I shared my LLM predictions last week, and one of them was that by 2029 "Someone will build a new browser using mainly AI-assisted coding and it won’t even be a surprise" https://simonwillison.net/2026/Jan/8/llm-predictions-for-202... and https://www.youtube.com/watch?v=lVDhQMiAbR8&t=3913s
This project from Cursor is the second attempt I've seen at this now! The other is this one: https://www.reddit.com/r/Anthropic/comments/1q4xfm0/over_chr...
Lets make someone pass the one we have, this experiment didn't seem to yield a functioning browser, why would we raise the bar?
The web needs to be more p5n friendly.
I took a 5-minute look at the layout crate here and... it doesn't look great:
1. Line height calculation is suspicious, the structure of the implementation also suggests inline spans aren't handled remotely correctly
2. Uhm... where is the bidi? Directionality has far reaching implications on an inline layout engine's design. This is not it.
3. It doesn't even consider itself a real engine:
// Estimate text width (rough approximation: 0.6 * font_size * char_count)
// In a real implementation, this would use font metrics
let char_count = text.chars().count() as f32;
let avg_char_width = font_size * 0.5; // Approximate average character width
let text_width = char_count * avg_char_width;
I could probably go on in perpetuity about the things wrong with this, even test it myself or something. But that's a waste of time I'm not undertaking.
Making a "browser" that renders a few particular web pages "correctly" is an order of magnitude easier than a browser that also actually cares about standards.
If this is how "A Browser for the modern age." looks then I want a time machine.
It reminds of having AI write me an MUI component the other day that implemented the "sx" prop [1] with some code that handles all the individual properties that were used by the component in that particular application, it might have been correct, the component at all was successful and well coded... but MUI provides a styled() function and a <Box> component, either one of which could have been used to make this component handle all the properties that "sx" is supposed to handle with as little as one line of code. I asked the agent "how would I do this using the tools that MUI provides to support sx" and had a great conversation and got a complete and clear understanding about the right way to do it but on the first try it wrote something crazy overcomplicated to handle the specific case as opposed to a general-purpose solution that was radically simple. That "web browser" was all like that.
[1] you can write something like sx={width: 4} and MUI multiplies 4 by the application scale and puts on, say, a width: 20px style
That said, I don't really find the critique that models have browser source code in their training data particularly interesting.
If they spat out a full, working implementation in response to a single prompt then sure, I'd be suspicious they were just regurgitating their training data.
But if you watch the transcripts for these kinds of projects you'll see them make thousands of independent changes, reacting to test failures and iterating towards an implementation that matches the overall goals of the project.
The fact that Firefox and Chrome and WebKit are likely buried in the training data somewhere might help them a bit, but it still looks to me more like an independent implementation that's influenced by those and many other sources.
> Given how badly my 2025 predictions aged I'm probably going to sit that one out! [1]
Seven days later you appear on the same podcast you appeared on in 2025 to share your LLM predictions for 2026.
What changed?
https://www.youtube.com/watch?v=8kUQWuK1L4w
The "discoverer" of APL tried to express as many problems as he could with his notation. First he found that notation expands and after some more expansion he found that it began shrinking.
The same goes to Forth, which provides means for a Sequitur-compressed [1] representation of a program.
[1] https://en.wikipedia.org/wiki/Sequitur_algorithm
Myself, I always strive to delete some code or replace some code with shorter version. First, to better understand it, second, to return back and read less.
> There are tons of tiny edge cases and standards to comply with where implementing one standard will break 3 others if not done carefully. AI can't do that right now.
Firstly the CI is completely broken on every commit, all tests have failed and its and looking closely at the code, it is exactly what you expect for unmaintainable slop.
Having more lines of code is not a good measure of robust software, especially if it does not work.
My angle has been a bit different: scaling autonomous coding for individual developers, and in a much simpler way. I love CLI agents, but I found myself wasting time babysitting terminals while waiting for turns to finish. At some point it clicked: what if I could just email them?
Email sounds backward, but that’s the feature. It’s universal, async, already collaborative. The agent sends me a focused update, I reply with guidance, and it keeps working on a server somewhere, or my laptop, while I’m not glued to my desk. There’s still a human in the loop, just without micromanagement.
