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Welcome! BoredReading is a fresh way to read high quality articles (updated every hour). Our goal is to curate (with your help) Michelin star quality articles (stuff that's really worth reading). We currently have articles in 0 categories from architecture, history, design, technology, and more. Grab a cup of freshly brewed coffee and start reading. This is the best way to increase your attention span, grow as a person, and get a better understanding of the world (or atleast that's why we built it).

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If you haven’t seen it, Manu has a new series called “People and Blogs” centered around a lovely goal: to both highlight wonderful human beings and their blogs, and also to promote a healthier way to inhabit the web and show that traditional social media is not the be all and end all when it comes to having an internet presence The format is a standard set of questions across a series of people who run their own personal blogs. He’s already interviewed a series of people, most of whom I was unfamiliar with, so it’s exciting to discover how much larger the world of RSS is! His latest interviewee was someone you might be familiar with: yours truly. My name is Jim Nielsen (not to be confused with Jim Nielsen, the California state senator, who still outranks me on Google). I talk about my personal history of blogging: my first blog was on Blogger…It mainly consisted of me posting pictures of ridiculous stuff I’d made in Photoshop — what we might call “memes” and “shitposting” now. And why...
a year ago

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More from Jim Nielsen’s Blog

Building WebSites With LLMS

And by LLMS I mean: (L)ots of (L)ittle ht(M)l page(S). I recently shipped some updates to my blog. Through the design/development process, I had some insights which made me question my knee-jerk reaction to building pieces of a page as JS-powered interactions on top of the existing document. With cross-document view transitions getting broader and broader support, I’m realizing that building in-page, progressively-enhanced interactions is more work than simply building two HTML pages and linking them. I’m calling this approach “lots of little HTML pages” in my head. As I find myself trying to build progressively-enhanced features with JavaScript — like a fly-out navigation menu, or an on-page search, or filtering content — I stop and ask myself: “Can I build this as a separate HTML page triggered by a link, rather than JavaScript-injected content built from a button?” I kinda love the results. I build separate, small HTML pages for each “interaction” I want, then I let CSS transitions take over and I get something that feels better than its JS counterpart for way less work. Allow me two quick examples. Example 1: Filtering Working on my homepage, I found myself wanting a list of posts filtered by some kind of criteria, like: The most recent posts The ones being trafficked the most The ones that’ve had lots of Hacker News traffic in the past My first impulse was to have a list of posts you can filter with JavaScript. But the more I built it, the more complicated it got. Each “list” of posts needed a slightly different set of data. And each one had a different sort order. What I thought was going to be “stick a bunch of <li>s in the DOM, and show hide some based on the current filter” turned into lots of data-x attributes, per-list sorting logic, etc. I realized quickly this wasn’t a trivial, progressively-enhanced feature. I didn’t want to write a bunch of client-side JavaScript for what would take me seconds to write on “the server” (my static site generator). Then I thought: Why don’t I just do this with my static site generator? Each filter can be its own, separate HTML page, and with CSS view transitions I’ll get a nice transition effect for free! Minutes later I had it all working — mostly, I had to learn a few small things about aspect ratio in transitions — plus I had fancy transitions between “tabs” for free! This really feels like a game-changer for simple sites. If you can keep your site simple, it’s easier to build traditional, JavaScript-powered on-page interactions as small, linked HTML pages. Example 2: Navigation This got me thinking: maybe I should do the same thing for my navigation? Usually I think “Ok, so I’ll have a hamburger icon with a bunch of navigational elements in it, and when it’s clicked you gotta reveal it, etc." And I thought, “What if it’s just a new HTML page?”[1] Because I’m using a static site generator, it’s really easy to create a new HTML page. A few minutes later and I had it. No client-side JS required. You navigate to the “Menu” and you get a page of options, with an “x” to simulate closing the menu and going back to where you were. I liked it so much for my navigation, I did the same thing with search. Clicking the icon doesn’t use JavaScript to inject new markup and animate things on screen. Nope. It’s just a link to a new page with CSS supporting a cross-document view transition. Granted, there are some trade-offs to this approach. But on the whole, I really like it. It was so easy to build and I know it’s going to be incredibly easy to maintain! I think this is a good example of leveraging the grain of the web. It’s really easy to build a simple website when you can shift your perspective to viewing on-page interactivity as simple HTML page navigations powered by cross document CSS transitions (rather than doing all of that as client-side JS). Jason Bradberry has a neat article that’s tangential to this idea over at Piccalil. It’s more from the design standpoint, but functionally it could work pretty much the same as this: your “menu” or “navigation” is its own page. ⏎ Email · Mastodon · Bluesky

