More from alexwlchan
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]
I’m sitting in a small coffee shop in Brooklyn. I have a warm drink, and it’s just started to snow outside. I’m visiting New York to see Operation Mincemeat on Broadway – I was at the dress rehearsal yesterday, and I’ll be at the opening preview tonight. I’ve seen this show more times than I care to count, and I hope US theater-goers love it as much as Brits. The people who make the show will tell you that it’s about a bunch of misfits who thought they could do something ridiculous, who had the audacity to believe in something unlikely. That’s certainly one way to see it. The musical tells the true story of a group of British spies who tried to fool Hitler with a dead body, fake papers, and an outrageous plan that could easily have failed. Decades later, the show’s creators would mirror that same spirit of unlikely ambition. Four friends, armed with their creativity, determination, and a wardrobe full of hats, created a new musical in a small London theatre. And after a series of transfers, they’re about to open the show under the bright lights of Broadway. But when I watch the show, I see a story about friendship. It’s about how we need our friends to help us, to inspire us, to push us to be the best versions of ourselves. I see the swaggering leader who needs a team to help him truly achieve. The nervous scientist who stands up for himself with the support of his friends. The enthusiastic secretary who learns wisdom and resilience from her elder. And so, I suppose, it’s fitting that I’m not in New York on my own. I’m here with friends – dozens of wonderful people who I met through this ridiculous show. At first, I was just an audience member. I sat in my seat, I watched the show, and I laughed and cried with equal measure. After the show, I waited at stage door to thank the cast. Then I came to see the show a second time. And a third. And a fourth. After a few trips, I started to see familiar faces waiting with me at stage door. So before the cast came out, we started chatting. Those conversations became a Twitter community, then a Discord, then a WhatsApp. We swapped fan art, merch, and stories of our favourite moments. We went to other shows together, and we hung out outside the theatre. I spent New Year’s Eve with a few of these friends, sitting on somebody’s floor and laughing about a bowl of limes like it was the funniest thing in the world. And now we’re together in New York. Meeting this kind, funny, and creative group of people might seem as unlikely as the premise of Mincemeat itself. But I believed it was possible, and here we are. I feel so lucky to have met these people, to take this ridiculous trip, to share these precious days with them. I know what a privilege this is – the time, the money, the ability to say let’s do this and make it happen. How many people can gather a dozen friends for even a single evening, let alone a trip halfway round the world? You might think it’s silly to travel this far for a theatre show, especially one we’ve seen plenty of times in London. Some people would never see the same show twice, and most of us are comfortably into double or triple-figures. Whenever somebody asks why, I don’t have a good answer. Because it’s fun? Because it’s moving? Because I enjoy it? I feel the need to justify it, as if there’s some logical reason that will make all of this okay. But maybe I don’t have to. Maybe joy doesn’t need justification. A theatre show doesn’t happen without people who care. Neither does a friendship. So much of our culture tells us that it’s not cool to care. It’s better to be detached, dismissive, disinterested. Enthusiasm is cringe. Sincerity is weakness. I’ve certainly felt that pressure – the urge to play it cool, to pretend I’m above it all. To act as if I only enjoy something a “normal” amount. Well, fuck that. I don’t know where the drive to be detached comes from. Maybe it’s to protect ourselves, a way to guard against disappointment. Maybe it’s to seem sophisticated, as if having passions makes us childish or less mature. Or perhaps it’s about control – if we stay detached, we never have to depend on others, we never have to trust in something bigger than ourselves. Being detached means you can’t get hurt – but you’ll also miss out on so much joy. I’m a big fan of being a big fan of things. So many of the best things in my life have come from caring, from letting myself be involved, from finding people who are a big fan of the same things as me. If I pretended not to care, I wouldn’t have any of that. Caring – deeply, foolishly, vulnerably – is how I connect with people. My friends and I care about this show, we care about each other, and we care about our joy. That care and love for each other is what brought us together, and without it we wouldn’t be here in this city. I know this is a once-in-a-lifetime trip. So many stars had to align – for us to meet, for the show we love to be successful, for us to be able to travel together. But if we didn’t care, none of those stars would have aligned. I know so many other friends who would have loved to be here but can’t be, for all kinds of reasons. Their absence isn’t for lack of caring, and they want the show to do well whether or not they’re here. I know they care, and that’s the important thing. To butcher Tennyson: I think it’s better to care about something you cannot affect, than to care about nothing at all. In a world that’s full of cynicism and spite and hatred, I feel that now more than ever. I’d recommend you go to the show if you haven’t already, but that’s not really the point of this post. Maybe you’ve already seen Operation Mincemeat, and it wasn’t for you. Maybe you’re not a theatre kid. Maybe you aren’t into musicals, or history, or war stories. That’s okay. I don’t mind if you care about different things to me. (Imagine how boring the world would be if we all cared about the same things!) But I want you to care about something. I want you to find it, find people who care about it too, and hold on to them. Because right now, in this city, with these people, at this show? I’m so glad I did. And I hope you find that sort of happiness too. Some of the people who made this trip special. Photo by Chloe, and taken from her Twitter. Timing note: I wrote this on February 15th, but I delayed posting it because I didn’t want to highlight the fact I was away from home. [If the formatting of this post looks odd in your feed reader, visit the original article]