It’s been surprisingly joyful and productive, and it feels closer to how real organizations already work. I’ve put together a small, usable tool around this and shared it here if anyone wants to try it or kick the tires: https://news.ycombinator.com/item?id=46629191
I'm not sure the approach of "completely autonomous coding" is the right way to go. I feel like maybe we'll be able to use it more effectively if we think of them as something to be used by a human to accomplish some thing instead, lean into letting the human drive the thing instead, because quality spirals so quickly out of control.
Even their README is kind of crappy. Ideally you want installation instructions right near the top, but it's broken into multiple files. The README link that says "running + architecture" (but the file is actually called browser_ui.md???) is hard to follow. There is no explicit list of dependencies, and again no explanation of how JavaScript execution works, or how rendering works, really.
It's impressive that they got such a big project to be built by agents and to compile, but this codebase... Feels like AI slop, and you couldn't pay me to maintain it. You could try to get AI agents to maintain it, but my prediction is that past some scale, they would have a hard time figuring out their own mess. You would just be left with permanent bugs you can't easily fix.
I can’t shake the feeling that simply being a shameless about copy-paste (ie copyright infringement), would let existing tools do much the same faster and more efficiently. Download Chromium, search-replace ‘Google’ with ‘ME!’, run Make… if I put that in a small app someone would explain that’s actually solvable as a bash one-liner.
There’s a lot of utility in better search and natural language interactions. The siren call of feedback loops plays with our sense of time and might be clouding or sense of progress and utility.
Anyone who has looked at AI art, read AI stories, listened to AI music, or really interacted with AI in any meaningfully critical way would recognize that this was the only predictable result given the current state of AI generated “content”. It’s extremely brittle, and collapses at the smallest bit of scrutiny.
But I guess (to continue steelmanning) the paradigm has shifted entirely. Why do we even need an entire browser for the whole internet? Why can’t we just vibe code a “browser” on demand for each web page we interact with?
I feel gross after writing this.
That agents can write a bunch of code by themselves? We already knew that, and what's even the point of that if the code doesn't work?
I feel like I'm still missing what this entire project and blogpost is about. Is it supposed to be all theoretical or what's the deal?
I guess the fundamental truth that I’m working towards for generative AI is that it appears to have asymptotic performance with respect to recreating whatever it’s trying to recreate. That is, you can throw unlimited computing power and unlimited time at trying to recreate something, but there will still be a missing essence that separates the recreation from the creation. In very small snippets, and for very large compute, there may be reasonable results, but it will never be able to completely replace what can be created in meatspace by meatpeople.
Whether the economics of the tradeoff between “nearly recreated” and “properly created” is net positive is what I think this constant ongoing debate is about. I don’t think it’s ever going to be “it always makes sense to generate content instead of hire someone for this”, but rather a more dirty, “in this case, we should generate content”.
Writing code one function at a time is the furthest thing than what is being showcased in TFA.
But that's the thing, it doesn't compile, has a ton of errors, CI seems broken since long... What exactly is supposed to impressive here, that it managed to generate a bunch of code that doesn't even compile?
What in the holy hackers is this even about? Am I missing something obvious here? How is this news?
Yeah, answers need to be given.
It's about hyping up cursor and writing a blog post. You're not supposed to look at or use the code, obviously.
I suspect the author of the post would agree. This feels much more like a experiment to push the limits of LLMs than anything they're looking to seriously use as a product (or even the basis of a product).
I think the more interesting question is when the approach of completely autonomous coding will be the right way to go. LLMs are definitely progressing along a spectrum of: Can't do it -> Can do it with help -> Can do it alone but code isn't great -> Can do it alone with good code. Right now I'd say they're only in that final step for very small projects (e.g. simple Python scripts), but it seems like an inevitability that they will get there for increasingly large ones.
So I guess they've achieved human parity then?
(I'll see myself out)
> Today's agents work well for focused tasks, but are slow for complex projects.
What does slow mean? Slower than humans? Need faster GPUs? What does it even imply? Too slow to produce the next token? Too slow in attempts to be usable? Need human intervention?
