skybrian's recent activity
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Comment on Introductions | July 2026 in ~talk
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Comment on Rewriting Bun in Rust in ~comp
skybrian Link ParentIt's overdetermined. Some other reasons to use your own company's product are that it's free to use for employees and already approved and set up, you want to become familiar with your company's...It's overdetermined. Some other reasons to use your own company's product are that it's free to use for employees and already approved and set up, you want to become familiar with your company's products, and you can help out by giving feedback. There are also the reasons stated in the article for doing the project.
Like what is he going to do, use OpenAI?
So it's definitely not an unbiased decision and yes, it might be good marketing, too. But saying things are "a marketing stunt," is mostly evidence that people like to choose the most cynical reason for doing a thing.
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Comment on Rewriting Bun in Rust in ~comp
skybrian LinkFrom the article: [...] [...] [...] [...] [...] [...] [...] [...]From the article:
Bun started as a line-for-line port of esbuild's JavaScript & TypeScript transpiler from Go to Zig. I wrote my first line of Zig on April 16, 2021. I bet on Zig after seeing the single-page Zig Language Reference on Hacker News and getting really excited about the low-level control and care for performance.
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For Bun, correctly handling the lifetimes of garbage-collected values and manually-managed values has been a major source of stability issues - most often small memory leaks and occasionally, crashes. Every memory allocation has to be meticulously reviewed. Where do these bytes get freed? How do we ensure it only gets freed once? Did we check for JavaScript exceptions properly? Is this garbage-collected pointer visible to the conservative stack scanner? Is this garbage collected memory or manually managed memory?
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A large percentage of bugs from that list are use-after-free, double-free, and "forgot to free" in an error path. In safe Rust, these are compiler errors and RAII-like automatic cleanup with
Drop. Compiler errors are a better feedback loop than a style guide.Historically, rewrites are a terrible idea. Excluding comments, Bun is 535,496 lines of Zig. A rewrite in another language would take a small team of engineers a full year. It would mean freezing bugfixes, security fixes or feature development for that time. The least risky approach to getting something shippable would be a mechanical port from Zig to Rust, with the minimal number of behavioral changes, using the exact same test suite we already use for testing Bun.
Fortunately, Bun's own test suite is written in TypeScript which means it doesn't depend on the runtime's programming language.
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Since merging the Rust port, we've completed 11 rounds of security review from Claude Code Security and addressed the findings.
We've also added 24/7 coverage-guided fuzzing of every parser in Bun — JavaScript, TypeScript, JSX, CSS, JSON5, JSONC, TOML, YAML, Markdown, INI, Bun Shell scripts, semver ranges, .patch files, and CSS colors. The fuzzer automatically sends the bugs it finds to Claude to submit a PR reproducing & fixing, and humans review the PRs. So far, it's executed our parsers 100 billion times which has led to around 15 PRs.
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So far, Bun v1.4.0 fixes 128 bugs that reproduce in v1.3.14. These range from memory leaks to crashes to miscolored help text.
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In Bun v1.3.14, every build leaks about 3 MB, forever — tools like dev servers that bundle on every request eventually run out of memory. In Bun v1.4.0, memory levels off:
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The initial changes in the Rust rewrite reduced binary size by 3.8 MB on Windows, 5.5 MB on macOS, and 6.8 MB on Linux. This is largely because we used too much
comptimein our Zig code.[...]
Claude Code v2.1.181 (released June 17th) and later use the Rust port of Bun. Startup got 10% faster on Linux but otherwise, barely anyone noticed. Boring is good.