As well as changing the way I organise my writing, last year I made some cosmetic improvements to this site. I design everything on this site myself, and I write the CSS by hand – I don’t use any third-party styles or frameworks. I don’t have any design training, and I don’t do design professionally, so I use this site as a place to learn and practice my design skills. It’s a continual work-in-progress, but I’d like to think it’s getting better over time. I design this site for readers. I write long, text-heavy posts with the occasional illustration or diagram, so I want something that will be comfortable to read and look good on a wide variety of browsers and devices. I get a lot of that “for free” by using semantic HTML and the default styles – most of my CSS is just cosmetic. Let’s go through some of the changes. Cleaning up the link styles This is what links used to look like: Every page has a tint colour, and then I was deriving different shades to style different links – a darker shade for visited links, a lighter shade for visited links in dark mode, and a background that appears on hover. I’m generating these new colours programatically, and I was so proud of getting that code working that I didn’t stop to think whether it was a good idea. In hindsight, I see several issues. The tint colour is meant to give the page a consistent visual appearance, but the different shades diluted that effect. I don’t think their meaning was especially obvious. How many readers ever worked it out? And the hover styles are actively unhelpful – just as you hover over a link you’re interested in, I’m making it harder to read! (At least in light mode – in dark mode, the hover style is barely legible.) One thing I noticed is that for certain tint colours, the “visited” colour I generated was barely distinguishable from the text colour. So I decided to lean into that in the new link styles: visited links are now the same colour as regular text. This new set of styles feels more coherent. I’m only using one shade of the tint colour, and I think the meaning is a bit clearer – only new-to-you links will get the pop of colour to stand out from the rest of the text. I’m happy to rely on underlines for the links you’ve already visited. And when you hover, the thick underline means you can see where you are, but the link text remains readable. Swapping out the font I swapped out the font, replacing Georgia with Charter. The difference is subtle, so I’d be surprised if anyone noticed: I’ve always used web safe fonts for this site – the fonts that are built into web browsers, and don’t need to be downloaded first. I’ve played with custom fonts from time to time, but there’s no font I like more enough to justify the hassle of loading a custom font. I still like Georgia, but I felt it was showing its age – it was designed in 1993 to look good on low-resolution screens, but looks a little chunky on modern displays. I think Charter looks nicer on high-resolution screens, but if you don’t have it installed then I fall back to Georgia. Making all the roundrects consistent I use a lot of rounded rectangles for components on this site, including article cards, blockquotes, and code blocks. For a long time they had similar but not identical styles, because I designed them all at different times. There were weird inconsistencies. For example, why does one roundrect have a 2px border, but another one is 3px? These are small details that nobody will ever notice directly, but undermine the sense of visual together-ness. I’ve done a complete overhaul of these styles, to make everything look more consistent. I’m leaning heavily on CSS variables, a relatively new CSS feature that I’ve really come to like. Variables make it much easier to use consistent values in different rules. I also tweaked the appearance: I’ve removed another two shades of the tint colour. (Yes, those shades were different from the ones used in links.) Colour draws your attention, so I’m trying to use it more carefully. A link says “click here”. A heading says “start here”. What does a blockquote or code snippet say? It’s just part of the text, so it shouldn’t be grabbing your attention. I think the neutral background also makes the syntax highlighting easier to read, because the tint colour isn’t clashing with the code colours. I could probably consolidate the shades of grey I’m using, but that’s a task for another day. I also removed the left indent on blockquotes and code blocks – I think it looks nicer to have a flush left edge for everything, and it means you can read more text on mobile screens. (That’s where I really felt the issues with the old design.) What’s next? By tidying up the design and reducing the number of unique elements, I’ve got a bit of room to add something new. For a while now I’ve wanted a place at the bottom of posts for common actions, or links to related and follow-up posts. As I do more and more long-form, reflective writing, I want to be able to say “if you liked this, you should read this too”. I want something that catches your eye, but doesn’t distract from the article you’re already reading. Louie Mantia has a version of this that I quite like: I’ve held off designing this because the existing pages felt too busy, but now I feel like I have space to add this – there aren’t as many clashing colours and components to compete for your attention. I’m still sketching out designs – my current idea is my rounded rectangle blocks, but with a coloured border instead of a subtle grey, but when I did a prototype, I feel like it’s missing something. I need to try a few more ideas. Watch this space! [If the formatting of this post looks odd in your feed reader, visit the original article]
