Browsers are pretty much the best case scenario for autonomous coding agents. A totally unique situation that mostly doesn't occur in the real world.
At a minimum:
1. You've got an incredibly clearly defined problem at the high level.
2. Extremely thorough tests for every part that build up in complexity.
3. Libraries, APIs, and tooling that are all compatible with one another because all of these technologies are built to work together already.
4. It's inherently a soft problem, you can make partial progress on it.
5. There's a reference implementation you can compare against.
6. You've got extremely detailed documentation and design docs.
7. It's a problem that inherently decomposes into separate components in a clear way.
8. The models are already trained not just on examples for every module, but on example browsers as a whole.
9. The done condition for this isn't a working browser, it's displaying something.
This isn't a realistic setup for anything that 99.99% of people work on. It's not even a realistic setup for what actual developers of browsers do who must implement new or fuzzy things that aren't in the specs.
Note 9. That's critical. Getting to the point where you can show simple pages is one thing. Getting to the point where you have a working production browser engine, that's not just 80% more work, it's probably considerably more than 100x more work.
It's a good benchmark for how agents can write very complex code. Browsers are likely among the most complex programs we have today (arguably more complex than many OSs). Even if the problem is well-defined, many sceptics would still say the complexity is beyond what agents can handle.
So first of all, as per my other comments on this threads and coming from a browser engineer: the autonomous coding agents failed miserably.
Whether it is the best case scenario in terms of benchmark, I am not so sure.
The Web is indeed standardized and there are many open-source implementations out there. But how to implement the Web in a novel way by definition means you are trying to solve some perceived problem with existing implementations.
So I would rephrase your statement as such: rewriting an existing engine in another language without any novelty might be the best case scenario for autonomous coding agents.
As an example of approaching the problem in a novel way: the Fastrender code seems obsessed with metering of resources. Implementing the Web with that constraint in mind would be an interesting problem and not obvious at all. That's not what the project is doing so far by the way, since the code is quite frankly a bunch of spaghetti that does not follow Web standards at all(in a way that is unrelated to the metering story, so the divergence from specs is not novel, it's just wrong).
Taffy a solid library choice, but it's probably the most robust ammunition for anyone who wants to argue that this shouldn't count as a "from scratch" rendering engine.
I don't think it detracts much if at all from FastRender as an example of what an army of coding agents can help a single engineer achieve in a few weeks of work.
I think the other question is how far away this is from a "working" browser. It isn't impossible to render a meaningful subset of HTML (especially when you use external libraries to handle a lot of this). The real difficulty is doing this (a) quickly, (b) correctly and (c) securely. All of those are very hard problems, and also quite tricky to verify.
I think this kind of approach is interesting, but it's a bit sad that Cursor didn't discuss how they close the feedback loop: testing/verification. As generating code becomes cheaper, I think effort will shift to how we can more cheaply and reliably determine whether an arbitrary piece of code meets a desired specification. For example did they use https://web-platform-tests.org/, fuzz testing (e.g. feed in random webpages and inform the LLM when the fuzzer finds crashes), etc? I would imagine truly scaling long-running autonomous coding would have an emphasis on this.
Of course Cursor may well have done this, but it wasn't super deeply discussed in their blog post.
I really enjoy reading your blog and it would be super cool to see you look at approaches people have to ensuring that LLM-produced code is reliable/correct.
I was gratified to learn that the project used my own AccessKit for accessibility (or at least attempted to; I haven't verified if it actually works at all; I doubt it)... then horrified to learn that it used a version that's over 2 years old.
For me, the biggest open question is currently "How autonomous is 'autonomous'?" because the commits make it clear there were multiple actors involved in contributing to the repository, and the timing/merges make it seem like a human might have been involved with choosing what to merge (but hard to know 100%) and also making smaller commits of their own. I'm really curious to understand what exactly "It ran uninterrupted for one week" means, which was one of Cursor's claims.
