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Slop is without a doubt everywhere. Be it in restaurant’s menus, music playlists, or online game storefronts, it is now impossible to escape it. Yet, I’ve never met anyone who was actually looking forward to consuming that. No matter how hard it’s shoved down our throats, the human longing to connect with other people through art still remains. Raymond Kotewicz They will not let you filter A simple solution would be to have slop filters. Automatically label AI-generated trash, and let users hide it if they don’t want to see it. So far, I don’t think any of the major internet platforms has implemented this. The common excuse for why they don’t let you easily filter out slop is that it would be technically too difficult. Let me be clear with everyone here: this is complete bullshit. We’re talking about companies that can hire some of the most talented engineers in the world. I don’t think anyone seriously believes they can’t come up with a way to guess if a file has been generated by AI or not. 90% of the slop out there stands out so much everyone can see how it was made after looking at it for 5 seconds. A more plausible reason for the lack of slop filter is that they are simply testing out the waters. They are checking if users will tolerate it. The question tech executives want answered is: will people buy this garbage? And if the answer to that question is yes, here’s what they will do: they will start generating it themselves. They don’t need the useless meat proxies who think typing ten words and pressing enter makes them creative. They definitely don’t want to verse anyone royalties when they could just be making a one time purchase of tokens. If you think this is not real, just ask Tim Sweeney. I bet he will be more than happy to confirm. Roman Navarro Curation by humans for humans So now that it’s clear that tech companies will not save us from drowning in slop, it’s time to turn back to actual humans instead. The good news is they are a lot of people who are here to help you. There are thousands of people on the internet who will search through mountains of content to find the perfect piece of art they want to share with the world. We don’t explicitly call them curators. They are streamers, bloggers, journalists, DJs, overly enthusiast fans, or simply your friends. Some of those people are seriously amazing. There are DJs out there who will spend thousands of hours digging through crates to craft you the perfect ninety minutes of music. Why do you still need those machine-generated playlists? The alternative to blindly trusting the output of a recommendation algorithm isn’t always to become an expert in every field of art. Sometimes you just have to put the trust you had in a machine back into another person. Back to the real world? The real long term solution would be to stop relying only on the internet for distribution. This is already a thing in the music industry. Every city has a record shop that will sell you a carefully selected set of vinyls. For many musicians, physical sales (and concerts) are a much better source of income than streaming. The same goes for the movie. While text-to-video AI is a thing, the real money isn’t made by generating 30 second TikTok dramas. The real money comes from people paying tickets to see a full-length movie in a theater. Sadly I’m not too optimistic about my own field: video games. Somehow it seems the direction we’ve taken is the opposite. We’re all in on online distributions platforms. We have fully accepted them as our new digital overlords. Next month GTA V is coming out without a disk in the box. Sony already announced that the next Playstation will not support physical media. Physical distribution is not even mentioned in the pitch of the average indie publisher. In Breda, the small Dutch city where I live, we used to have two dedicated video games shop. Higher interests rates, expensive rents, and various policy failures have done their jobs: they are both closed. I don’t think this is a good sign for the survivability of the gaming industry.