Last year I wrote about using static websites for tiny archives. The idea is that I create tiny websites to store and describe my digital collections. There are several reasons I like this approach: HTML is flexible and lets me display data in a variety of ways; it’s likely to remain readable for a long time; it lets me add more context than a folder full of files. I’m converting more and more of my local data to be stored in static websites – paperwork I’ve scanned, screenshots I’ve taken, and web pages I’ve bookmarked. I really like this approach. I got a lot of positive feedback, but the most common reply was “please share some source code”. People wanted to see examples of the HTML and JavaScript I was using I deliberately omitted any code from the original post, because I wanted to focus on the concept, not the detail. I was trying to persuade you that static websites are a good idea for storing small archives and data sets, and I didn’t want to get distracted by the implementation. There’s also no single code base I could share – every site I build is different, and the code is often scrappy or poorly documented. I’ve built dozens of small sites this way, and there’s no site that serves as a good example of this approach – they’re all built differently, implement a subset of my ideas, or have hard-coded details. Even if I shared some source code, it would be difficult to read or understand what’s going on. However, there’s clearly an appetite for that sort of explanation, so this follow-up post will discuss the “how” rather than the “why”. There’s a lot of code, especially JavaScript, which I’ll explain in small digestible snippets. That’s another reason I didn’t describe this in the original post – I didn’t want anyone to feel overwhelmed or put off. A lot of what I’m describing here is nice-to-have, not essential. You can get started with something pretty simple. I’ll go through a feature at a time, as if we were building a new static site. I’ll use bookmarks as an example, but there’s nothing in this post that’s specific to bookmarking. If you’d like to see everything working together, check out the demo site. It includes the full code for all the sections in this post. Let’s dive in! Start with a hand-written HTML page (demo) Reduce repetition with JavaScript templates (demo) Add filtering to find specific items (demo) Introduce sorting to bring order to your data (demo) Use pagination to break up long lists (demo) Provide feedback with loading states and error handling (demo 1, demo 2) Test the code with QUnit and Playwright Manipulate the metadata with Python Store the website code in Git Closing thoughts demo) A website can be a single HTML file you edit by hand. Open a text editor like TextEdit or Notepad, copy-paste the following text, and save it in a file named bookmarks.html. <h1>Bookmarks</h1> <ul> <li><a href="https://estherschindler.medium.com/the-old-family-photos-project-lessons-in-creating-family-photos-that-people-want-to-keep-ea3909129943">Lessons in creating family photos that people want to keep, by Esther Schindler (2018)</a></li> <li><a href="https://www.theatlantic.com/technology/archive/2015/01/why-i-am-not-a-maker/384767/">Why I Am Not a Maker, by Debbie Chachra (The Atlantic, 2015)</a></li> <li><a href="https://meyerweb.com/eric/thoughts/2014/06/10/so-many-nevers/">So Many Nevers, by Eric Meyer (2014)</a></li> </ul> If you open this file in your web browser, you’ll see a list of three links. You can also check out my demo page to see this in action. This is an excellent way to build a website. If you stop here, you’ve got all the flexibility and portability of HTML, and this file will remain readable for a very long time. I build a lot of sites this way. I like it for small data sets that I know are never going to change, or which change very slowly. It’s simple, future-proof, and easy to edit if I ever need to. demo) As you store more data, it gets a bit tedious to keep copying the HTML markup for each item. Wouldn’t it be useful if we could push it into a reusable template? When a site gets bigger, I convert the metadata into JSON, then I use JavaScript and template literals to render it on the page. Let’s start with a simple example of metadata in JSON. My real data has more fields, like date saved or a list of keyword tags, but this is enough to get the idea: const bookmarks = [ { "url": "https://estherschindler.medium.com/the-old-family-photos-project-lessons-in-creating-family-photos-that-people-want-to-keep-ea3909129943", "title": "Lessons in creating family photos that people want to keep, by Esther Schindler (2018)" }, { "url": "https://www.theatlantic.com/technology/archive/2015/01/why-i-am-not-a-maker/384767/", "title": "Why I Am Not a Maker, by Debbie Chachra (The Atlantic, 2015)" }, { "url": "https://meyerweb.com/eric/thoughts/2014/06/10/so-many-nevers/", "title": "So Many Nevers, by Eric Meyer (2014)" } ]; Then I have a function that renders the data for a single bookmark as HTML: function Bookmark(bookmark) { return ` <li> <a href="${bookmark.url}">${bookmark.title}</a> </li> `; } Having a function that returns HTML is inspired by React and Next.js, where code is split into “components” that each render part of the web app. This function is simpler than what you’d get in React. Part of React’s behaviour is that it will re-render the page if the data changes, but my function won’t do that. That’s okay, because my data isn’t going to change. The HTML gets rendered once when the page loads, and that’s enough. I’m using a template literal because I find it simple and readable. It looks pretty close to the actual HTML, so I have a pretty good idea of what’s going to appear on the page. Template literals are dangerous if you’re getting data from an untrusted source – it could allow somebody to inject arbitrary HTML into your page – but I’m writing all my metadata, so I trust it. I know there are other ways to construct HTML in JavaScript, like document.createElement(), the <template> element, or Web Components – but template literals have always been sufficient for me, and I’ve never had a reason to explore other options. Now we have to call this function when the page loads, and render the list of bookmarks. Here’s the rest of the code: <script> window.addEventListener("DOMContentLoaded", () => { document.querySelector("#listOfBookmarks").innerHTML = bookmarks.map(Bookmark).join(""); }); </script> <h1>Bookmarks</h1> <ul id="listOfBookmarks"></ul> I’m listening for the DOMContentLoaded event, which occurs when the HTML page has been fully parsed. When that event occurs, it looks for <ul id="listOfBookmarks"> in the page, and inserts the HTML for the list of bookmarks. We