I've reached out to the engineer who seemed to have run the experiment, who hopefully can shed some more light on it and (hopefully) my update to https://news.ycombinator.com/item?id=46646777 will include the replies and more investigations.
Why attempt something that has abundant number of libraries to pick and choose? To me, however impressive it is, 'browser build from scratch' simply overstates it. Why not attempt something like a 3D game where it's hard to find open source code to use?
Any views on the nature of "maintainability" shifting now? If a fleet of agents demonstrated the ability to bootstrap a project like that, would that be enough indication to you that orchestration would be able to carry the code base forward? I've seen fully llm'd codebases hit a certain critical weight where agents struggled to maintain coherent feature development, keeping patterns aligned, as well as spiralling into quick fixes.
> I think somebody will have built a full web browser mostly using AI assistance, and it won’t even be surprising
> When I made my 2029 prediction this is more-or-less the quality of result I had in mind.
There seems to be a lot of compensation and leniency made by the author here.
So, it is seemingly impressive that someone was able to use agents to build a browser.
But they used trillions of tokens? This equates to millions of dollars of spend. Are we really happy with this?
The browser itself is not fully complete. There's rendering glitches stated in the article. So millions of dollars for something that has obvious bugs.
This is also pure agent code. Can a code base like this ever be maintained by a team of humans? Are you vendor locked into a specific model if you want to build more features? How will support work? How will releases work? The lack of reflection over the rest of the software lifecycle except building is shocking.
So I'm not sure after reflecting, whether any of this is impressive outside of "someone with unlimited tokens built a browser using ai agents". It's the same class of problem being solved over and over again. Nothing new is really being done here.
Maybe it's just me but there's much more to software than just building.
If an AI system autonomously built a rocket and went to the moon, would you call it unimpressive because it's already been done? The moving of goalposts is shocking.
This is one of the reasons why I just wrote a testing book (beta reviews giving feedback now). Testing is one of those boring subjects that many programmers ignore. But it just got very relevant. Especially TDD.
No, OP is merely an AI deepthroater that will blindly swallow whatever drivel is put out by AI companies and then "benchmark" it by having it generate a pelican (oh and he got early access to the model), then call whatever he puts out "AI optimism"
The reality of things is, AI still can't handle long running tasks without blowing $500k worth of tokens for an end result that doesn't work, and further work is another $100k worth to get nothing novel.
Agentic coding is a card castle built on another card castle (test time compute) built on another card castle (token prediction) the mere fact that using lot of iterations and compute works maybe tells us that nothing is really elegant about the things we craft.
So AI makes it cheaper to remix anything already-seen, or anything with a stable pattern, if you’re willing to throw enough resources at it.
AI makes it cheap (eventually almost free) to traverse the already-discovered and reach the edge of uncharted territory. If we think of a sphere, where we start at the center, and the surface is the edge of uncharted territory, then AI lets you move instantly to the surface.
If anything solved becomes cheap to re-instantiate, does R&D reach a point where it can’t ever pay off? Why would one pay for the long-researched thing when they can get it for free tomorrow? There will be some value in having it today, just like having knowledge about a stock today is more valuable than the same knowledge learned tomorrow. But does value itself go away for anything digital, and only remain for anything non-copyable?
The volume of a sphere grows faster than the surface area. But if traversing the interior is instant and frictionless, what does that imply?
> The volume of a sphere grows faster than the surface area. But if traversing the interior is instant and frictionless, what does that imply?
It's nearly frictionless, not frictionless because someone has to use the output (or at least verify it works). Also, why do you think the "shape" of the knowledge is spherical? I don't assume to know the shape but whatever it is, it has to be a fractal-like, branching, repeating pattern.
The fundamental idea that modern LLMs can only ever remix, even if its technically true (doubt), in my opinion only says to me that all knowledge is only ever a remix, perhaps even mathematically so. Anyone who still keeps implying these are statistical parrots or whatever is just going to regret these decisions in the future.