4 days ago 5 votes
AX, DX, UX

Matt Biilman, CEO of Netlify, published an interesting piece called “Introducing AX: Why Agent Experience Matters” where he argues the coming importance of a new “X” (experience) in software: the agent experience, meaning the experience your users’ AI agents will have as automated users of products/platforms. Too many companies are focusing on adding shallow AI features all over their products or building yet another AI agent. The real breakthrough will be thinking about how your customers’ favorite agents can help them derive more value from your product. This requires thinking deeply about agents as a persona your team is building and developing for. In this future, software that can’t be used by an automated agent will feel less powerful and more burdensome to deal with, whereas software that AI agents can use on your behalf will become incredibly capable and efficient. So you have to start thinking about these new “users” of your product: Is it simple for an Agent to get access to operating a platform on behalf of a user? Are there clean, well described APIs that agents can operate? Are there machine-ready documentation and context for LLMs and agents to properly use the available platform and SDKs? Addressing the distinct needs of agents through better AX, will improve their usefulness for the benefit of the human user. In summary: We need to start focusing on AX or “agent experience” — the holistic experience AI agents will have as the user of a product or platform. The idea is: teams focus more time and attention on “AX” (agent experience) so that human end-users can bring their favorite agents to our platforms/products and increase productivity. But I’m afraid the reality will be that the limited time and resources teams spend today building stuff for humans will instead get spent building stuff for robots, and as a byproduct everything human-centric about software will become increasingly subpar as we rationalize to ourselves, “Software doesn’t need to be good for human because humans don’t use software anymore. Their robots do!” In that world, anybody complaining about bad UX will be told to shift to using the AX because “that’s where we spent all our time and effort to make your experience great”. Prior Art: DX DX in theory: make the DX for people who are building UX really great and they’ll be able to deliver more value faster. DX in practice: DX requires trade-offs, and a spotlight on DX concerns means UX concerns take a back seat. Ultimately, some DX concerns end up trumping UX concerns because “we’ll ship more value faster”, but the result is an overall degradation of UX because DX was prioritized first. Ultimately, time and resources are constraining factors and trade-offs have to be made somewhere, so they’re made for and in behalf of the people who make the software because they’re the ones who feel the pain directly. User pain is only indirect. Future Art: AX AX in theory: build great stuff for agents (AX) so people can use stuff more efficiently by bringing their own tools. AX in practice: time and resources being finite, AX trumps UX with the rationale being: “It’s ok if the human bit (UX) is a bit sloppy and obtuse because we’ll make the robot bit (AX) so good people won’t ever care about how poor the UX is because they’ll never use it!” But I think we know how that plays out. A few companies may do that well, but most software will become even more confusing and obtuse to humans because most thought and care is poured into the robot experience of the product. The thinking will be: “No need to pour extra care and thought into the inefficient experience some humans might have. Better to make the agent experience really great, so humans won’t want to interface with our thing manually.” In other words: we don’t have the time or resources to worry about the manual human experience because we’ve got all these robots to worry about! It appears there’s no need to fear AI becoming sentient and replacing us humans. We’ll phase ourselves out long before the robots ever become self-aware. All that said, I’m not against the idea of “AX” but I do think the North Star of any “X” should remain centered on the (human) end-user. UX over AX over DX. Email · Mastodon · Bluesky

6 days ago 10 votes
Can You Get Better Doing a Bad Job?

Rick Rubin has an interview with Woody Harrelson on his podcast Tetragrammaton. Right at the beginning Woody talks about his experience acting and how he’s had roles that did’t turn out very well. He says sometimes he comes away from those experiences feeling dirty, like “I never connected to that, it never resonated, and now I feel like I sold myself...Why did I do that?!” Then Rick asks him: even in those cases, do you feel like you got better at your craft because you did your job? Woody’s response: I think when you do your job badly you never really get better at your craft. Seems relevant to making websites. I’ve built websites on technology stacks I knew didn’t feel fit for their context and Woody’s experience rings true. You just don’t feel right, like a little voice that says, “You knew that wasn’t going to turn out very good. Why did you do that??” I don’t know if I’d go so far as to say I didn’t get better because of it. Experience is a hard teacher. Perhaps, from a technical standpoint, my skillset didn’t get any better. But from an experiential standpoint, my judgement got better. I learned to avoid (or try to re-structure) work that’s being carried out in a way that doesn’t align with its own purpose and essence. Granted, that kind of alignment is difficult. If it makes you feel any better, even Woody admits this is not an easy thing to do: I would think after all this time, surely I’m not going to be doing stuff I’m not proud of. Or be a part of something I’m not proud of. But damn...it still happens. Email · Mastodon · Bluesky