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Bun v1.3.14 was the last version of Bun written in Zig. Bun v1.4.0 will be the first version of Bun written in Rust. It's available in canary now - please report any issues you find:
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Rewriting Bun in Rust
11 votes -
Comment on Why the West stopped making land in ~society
skybrian LinkFrom the article: [...] [...] [...] [...] [...]From the article:
In total, around eight percent of the land in America’s major coastal cities was underwater in the 1890s and has since been reclaimed. This includes the land under several major airports, like Newark, Logan, and SFO, as well as neighborhoods like the Financial District in San Francisco, the Back Bay in Boston, and Camden in Philadelphia. Some cities, like Boston and Charleston, have doubled in size by reclaiming land.
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Today, reclamation should be more common than ever. Land values in some cities are thirty times what they were in 1950, and high-tide flooding is four to eight times as frequent. Reclamation could extend and protect our coastal cities as it has for centuries. But rather than reclaim more land, we have virtually ceased to reclaim any at all. Since the completion of Battery Park City in 1976, there has not been a single major urban land reclamation project in the United States and only a handful of port expansions.
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The timing points to a third explanation. Reclamation stopped abruptly in the 1970s when a wave of environmental regulations made it enormously expensive to reshape the landscape. And it halted at the same time in every other country that passed similar laws.
If the legal barriers to reclamation were lifted, we could build hundreds of thousands of new homes near the centers of our most valuable cities. We could build new airports to refresh ailing transportation infrastructure, and we could protect low-lying coastal areas from sea level rise. The disappearance of land reclamation is a choice that we have the power to undo.
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Hundreds of square miles of water near major American cities are shallow enough to reclaim easily. When Boston’s Back Bay was filled in the late-nineteenth century, it had an average depth of 20 feet. Two thirds of the San Francisco Bay is shallower than this. The South Bay, adjoining Silicon Valley, is less than six feet deep. Outside the main navigation channels, most of New York harbor is less than ten feet deep. Almost all of the water between Miami and Miami Beach, and along the shelf extending south to Key West, is also less than ten feet deep. All of these could be candidates for reclamation.
Nor is depth a hard limit if land values are high enough. Monaco’s recent Mareterra project expanded the country’s land area by three percent by dredging down to 164 feet.
Soil and rock conditions also matter, but much of the American coastline has better conditions than sites where reclamation has already succeeded. Singapore’s Jurong Island is a massive industrial zone created on land reclaimed between 1995 and 2009. The water was more than 50 feet deep and atop another 50 feet of soft marine clay, which settles more than other materials under pressure. To reclaim the land, these layers had to be threaded with long plastic tubes to allow drainage and loaded with a large, heavy quantity of extra soil.
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Today, downtown land values are high enough to justify reclamation costs even in places where suburban land is plentiful. In 1955, the California Division of Water Resources estimated that reclaiming 23,000 acres of land on the west side of the San Francisco Bay would cost about $330,000 per acre in 2025 dollars. Today, the cost of an acre of single-family-zoned land in San Francisco County averages $24 million, more than 70 times the estimated reclamation cost. Even the cheapest county in the Bay Area exceeds $330,000 per acre.
Despite the space opened up by cars, trains, and buses, America’s biggest cities face a massive land shortage. Even as the transportation explanation has weakened, reclamation hasn’t returned. Boston’s Back Bay reclamation was profitable when land sold for the equivalent of $40 per square foot. Land in major cities today sells for ten to a hundred times that, and dredging capacity has grown nearly tenfold since 1950. Yet urban reclamation has disappeared even in the most land-constrained cities in the country.
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Reclamation has continued only in countries where environmental regulation is less onerous, like China, Singapore, and Japan. China has reclaimed over 5,000 square kilometers in the first two decades of this century, including a new city of 500,000 outside of Shanghai, and Singapore has expanded its territory by over a quarter since 1975.
Japan does have high environmental standards and a NEPA-style law requiring environmental impact reports. But Japan’s system has two advantages: clear numeric thresholds for when reports are required and courts that make it harder to overturn agency decisions. Japanese infrastructure projects don’t need the defense against legal challenges that make Western projects take decades. The result is abundant housing and infrastructure, including multiple airports, port facilities, and residential areas built on reclaimed land.