In April, the Godot foundation organized a conference close to their home base: GodotCon Amsterdam. It was a good occasion for us to showcase Paradox Tower and get some beta feedback from other developers. Most importantly it was two entire days of talks about game development in Godot. Here are some of the ones I recommend checking out if you are into it. 1000 rigged AnimationPlayers in 3D, impossible? Having a lot of animated 3D objects in Godot can seriously damage performance. This is something to look for if you’re developing for systems with limited resources, which may rely on the Compatibility renderer (like Android or Switch). Bertrand Bordage explains how to get around this limitation using Vertex Animation Textures. It basically consists of baking an animation into a shader. I had previously never heard of it and this talk really seems like a doable solution for Switch ports of 3D games. The Clone Wars: Defending Godot Games From Reupload Scams Godot games are very easy to reverse engineer. Many developers assume this is not an issue and will only be used by modders and curious players. Sadly, there’s a cottage industry of shady publishers who will take advantage of this to copy your game. I’ve previously talked a bit about technical countermeasures you can use to make this process harder. In this talk, Yasen Dinkov also explores the legal and marketing aspects of the problem. He provides ideas that can make taking down illegal store pages much easier. From model to gameplay in seconds: setting up your Blender Godot pipeline Doing a lot of manual steps can kill your productivity. A big source of this is manually importing 3D assets and setting them up as Godot scenes. Godot comes with a set of tools to speed that up: post-import scripts. Here, Marion Allard explains how to make the best use of it, with step-by-step examples using Blender objects: Make your own fractal! A more relaxed talk to finish, with a short and fun introduction to fractals by Marta Imke. It explores visual math and the different ways you can build fractal images out of simple functions. This is definitely something that can be useful if you want to create some trippy visual effects.
In the latest Steam client update, Valve unveiled major changes to its store’s home page. One of the main difference is that the “Popular Upcoming” section, which shows upcoming games now shows a lot fewer games. This seems to worry indie developers who relied on this section as a major driver of visibility around launch. I personally think this is will not change much to how indie games are marketed and might even be a positive for many niche developers. Popular upcoming is dead Now that game development is more accessible than ever, and anyone can post their games on Steam and Epic. Since all those new developers need to market their games, this has created a cottage industry of game marketing influencers. The strategy they teach is basically always a variant of: Make a game (and a demo) Get a lot of people to play the demo during Steam Next Fest Get 6000+ Wishlist Appear in Popular Upcoming ??? Profit Since everyone is running around with approximately the same strategy, it became a very crowded trade. With everyone publishing a more and more polished demo, Next Fest began to have diminishing returns for most developers. In the same way Popular Upcoming was very impactful when maybe 1 or 2 games appeared in it every day, but if it shows a dozen game, it’s not a guarantee of success anymore. And let’s be honest, players have probably been paying less and less attention to this section of the store for a while. Basically every shovelware publisher knows how to game the algorithm and get their new version of Slop Simulator 2027 in their. Long live the Personal Calendar In the same update, Valve introduced a new feature: the Personal Calendar. It’s a whole page which aims at making players discover games that are personalized to their taste. The recommendation algorithm seems to work somewhat decently. I do not think it is entirely based on tags, since it seems to recommend me a lot of “Open World Survival Craft” games, which is not something I usually play. It may be using wishlist behavior of similar players to make recommendations. One of the major differences is that it shows a calendar for the next eight weeks, so games can have a much longer visibility window. If a player of your target audience didn’t browse Steam on the day before your launch, it would miss you on popular upcoming, while now they can It also shows recently released games, in the past 7 days and past month. Contrary to the homepage “Popular New Releases” tab (ex “New & Trending”), which requires a lot of active players, this section seems accessible even to unknown games. Steam is currently recommending me Imago Season with at the moment 0 review and only 2 concurrent players. A new era for niche games? Before this change, it was extremely difficult for developers to market niche games. I know it from first-hand experience, since I have no doubt Dice ‘n Goblins would have been easier to market if it was not the weirdest combination of ideas possible. Because of this, a lot of new indie game developers were advised to stick to a very popular genre. You love real-time strategy? Too bad, you’ll have to make an action roguelike instead. This has led to a loss of creativity in the indie game world. With many developers working on genres that do not interest them, using recycled ideas and aesthetics. I do believe that this new version of Steam might swing the pendulum in the other way. Now that players of niche games can get recommendation customized to their taste, making games for a non-mainstream audience can become viable again.