a week ago 7 votes
Limitations vs. Capabilities

Andy Jiang over on the Deno blog writes “If you're not using npm specifiers, you're doing it wrong”: During the early days of Deno, we recommended importing npm packages via HTTP with transpile services such as esm.sh and unpkg.com. However, there are limitations to importing npm packages this way, such as lack of install hooks, duplicate dependency resolution issues, loading data files, etc. I know, I know, here I go harping on http imports again, but this article reinforces to me that one man’s “limitations” are another man’s “features”. For me, the limitations (i.e. constraints) of HTTP imports in Deno were a feature. I loved it precisely because it encouraged me to do something different than what node/npm encouraged. It encouraged me to 1) do less, and 2) be more web-like. Trying to do more with less is a great way to foster creativity. Plus, doing less means you have less to worry about. Take, for example, install hooks (since they’re mentioned in the article). Install hooks are a security vector. Use them and you’re trading ease for additional security concerns. Don’t use them and you have zero additional security concerns. (In the vein of being webby: browsers don’t offer install hooks on <script> tags.) I get it, though. It’s hard to advocate for restraint and simplicity in the face of gaining adoption within the web-industrial-complex. Giving people what they want — what they’re used to — is easier than teaching them to change their ways. Note to self: when you choose to use tools with practices, patterns, and recommendations designed for industrial-level use, you’re gonna get industrial-level side effects, industrial-level problems, and industrial-level complexity as a byproduct. As much as its grown, the web still has grassroots in being a programming platform accessible by regular people because making a website was meant to be for everyone. I would love a JavaScript runtime aligned with that ethos. Maybe with initiatives like project Fugu that runtime will actually be the browser. Email · Mastodon · Bluesky

a week ago 11 votes
Sanding UI, pt. II

Let’s say you make a UI to gather some user feedback. Nothing complicated. Just a thumbs up/down widget. It starts out neutral, but when the user clicks up or down, you highlight what they clicked an de-emphasize/disable the other (so it requires an explicit toggle to change your mind). So you implement it. Ship it. Cool. Works right? Well, per my previous article about “sanding” a user interface UI by clicking around a lot, did you click on it a lot? If you do, you’ll find that doing so selects the thumbs up/down icon as if it were text: So now you have this weird text selection that’s a bit of an eye sore. It’s not relevant to text selection because it’s not text. It’s an SVG. So the selection UI that appears is misleading and distracting. One possible fix: leverage the user-select: none property in CSS which makes it not selectable. When the user clicks multiple times to toggle, no text selection UI will appear. Cool. Great! Another reason to click around a lot. You can ensure any rough edges are smoothed out, and any “UI splinters” are ones you get (and fix) in place of your users. Email · Mastodon · Bluesky

a week ago 14 votes

More in programming

AI: Where in the Loop Should Humans Go?