have to wait for this event so the <ul> actually exists. If we tried to run it immediately, it might run before the <ul> exists, and then it wouldn’t know where to insert the HTML. I’m using querySelector() to find the <ul> I want to modify – this is a newer alternative to functions like getElementById(). It’s quite flexible, because I can target any CSS selector, and I find CSS rules easier to remember than the family of getElementBy* functions. Although it’s slightly slower in benchmarks, the difference is negligible and it’s easier for me to remember. If you want to see this page working, check out the demo page. I use this pattern as a starting point for a lot of my static sites – metadata in JSON, some functions that render HTML, and an event listener that renders the whole page after it loads. Once I have the basic site, I add data, render more HTML, and write CSS styles to make it look pretty. This is where I can have fun, and really customise each site. I keep tweaking until I have something I like. I’m ignoring CSS because that could be a whole other post, and there’s a vintage charm to unstyled HTML – it’s fine for what we’re discussing today. What else can we do? demo) As the list gets even longer, it’s useful to have a way to find specific items in the list – I don’t want to scroll the whole thing every time. I like adding keyword tags to my data, and then filtering for items with particular tags. If I add other metadata fields, I could filter on those too. Here’s a brief sketch of the sort of interface I like: I like to be able to define a series of filters, and apply them to focus on a specific subset of items. I like to combine multiple filters to refine my search, and to see a list of applied filters with a way to remove them, if I’ve filtered too far. I like to apply filters from a global menu, or to use controls on each item to find similar items. I use URL query parameters to store the list of currently-applied filters, for example: bookmarks.html?tag=animals&tag=wtf&publicationYear=2025 This means that any UI element that adds or removes a filter is a link to a new URL, so clicking it loads a new page, which triggers a complete re-render with the new filters. When I write filtering code, I try to make it as easy as possible to define new filters. Every site needs a slightly different set of filters, but the overall principle is always the same: here’s a long list of items, find the items that match these rules. Let’s start by expanding our data model to include a couple of new fields: const bookmarks = [ { "url": "https://estherschindler.medium.com/the-old-family-photos-project-lessons-in-creating-family-photos-that-people-want-to-keep-ea3909129943", "title": "Lessons in creating family photos that people want to keep, by Esther Schindler (2018)", "tags": ["photography", "preservation"], "publicationYear": "2018" }, … ]; Then we can define some filters we might use to narrow the list: const bookmarkFilters = [ { id: 'tag', label: 'tagged with', filterFn: (bookmark, tagName) => bookmark.tags.includes(tagName), }, { id: 'publicationYear', label: 'published in', filterFn: (bookmark, year) => bookmark.publicationYear === year, }, ]; Each filter has three fields: id matches the name of the associated URL query parameter label is how the filter will be described in the list of applied filters filterFn is a function that takes two arguments: a bookmark, and a filter value, and returns true/false depending on whether the bookmark matches this filter This list is the only place where I need to customise the filters for a particular site; the rest of the filtering code is completely generic. This means there’s only one place I need to make changes if I want to add or remove filters. The next piece of the filtering code is a generic function that filters a list of items, and takes the list of filters as an argument: /* * Filter a list of items. * * This function takes the list of items and available filters, and the * URL query parameters passed to the page. * * This function returns a list with the items that match these filters, * and a list of filters that have been applied. */ function filterItems({ items, filters, params }) { // By default, all items match, and no filters are applied. var matchingItems = items; var appliedFilters = []; // Go through the URL query params one by one, and look to // see if there's a matching filter. for (const [key, value] of params) { console.debug(`Checking query parameter ${key}`); const matchingFilter = filters.find(f => f.id === key); if (typeof matchingFilter === 'undefined') { continue; } // There's a matching filter! Go ahead and filter the // list of items to only those that match. console.debug(`Detected filter ${JSON.stringify(matchingFilter)}`); matchingItems = matchingItems.filter( item => matchingFilter.filterFn(item, value) ); // Construct a new query string that doesn't include // this filter. const altQuery = new URLSearchParams(params); altQuery.delete(key, value); const linkToRemove = "?" + altQuery.toString(); appliedFilters.push({ type: matchingFilter.id, label: matchingFilter.label, value, linkToRemove, }) } return { matchingItems, appliedFilters }; } This function doesn’t care what sort of items I’m passing, or what the actual filters are, so I can reuse it between different sites. It returns the list of matching items, and the list of applied filters. The latter allows me to show that list on the page. linkToRemove is a link to the same page with this filter removed, but keeping any other filters. This lets us provide a button that removes the filter. The final step is to wire this filtering into the page render. We need to make sure we only show items that match the filter, and show the user a list of applied filters. Here’s the new code: <script> window.addEventListener("DOMContentLoaded", () => { const params = new URLSearchParams(window.location.search); const { matchingItems: matchingBookmarks, appliedFilters } = filterItems({ items: bookmarks, filters: bookmarkFilters, params: params, }); document.querySelector("#appliedFilters").innerHTML = appliedFilters .map(f => `<li>${f.label}: ${f.value} <a href="${f.linkToRemove}">(remove)</a></li>`) .join(""); document.querySelector("#listOfBookmarks").innerHTML = matchingBookmarks.map(Bookmark).join(""); }); </script> <h1>Bookmarks</h1> <p>Applied