Lately, I’ve been experimenting with building games in Ebitengine. It’s a 2D engine that lets you create games using the Go programming language. To be able to iterate faster on visual effects, I have created a tool that lets me preview shaders: Luluka. Kage Shaders For those who feel like they missed an episode, shaders are small programs running on the GPU which can be used to modify the pixels of an image. In games, they are used everywhere to control how a game looks. They can be used create special effects like blurring an image or making your screen look like a CRT from the 90s. Ebitengine has its own shader language called Kage. It is very convenient because it has a syntax extremely close to Go. Close enough that you can even run go fmt to format your Kage files. Under the hood, Ebitengine will automatically convert Kage shaders into a format understandable by the GPU. Quasilyte’s article about Ebitengine shaders is generally the most comprehensive introduction to the subject. You can also learn more about them in the Kage’s desk. Luluka One of the main difference between Ebitengine and other game engines like Godot, is that it doesn’t have a visual editor. This is generally fine, since I’ve spent most of my career avoiding What-You-See-Is-What-You-Get tools in favor of staying inside NeoVim. But for shaders, you can end up spending a lot of time tweaking a few variables until you get them to look right. That’s why I decided to build my own tool that would let me work on a shader in isolation, and quickly change the variables we give it. You can install Luluka using the following command: go install github.com/Tsukumogami-Software/[email protected] Run a shader by pointing it straight to the file, passing textures with -i and uniform values with -u: luluka sample/transition.kage -i image2.png -i image.png -u Steepness:80 -u Seed.0:15 -u Seed.1:100 -u Seed.2:5000 -u Seed.3:5000 -u Speed:0.08 For more convenience, you can use a YAML file to pass your uniform values. This is especially practical when working with arrays or matrices, since commands can get very long: Steepness: 80 Seed: [15.0, 100.0, 5000.0] Speed: 0.08 luluka sample/transition.kage -i image2.png -i image.png -v values.yaml
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As with my last post on AI, let me open with a statement: Just because most people hold AI a certain way, doesn’t mean that we always must, either now or in the future. As I noted in that piece, and my shades of gray post, I am well aware — and agree with — … Continue reading Must we always use LLMs this way? →
TokyoDev has grown substantially over the past few years. We’ve expanded our community, dramatically increased the scope and frequency of our events, and have curated a library of articles for international developers. Despite all that, the way we run the business hasn’t changed much. I’ve continued to coordinate most of these activities myself, making the final decisions and keeping everything connected. In short, TokyoDev cannot run without my daily input. That approach worked well when TokyoDev was smaller, but it’s increasingly limiting how much we can accomplish. To help the business grow further, I wanted to find someone to take over some of my responsibilities and bring a fresh perspective. This is a significant step for me personally, as while I have worked with many contractors over the years, I have never actually hired a regular employee. Most importantly, I wanted to ensure that this person was someone I could trust to understand what TokyoDev stands for, and to make decisions in line with our philosophy. So I’m happy to announce that Scott Rothrock is joining TokyoDev full time as our Head of Operations. About Scott Scott has lived in Japan for nearly 20 years and has worked as a software engineer in multiple industries and environments, including small Japanese-owned businesses, overseas employers, and international companies based in Japan. Until the end of October 2026, he was the head of the Japan Markets team at Kraken Technologies, where he led an international team of software engineers in delivering complicated, regulated products on strict timelines for Japanese electricity and gas retailers, while also ensuring compliance with evolving organizational and global standards. He’s made a name for himself both inside and outside Japan as a kind, effective mentor who can cultivate great engineers and leaders. Scott’s previous role in TokyoDev Some of you may already know Scott in another capacity, as he has been involved with TokyoDev since 2022. He began as a community volunteer and later became our community