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If all your time is spent watching output tokens, where do your input tokens come from? Letting an agent rip on full auto is basically doom scrolling. Even worse if you're doom scrolling while the agent runs. We humans love frying our dopamine receptors. This feels great until you realize what you were offloading: the struggle. The part where you fail. Failure is the entire point. You don't make progress in the gym unless you take a set at least close to failure. The muscle only adapts when it's forced to. It is no different for the brain. Cognitive Atrophy It is very hard to admit to yourself that your skills have atrophied. It is even harder to admit this to other people. I will admit that over the past several months my brain has gotten smoother (and I wasn't even on Twitter much!). Recently, I had written an abstraction for my diff viewer (diffy), an element system with a macro that lets agents write html-like code in rust for native ui (they reason better with this). But it wasn't adopted everywhere in the repo yet, so when I asked for a new feature, the model decided to hand paint it straight to the viewport instead. Every behavior the element system gives you for free was just... missing. Text wasn't selectable. Hover highlights wouldn't go away. And since I wasn't looking closely, it iterated on the slop and produced more slop, more bugs. I just kept saying continue. I lost a whole day untangling it, and the funny part is that once I actually looked at what it had built, every bug was the same bug. When you hit a roadblock and your immediate reaction is to reach for something else (previously, this used to be other people, but now it is a language model) you are essentially skipping the part where you actually learn to solve the problem. It is funny how one of the best "learning tools" has turned out to be the number one cause (anecdotal. sue me) of the lack of learning! It's been a few months since I started writing this, and things have gotten more dire. Several major software services barely work now, grown engineers I once respected are writing somber posts about missing a language model that was banned for a while. Mourning. For model weights. It's all so dystopian. It didn't work, but boy was it beautiful. As the agents get better, one is basically expected to produce code at an alarming rate. The timeline to get something done is compressed but the time it takes to come up with solutions to hard problems has not. There are usually a few good abstractions one can come up with that balance the upsides and tradeoffs for most software problems. However it is currently trivial to turn your brain off and let the slop flow. The code will be complex. It might look like it all works, but something always breaks. And the solution to that? More slop. Software quality is collapsing as a result, and the societal expectation that engineers understand what they ship is disappearing. You never understood the code in the first place. So when you need to change it, you're asking the same stateless clanker to modify code it has no memory of writing. All output tokens and zero thinking tokens. A lower barrier of entry to write software doesn't imply the standards for good software must be lowered. The culture of doing things because they said you couldn't. The growing trend is to do things because you now can (supposedly), but we used to try and do things because we could not out of sheer stubbornness. Carmack and gang shipped QuakeWorld with client-side prediction over dial-up when the conventional wisdom was that twitch shooters over the internet were unplayable. This only happened because Quake's original netcode was laggy and everyone hated it. (They fixed it in a month.) George Dantzig arrived late to class, mistook two "unsolvable" statistics problems for homework, and solved them. Nobody told him they were impossible, so he just did the work. Andrew Wiles spent seven years alone in his attic working on Fermat's Last Theorem, a problem mathematicians had given up on for 350 years. He announced the proof, a reviewer found a hole in it, and he spent another year fixing that too. Notice that all three of them became who they are because of the struggle, not despite it. The people benefiting most from generative tools today, say Terence Tao or Mitchell Hashimoto, already put in the time, so when they offload work they're just skipping the typing. When people like you and me (if this is not you, then I apologize) offload, we skip the grind itself. With language models, easy tasks got easier, hard tasks stayed hard. The hard part was never the task itself. @codex how do I fix this I don't know, I am figuring this out as I go. The amount of time I have spent actually programming has been dropping month over month this year. I used to have a coding stats section on my website that would track hours I spent writing code split by language, recently I had updated it to this: and it made me quite sad. I do think that sometimes all you need is to realize that the thing you are doing is actually detrimental to your growth. Consistency matters more than one would assume. If you consistently take some time away from these tools and actually use your brain, that alone is already significantly better than offloading your thoughts. Solve the problems yourself. Or at least try, fail, and spend time thinking. There is seemingly no "learning" phase anymore. You are expected to just know things. Learning is fun, don't let anyone take this away from you. I've written about this before. It is probably going to be slow, learning takes time and effort. You will feel stupid (I feel stupid). This is a good feeling, because there exists a world where you are no longer stupid and the path towards it is learning. Books still exist! Libraries are still open, notebooks waiting to be written in. Read more. Write more. But If you really do care about improving yourself, be honest and use these models for what they are, highly efficient filters of zettabytes of data (the internet is estimated to be 175-240 zettabytes (10^{21} bytes)). It was extremely difficult to identify what one needed to read to learn niche topics even like 2 years ago. I remember asking a good friend of mine to recommend material to dive deep into learning about SIMD, and honestly there wasn't much stuff to read except the Intel Intrinsics Guide. And if you've ever taken a look at that, it is quite cancerous for a first-time reader. Language models are super useful here because you can point them at material and you can ask questions that pertain to the thing you care about and it will simply just tell you the correct things. Conclusion One good thing in this age of slop is to consume knowledge at an unbelievable pace. I don't necessarily mean using only model output for learning (I don't trust them to learn any topic more than a shallow amount), but rather using them to help sift through the plethora of information available out there and identifying the right things to read. Human slop exists too and using a language model to supplement your learning might help keep you sane (ironically). I like using these models to write code that I tell it to write (outside of work I enjoy doing it myself entirely), and I am largely disinterested in asking it what I should write. There are exceptions of course, because not everyone is working on scaling software services which has largely been solved (but slowly being forgotten), but that would be for you to decide. The best model you have access to (and it has solved continual learning) is, and always has been, the one inside your skull. It's time to scale up its input tokens.