After reading that post it feels so basic to sit here, watching my single humble claude code agent go along with its work... confident, but brittle and so easily distracted.
The complex thing is that you would need to take into account the energy used to feed the programmers, the energy used for their education or simply them growing up to the age they are working. For the LLMs it would have to take into account energy used for the GPU, the machine building the GPUs, datacenters, engineers maintaining it, their education etc etc. It’s so complex to really estimate these things from bottom up if you are not only looking locally, it feels impossible…
The more I think about LLMs the stranger it feels trying to grasp what they are.
To me, when I'm working with them, they don't feel intelligence but rather an attempt at mimicking it.
You can never trust, that the AI actually did something smart or dump. The judge always has to be you.
It's ability to pattern match it's way through a code base is impressive until it's not and you always have to pull it back to reality when it goes astray.
It's ability to plan ahead is so limited and it's way of "remembering" is so basic. Every day it's a bit like 50 first dates.
Nonetheless seeing what can be achieved with this pseudo intelligence tool makes me feel a little in awe. It's the contrast between not being intelligence and achieving clearly useful outcomes if stirred correctly and the feeling that we just started to understand how to interact with this alien.
> they don't feel intelligence but rather an attempt at mimicking it
Because that's exactly what they are. An LLM is just a big optimization function with the objective "return the most probabilistically plausible sequence of words in a given context".
There is no higher thinking. They were literally built as a mimicry of intelligence.
But you can automate much of that work by having good tests. Why vibe-test AI code when you can code-test it? Spend your extra time thinking how to make testing even better.
It's a compressed database with diffuse indices. It's using probability matching rather than pattern matching. Write operations are called 'training' and 'fine-tuning'.
If you find yourself 50-first-dating your LLMs, it may be worth it to invest some energy into building up some better context indexing of both the codebase itself and of your roadmap.
$ time target/release/fetch_and_render "https://www.lauf-goethe-lauf.de/"
real 0m0,685s
user 0m0,548s
sys 0m0,070s
$ time chromium --headless --disable-gpu --screenshot=out.png --window-size=1200,800 https://www.lauf-goethe-lauf.de/
real 0m1,099s
user 0m0,927s
sys 0m0,692s
# edit: with a hot-standby chrome and a running node instance a can reach 0,369s seconds here
I'm a maintainer of Servo which is another web engine project.
Although I dissented on the decision, we banned the use of AI. Outside of the project I've been enjoying agentic coding and I do think it can be used already today to build production-grade software of browser-like complexity.
But this project shows that autonomous agents without human oversight is not the way forward.
Why? Because the generated code makes little sense from a conceptual perspective and does not provide a foundation on which to eventually build an entire web engine.
For example, I've just looked into the IndexedDB implementation, which happens to be what I am working on at the moment in Servo.
Now, my work in Servo is incomplete, but conceptually the code that is in place makes sense and there is a clear path towards eventually implementing the thing as a whole.
In Fastrender, you see an Arc<Mutex<Database>> which is never going to work, because by definition a production browser engine will have to involve multiple processes. That doesn't mean you need the IPC in a prototype, but you certainly should not have shared state--some simple messaging between threads or tasks would do.
The above is an easy coding fix for the AI, but it requires input from a human with a pretty good idea of what the architecture should look like.
For comparison, when I look at the code in Ladybird, yet another browser project, I can immediately find my way around what for me is a stranger codebase: not just a single file but across large swaths of the project and understand things like how their rendering loop works. With Fastrender I find it hard to find my way around, despite all the architectural diagrams in the README.
So what do I propose instead of long-running autonomous agents? The focus should shift towards demonstrating how AI can effectively assist humans in building well-architected software. The AI is great at coding, but you eventually run into what I call conceptual bottlenecks, which can be overcome with human oversight. I've written about this elsewhere: https://medium.com/@polyglot_factotum/on-writing-with-ai-87c...