manager, which also included responsibilities such as managing our article pipeline from submission to publishing. When I created TokyoDev’s Discord, I had no previous experience running an online community. Scott joined soon after I started and began helping other members, stepping in when people behaved inappropriately, and configuring our roles and permissions and safety features. I was impressed by his initiative and asked him to become an official moderator in January 2023. Outside of his role as a moderator, Scott has always pushed TokyoDev to grow; for example, he arranged internet events such as livestreamed AMAs and interviews. He organized TokyoDev’s first meetup as a volunteer in February 2023, which taught me that there was interest in TokyoDev events outside of the internet. By the end of 2023, I had brought him on as our part-time community manager. What I saw throughout his tenure was that Scott continued to take initiative in expanding TokyoDev. Scott was one of our first external contributors, writing about his experience living with a disability in Japan. He encouraged me to expand and refine both our editing and submission processes so that we could reach out to even more contributors and platform their perspectives. The editorial team now consists of him and Rebecca Callahan, and they’ve had a hand in every article that TokyoDev has published. In other words, since 2022, Scott has shown that he cares about TokyoDev as much as I do. He is invested in what we do and why we do it, and shares my vision for what TokyoDev can become. What changes now Scott’s experiences as an engineer, a mentor, and a leader make me confident that, as our Head of Operations, he will continue to help TokyoDev mature. A big part of what has made TokyoDev successful is that I see the software developers who use our site as my peers and genuinely want to help them. Scott shares that perspective, and will join me in ensuring that TokyoDev remains true to its principles while we grow. So what changes in the short term? Nothing dramatic. I will teach Scott about managing TokyoDev as a business, and he will document our processes and products as a part of that learning. He will also look for areas of improvement while ensuring that we remain compliant with regulations and labor law. Over time, I will hand over some other responsibilities. I will continue to run TokyoDev, but TokyoDev will become less dependent on me for its day-to-day operations. Long term, I’m excited about what this will enable us to do. TokyoDev has always been about helping international software developers build successful careers in Japan, and bringing Scott on full time puts us in a much better position to continue doing that. We look forward to writing the next chapter of TokyoDev together.
Kidz Fun Art is a drawing, colouring and animation app for kids that I’ve been building since 2021. It runs in the browser also as a simple wrapper app on iOS, iPadOS and Windows. It wasn’t getting a great click through rate on Google, even for people who directly searched for it. So, for two days I took a hard look at how fast it loads and how search engines see it. It wasn’t flattering. This post covers what I found, what I changed and what the graphs say now. Short version: on a simulated slow phone, the homepage’s Largest Contentful Paint got about 2.6× faster (15.9 s to 6.1 s) and its first load is about 82% smaller (4.2 MB to 0.75 MB). I also fixed a set of SEO mistakes that are, frankly, embarrassing to write down. I’ll write them down anyway. Part 1: Google had no idea what the site was for I started by reading 16 months of Google Search Console data. 9,380 clicks in total. About 9,200 came from people who had typed “kidz fun art” or a close spelling. People searching for what the app actually does (“coloring app for kids”, “ai art for kids”, “make a comic book”) sent the site 164 clicks. In 16 months. Total. When there was a relevant page, Google ranked it well. The site was #1 for things like “drawing app for kids ipad” and a German query that means “paint by numbers app without ads”. There just weren’t many relevant pages. Then I looked at the code, and the cause was obvious. The homepage <title> was “Kidz Fun Art” in all 12 languages. That’s a fine name, and a terrible title. It tells a search engine nothing. The homepage’s first <h1> was the pricing table’s “Choose your experience”. I was, in effect, telling Google that the most important thing on my homepage was the checkout. Every blog post had the same meta description: a generic line about “news, updates, tips”. Each post’s front matter already had a perfectly good