Glimpse v1.0 Glimpse can now build call graphs, showing you exactly how functions relate to each other in your codebase. Video # what does main call? glimpse code :main # what calls this function? (reverse call graph) glimpse code :process_request --callers # limit the depth glimpse code :build --depth 3 This works by parsing your code with tree-sitter, extracting function definitions and calls, then resolving those calls to their actual definitions. Precise mode Sometimes tree-sitter based resolution isn’t enough. Maybe you’re dealing with dynamic dispatch, generics, or just a language with particularly complex module resolution. For this, Glimpse can use LSPs to resolve definitions semantically. glimpse code :main --precise This spins up actual LSP servers and uses goto-definition / goto-implementation to resolve calls. It’s slower, but accurate. Glimpse will attempt to auto-install the LSP servers for you. Indexing Glimpse eagerly caches whatever it finds into an incremental index. But you can choose to pre-build the index ahead of time for instant queries. # build the index glimpse index build # with LSP for precise resolution glimpse index build --precise # check what you've got glimpse index status The index stores all the definitions, calls, and resolutions so subsequent queries are fast. Language support Glimpse now supports: Go, Rust, C, C++, Python, TypeScript, JavaScript, Zig, Java, Scala, Nix, Lua, Ruby, C#, Kotlin, Swift, and Haskell. Each language has custom tree-sitter queries for extracting definitions, calls, and imports. The grammars are downloaded and compiled automatically on first use. Try it # install cargo install glimpse # or with homebrew brew tap seatedro/glimpse && brew install glimpse # or with nix nix profile install github:seatedro/glimpse # then just glimpse code :main
This is a general devlog covering what I’ve been up to over the past few months! Haskell Hakyll is a Haskell library for generating static websites (yes, you do not, in fact, need Next.js for your personal site). The previous iteration of my website was written with Go + templ, and it was not static. I wrote everything myself, and it was, quite frankly, a horror to maintain. The templ LSP actually hinders more than it helps, and since I did not statically generate the content, it was kind of slow for a personal website. I did not like it. I added some very minor CSS fixes and a theme toggle button for my friend’s new website ludwigabap.com, and I saw the light that is Hakyll. :kneel: So I nerd-sniped myself into rewriting my personal website in Hakyll so that it generates beautiful HTML at build time (it uses Tailwind, too). All the dynamic bits of the website, like my silly stats and coding time, are served from a single Rust Axum server binary on my Hetzner VM that serves other stuff too (Memegrep’s server, among other things). Nix-pill This segues into my next nerd-snipe, or rather, my favorite new pill: the Nix pill. My entire website is built with a single Nix flake, including the Hakyll build step, the Cargo build step, and generating the resulting Docker image. Although, to be honest, the learning curve is very steep and I simply do not have it in me to master this language anytime soon, I like the principles, and it seems to me the least bad build system out there. I do most of my work on a MacBook running a NixOS VM as well. (seatedro/dotnix on GitHub.) It’s so much nicer to have a real dev workflow versus whatever the fuck macOS is. Sorry, but Homebrew is not good software. Ember The reason I started programming was that I was playing a game called Midtown Madness as a wee lad and decided that one day I would build a cool game like that. So, after 15 years, I’ve decided to embark on a small adventure to build a cool physics simulation (and eventually a voxel) engine. It’s written entirely in Zig because I enjoy writing Zig and did not want to touch C++ (although there were times I thought about committing this sin). When Ember gets far enough, I might write separate devlogs, but with what’s written so far, there isn’t enough to justify one. I was knee-deep in the mines because I wanted extremely specific features like multi-viewport docking with ImGui, and the off-the-shelf Zig libraries did not want to ship that for some reason. So there were many adventures in getting this shit to compile neatly. Ember now uses SDL3 for windowing and has an abstract rendering API (rudimentary, but the backends are in place) with SDLRenderer3, OpenGL, and WGPU-native backends that can be neatly switched at comptime. I’m using a bunch of cool resources to learn more about how to do this because I’ve never really written any game-dev related code before. I just want to build some cool simulations, and I will get there no matter what. github / seatedro / ember Thanatos After 5 years, I have finally built a new personal computer. I will be running NixOS as my main operating system with Hyprland and Wayland. I will also have Windows installed on a separate drive mainly for video games, though I expect to use Linux more often. It’s so nice not having to worry about my 256 GB SSD getting filled up on my MacBook Air. :> Exa Also, I joined Exa last month to work on the back-end team, and it has been so fun! I am soaking up as much knowledge from my significantly smarter peers at a good pace. As per custom, I revisited @ludwigABAP’s post: On becoming competitive when joining a new company. (PS: it’s on a new website.) Fin Back to the code cave I go—going to extract the polymorphic.