This is a re-publishing of a blog post I originally wrote for work, but wanted on my own blog as well. AI is everywhere, and its impressive claims are leading to rapid adoption. At this stage, I’d qualify it as charismatic technology—something that under-delivers on what it promises, but promises so much that the industry still leverages it because we believe it will eventually deliver on these claims. This is a known pattern. In this post, I’ll use the example of automation deployments to go over known patterns and risks in order to provide you with a list of questions to ask about potential AI solutions. I’ll first cover a short list of base assumptions, and then borrow from scholars of cognitive systems engineering and resilience engineering to list said criteria. At the core of it is the idea that when we say we want humans in the loop, it really matters where in the loop they are. My base assumptions The first thing I’m going to say is that we currently do not have Artificial General Intelligence (AGI). I don’t care whether we have it in 2 years or 40 years or never; if I’m looking to deploy a tool (or an agent) that is supposed to do stuff to my production environments, it has to be able to do it now. I am not looking to be impressed, I am looking to make my life and the system better. Another mechanism I want you to keep in mind is something called the context gap. In a nutshell, any model or automation is constructed from a narrow definition of a controlled environment, which can expand as it gains autonomy, but remains limited. By comparison, people in a system start from a broad situation and narrow definitions down and add constraints to make problem-solving tractable. One side starts from a narrow context, and one starts from a wide one—so in practice, with humans and machines, you end up seeing a type of teamwork where one constantly updates the other: The optimal solution of a model is not an optimal solution of a problem unless the model is a perfect representation of the problem, which it never is.  — Ackoff (1979, p. 97) Because of that mindset, I will disregard all arguments of “it’s coming soon” and “it’s getting better real fast” and instead frame what current LLM solutions are shaped like: tools and automation. As it turns out, there are lots of studies about ergonomics, tool design, collaborative design, where semi-autonomous components fit into sociotechnical systems, and how they tend to fail. Additionally, I’ll borrow from the framing used by people who study joint cognitive systems: rather than looking only at the abilities of what a single person or tool can do, we’re going to look at the overall performance of the joint system. This is important because if you have a tool that is built to be operated like an autonomous agent, you can get weird results in your integration. You’re essentially building an interface for the wrong kind of component—like using a joystick to ride a bicycle. This lens will assist us in establishing general criteria about where the problems will likely be without having to test for every single one and evaluate them on benchmarks against each other. Questions you'll want to ask The following list of questions is meant to act as reminders—abstracting away all the theory from research papers you’d need to read—to let you think through some of the important stuff your teams should track, whether they are engineers using code generation, SREs using AIOps, or managers and execs making the call to adopt new tooling. Are you better even after the tool is taken away? An interesting warning comes from studying how LLMs function as learning aides. The researchers found that people who trained using LLMs tended to fail tests more when the LLMs were taken away compared to people who never studied with them, except if the prompts were specifically (and successfully) designed to help people learn. Likewise, it’s been known for decades that when automation handles standard challenges, the operators expected to take over when they reach their limits end up worse off and generally require more training to keep the overall system performant. While people can feel like they’re getting better and more productive with tool assistance, it doesn’t necessarily follow that they are learning or improving. Over time, there’s a serious risk that your overall system’s performance will be limited to what the automation can do—because without proper design, people keeping the automation in check will gradually lose the skills they had developed prior. Are you augmenting the person or the computer? Traditionally successful tools tend to work on the principle that they improve the physical or mental abilities of their operator: search tools let you go through more data than you could on your own and shift demands to external memory, a bicycle more effectively transmits force for locomotion, a blind spot alert on your car can extend your ability to pay attention to your surroundings, and so on. Automation that augments users therefore tends to be easier to direct, and sort of extends the person’s abilities, rather than acting based on preset goals and framing. Automation that augments a machine tends to broaden the device’s scope and control by leveraging some known effects of their environment and successfully hiding them away. For software folks, an autoscaling controller is a good example of the latter. Neither is fundamentally better nor worse than the other—but you should figure out what kind of automation you’re getting, because they fail differently. Augmenting the user implies that they can tackle a broader variety of challenges effectively. Augmenting the computers tends to mean that when the component reaches its limits, the challenges are worse for the operator. Is it turning you into a monitor rather than helping build an understanding? If your job is to look at the tool go and then say whether it was doing a good or bad job (and maybe take over if it does a bad job), you’re going to have problems. It has long been known that people adapt to their tools, and automation can create complacency. Self-driving cars that generally self-drive themselves well but still require a monitor are not effectively monitored. Instead, having AI that supports people or adds perspectives to the work an operator is already doing tends to yield better long-term results than patterns where the human learns to mostly delegate and focus elsewhere. (As a side note, this is why I tend to dislike incident summarizers. Don’t make it so people stop trying to piece together what happened! Instead, I prefer seeing tools that look at your summaries to remind you of items you may have forgotten, or that look for linguistic cues that point to biases or reductive points of view.) Does it pigeonhole what you can look at? When evaluating a tool, you should ask questions about where the automation lands: Does it let you look at the world more effectively? Does it tell you where to look in the world? Does it force you to look somewhere specific? Does it tell you to do something specific? Does it force you to do something? This is a bit of a hybrid between “Does it extend you?” and “Is it turning you into a monitor?” The five questions above let you figure that out. As the tool becomes a source of assertions or constraints (rather than a source of information and options), the operator becomes someone who interacts with the world from inside the tool rather than someone who interacts with the world with the tool’s help. The tool stops being a tool and becomes a representation of the whole system, which means whatever limitations and internal constraints it has are then transmitted to your users. Is it a built-in distraction? People tend to do multiple tasks over many contexts. Some automated systems are built with alarms or alerts that require stealing someone’s focus, and unless they truly are the most critical thing their users could give attention to, they are going to be an annoyance that can lower the effectiveness of the overall system. What perspectives does it bake in? Tools tend to embody a given perspective. For example, AIOps tools that are built to find a root cause will likely carry the conceptual framework behind root causes in their design. More subtly, these perspectives are sometimes hidden in the type of data you get: if your AIOps agent can only see alerts, your telemetry data, and maybe your code, it will rarely be a source of suggestions on how to improve your workflows because that