filters:</p> <ul id="appliedFilters"></ul> <p>Bookmarks:</p> <ul id="listOfBookmarks"></ul> I stick to simple filters that can be phrased as a yes/no question, and I rely on my past self to have written sufficiently useful metadata. At least in static sites, I’ve never implemented anything like a fuzzy text search, where it’s less obvious whether a particular item should match. You can check out the filtering code on the demo page. demo) The next feature I usually implement is sorting. I build a dropdown menu with all the options, and picking one reloads the page with the new sort order. Here’s a quick design sketch: For example, I often sort by the date I saved an item, so I can find an item I saved recently. Another sort order I often use is “random”, which shuffles the items and is a fun way to explore the data. As with filters, I put the current sort order in a query parameter, for example: bookmarks.html?sortOrder=titleAtoZ As before, I want to write this in a generic way and share code between different sites. Let’s start by defining a list of sort options: const bookmarkSortOptions = [ { id: 'titleAtoZ', label: 'title (A to Z)', compareFn: (a, b) => a.title > b.title ? 1 : -1, }, { id: 'publicationYear', label: 'publication year (newest first)', compareFn: (a, b) => Number(b.publicationYear) - Number(a.publicationYear), }, ]; Each sort option has three fields: id is the value that will appear in the URL query parameter label is the human-readable label that will appear in the dropdown compareFn(a, b) is a function that compares two items, and will be passed directly to the JavaScript sort function. If it returns a negative value, then a sorts before b. If it returns a positve value, then a sorts after b. Next, we can define a function that will sort a list of items: /* * Sort a list of items. * * This function takes the list of items and available options, and the * URL query parameters passed to the page. * * It returns a list with the items in sorted order, and the * sort order that was applied. */ function sortItems({ items, sortOptions, params }) { // Did the user pass a sort order in the query parameters? const sortOrderId = getSortOrder(params); // What sort order are we using? // // Look for a matching sort option, or use the default if the sort // order is null/unrecognised. For now, use the first defined // sort order as the default. const defaultSort = sortOptions[0]; const selectedSort = sortOptions.find(s => s.id === sortOrderId) || defaultSort; console.debug(`Selected sort: ${JSON.stringify(selectedSort)}`); // Now apply the sort to the list of items. const sortedItems = items.sort(selectedSort.compareFn); return { sortedItems, appliedSortOrder: selectedSort }; } /* Get the current sort order from the URL query parameters. */ function getSortOrder(params) { return params.get("sortOrder"); } This function works with any list of items and sort orders, making it easy to reuse across different sites. I only have to define the list of sort orders once. This approach makes it easy to add new sort orders, and to write a component that renders a dropdown menu to pick the sort order: /* * Create a dropdown control to choose the sort order. When you pick * a different value, the page reloads with the new sort. */ function SortOrderDropdown({ sortOptions, appliedSortOrder }) { return ` <select onchange="setSortOrder(this.value)"> ${ sortOptions .map(({ id, label }) => ` <option value="${id}" ${id === appliedSortOrder.id ? 'selected' : ''}> ${label} </option> `) .join("") } </select> `; } function setSortOrder(sortOrderId) { const params = new URLSearchParams(window.location.search); params.set("sortOrder", sortOrderId); window.location.search = params.toString(); } Finally, we can wire the sorting code into the rest of the app. After filtering, we sort the items and then render the sorted list. We also show the sort controls on the page: <script> window.addEventListener("DOMContentLoaded", () => { const params = new URLSearchParams(window.location.search); const { matchingItems: matchingBookmarks, appliedFilters } = filterItems(…); … const { sortedItems: sortedBookmarks, appliedSortOrder } = sortItems({ items: matchingBookmarks, sortOptions: bookmarkSortOptions, params, }); document.querySelector("#sortOrder").innerHTML += SortOrderDropdown({ sortOptions: bookmarkSortOptions, appliedSortOrder }); document.querySelector("#listOfBookmarks").innerHTML = sortedBookmarks.map(Bookmark).join(""); }); </script> <p id="sortOrder">Sort by:</p> You can check out the sorting code on the demo page. demo) If you have a really long list of items, you may want to break them into multiple pages. This isn’t something I do very often. Modern web browsers are very performant, and you can put thousands of elements on the page without breaking a sweat. I’ve only had to add pagination in a couple of very image-heavy sites – if it’s a text-based site, I just show everything. (You may notice that, for example, there are no paginated lists anywhere on this site. By writing lean HTML, I can fit all my lists on a single page.) If I do want pagination, I stick to a classic design: As with other features, I use a URL query parameter to track the current page number: bookmarks.html?pageNumber=2 This code can be written in a completely generic way – it doesn’t have to care what sort of items we’re paginating. First, let’s write a function that will select a page of items for us. If we’re on page N, what items should we be showing? /* * Get a page of items. * * This function will reduce the list of items to the items that should * be shown on this particular page. */ function paginateItems({ items, pageNumber, pageSize }) { // Page numbers are 1-indexed, so page 1 corresponds to // the indices 0…(pageSize - 1). const startOfPage = (pageNumber - 1) * pageSize; const endOfPage = pageNumber * pageSize; const thisPage = items.slice(startOfPage, endOfPage); return { thisPage, totalPages: Math.ceil(items.length / pageSize), }; } In some of my sites, the page size is a suggestion rather than a hard rule. If there are 27 items and the page size is 25, I think it’s nicer to show all the items on one page than push a few items onto a second page which barely has anything on it. But that might reflect my general dislike of