This piece is made and written to keep the bubble inflating further.
The implied future here is _unreal cool_. Swarms of coding agents that can build anything, with little oversight. Long-running projects that converge on high-quality, complex projects.
But the examples feel thin. Web browsers, Excel, and Windows 7 exist, and they specifically exist in the LLM's training sets. The closest to real code is what they've done with Cursor's codebase .... but it's not merged yet.
I don't want to say, call me when it's merged. But I'm not worried about agents ability to produce millions of lines of code. I'm worried about their ability to intersect with the humans in the real world, both as users of that code and developers who want to build on top of it.
In my experience agents don't converge on anything. They diverge into low-quality monstrosities which at some point become entirely unusable.
I would go even further, why have they not created at least one less complex project that is working and ready to be checked out? To me it sounds like having a carrot dangle in front of the face of VC investors: 'Look, we are almost there to replace legions of software developers! Imagine the market size and potential cost reductions for companies.'
LLMs are definitely an exciting new tool and they are going to change a lot. But are they worth $B for everything being stamped 'AI'? The future will tell. Looking back the dotcom boom hype felt exactly the same.
The difference with the dotcom boom is that at the time there was a lot more optimism to build a better future. The AI gold rush seems to be focused on getting giga-rich while fscking the bigger part of humanity.
There's just a bit over 3 browsers, 1 serious excel-like and small part of windows user side. That's really not enough for training for replicating those specific tasks.
because it is absolutely impossible to review that code and there is gazillion issues there.
The only way it can get merged is YOLO and then fix issues for months in prod which kinda defeats the purpose and brings gains close to zero.
This is how I think about it. I care about asymptotics. What initial conditions (model(s) x workflow/harness x input text artefacts) causes convergence to the best steady state? The number of lines of code doesn't have to grow, it could also shrink. It's about the best output.
But what would be the point of re-creating existing applications? It would be useful if you can produce a better version of those applications. But the point in this experiment was to produce something "from scratch" I think. Impressive yes, but is it useful?
A more practically useful task would be for Mozilla Foundation and others to ask AI to fix all bugs in their application(s). And perhaps they are trying to do that, let's wait and see.
I think any large piece of software with well established standards and test suites will be able to be quickly rewritten and optimized by coding agents.
[1] https://github.com/sberan/tjs
[2] /spawn-perf-agents claude command: https://github.com/sberan/tjs/blob/main/.claude/commands/spa...
I'd want to see some system, that 100%s the first task, saturation, does a great job on the next, then does a valiant effort on the third, then finally makes something promising but as yet unusable on the last.
This way we could see that scaling up difficulty results in a gradual decline in quality, and could have a decent measurement of where we are at and where we are going.
> Another experiment was doing an in-place migration of Solid to React in the Cursor codebase. It took over 3 weeks with +266K/-193K edits. As we've started to test the changes, we do believe it's possible to merge this change.
In my view, this post does not go into sufficient detail or nuance to warrant any serious discussion, and the sparseness of info mostly implies failure, especially in the browser case.
It _is_ impressive that the browser repo can do _anything at all_, but if there was anything more noteworthy than that, I feel they'd go into more detail than volume metrics like 30K commits, 1M LoC. For instance, the entire capability on display could be constrained to a handful of lines that delegate to other libs.
And, it "is possible" to merge any change that avoids regressions, but the majority of our craft asks the question "Is it possible to merge _the next_ change? And the next, and the 100th?"
If they merge the MR they're walking the walk.
If they present more analysis of the browser it's worth the talk (not that useful a test if they didn't scrutinize it beyond "it renders")
Until then, it's a mountain of inscrutable agent output that manages to compile, and that contains an execution pathway which can screenshot apple.com by some undiscovered mechanism.
By far the most useful metric is to have a live system running for a year with widespread usage that produces a lower number of bugs than that of a codebase created by humans.
Until that happens, my skeptic hat will remain firmly on my head.
But is this actually true? They don't say that as far as I can tell, and it also doesn't compile for me nor their own CI it seems.
If you can't reproduce or compile the experiment then it really doesn't work at all and nothing but a hype piece.