There is one very good idea in the project: adding the web standards directly in the repo so it can be used as context by the AI and humans alike. Any project can apply this by adding specs and other artifacts right next to the code. I've been doing this myself with TLA+, see https://medium.com/@polyglot_factotum/tla-in-support-of-ai-c...
To further ground the AI code output, I suggest telling it to document the code with the corresponding lines from the spec.
Back in early 2025 when we had those discussions in Servo about whether to allow some use of AI, I wrote this guide https://gist.github.com/gterzian/26d07e24d7fc59f5c713ecff35d...
which I think is also the kind of context you want to give the AI. Note that this was back in the days of accepting edits with tabs...
Though the fact that the code is so incoherent and inconsistent plausibly makes it more impressive that they still managed to make something that works at all, and weakens the argument that "all they did was copy/translate some existing other things to Rust."
That said, it's possible that none of that code even gets executed at run time, and the only code that is actually run is some translated glue code, with the other million lines essentially dead, so who knows.
So we've graduated from unmaintainable slop code to unusable slop products. Sorry, this just doesn't feel like progress toward any meaningful future. But I'm sure it will unburden lots of investors of their money.
The whole industry is like one of those projects that claims "90% finished" from the time of the first demo, then for the next N years, all the way up until the project is eventually canceled. Except this project already has trillions of dollars at stake.
I am waiting for that guy or a team that uses LLMs to write the most optimal version of Windows in existence, something that even surpasses what Microsoft has done over the years and honestly looking at the current state of Windows 11, it really feels like it shouldn't even be that hard to make something more user friendly
Considering Microsoft's significant (and vocal) investment in LLMs, I fear the current state of Windows 11 is related to a team trying to do exactly that.
The problem there is the same problem with AI-generated commercial feature films: the copyrightability of the output of LLMs remains an unexplored morass of legal questions, and no big name is going to put their name on something until that's adjudicated.
light_hue_1|1 month ago
At a minimum:
1. You've got an incredibly clearly defined problem at the high level.
2. Extremely thorough tests for every part that build up in complexity.
3. Libraries, APIs, and tooling that are all compatible with one another because all of these technologies are built to work together already.
4. It's inherently a soft problem, you can make partial progress on it.
5. There's a reference implementation you can compare against.
6. You've got extremely detailed documentation and design docs.
7. It's a problem that inherently decomposes into separate components in a clear way.
8. The models are already trained not just on examples for every module, but on example browsers as a whole.
9. The done condition for this isn't a working browser, it's displaying something.
This isn't a realistic setup for anything that 99.99% of people work on. It's not even a realistic setup for what actual developers of browsers do who must implement new or fuzzy things that aren't in the specs.
Note 9. That's critical. Getting to the point where you can show simple pages is one thing. Getting to the point where you have a working production browser engine, that's not just 80% more work, it's probably considerably more than 100x more work.
maleldil|1 month ago
polyglotfacto|1 month ago
Whether it is the best case scenario in terms of benchmark, I am not so sure.
The Web is indeed standardized and there are many open-source implementations out there. But how to implement the Web in a novel way by definition means you are trying to solve some perceived problem with existing implementations.
So I would rephrase your statement as such: rewriting an existing engine in another language without any novelty might be the best case scenario for autonomous coding agents.
As an example of approaching the problem in a novel way: the Fastrender code seems obsessed with metering of resources. Implementing the Web with that constraint in mind would be an interesting problem and not obvious at all. That's not what the project is doing so far by the way, since the code is quite frankly a bunch of spaghetti that does not follow Web standards at all(in a way that is unrelated to the metering story, so the divergence from specs is not novel, it's just wrong).
simonw|1 month ago
Most of the big ones are things like skia, harfbuzz, wgpu - all totally reasonable IMO.
The two that stand out for me as more notable are html5ever for parsing HTML and taffy for handling CSS grids and flexbox - that's vendored with an explanation of some minor changes here: https://github.com/wilsonzlin/fastrender/blob/19bf1036105d4e...
