower than C. It's in the top 3 of languages now.
Worse but more convenient always wins.
The benchmark is a more generally interesting part of the launch materials, so I figured it had its own separate home here.
Me: hmm, this title confuses and infuriates Rob.
[Clicks link]
Me: Sees same title, repeat feelings of confusion and infuration
[Scrolls article down on my smartphone]
Me: Sees jpg with the same title, repeat feelings of co fusion and infuriation.
[Closes tab]
[Continues living rest of my life]
I hope this feedback is well received and understood.
When you think of everything it takes for an AI to use what the article calls a "vision agent" then it seems as if using a purpose-made API ought to be MANY orders of magnitude faster.
Using CLI tools is much faster and token-efficient. I developed ten apps in the last two months. One reached 10,000+ monthly active users.
I ask Codex to generate SVG line by line and backtrack edit, ask it to use Inkscape to generate icons, etc...
I developed all this on $20 codex sub.
Well I am competing with geoip provider like maxmind.
I developed custom traceroute and ping service to geolocate IPs with very high accuracy beating products like digital element, maxmind, ipinfo
These companies have huge teams. But my 3 people company already beat them.
Code doesn't matter much, it's not an opensource project.
My free app is http://macrocodex.app which I've developed along with a fitness coach.
I am currently beating companies with 20-30 developers and closing more deals while having 1/10th of the staff.
I am simply very excited about all this.
Nobody cares show you solve the problem, or if your code is ugly. As long as it's reliable and without downtime, you aren't breaking things and causing your customer headache, you are winning.
Even before AI, bad code existed. Not every company had 10x developer writing beautiful idiomatic rust code.
AI is just a tool, people who are trying to generate whole codebase with it are doing something very wrong. You can write code faster with AI provided you understand its strength and weakness
Heh, you're in for a rude awakening, sometime in the future :) But I won't spoil the surprise, you clearly have made up your mind about what to focus on.
> My free app is http://macrocodex.app which I've developed along with a fitness coach.
Crazy, this app you've run for ~1-2 months has 10K active users already, even though there is zero info about who runs it, zero reviews, and says "Download on the App Store" on the landing page even though you then ask people to use the web app, impressive.
I don't think anyone said using AI can't produce a ton of code really quickly, and no one is finding that difficult to manage either. But most of us software engineers are trying to build long-lasting codebases with AI too, then "less === better" typically, so it's not about being able to spit out features as fast as possible, but avoid the evergrowing codebase from collapsing on top of itself, and each prompt not getting slower and slower, but as fast as on a greenfield project.
Sounds like you've found the holy grail of being able to avoid that, kudos if so. Judging by you giving zero care to how the design and architecture actually is, I kind of find that hard to believe. But, if it works for you, it works for you, not up to me or others to dictate how you build stuff, hope you enjoy it, however you build stuff :)
It breaks like 80% of the time for me, and it's incredibly slow. Having it use Playwright (bonus: can test in FF/Saf too) was a big improvement.
The EHRs could give companies like Akasa API access so Akasa could then just run NLP, but the EHR vendors don't grant various third parties API access for various reasons, so instead Akasa gets a seat license for each medical system they service and uses computer vision to read the screen (a cadre of Akasa medical coders review errors to stay up to date with unannounced changes from the EHR vendors) and then runs the NLP to figure out which CPT codes to assign to actually put in a bill and send the payer so the hospitals can stay afloat.
So this 45x delta is how much more the medical systems pay Akasa because Epic won't work with Akasa.
This is but one example of why US medical bills are outrageously high.