pagination, and it’s definitely a nice-to-have rather than a required feature. Once we know what page we’re on and how many pages there are, we can create a component to render some basic pagination controls: /* * Renders a list of pagination controls. * * This includes links to prev/next pages and the current page number. */ function PaginationControls({ pageNumber, totalPages, params }) { // If there are no pages, we don't need pagination controls. if (totalPages === 1) { return ""; } // Do we need a link to the previous page? Only if we're past page 1. if (pageNumber > 1) { const prevPageUrl = setPageNumber({ params, pageNumber: pageNumber - 1 }); prevPageLink = `<a href="${prevPageUrl}">← prev</a>`; } else { prevPageLink = null; } // Do we need a link to the next page? Only if we're before // the last page. if (pageNumber < totalPages) { const nextPageUrl = setPageNumber({ params, pageNumber: pageNumber + 1 }); nextPageLink = `<a href="${nextPageUrl}">next →</a>`; } else { nextPageLink = null; } const pageText = `Page ${pageNumber} of ${totalPages}`; // Construct the final result. return [prevPageLink, pageText, nextPageLink] .filter(p => p !== null) .join(" / "); } /* Returns a URL that points to the new page number. */ function setPageNumber({ params, pageNumber }) { const updatedParams = new URLSearchParams(params); updatedParams.set("pageNumber", pageNumber); return `?${updatedParams.toString()}`; } Finally, let’s wire this code into the rest of the app. We get the page number from the URL query parameters, paginate the list of filtered and sorted items, and show some pagination controls: <script> /* Get the current page number. */ function getPageNumber(params) { return Number(params.get("pageNumber")) || 1; } window.addEventListener("DOMContentLoaded", () => { const params = new URLSearchParams(window.location.search); const { matchingItems: matchingBookmarks, appliedFilters } = filterItems(…); const { sortedItems: sortedBookmarks, appliedSortOrder } = sortItems(…); const pageNumber = getPageNumber(params); const { thisPage: thisPageOfBookmarks, totalPages } = paginateItems({ items: sortedBookmarks, pageNumber, pageSize: 25, }); document.querySelector("#paginationControls").innerHTML += PaginationControls({ pageNumber, totalPages, params }); document.querySelector("#listOfBookmarks").innerHTML = thisPageOfBookmarks.map(Bookmark).join(""); }); </script> <p id="paginationControls">Pagination controls: </p> One thing that makes pagination a little tricky is that it affects filtering and sorting as well – when you change either of those, you probably want to reset to the first page. For example, if you’re filtering for animals and you’re on page 3, then you add a second filter for giraffes, you should reset to page 1. If you stay on page 3, it might be confusing if there are less than 3 pages of results with the new filter. The key to this is calling params.delete("pageNumber") when you update the URL query parameters. You can play with the pagination on the demo page. demo 1, demo 2) One problem with relying on JavaScript to render the page is that sometimes JavaScript goes wrong. For example, I write a lot of my metadata by hand, and a typo can create invalid JSON and break the page. There are also people who disable JavaScript, or sometimes it just doesn’t work. If I’m using the site, I can open the Developer Tools in my web browser and start debugging there – but that’s not a great experience. If you’re not expecting something to go wrong, it will just look like the page is taking a long time to load. We can do better. To start, we can add a <noscript> element that explains to users that they need to enable JavaScript. This will only be shown if they’ve disabled JavaScript: <noscript> <strong>You need to enable JavaScript to use this site!</strong> </noscript> I have a demo page which disables JavaScript, so you can see how the noscript tag behaves. This won’t help if JavaScript is broken rather than disabled, so we also need to add error handling. We can listen for the error event on the window, and report an error to the user – for example, if a script fails to load. <div id="errors"></div> <script> window.addEventListener("error", function(event) { document .querySelector('#errors') .innerHTML = `<strong>Something went wrong when loading the page!</strong>`; }); </script> We can also attach an onerror handler to specific script tags, which allows us to customise the error message – we can tell the user that a particular file failed to load. <script src="app.js" onerror="alert('Something went wrong while loading app.js')"></script> I have another demo page which has a basic error handler. Finally, I like to include a loading indicator, or some placeholder text that will be replaced when the page will finish loading – this tells the user where they can expect to see something load in. <ul id="listOfBookmarks">Loading…</ul> It’s somewhat rare for me to add a loading indicator or error handling, just because I’m the only user of my static sites, and it’s easier for me to use the developer tools when something breaks. But providing mechanisms for the user to understand what’s going on is crucial if you want to build static sites like this that other people will use. Test the code with QUnit and Playwright If I’m writing a very complicated viewer, it’s helpful to have tests. I’ve found two test frameworks that I particularly like for this purpose. QUnit is a JavaScript library that I use for unit testing – to me, that means testing individual functions and components. For example, QUnit was very helpful when I was writing the early iterations of the sorting and filtering code, and writing tests caught a number of mistakes. You can run QUnit in the browser, and it only requires two files, so I can test a project without creating a whole JavaScript build system or dependency tree. Here’s an example of a QUnit test: QUnit.test("sorts bookmarks by title", function(assert) { // Create three bookmarks with different titles const bookmarkA = { title: "Almanac for apples" }; const bookmarkC = { title: "Compendium of coconuts" }; const bookmarkP = { title: "Page about papayas" }; const params = new URLSearchParams("sortOrder=titleAtoZ"); // Pass the bookmarks in the wrong order, so they can't be sorted // correctly "by accident" const { sortedItems, appliedSortOrder } = sortItems({ items: [bookmarkC, bookmarkA, bookmarkP], sortOptions: bookmarkSortOptions, params, }); // Check the bookmarks have been sorted in the right order assert.deepEqual(sortedItems, [bookmarkA, bookmarkC, bookmarkP]); }); You can see this test running in the browser in my demo page. Playwright is a testing library that can open a web app in a real web browser, interact with the page, and check that the app behaves correctly. It’s often used for dynamic web apps, but it works just as well for static pages. For example, it can test that if you select a new sort order, the page reloads and show results in the correct order. Here’s an example of a simple test written with Playwright in Python: from playwright.sync_api import expect, sync_playwright with sync_playwright() as p: browser = p.webkit.launch() # Open the HTML file in the browser page = browser.new_page() page.goto('file:///Users/alexwlchan/Sites/sorting.html') # Look for an <li> element with one of the bookmarks -- this will # only appear if the page has rendered correctly. expect(page.get_by_text("So Many Nevers")).to_be_visible() browser.close() These tools are a great safety net for catching mistakes, but I don’t always need them. I only write tests for my more complicated sites – when the sorting/filtering code is particularly complex, there’s a lot of rendering code, or I anticipate making major changes in future. I don’t bother with tests when the site is simple and unlikely to change, and I can just do manual checks when I write it the first time. Tests are less useful if I know I’ll never make changes. This is getting away from the idea of a self-contained static website, because now I’m relying on third-party code, and for Playwright I need to maintain a working Python environment. I’m okay with this, because the website is still usable even if I can no longer run the tests. These are useful sidecar tools, but I only need them if I’m making changes. If I finish a site and I know I won’t change it again, I don’t need to worry about whether the tests will still work years later. Manipulate the metadata with Python For small sites, we could write all this JavaScript directly in <script> tags or in a single file. As we get more data, splitting the metadata and application logic makes everything easier to manage. One pattern I’ve adopted is to put all the item metadata into a single, standalone JavaScript file that assigns a single variable: const bookmarks = […]; and then load that file in the HTML page with a <script src="metadata.js"> element. I use JavaScript rather than pure JSON because browsers don’t allow fetching local JSON files via file://. If you open an HTML page without a web server, the browser will block requests to fetch a JSON file because of security restrictions. By storing data in a JavaScript file instead, I can load it with a simple <script> tag. I wrote a small Python library javascript-data-files that lets me interact with JSON stored this way. This allows me to write scripts that add data to the metadata file (like saving a new bookmark) or to verify the existing metadata (like checking that I have an archived copy of every bookmark). I’ll write more about this in future posts, because this one is long enough already. For example, let’s add a new bookmark to the metadata.js file: from javascript_data_files import read_js, write_js bookmarks = read_js("metadata.js", varname="bookmarks") bookmarks.append({ "url": "https://www.theguardian.com/lifeandstyle/2019/jan/13/ella-risbridger-john-underwood-friendship-life-new-family", "title": "When my world fell apart, my friends became my family, by Ella Risbridger (2019)" }) write_js("metadata.js", varname="bookmarks", value=bookmarks) We’re starting to blur the line between a static site and a static site generator. These scripts only work if I have a working Python environment, which is less future-proof than pure HTML. I’m happy with this compromise, because the website is fully functional without them – I only need to run these scripts if I’m modifying the metadata. If I stop making changes and the Python environment breaks, I can still read everything I’ve already saved. Store the website code in Git I create Git repositories for all of my local websites. This allows me to track changes, and it means I can experiment freely – I can always roll back if I break something. These Git repositories only live on my local machine. I run git init . in the folder, I create commits to record any changes, and that’s it. I don’t push the repository to GitHub or another remote Git server. (Although I do have backups of every site, of course.) Git has a lot of features for writing code in a collaborative environment, but I don’t need any of those here – I’m the only person working on these sites. Most of the time, I just use two commands: $ git add bookmarks.html $ git commit -m "Add filtering by author" This creates a labelled snapshot of my latest changes to bookmarks.html. I only track the text files in Git – the HTML, CSS, and JavaScript. I don’t track binary files like images and videos. Git struggles with those larger files, and I don’t edit those as much as the text files, so having them in version control is less useful. I write a gitignore file to ignore all of them. Closing thoughts There are lots of ideas here, but you don’t need to use all of them – most of my sites only use a few. Every site is different, and you can pick what makes most sense for your project. If you’re building a static site for a tiny archive, start with a simple HTML file. Add features like templates, sorting, and filtering incrementally as they become useful. You don’t need to add them all upfront – that can make things more complicated than they need to be. This approach can scale from simple collections to sophisticated archives. A static website built with HTML and JavaScript is easy to maintain and modify, has no external dependencies, and is future-proof against a lot of technological changes. I’ve come to love using static websites to store my local data. They’re flexible, resilient, and surprisingly powerful. I hope you’ll consider it too, and that these ideas help you get started. [If the formatting of this post looks odd in your feed reader, visit the original article]