Taffy a solid library choice, but it's probably the most robust ammunition for anyone who wants to argue that this shouldn't count as a "from scratch" rendering engine.
I don't think it detracts much if at all from FastRender as an example of what an army of coding agents can help a single engineer achieve in a few weeks of work.
sealeck|1 month ago
I think this kind of approach is interesting, but it's a bit sad that Cursor didn't discuss how they close the feedback loop: testing/verification. As generating code becomes cheaper, I think effort will shift to how we can more cheaply and reliably determine whether an arbitrary piece of code meets a desired specification. For example did they use https://web-platform-tests.org/, fuzz testing (e.g. feed in random webpages and inform the LLM when the fuzzer finds crashes), etc? I would imagine truly scaling long-running autonomous coding would have an emphasis on this.
Of course Cursor may well have done this, but it wasn't super deeply discussed in their blog post.
I really enjoy reading your blog and it would be super cool to see you look at approaches people have to ensuring that LLM-produced code is reliable/correct.
mwcampbell|1 month ago
embedding-shape|1 month ago
I've reached out to the engineer who seemed to have run the experiment, who hopefully can shed some more light on it and (hopefully) my update to https://news.ycombinator.com/item?id=46646777 will include the replies and more investigations.
shubhamjain|1 month ago
janoelze|1 month ago
teaearlgraycold|1 month ago
tabs_or_spaces|1 month ago
> When I made my 2029 prediction this is more-or-less the quality of result I had in mind.
There seems to be a lot of compensation and leniency made by the author here.
So, it is seemingly impressive that someone was able to use agents to build a browser.
But they used trillions of tokens? This equates to millions of dollars of spend. Are we really happy with this?
The browser itself is not fully complete. There's rendering glitches stated in the article. So millions of dollars for something that has obvious bugs.
This is also pure agent code. Can a code base like this ever be maintained by a team of humans? Are you vendor locked into a specific model if you want to build more features? How will support work? How will releases work? The lack of reflection over the rest of the software lifecycle except building is shocking.
So I'm not sure after reflecting, whether any of this is impressive outside of "someone with unlimited tokens built a browser using ai agents". It's the same class of problem being solved over and over again. Nothing new is really being done here.
Maybe it's just me but there's much more to software than just building.
simianwords|1 month ago
Yes, arguably 5 million is a fair price and cheaper than what it would take to pay humans.
laterium|1 month ago
andrewchambers|1 month ago
utopiah|1 month ago
__mharrison__|1 month ago
well_ackshually|1 month ago
The reality of things is, AI still can't handle long running tasks without blowing $500k worth of tokens for an end result that doesn't work, and further work is another $100k worth to get nothing novel.
retinaros|1 month ago
halfcat|1 month ago
AI makes it cheap (eventually almost free) to traverse the already-discovered and reach the edge of uncharted territory. If we think of a sphere, where we start at the center, and the surface is the edge of uncharted territory, then AI lets you move instantly to the surface.
If anything solved becomes cheap to re-instantiate, does R&D reach a point where it can’t ever pay off? Why would one pay for the long-researched thing when they can get it for free tomorrow? There will be some value in having it today, just like having knowledge about a stock today is more valuable than the same knowledge learned tomorrow. But does value itself go away for anything digital, and only remain for anything non-copyable?
The volume of a sphere grows faster than the surface area. But if traversing the interior is instant and frictionless, what does that imply?
tornikeo|1 month ago
It's nearly frictionless, not frictionless because someone has to use the output (or at least verify it works). Also, why do you think the "shape" of the knowledge is spherical? I don't assume to know the shape but whatever it is, it has to be a fractal-like, branching, repeating pattern.
ramraj07|1 month ago
ukuina|1 month ago
vedmakk|1 month ago
swordsith|1 month ago
ramon156|1 month ago
Not saying that this only happens with LLMs, in fact it should be compared against e.g. a dev team of 4-5
cocoto|1 month ago
xnx|1 month ago
Chipshuffle|1 month ago
It's ability to pattern match it's way through a code base is impressive until it's not and you always have to pull it back to reality when it goes astray.