excerpt field that nobody read. Blog posts had two <h1>s: the site header and the post title. There was no structured data at all. The sitemap that redirected every single URL This one stings. The sitemap is the one file whose whole job is to say “here are my URLs, please index them”. Mine listed 17 URLs, and every one of them redirected. The sitemap build used an environment variable that resolved to kidzfun.art. The site lives on www.kidzfun.art, so every URL got a 308. Only the English blog posts were listed. The other 11 languages appeared only as hreflang alternates, which pointed at redirecting URLs too. There were no <link rel="canonical"> tags on any page and no hreflang in the page <head>. Google was left to guess which URL was the real one, and it guessed wrong a lot. What I changed: The sitemap is now built on https://www.kidzfun.art, with 204 URLs: every home page, blog index, blog post, About page and gallery page, in all 12 languages. Each one lists all of its language alternates plus an x-default. A shared SeoLinks component adds an absolute canonical (www, no query string) and the full hreflang set to every public page. The ?v= cache-busting URLs that had been showing up as duplicates now canonicalise to the clean URL. /xx/blog/yy/... URLs, which returned a 500 in production (oops), now 308 to /blog/yy/.... Login, account, debug, export and embed pages send X-Robots-Tag: noindex, follow from next.config.js, so they stop using up crawl budget. robots.txt now mentions the sitemap. Novel idea. Why Google skipped half the site Search Console’s Pages report had 93 of 174 known pages not indexed. Every reason on that list traces back to the mistakes above: “Page with redirect” (22): that sitemap, plus internal links to the non-www host. “Duplicate without user-selected canonical” (18): I hadn’t selected any canonicals. Fair cop. “Crawled, currently not indexed” (39): mostly translated posts that Google reached through redirecting URLs, plus thin pages like /login. “Discovered, currently not indexed” (12): Google found these and decided they weren’t worth a visit. With no sitemap entry and no hreflang, I hadn’t given it a reason to. On-page fixes Titles and descriptions: Each blog post got its own seoTitle and description, in all 12 languages. The meta description now comes from the post’s excerpt, which is what that field was for all along. The About pages take their title and description from their own localized Markdown. They had been sharing an English title and a generic description. One <h1> per page: A # heading inside a post’s Markdown now renders as an <h2>, so the post title is the only <h1>. The About page opts back in with a keepH1 prop. Its Markdown heading is the page title. This fix arrived in two parts. The first part left the About page with zero <h1>s. I noticed, eventually. Structured data and social tags: JSON-LD for the home page (the app), blog posts (articles) and the embedded videos. Open Graph and X/Twitter tags on every public page, so shared links get a proper card. A real 404 for deleted gallery images: The AI gallery shows a friendly “AI Artwork Not Found” page when an image has been deleted. It used to return 200 OK, which Google calls a “soft 404” and dislikes. It now returns a 404 with noindex, and a 503 if the Firestore lookup itself fails, so a database hiccup tells crawlers “come back later” rather than “this is gone”. Part 2: Making the homepage fast The homepage is the app. It works offline, so every dialog (save, stickers, colouring pages, “get an adult”, “no wifi”) is server-rendered into the HTML up front. That’s a deliberate trade-off, and I kept it because I want new users to be able to immediately be able to start drawing as soon as they launch the app. What I hadn’t thought about was what it meant for the network. What the homepage downloaded before the app could start I measured the live site with a Lighthouse-style mobile profile: slow 4G (1.6 Mbps, 150 ms latency), 4× CPU slowdown, empty cache. First load: 4.5 MB over 198 requests. The homepage HTML alone was 358 KB, with about 77 <img> tags in hidden dialogs. The browser dutifully fetched every one of those images. The script that starts the app (main.min.js, 191 KB) sat at the very end of the document, so the browser found it last and gave it whatever bandwidth was left. The app script finished arriving at about 14 seconds. The CPU was not the problem. Every long task was under 300 ms. This was purely about bandwidth and request order. I had built a very polite queue and put my own app at the back of it. Watch the queue The waterfall is a simplified illustration. The