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Why the West stopped making land
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Comment on A conversation on rent control in ~society
skybrian LinkFrom the article:From the article:
In Mason’s view, the evidence that rent regulations discourage construction has been widely overstated: When designed well — and paired with zoning reforms — rent controls can protect tenants from displacement without reducing the long-term supply of housing.
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A conversation on rent control
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Comment on Finland offers a glimpse of the hurdles Chinese electric vehicle makers face in winning over Europe – consumers ask whether the cars spy on them, or if they have kill switches in ~transport
skybrian Link ParentWe discussed "impaired driving detection" here. Since the NHTSA hasn't published any regulations (they don't think it's feasible), maybe the car manufacturers don't have to do anything? That is,...We discussed "impaired driving detection" here. Since the NHTSA hasn't published any regulations (they don't think it's feasible), maybe the car manufacturers don't have to do anything? That is, it's the NHTSA that's not in compliance with the law. (Lots of that happening with this administration.)
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Comment on Sports entertainment makes me angry in ~talk
skybrian Link ParentThat's clearly insulting. I don't think my post was much like that at all.That's clearly insulting. I don't think my post was much like that at all.
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Comment on Sports entertainment makes me angry in ~talk
skybrian Link ParentWell, I think I sufficiently acknowledged that some parodies are better than others.Well, I think I sufficiently acknowledged that some parodies are better than others.
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Comment on Sports entertainment makes me angry in ~talk
skybrian (edited )Link ParentParodies are commonly shared on social media because it's easy and people think it's clever. Sometimes it actually is clever! (With the Onion, often it's just the headline that's clever.) Sure, it...Parodies are commonly shared on social media because it's easy and people think it's clever. Sometimes it actually is clever! (With the Onion, often it's just the headline that's clever.)
Sure, it should be allowed and occasionally it's fun, but often it's mean-spirited and not the sort of thing I like to see in my feed.
What's the underlying point in this case? It's basically a rough analogy. How is criticizing modern art similar to criticizing sports? Well, it's similar in some ways and different in others.
With a bit more thought, it might be possible to figure out something meaningful to say without the sneer that parody often adds?
Off the top of my head, there's clearly a lot of money that goes into sports, and like modern art it can seem rather excessive and wasteful, to people who aren't into it. And that's not even taking into account sports betting.
(I'm not going to go around calling it "sportsball" though, because I see no reason to be mean about it.)
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Comment on Your AI is not a tool in ~tech
skybrian Link ParentYes, guilty. So it’s about addiction? Is AI use addictive like social media or games?Yes, guilty. So it’s about addiction? Is AI use addictive like social media or games?
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Comment on Your AI is not a tool in ~tech
skybrian LinkThat’s rather ominous, but vague. What would be an example of the sort of thing they’re warning about?That’s rather ominous, but vague. What would be an example of the sort of thing they’re warning about?
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Comment on Small AI models gain traction around the world in ~tech
skybrian LinkFrom the article: [...] [...] [...]From the article:
Small AI is a far cry from wealthy nations’ colossal large language models (LLMs), hyperscale data centers, multibillion-dollar investments, and debates about AI consciousness. But for millions of people around the world, the only AI that matters, and often the only kind available, is small. (According to a World Bank Report issued in November, only 0.7 percent of internet users in the world’s poorest countries have used ChatGPT, compared to a quarter of all internet users in the most developed nations.)
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For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.
Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.
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In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.
The second reason Rovai cites is the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.
Rovai illustrated these reasons on a Zoom call, using one of his most recent experiments. Holding up a device, he says, “This is the new Arduino UNO Q—a US $50 device with a Qualcomm chipset. I’m running a language model here, which collects data from sensors and analyzes that data to detect tiny pools of water where mosquitoes might be breeding. It takes 3 watts to run it.”