I guess probably at some point, something compiled, but cba to try to find that commit. I guess they should've left it in a better state before doing that blog post.
I do use AI heavily so I resorted to actually turning on warnings as errors in the rust codebases I work in.
Product is still fairly beta, but in Sculptor[^1] we have an MCP that provides agent & human with suggestions along the lines of "the agent didn't actually integrate the new module" or "the agent didn't actually run the tests after writing them." It leads to some interesting observations & challenges - the agents still really like ignoring tool calls compared to human messages b/c they "know better" (and sometimes they do).
It is also close to impossible run any node ecosystem without getting a wall of warnings.
You are an extreme outlier for putting in the work to fix all warnings
Haven't found that myself, are you talking about TypeScript warnings perhaps? Because I'm mostly using just JavaScript and try to steer clear of TypeScript projects, and AFAIK, JavaScript the language nor runtimes don't really have warnings, except for deprecations, are those the ones you're talking about?
I started looking in the Cargo.toml to at least get an idea how the project was constructed. I saw there that rather than being built from scratch as the post seemed to imply that almost every core component was simply pulled in from an open source library. quickjs engine, wgpu graphics, winit windowing & input, egui for ui, html parsing, the list goes on. On twitter their CEO explicitly stated that it uses a "custom js vm" which seemed particularly misleading / untrue to me.
Integrating all of these existing components is still super impressive for these models to do autonomously, so I'm just at a loss how to feel when it does something impressive but they then feel the need to misrepresent so much. I guess I just have a lot less respect and trust for the cursor leadership, but maybe a little relief knowing that soon I may just generate my own custom cursor!
https://github.com/dioxuslabs/blitz
Maybe we ended up in the training data!
Great work by the way, blitz seems to be coming along nicely, and I even see you guys created a proto browser yourselves which is pretty cool, actually functional unlike Cursor's.
Take a screenshot and take it to your manager / investor and make a presentation “Imagine what is now possible for our business”.
Get promoted / exit, move to other pastures and let them figure it out.
It's hard to avoid the impression that this is an unverified pile of slop that may have actually never worked.
The CI process certainly hasn't succeeded for the vast majority of commits.
Baffling, really.
Looking at OAI API pricing, 5.2 Codex is $14 per 1 million output tokens. Which makes cool $14m for 1 trillion tokens (multiplied by whatever the plural is). For something that "kind of works".
Its a nice ad for OAI and Anysphere, but maybe next time - just donate the money to a browser team?
What is `FrameState::render_placeholder`?
``` pub fn render_placeholder(&self, frame_id: FrameId) -> Result<FrameBuffer, String> { let (width, height) = self.viewport_css; let len = (width as usize) .checked_mul(height as usize) .and_then(|px| px.checked_mul(4)) .ok_or_else(|| "viewport size overflow".to_string())?;
if len > MAX_FRAME_BYTES {
return Err(format!(
"requested frame buffer too large: {width}x{height} => {len} bytes"
));
}
// Deterministic per-frame fill color to help catch cross-talk in tests/debugging.
let id = frame_id.0;
let url_hash = match self.navigation.as_ref() {
Some(IframeNavigation::Url(url)) => Self::url_hash(url),
Some(IframeNavigation::AboutBlank) => Self::url_hash("about:blank"),
Some(IframeNavigation::Srcdoc { content_hash }) => {
let folded = (*content_hash as u32) ^ ((*content_hash >> 32) as u32);
Self::url_hash("about:srcdoc") ^ folded
}
None => 0,
};
let r = (id as u8) ^ (url_hash as u8);
let g = ((id >> 8) as u8) ^ ((url_hash >> 8) as u8);
let b = ((id >> 16) as u8) ^ ((url_hash >> 16) as u8);
let a = 0xFF;
let mut rgba8 = vec![0u8; len];
for px in rgba8.chunks_exact_mut(4) {
px[0] = r;
px[1] = g;
px[2] = b;
px[3] = a;
}
Ok(FrameBuffer {
width,
height,
rgba8,
})
}What is it doing in these diffs?
https://github.com/wilsonzlin/fastrender/commit/f4a0974594e3...
I'd be really curious to see the amount of work/rework over time, and the token/time cost for each additional actual completed test case.