milestone times (first paint, LCP, load) are measured: slow 4G + 4× CPU, local production build, median of 3 runs. My Largest Contentful Paint was the “Contact support” link The canvas doesn’t count toward LCP, and the toolbar buttons are small. So on a slow load the largest thing painted was… the loading dialog. The loading dialog fades in its “Taking a while? Contact support” text after 5 to 7.5 seconds. On a slow connection, that text became the page’s LCP element, at 15.9 s. I was measuring my page speed by how long it took to suggest the user complain about my page speed. There’s a lesson in there somewhere. The fix wasn’t to change the dialog. It was to make the app start sooner, so it paints before that text ever appears. What I changed Preload the app script. I added <link rel="preload" as="script"> in <head> for main.min.js and errorHandler.min.js. The <script> tags stay where they were, so execution order doesn’t change. The browser just starts fetching them before any image. This one line did more than anything else on this list. It’s a little humbling. Turn the three dialog GIFs into MP4s. Three animated GIFs: an arrow pushing pictures into a tablet (927 KB), a dancing rainbow pencil (650 KB) and a paper plane (305 KB). That’s 1.9 MB of decoration in dialogs most visitors never open on their first visit. I re-encoded them with ffmpeg: H.264 High profile, -preset veryslow -crf 20, yuv420p, +faststart, constant frame rate taken from each GIF’s frame delay. The commands are committed as scripts/gif-to-mp4.sh. The result is 130 KB in total, with SSIM between 0.988 and 0.993 against the originals. The pencil needed tpad=stop_mode=clone:stop_duration=0.2, because ffmpeg’s GIF reader drops the final 250 ms hold and the dance lost its big finish. The new <video autoplay loop muted playsinline> tags render at exactly the same size and class as the old <img> tags. Alt text moved to aria-label. Locked colouring thumbnails were full-size PNGs. In the colouring page picker, locked pages used the full-size colouring PNG as their background image, inlined into the HTML. That came to about 1.3 MB on every first visit, to draw thumbnails in a dialog that was closed. They now use the small image, set when the picker opens, like the unlocked ones always did. Sticker thumbnails wait their turn. I tried loading="lazy" first. It didn’t help: Chrome fetched the images before the stylesheet that hides the dialogs had arrived, so as far as it knew they were visible. Sticker thumbnails and the media library’s YouTube thumbnail now use data-deferred-src. They load 3 seconds after the load event, or as soon as their dialog opens, whichever comes first. The service worker preload starts after load. The offline preload queue (about 2 MB of colouring pages and stickers) used to start while the page was still loading and compete with it. Now it starts after the load event. Same files, same offline result, no fighting for bandwidth. Analytics is async. Nothing waited for it, so nothing needed to block on it. Cache headers. /images/*: max-age=86400, stale-while-revalidate=604800. It had been max-age=0, so every image was re-checked on every visit. Versioned /js, /css and /addToHomescreen files are immutable for a year in production. Offline still has to work Offline support is a core feature, so “just don’t download it” was never an option. Some of the changes above put it at risk, and a couple of things turned up along the way: Safari plays video from byte ranges. Safari requests video with Range: bytes=.... Answer that with a full cached response and the video won’t play. The service worker now answers Range requests from its cache with a proper 206 Partial Content slice (lib/util/rangeResponse.ts, with unit tests). First-visit gap: on a first visit the service worker isn’t controlling the page yet, so dialog images only reached the offline cache on the second online visit. The page’s own dialog images and videos now go to the front of the offline preload queue on the first visit. I nearly downloaded my whole site, 12 times over. The service worker cached every <link href> it found in the page. The new SEO work had just added canonical and hreflang <link>s pointing at all 12 localized homepages. So every new visitor would have downloaded about 12 extra homepages, in languages they don’t speak, “for offline use”. I caught it before production. The service worker now skips rel=canonical and rel=alternate. Safari stopped the service worker mid-download. The old preload queue ran on timers after the request had been answered. Safari considers an idle service worker fair game, so it stopped it after the first