isn’t part of its world. In roles that are inherently about pulling context from many disconnected sources, how on earth is automation going to make the right decisions? And moreover, who’s accountable for when it makes a poor decision on incomplete data? Surely not the buyer who installed it! This is also one of the many ways in which automation can reinforce biases—not just based on what is in its training data, but also based on its own structure and what inputs were considered most important at design time. The tool can itself become a keyhole through which your conclusions are guided. Is it going to become a hero? A common trope in incident response is heroes—the few people who know everything inside and out, and who end up being necessary bottlenecks to all emergencies. They can’t go away for vacation, they’re too busy to train others, they develop blind spots that nobody can fix, and they can’t be replaced. To avoid this, you have to maintain a continuous awareness of who knows what, and crosstrain each other to always have enough redundancy. If you have a team of multiple engineers and you add AI to it, having it do all of the tasks of a specific kind means it becomes a de facto hero to your team. If that’s okay, be aware that any outages or dysfunction in the AI agent would likely have no practical workaround. You will essentially have offshored part of your ops. Do you need it to be perfect? What a thing promises to be is never what it is—otherwise AWS would be enough, and Kubernetes would be enough, and JIRA would be enough, and the software would work fine with no one needing to fix things. That just doesn’t happen. Ever. Even if it’s really, really good, it’s gonna have outages and surprises, and it’ll mess up here and there, no matter what it is. We aren’t building an omnipotent computer god, we’re building imperfect software. You’ll want to seriously consider whether the tradeoffs you’d make in terms of quality and cost are worth it, and this is going to be a case-by-case basis. Just be careful not to fix the problem by adding a human in the loop that acts as a monitor! Is it doing the whole job or a fraction of it? We don’t notice major parts of our own jobs because they feel natural. A classic pattern here is one of AIs getting better at diagnosing patients, except the benchmarks are usually run on a patient chart where most of the relevant observations have already been made by someone else. Similarly, we often see AI pass a test with flying colors while it still can’t be productive at the job the test represents. People in general have adopted a model of cognition based on information processing that’s very similar to how computers work (get data in, think, output stuff, rinse and repeat), but for decades, there have been multiple disciplines that looked harder at situated work and cognition, moving past that model. Key patterns of cognition are not just in the mind, but are also embedded in the environment and in the interactions we have with each other. Be wary of acquiring a solution that solves what you think the problem is rather than what it actually is. We routinely show we don’t accurately know the latter. What if we have more than one? You probably know how straightforward it can be to write a toy project on your own, with full control of every refactor. You probably also know how this stops being true as your team grows. As it stands today, a lot of AI agents are built within a snapshot of the current world: one or few AI tools added to teams that are mostly made up of people. By analogy, this would be like everyone selling you a computer assuming it were the first and only electronic device inside your household. Problems arise when you go beyond these assumptions: maybe AI that writes code has to go through a code review process, but what if that code review is done by another unrelated AI agent? What happens when you get to operations and common mode failures impact components from various teams that all have agents empowered to go fix things to the best of their ability with the available data? Are they going to clash with people, or even with each other? Humans also have that ability and tend to solve it via processes and procedures, explicit coordination, announcing what they’ll do before they do it, and calling upon each other when they need help. Will multiple agents require something equivalent, and if so, do you have it in place? How do they cope with limited context? Some changes that cause issues might be safe to roll back, some not (maybe they include database migrations, maybe it is better to be down than corrupting data), and some may contain changes that rolling back wouldn’t fix (maybe the workload is controlled by one or more feature flags). Knowing what to do in these situations can sometimes be understood from code or release notes, but some situations can require different workflows involving broader parts of the organization. A risk of automation without context is that if you have situations where waiting or doing little is the best option, then you’ll need to either have automation that requires input to act, or a set of actions to quickly disable multiple types of automation as fast as possible. Many of these may exist at the same time, and it becomes the operators’ jobs to not only maintain their own context, but also maintain a mental model of the context each of these pieces of automation has access to. The fancier your agents, the fancier your operators’ understanding and abilities must be to properly orchestrate them. The more surprising your landscape is, the harder it can become to manage with semi-autonomous elements roaming around. After an outage or incident, who does the learning and who does the fixing? One way to track accountability in a system is to figure out who ends up having to learn lessons and change how things are done. It’s not always the same people or teams, and generally, learning will happen whether you want it or not. This is more of a rhetorical question right now, because I expect that in most cases, when things go wrong, whoever is expected to monitor the AI tool is going to have to steer it in a better direction and fix it (if they can); if it can’t be fixed, then the expectation will be that the automation, as a tool, will be used more judiciously in the future. In a nutshell, if the expectation is that your engineers are going to be doing the learning and tweaking, your AI isn’t an independent agent—it’s a tool that cosplays as an independent agent. Do what you will—just be mindful All in all, none of the above questions flat out say you should not use AI, nor where exactly in the loop you should put people. The key point is that you should ask that question and be aware that just adding whatever to your system is not going to substitute workers away. It will, instead, transform work and create new patterns and weaknesses. Some of these patterns are known and well-studied. We don’t have to go rushing to rediscover them all through failures as if we were the first to ever automate something. If AI ever gets so good and so smart that it’s better than all your engineers, it won’t make a difference whether you adopt it only once it’s good. In the meanwhile, these things do matter and have real impacts, so please design your systems responsibly. If you’re interested to know more about the theoretical elements underpinning this post, the following references—on top of whatever was already linked in the text—might be of interest: Books: Joint Cognitive Systems: Foundations of Cognitive Systems Engineering by Erik Hollnagel Joint Cognitive Systems: Patterns in Cognitive Systems Engineering by David D. Woods Cognition in the Wild by Edwin Hutchins Behind Human Error by David D. Woods, Sydney Dekker, Richard Cook, Leila Johannesen, Nadine Sarter Papers: Ironies of Automation by Lisanne Bainbridge The French-Speaking Ergonomists’ Approach to Work Activity by Daniellou How in the World Did We Ever Get into That Mode? Mode Error and Awareness in Supervisory Control by Nadine Sarter Can We Ever Escape from Data Overload? A Cognitive Systems Diagnosis by David D. Woods Ten Challenges for Making Automation a “Team Player” in Joint Human-Agent Activity by Gary Klein and David D. Woods MABA-MABA or Abracadabra? Progress on Human–Automation Co-ordination by Sidney Dekker Managing the Hidden Costs of Coordination by Laura Maguire Designing for Expertise by David D. Woods The Impact of Generative AI on Critical Thinking by Lee et al.