I can create a web browser in under a minute in Copilot if I ask it to build a WinForms project that embeds the WebView2 "Edge" component and just adds an address bar and a back button.
If one vulnerability exists in those crates well, thats that.
It kinda blows my mind that this is possible, to build a browser engine that approximates a somewhat working website renderer.
Even if we take the most pessimistic interpretation of events ( heavy human steering, relies on existing libraries, sloppy code quality at places, not all versions compile etc)
The positive views are mostly from people who point out that what matters in the end is what the code does, not what it looks like, e.g. users don't see the code, nor do they care about the code, and that even for businesses who do care, LLMs may be the ones who have to pay down any technical debt that builds up.
* Anyone in a field where mistakes are expensive. In one project, I asked the LLM to code-review itself and it found security vulnerabilities in its own solutions. It's probably still got more I don't know about.
** In the original sense of just letting the LLM do whatever it wanted in response to the prompt, never reading or code reviewing the result myself until the end.
I'm very bullish on LLMs building software, but this doesn't mean the death of software products anymore than 3D printers meant the death of factories.
The hype may be similar, if that's your point then I agree, but the weakness of 3D printing is the range of materials and the conditions needed to work with them (titanium is merely extremely difficult, but no sane government will let the general public buy tetrafluoroethylene as a feedstock), while the weakness of machine learning (even more broadly than LLMs) is the number of examples they require in order to learn stuff.
* A small statically generated Hugo website but with some clever linking/taxonomy stuff. This was a fairly self-contained project that is now 'finished' but wouldn't hvae taken me more than a few days to code up from scratch. * A scientific simulation package, to try and do a clean refresh of an existing one which i can point at for implementation details but which has some technical problems I would like to reduce/remove.
Claude code absolutely smashed the first one - no issues at all. With the second, no matter what I tried, it just made lots of mistakes, even when I just told it to copy the problematic parts and transpose them into the new structure. It basically got to a point where it wasn't correct and it didn't seem to be able to get out of a bit of a 'doom loop' and required manual intervention, no matter how much prompting and hints I gave it.
Did sign up for Claude Code myself this week, too, given the $10/month promo. I have experience with AI by using AWS Kiro at work and directly prompting Claude Opus for convos. After just 2 days and ~5-6 vibe coding sessions in total I got a working Life-OS-App created for my needs.
- Clone of Todoist with the features that I actually use/want. Projects, Tags, due dates, quick adding with a todoist like text-aware input (e.g. !p1, Today etc.)
- A fantastical like calendar. Again, 80% of the features I used from Fantastical
- A Habit Tracker
- A Goal Tracker (Quarterly / Yearly)
- A dashboard page showing todays summary with single click edit/complete marking
- User authentication and sharing of various features (e.g. tasks)
- Docker deployment which will eventually run on my NAS
I'm going to add a few more things and cancel quite a few subscriptions. It one-shots all tasks within minutes. It's wild. I can code but didn't bother looking at the code myself, because ... why.
Even though do not earn US Tech money, am tempted to buy the max subscription for a month or two although the price is still hard to swallow.
Claude and vibe coding is wild. If I can clone todoist within a few vibe coding sessions and then implement any additional/new feature I want within minutes instead proposing, praying and then waiting for months, why would I pay $$$...
It a broken mess that probably implements 0.00001% of Excel. And its 1.2m locs.
With codebases developed in this way - either they need to figure out how agents are going to maintain them (in which case SWE as we know is dead - it will only be limited to those that can spend trillions of tokens, or they are going to remain weird demos.
It turns out to matter a whole lot less than you would expect. Coding Agents are really good at using grep and writing out plans to files, which means they can operate successfully against way more code than fits in their context at a single time.
Interestingly, recently it seems to me like codex is actually compressing early and often so that it stays in the smarter-feeling reasoning zone of the first 1/3rd of the window, which is a neat solution for this, albeit with the caveat of post-compression behavior differences cropping up more often.
Presumably the security and validation of code still needs work, I haven't read anything that indicates those are solved yet, so people still need to read and understand the code, but we're at the "can do massive projects that work" stage.