few images. The page now sends images in batches of 10, and the service worker caches each batch before it replies. A page lock-up I’d never noticed. resizeDialog added a new “image loaded” listener every time the window resized. With lots of images loading at once, the listeners multiplied until the page locked up. It now attaches one listener per image, which, in hindsight, is how many it needed. Part 3: The blog Blog index cards used the full 1920 px screenshots as covers, up to 500 KB each, in a box about 200 px wide. Each card was a little billboard, shrunk down by the browser on every visit. Covers now go through next/image with sizes that match the actual card widths, so the browser gets a resized WebP. The first few cards get priority. The rest load lazily. The first sizes hint was too small at some widths and made covers blurry. It now matches the real card widths, up to about 630 px between 769 px and 1028 px wide. Post pages: The cover image gets fetchpriority="high", and images further down the post load lazily. Five images were recompressed in place, keeping the same filenames and dimensions. The PNGs came out about 4× smaller and the JPEGs about 40% smaller. I deleted a 1.1 MB cover.png that nothing referenced. All 12 translations of that post used cover.jpg. The PNG had just been sitting in public/ taking up space. Results on the slow 4G profile: /blog LCP went from 13.3 s to 1.8 s. A typical post went from 3.9 s to 1.7 s. The gallery didn’t get faster, on purpose. It got a 5-minute edge cache and a cached Firestore image count, which fixed its slow server responses. I decided not to change its 20 full-size PNG thumbnails, so its lab LCP barely moved (8.4 s to 8.6 s). That’s a choice, and the chart owns it. Part 4: Lighthouse homework After the speed work went live I ran Lighthouse on the production homepage (mobile) and cleaned up what it flagged. Performance stayed in the low 70s, so I have some more work to do, but it’s moved nicely in the right direction. Part 5: Telling the search engines Fixing the site is half the job. The other half is getting crawlers to come back and notice. Google Search Console: Resubmitted the www sitemap. Clicked “Validate fix” on “Duplicate without user-selected canonical” and on the redirect errors. Requested indexing for the main language homepages and the top blog posts. The quota is about 10 a day, so it’ll take a few days. Wrote down a baseline from the Performance report, so in 4 to 8 weeks I can tell whether any of this worked. Bing Webmaster Tools: Submitted all 204 sitemap URLs through URL Submission. The daily quota is 10,000, so no trickle-feeding needed. Bing’s index also feeds DuckDuckGo and several AI search tools, so it’s cheap extra reach. Most of Bing’s “SEO recommendations” pointed at old ?v= URLs and old titles. They’ll clear as Bing recrawls. What I’m not watching: the Lighthouse score. Google ranks on real-user Chrome data (the Chrome UX Report): a rolling 28-day window, judged at the 75th percentile. So the honest answer to “did it work?” is “ask me in a month”. How I kept from breaking things The app has hundreds of unit and integration tests, all of which still pass (even though they do take over 30 minutes to run). I also manually regression tested all the riskiest changes. Hopefully I don’t make things worse before they get better… Lessons, mostly at my own expense Read your own sitemap. Actually open it and click a URL. Mine had been quietly redirecting for who knows how long. <link rel="preload"> for the script that boots your app is the cheapest big win there is. Browsers fetch resources in the order they find them. If your app script is at the bottom of a 358 KB document full of images, it really is last in line. Check what your LCP element actually is before you try to make it faster. Mine was a “Contact support” link. GIF is an image format from 1987 that I was using as a video format. A real video codec beats it by an order of magnitude at the same quality. try/catch doesn’t catch promise rejections. I knew that. My code didn’t. Every <link> you add to <head> is something your service worker might decide to download. Speed scores are for diagnosis. Rankings come from real users. Fix what the lab finds, then wait for the field data. Epilogue You’re still here? Wow. Cool. After all this, I’m hoping that users notice nothing other than quicker app start times. I’m looking forward to keeping an eye on traffic and retention in the coming weeks. Wish me luck!
Five months ago, we rebuilt our warehouse on DuckDB instead of ClickHouse. We did it because a shared ClickHouse cluster couldn't handle hours-long…