yesterday 4 votes
AMD YOLO

AMD is sending us the two MI300X boxes we asked for. They are in the mail. It took a bit, but AMD passed my cultural test. I now believe they aren’t going to shoot themselves in the foot on software, and if that’s true, there’s absolutely no reason they should be worth 1/16th of NVIDIA. CUDA isn’t really the moat people think it is, it was just an early ecosystem. tiny corp has a fully sovereign AMD stack, and soon we’ll port it to the MI300X. You won’t even have to use tinygrad proper, tinygrad has a torch frontend now. Either NVIDIA is super overvalued or AMD is undervalued. If the petaflop gets commoditized (tiny corp’s mission), the current situation doesn’t make any sense. The hardware is similar, AMD even got the double throughput Tensor Cores on RDNA4 (NVIDIA artificially halves this on their cards, soon they won’t be able to). I’m betting on AMD being undervalued, and that the demand for AI has barely started. With good software, the MI300X should outperform the H100. In for a quarter million. Long term. It can always dip short term, but check back in 5 years.

yesterday 2 votes
whippet lab notebook: untagged mallocs, bis

Earlier this weekGuileWhippet But now I do! Today’s note is about how we can support untagged allocations of a few different kinds in Whippet’s .mostly-marking collector Why bother supporting untagged allocations at all? Well, if I had my way, I wouldn’t; I would just slog through Guile and fix all uses to be tagged. There are only a finite number of use sites and I could get to them all in a month or so. The problem comes for uses of from outside itself, in C extensions and embedding programs. These users are loathe to adapt to any kind of change, and garbage-collection-related changes are the worst. So, somehow, we need to support these users if we are not to break the Guile community.scm_gc_malloclibguile The problem with , though, is that it is missing an expression of intent, notably as regards tagging. You can use it to allocate an object that has a tag and thus can be traced precisely, or you can use it to allocate, well, anything else. I think we will have to add an API for the tagged case and assume that anything that goes through is requesting an untagged, conservatively-scanned block of memory. Similarly for : you could be allocating a tagged object that happens to not contain pointers, or you could be allocating an untagged array of whatever. A new API is needed there too for pointerless untagged allocations.scm_gc_mallocscm_gc_mallocscm_gc_malloc_pointerless Recall that the mostly-marking collector can be built in a number of different ways: it can support conservative and/or precise roots, it can trace the heap precisely or conservatively, it can be generational or not, and the collector can use multiple threads during pauses or not. Consider a basic configuration with precise roots. You can make tagged pointerless allocations just fine: the trace function for that tag is just trivial. You would like to extend the collector with the ability to make pointerless allocations, for raw data. How to do this?untagged Consider first that when the collector goes to trace an object, it can’t use bits inside the object to discriminate between the tagged and untagged cases. Fortunately though . Of those 8 bits, 3 are used for the mark (five different states, allowing for future concurrent tracing), two for the , one to indicate whether the object is pinned or not, and one to indicate the end of the object, so that we can determine object bounds just by scanning the metadata byte array. That leaves 1 bit, and we can use it to indicate untagged pointerless allocations. Hooray!the main space of the mostly-marking collector has one metadata byte for each 16 bytes of payloadprecise field-logging write barrier However there is a wrinkle: when Whippet decides the it should evacuate an object, it tracks the evacuation state in the object itself; the embedder has to provide an implementation of a , allowing the collector to detect whether an object is forwarded or not, to claim an object for forwarding, to commit a forwarding pointer, and so on. We can’t do that for raw data, because all bit states belong to the object, not the collector or the embedder. So, we have to set the “pinned” bit on the object, indicating that these objects can’t move.little state machine We could in theory manage the forwarding state in the metadata byte, but we don’t have the bits to do that currently; maybe some day. For now, untagged pointerless allocations are pinned. You might also want to support untagged allocations that contain pointers to other GC-managed objects. In this case you would want these untagged allocations to be scanned conservatively. We can do this, but if we do, it will pin all objects. Thing is, conservative stack roots is a kind of a sweet spot in language run-time design. You get to avoid constraining your compiler, you avoid a class of bugs related to rooting, but you can still support compaction of the heap. How is this, you ask? Well, consider that you can move any object for which we can precisely enumerate the incoming references. This is trivially the case for precise roots and precise tracing. For conservative roots, we don’t know whether a given edge is really an object reference or not, so we have to conservatively avoid moving those objects. But once you are done tracing conservative edges, any live object that hasn’t yet been traced is fair game for evacuation, because none of its predecessors have yet been visited. But once you add conservatively-traced objects back into the mix, you don’t know when you are done tracing conservative edges; you could always discover another conservatively-traced object later in the trace, so you have to pin everything. The good news, though, is that we have gained an easier migration path. I can now shove Whippet into Guile and get it running even before I have removed untagged allocations. Once I have done so, I will be able to allow for compaction / evacuation; things only get better from here. Also as a side benefit, the mostly-marking collector’s heap-conservative configurations are now faster, because we have metadata attached to objects which allows tracing to skip known-pointerless objects. This regains an optimization that BDW has long had via its , used in Guile since time out of mind.GC_malloc_atomic With support for untagged allocations, I think I am finally ready to start getting Whippet into Guile itself. Happy hacking, and see you on the other side! inside and outside on intent on data on slop fin