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Convinced that millions of people are already benefiting from these kinds of applications, the World Bank now actively promotes small AI with grants, mentorship programs, financing, technical advice, and models of government policies that are friendly for small-AI development. For example, in Rwanda, the World Bank is backing a government program to help low-income households get devices that can run AI.
All that said, no one claims that large language models are going away entirely. To create a generative AI that can run on a phone or other small device requires the architectural insights, data processing, and results of a larger model, Rovai says. “We need the big models to create these smaller models.”
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Small AI models gain traction around the world
15 votes -
Comment on Cambria, California banned fireworks. Then came the dogs. in ~society
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Comment on A global workspace in language models in ~comp
skybrian LinkFrom the article: [...]From the article:
In a new paper, we present evidence that a similar distinction has emerged in modern language models like Claude. We find that Claude has developed a small collection of internal neural patterns that, compared to all its other internal processing, play a special role.
We call the collection of these patterns the J-space—named after the technique we used to find them, involving a mathematical concept called the Jacobian. Each J-space pattern is linked to a particular word. But when one of these patterns lights up, it doesn’t mean the model is saying that word—just that the word is on its mind. If you've heard of language models having a "scratchpad" or “chain of thought”—text they write to themselves while reasoning—the J-space is something different. It operates silently, in the model’s internal neural activations, allowing the model to think about a concept without writing it down. Notably, the J-space wasn’t designed or programmed by us, but instead emerged on its own during Claude’s training process.
We find that the J-space has a number of unique properties, compared to the rest of Claude's processing:
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Claude can report on these representations. If you ask Claude what it's thinking about, it will tell you what’s in the J-space. Non-J-space representations are less reportable.
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It can also modulate them on request. If you ask Claude to think about something, or solve a problem silently in its head, it will light up the appropriate patterns in its J-space. By contrast, it has trouble modulating patterns not in the J-space.
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Claude uses its J-space for internal reasoning. If you ask Claude to solve a problem that requires multiple steps, the intermediate steps will light up in its J-space, even when it doesn’t say them out loud. These J-space patterns causally mediate its performance in such tasks, despite being smaller in magnitude than other representations.
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Representations in the J-space can be used flexibly for many tasks—for example, once “France” has lit up in Claude’s J-space, the model can recall its capital, or its national currency, or the continent it belongs to.
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However, despite its important role, the J-space is not involved in most of what a language model does—speaking fluently, recalling simple facts, using correct grammar, etc. In experiments where we prevented Claude from using its J-space, it still interacted normally, but lost its higher-order cognitive functions.
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This post is a short summary of a much more extensive research paper, where you can find more detail on our experiments. We’ve also released a code repository with an open-source implementation of the core methods, and have partnered with Neuronpedia to provide an interactive demo of our methods on open-weights models. To provide additional perspectives on the broader implications of this work, we also invited commentary from several experts in neuroscience, philosophy, and LLM interpretability, which can be viewed here.
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A global workspace in language models
5 votes -
Comment on Agentic test processes, LLM benchmarks, and other notes on agentic coding in ~comp
skybrian Link ParentI switched to GPT-5.5 recently, because I can connect the $20/month ChatGPT subscription that I already had to the coding agent I'm using, with the result that there's no extra charge for me. It...I switched to GPT-5.5 recently, because I can connect the $20/month ChatGPT subscription that I already had to the coding agent I'm using, with the result that there's no extra charge for me. It seems okay and is a lot more no-nonsense. One thing that takes a bit of getting used to is that it's somewhat more careful to follow whatever instructions you give it.
Hi, I’ve posted lots of links here for many years. This year I used AI to write code to semi-automate it, in case you’re wondering about how the quotes happen. There’s no AI involved in choosing which links to post, though, and I tap each quoted paragraph myself.
All the links I post here are also archived to my personal links website.
There’s a little-used bio on your settings page that I recommend filling in if you want people to remember who you are. It shows up in the sidebar on your profile page.