It's ability to plan ahead is so limited and it's way of "remembering" is so basic. Every day it's a bit like 50 first dates.
Nonetheless seeing what can be achieved with this pseudo intelligence tool makes me feel a little in awe. It's the contrast between not being intelligence and achieving clearly useful outcomes if stirred correctly and the feeling that we just started to understand how to interact with this alien.
Gazoche|1 month ago
Because that's exactly what they are. An LLM is just a big optimization function with the objective "return the most probabilistically plausible sequence of words in a given context".
There is no higher thinking. They were literally built as a mimicry of intelligence.
visarga|1 month ago
But you can automate much of that work by having good tests. Why vibe-test AI code when you can code-test it? Spend your extra time thinking how to make testing even better.
cess11|1 month ago
NiloCK|1 month ago
gforce_de|1 month ago
tinyhouse|1 month ago
Deevian|1 month ago
aronowb14|1 month ago
polyglotfacto|1 month ago
Although I dissented on the decision, we banned the use of AI. Outside of the project I've been enjoying agentic coding and I do think it can be used already today to build production-grade software of browser-like complexity.
But this project shows that autonomous agents without human oversight is not the way forward.
Why? Because the generated code makes little sense from a conceptual perspective and does not provide a foundation on which to eventually build an entire web engine.
For example, I've just looked into the IndexedDB implementation, which happens to be what I am working on at the moment in Servo.
Now, my work in Servo is incomplete, but conceptually the code that is in place makes sense and there is a clear path towards eventually implementing the thing as a whole.
In Fastrender, you see an Arc<Mutex<Database>> which is never going to work, because by definition a production browser engine will have to involve multiple processes. That doesn't mean you need the IPC in a prototype, but you certainly should not have shared state--some simple messaging between threads or tasks would do.
The above is an easy coding fix for the AI, but it requires input from a human with a pretty good idea of what the architecture should look like.
For comparison, when I look at the code in Ladybird, yet another browser project, I can immediately find my way around what for me is a stranger codebase: not just a single file but across large swaths of the project and understand things like how their rendering loop works. With Fastrender I find it hard to find my way around, despite all the architectural diagrams in the README.
So what do I propose instead of long-running autonomous agents? The focus should shift towards demonstrating how AI can effectively assist humans in building well-architected software. The AI is great at coding, but you eventually run into what I call conceptual bottlenecks, which can be overcome with human oversight. I've written about this elsewhere: https://medium.com/@polyglot_factotum/on-writing-with-ai-87c...
There is one very good idea in the project: adding the web standards directly in the repo so it can be used as context by the AI and humans alike. Any project can apply this by adding specs and other artifacts right next to the code. I've been doing this myself with TLA+, see https://medium.com/@polyglot_factotum/tla-in-support-of-ai-c...
To further ground the AI code output, I suggest telling it to document the code with the corresponding lines from the spec.
Back in early 2025 when we had those discussions in Servo about whether to allow some use of AI, I wrote this guide https://gist.github.com/gterzian/26d07e24d7fc59f5c713ecff35d... which I think is also the kind of context you want to give the AI. Note that this was back in the days of accepting edits with tabs...
daxfohl|1 month ago
That said, it's possible that none of that code even gets executed at run time, and the only code that is actually run is some translated glue code, with the other million lines essentially dead, so who knows.
simonw|1 month ago
daxfohl|1 month ago
daxfohl|1 month ago
Agent_Builder|1 month ago
[deleted]
faeyanpiraat|1 month ago
anilgulecha|1 month ago
I think a good abstractions design and good test suite will make it break success of future coding projects.
vivzkestrel|1 month ago
kimixa|1 month ago
bandrami|1 month ago