yesterday 2 votes
Creating static map images with OpenStreetMap, Web Mercator, and Pillow

I’ve been working on a project where I need to plot points on a map. I don’t need an interactive or dynamic visualisation – just a static map with coloured dots for each coordinate. I’ve created maps on the web using Leaflet.js, which load map data from OpenStreetMap (OSM) and support zooming and panning – but for this project, I want a standalone image rather than something I embed in a web page. I want to put in coordinates, and get a PNG image back. This feels like it should be straightforward. There are lots of Python libraries for data visualisation, but it’s not an area I’ve ever explored in detail. I don’t know how to use these libraries, and despite trying I couldn’t work out how to accomplish this seemingly simple task. I made several attempts with libraries like matplotlib and plotly, but I felt like I was fighting the tools. Rather than persist, I wrote my own solution with “lower level” tools. The key was a page on the OpenStreetMap wiki explaining how to convert lat/lon coordinates into the pixel system used by OSM tiles. In particular, it allowed me to break the process into two steps: Get a “base map” image that covers the entire world Convert lat/lon coordinates into xy coordinates that can be overlaid on this image Let’s go through those steps. Get a “base map” image that covers the entire world Let’s talk about how OpenStreetMap works, and in particular their image tiles. If you start at the most zoomed-out level, OSM represents the entire world with a single 256×256 pixel square. This is the Web Mercator projection, and you don’t get much detail – just a rough outline of the world. We can zoom in, and this tile splits into four new tiles of the same size. There are twice as many pixels along each edge, and each tile has more detail. Notice that country boundaries are visible now, but we can’t see any names yet. We can zoom in even further, and each of these tiles split again. There still aren’t any text labels, but the map is getting more detailed and we can see small features that weren’t visible before. You get the idea – we could keep zooming, and we’d get more and more tiles, each with more detail. This tile system means you can get detailed information for a specific area, without loading the entire world. For example, if I’m looking at street information in Britain, I only need the detailed tiles for that part of the world. I don’t need the detailed tiles for Bolivia at the same time. OpenStreetMap will only give you 256×256 pixels at a time, but we can download every tile and stitch them together, one-by-one. Here’s a Python script that enumerates all the tiles at a particular zoom level, downloads them, and uses the Pillow library to combine them into a single large image: #!/usr/bin/env python3 """ Download all the map tiles for a particular zoom level from OpenStreetMap, and stitch them into a single image. """ import io import itertools import httpx from PIL import Image zoom_level = 2 width = 256 * 2**zoom_level height = 256 * (2**zoom_level) im = Image.new("RGB", (width, height)) for x, y in itertools.product(range(2**zoom_level), range(2**zoom_level)): resp = httpx.get(f"https://tile.openstreetmap.org/{zoom_level}/{x}/{y}.png", timeout=50) resp.raise_for_status() im_buffer = Image.open(io.BytesIO(resp.content)) im.paste(im_buffer, (x * 256, y * 256)) out_path = f"map_{zoom_level}.png" im.save(out_path) print(out_path) The higher the zoom level, the more tiles you need to download, and the larger the final image will be. I ran this script up to zoom level 6, and this is the data involved: Zoom level Number of tiles Pixels File size 0 1 256×256 17.1 kB 1 4 512×512 56.3 kB 2 16 1024×1024 155.2 kB 3 64 2048×2048 506.4 kB 4 256 4096×4096 2.7 MB 5 1,024 8192×8192 13.9 MB 6 4,096 16384×16384 46.1 MB I can just about open that zoom level 6 image on my computer, but it’s struggling. I didn’t try opening zoom level 7 – that includes 16,384 tiles, and I’d probably run out of memory. For most static images, zoom level 3 or 4 should be sufficient – I ended up a base map from zoom level 4 for my project. It takes a minute or so to download all the tiles from OpenStreetMap, but you only need to request it once, and then you have a static image you can use again and again. This is a particularly good approach if you want to draw a lot of maps. OpenStreetMap is provided for free, and we want to be a respectful user of the service. Downloading all the map tiles once is more efficient than making repeated requests for the same data. Overlay lat/lon coordinates on this base map Now we have an image with a map of the whole world, we need to overlay our lat/lon coordinates as points on this map. I found instructions on the OpenStreetMap wiki which explain how to convert GPS coordinates into a position on the unit square, which we can in turn add to our map. They outline a straightforward algorithm, which I implemented in Python: import math def convert_gps_coordinates_to_unit_xy( *, latitude: float, longitude: float ) -> tuple[float, float]: """ Convert GPS coordinates to positions on the unit square, which can be plotted on a Web Mercator projection of the world. This expects the coordinates to be specified in **degrees**. The result will be (x, y) coordinates: - x will fall in the range (0, 1). x=0 is the left (180° west) edge of the map. x=1 is the right (180° east) edge of the map. x=0.5 is the middle, the prime meridian. - y will fall in the range (0, 1). y=0 is the top (north) edge of the map, at 85.0511 °N. y=1 is the bottom (south) edge of the map, at 85.0511 °S. y=0.5 is the middle, the equator. """ # This is based on instructions from the OpenStreetMap Wiki: # https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#Example:_Convert_a_GPS_coordinate_to_a_pixel_position_in_a_Web_Mercator_tile # (Retrieved 16 January 2025) # Convert the coordinate to the Web Mercator projection # (https://epsg.io/3857) # # x = longitude # y = arsinh(tan(latitude)) # x_webm = longitude y_webm = math.asinh(math.tan(math.radians(latitude))) # Transform the projected point onto the unit square # # x = 0.5 + x / 360 # y = 0.5 - y / 2π # x_unit = 0.5 + x_webm / 360 y_unit = 0.5 - y_webm / (2 * math.pi) return x_unit, y_unit Their documentation includes a worked example using the coordinates of the Hachiko Statue. We can run our code, and check we get the same results: >>> convert_gps_coordinates_to_unit_xy(latitude=35.6590699, longitude=139.7006793) (0.8880574425, 0.39385379958274735) Most users of OpenStreetMap tiles will use these unit positions to select the tiles they need, and then dowload those images – but we can also position these points directly on the global map. I wrote some more Pillow code that converts GPS coordinates to these unit positions, scales those unit positions to the size of the entire map, then draws a coloured circle at each point on the map. Here’s the code: from PIL import Image, ImageDraw gps_coordinates = [ # Hachiko Memorial Statue in Tokyo {"latitude": 35.6590699, "longitude": 139.7006793}, # Greyfriars Bobby in Edinburgh {"latitude": 55.9469224, "longitude": -3.1913043}, # Fido Statue in Tuscany {"latitude": 43.955101, "longitude": 11.388186}, ] im = Image.open("base_map.png") draw = ImageDraw.Draw(im) for coord in gps_coordinates: x, y = convert_gps_coordinates_to_unit_xy(**coord) radius = 32 draw.ellipse( [ x * im.width - radius, y * im.height - radius, x * im.width + radius, y * im.height + radius, ], fill="red", ) im.save("map_with_dots.png") and here’s the map it produces: The nice thing about writing this code in Pillow is that it’s a library I already know how to use, and so I can customise it if I need to. I can change the shape and colour of the points, or crop to specific regions, or add text to the image. I’m sure more sophisticated data visualisation libraries can do all this, and more – but I wouldn’t know how. The downside is that if I need more advanced features, I’ll have to write them myself. I’m okay with that – trading sophistication for simplicity. I didn’t need to learn a complex visualization library – I was able to write code I can read and understand. In a world full of AI-generating code, writing something I know I understand feels more important than ever. [If the formatting of this post looks odd in your feed reader, visit the original article]