Division of labor and planning and hierarchy are all rapidly advancing, the orchestration and coordination capabilities are going to explode in '26.
Who created those agents and gives them the tasks to work on. Who created the tests? AI, or the humans?
If AI could reach the point where we actually trusted the output, then we might stop checking it.
It's a very real issue, people just seem to assume their code is wrong rather than the compiler. I've personally reported 12 GCC bugs over the last 2 years and there's 1239 open wrong-code bugs currently.
Here's an example of a simple one in the C frontend that has existed since GCC 4.7: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105180
AI coding agents are still a huge force-multiplier if you take this approach, though.
It would be walking the motorcycle.
Interesting times ahead.
All code interactions all happen through agents.
I suppose the question is if the agents only produce Swiss cheese solutions at scale and there's no way to fill in those gaps (at scale). Then yeah fully agentic coding is probably a pipe dream.
On the other hand if you can stand up a code generation machine where it's watts + Gpus + time => software products. Then well... It's only a matter of time until app stores entirely disappear or get really weird. It's hard to fathom the change that's coming to our profession in this world.
boys are trying to single shot a browser when a moderate complex task can derail a repo. there’s no good amount of info which might be deliberate but from what i can pick, their value add was “distributed computing and organisational design” but that too they simplified. i agree that simplicity is always the first option but flat filesystem structure without standards will not work. period.
Of course it creates bottlenecks, since code quality takes time and people don’t get it right on the first try when the changes are complex. I could also be faster if I pushed directly to prod!
Don’t get me wrong. I use these tools, and I can see the productivity gains. But I also believe the only way to achieve the results they show is to sacrifice quality, because no software engineer can review the changes at the same speed the agent generates code. They may solve that problem, or maybe the industry will change so only output and LOC matter, but until then I will keep cursing the agent until I get the result I want.
Things like integration creating bottlenecks or a lack of consistent top down direction leading to small risk adverse changes instead of bold redesigns. All things I’ve seen before.
(Or are they?)
> Our mission is to automate coding
But, if I'm being fair, a full working browser from scratch is just as good.
I really dislike this as a measure. A LLM on CPU is also long running cause it’s slow.
I get what it’s meant to convey but time is such a terrible measure of anything if tk/s isn’t static
I think the north-star metric for a multi-agent orchestrator system would be how much did it cost to get this done. how much better could we have done? should we have used a cheaper model for doing a trivial task and an expensive one to monitor it?
Sometimes workers will task other workers and act as a planner if the task is more complex.
It’s a good setup but it’s nothing like Claude Code.
The tokens were “expensive” from the minds of humans …
I think this situation emphasizes the importance of (something like) Agile. To produce something useful can only happen via experimentation and getting feedback from actual users, and re-iterating relentlessly.
https://taonexus.com/publicfiles/jan2026/172toy-browser.py.t... (turn the sound down, it's a bit loud if you interact with the built-in Tetris clone.)
You can run it after installing the packages, "pip install requests pillow urllib3 numpy simpleaudio"
I livestreamed the latest version here 2 weeks ago, it's a ten minute video:
https://www.youtube.com/watch?v=4xdIMmrLMLo&t=45s
I'm posting from that web browser. As an easter egg, mine has a cool Tetris clone (called Pentrix) based on pieces with 5 segments, the button for this is at the upper-right.
If you have any feature suggestions for what you want in a browser, please make them here:
Which is not an optimization. This is coming from a Rust dev; Rewriting it in Rust is not the optimization.
Also, I do not believe they actually reviewed the SolidJS->React PR. This PR is incredibly unrealistic and should've been done with either stacker PRs or incremental non-breaking changes.
None of this feels organic, can we stop pretending it is?
To continue the pessimistic tone, none of the writing went in-depth. I did not gain any knowledge, just a marketing post.
I think "intra-context" tooling is already dead. It's too narrow.
It's all "extra-context" now: how one instruments for multiple agents, at multiple times, handling things.
Personally, I think the best tool in this realm will come from open source, and be agnostic (many agents from many places interacting), in order to leverage differences between subtle provider qualities (speed, price and so on).
Building a browser is an interesting and expensive experiment. How much did it cost?