In my previous post, there was a first for this site: I embedded a post from Mastodon. Like many social media services, Mastodon has built-in support for embedding posts. If you’re looking at a public post, you can get a snippet of HTML and JavaScript to show that post in another web page. You add that snippet to your page, and when somebody opens it, the snippet will appear as a Mastodon post. It’s quick, easy, and not how I did it. When I want to embed post from social media sites, I don’t use the native embed. Instead, I write my own HTML and CSS to mimic their appearance, and it looks pretty close to the real thing. Here’s a comparison of a native/custom Mastodon embed – they’re not exactly the same, but close enough that you probably wouldn’t notice unless you were looking: This is something I’ve been doing for over a decade – I got the original idea from Dr Drang, who does something similar for tweets. (He wrote that post in 2012, and it highlights the value of resilient embeds – two of the four tweets he’s quoted are no longer available. The post would be harder to read if you couldn’t see the tweets he was quoting and replying to.) Many years ago, I copied Dr Drang’s code, created my own variant, and I used that for embedding tweets. I’ve now created another variant that works for Mastodon toots, and I have unfinished branches with more variants for Instagram and Bluesky. Why do I prefer my embeds? There are several reasons: My embeds are smaller and faster. Mastodon posts are short, and yet the native embed downloads nearly a megabyte of data to display 88 words of text – including the audio file boop.mp3, for reasons I can’t imagine. Meanwhile my custom embed requires just 35KB. I try to keep this site pretty lean and lightweight – the average size of an HTML page is just 13KB. Adding a megabyte of data for an embed would undo all that hard work. My embeds don’t require any JavaScript, third-party or otherwise. You don’t need JS to show static content, and adding third-party code introduces a privacy risk for my readers. I’m not completely opposed to JavaScript, but it’s massively overused on the modern web. It’s useful for interactive elements, but I really don’t need it on this content-only site. My embeds are more resilient. Because I have no dependency on the Mastodon server, it doesn’t matter if the server goes away or the toot is deleted. My page will be unaffected. This is why many people include social media posts as images, or copy the text into a blockquote. We’re in a time of increased tumult and instability for social media platforms, but their woes aren’t going to leave holes in my posts. My embeds support dark mode. A few years ago I added dark mode to this site. It’s not something I use myself, but I know it’s important to a lot of people and it was a fun little project. The native Mastodon embeds always show toots in light mode, whereas my embeds will adapt to your preference: On the other hand, the argument in favour of native embeds is that they need minimal effort, they should always work, and they support more features. My custom embeds can’t do pictures, or link previews, or quote toots, because I’ve never embedded a toot that uses those. If/when I do, I’ll have to write the code to support that. I’ll find that fun, but most people would find that annoying. I don’t know what accessibility is like for native embeds. My custom embeds only use a handful of semantic HTML elements, so they get a lot of good behaviour “by default from the browser. I hope native embeds are good for accessibility, but I don’t know enough to say whether my approach is better or worse in that regard. How does it work? I have some HTML and CSS that render the embedded toot. Here’s the entirety of the HTML – I’ve tweaked this ever so slightly for readability, but the key parts are there. <blockquote class="mastodon-embed"> <div class="header"> <a class="name_header" href="https://code4lib.social/@linguistory"> <img class="avatar" src="linguistory.jpg" alt=""> <div class="name"> <span class="display_name">James Truitt (he/him)</span> <span class="account_name">@linguistory@code4lib.social</span> </div> </a> <img class="mastodon_logo" src="logo.svg"> </div> <p class="text"> Do any <a href="https://code4lib.social/tags/digipres">#digipres</a> folks happen to have a handy repo of small invalid bags for testing purposes? <br> <br> I'm trying to automate our ingest process, and want to make sure I'm accounting for as many broken expectations as possible. </p> <p class="meta"> <a href="https://code4lib.social/@linguistory/113924700205617006">31 Jan 2025 at 19:49</a> </p> </blockquote> The CSS styles are a bit long to include here, but you can see them by reading the source code of my demo page. I’m using CSS grid layout to lay out the different components, but otherwise nothing too complicated. I designed my custom embed by creating two HTML files: one with a native embed, and one with my custom embed. I used the developer tools to get key values from the native embed, like colours and spacing, then I kept adding styles to my custom embed until it looked about right. When I want to embed a toot now, I write a line like: {% mastodon https://code4lib.social/@linguistory/113924700205617006 %} This calls a Jekyll plugin that replaces this line with an embedded toot. This code is very scrappy and poorly documented, so it may not be especially easy to adapt to your own site – if you want to do this, start from the HTML and CSS instead. Like everything on this site, my Mastodon embeds are a work-in-progress and not something that everybody should copy. The built-in embeds are quick, easy, and convenient, and they’re what most people should use. But what I like about having my own website is that when I do want to spend an unreasonable amount of effort on something, and do it just because I think it’s fun, I can do that, and nobody can stop me. [If the formatting of this post looks odd in your feed reader, visit the original article]
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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.
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.
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
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]
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.