yesterday 4 votes
Introducing the blogroll

This website has a new section: blogroll.opml! A blogroll is a list of blogs - a lightweight way of people recommending other people’s writing on the indieweb. What it includes The blogs that I included are just sampled from my many RSS subscriptions that I keep in my Feedbin reader. I’m subscribed to about 200 RSS feeds, the majority of which are dead or only publish once a year. I like that about blogs, that there’s no expectation of getting a post out every single day, like there is in more algorithmically-driven media. If someone who I interacted with on the internet years ago decides to restart their writing, that’s great! There’s no reason to prune all the quiet feeds. The picks are oriented toward what I’m into: niches, blogs that have a loose topic but don’t try to be general-interest, people with distinctive writing. If you import all of the feeds into your RSS reader, you’ll probably end up unsubscribing from some of them because some of the experimental electric guitar design or bonsai news is not what you’re into. Seems fine, or you’ll discover a new interest! How it works Ruben Schade figured out a brilliant way to show blogrolls and I copied him. Check out his post on styling OPML and RSS with XSLT to XHTML for how it works. My only additions to that scheme were making the blogroll page blend into the rest of the website by using an include tag with Jekyll to add the basic site skeleton, and adding a link with the download attribute to provide a simple way to download the OPML file. Oddly, if you try to save the OPML page using Save as… in Firefox, Firefox will save the transformed output via the XSLT, rather than the raw source code. XSLT is such an odd and rare part of the web ecosystem, I had to use it.

yesterday 2 votes