Look everything up Pretty much the best way to learn that I have found is to refrain from suprressing your innate curosity and let it go wild. Most of the time, you will encounter a term or concept that you do not know, instead of glancing at it briefly, go all in. Google it, read the wiki page, found someone’s blog post? Read it. Watch that youtube video (I only do this if it’s not 3 hours like your average Sphaerophoria stream). Dive head first into rabbit holes. I’ll outline an average day where I have two types of goals: - Concrete goals: Like finish implementing X feature in Y project - Loose goals: Learn about X or Y. I will include rough timestamps but they are pretty much meaningless because productivity levels vary immensely throughout the day. 9:00 AM Decided to learn about document parsing. Links: EPUB specification, I found out that EPUBs are just an archive with HTML, and nearly had a crisis. (Now I know.) Immediate questions: so I need to parse the EPUB, extract metadata, fetch the XML and parse that too. Eventually I would have to use a WebView to render the HTML/CSS on the screen to render the book. (Unfortunate.) Found out that in order to read the PDF spec, you need to pay like 350 swiss francs! Spend time perusing SwiftUI docs, and asking grok about how I can render things on the screen with Swift. Fiddle with XCode, marvel at how Swift gets pretty much everything right but uses func ... for declaring functions. It has Result<T, E> though, so forgiven. 11:00 AM Began reading Computer Systems: A Programmer’s Perspective (CSAPP) Studied numeric representations, created Anki cards for hexadecimal conversion 12:00 PM Rabbit holed into learning so much about UTF-8 Bookmarked to learn more about UTF-8 and UTF-16 and writing a parser for it later (ILY @zack_overflow) 1:00 PM Taking a break, lunch + watching a movie. 2:30 PM Read Jon Olick’s single file resize implementation in C++ as reference (ILY @gizmobly) to roll my own resize library for use in glyph Rabbit holed into learning more about Sinc filters and the Lanczos Kernel. 3:30 PM Shifted to working on memegrep(v2). This is where I already know the goals I want to accomplish before the day ends. I implemented a pub/sub flow to help with scale when users upload their private memes. Rough sketch in mind: user uploads meme(s) → server receives req → insert skeleton into db → queue upload → return 201 to user immediately → worker picks up task Spent the next 6 hours coding without even realizing 6 hours had passed (bliss) Ended up with multi-file upload, search, deployed a CLIP model, added all the scaffolding needed in the UI for this. 9:30 PM I posted something about pointers on twitter and ended up reading some history about the nomenclature just for fun. Links: pointers, handles are the better pointers Also saw something about reference counting being used in the libvips library earlier in the day, so decided to check out their implementation since i’ve only used it in rust quite often. reference counting, Rc and Arc 11:00 PM Was in bed and saw a post linking an amazing article by Valve on Source Engine Networking, so ended up being a nice and light read. At the end of the day I ended up with more questions, but I definitely had more answers than when I started! Here’s a DAG of my exploration for fun:
two-weeks I built a website (twoweeksisallyouneed dot com) with just Claude 3.5 Sonnet, zero lines of code written by me. Why I did this So a couple weeks back I had a computer vision midterm and i was allowed a single page of notes. I decided to use Claude to generate a cheat sheet in LaTeX. It was crazy lol, I was able to cook up something usable in 15 mins. pic.twitter.com/0tvIsdv34d — ro/nin (@seatedro) October 24, 2024 Someone had asked me to make a similar cheatsheet for ML, but I thought why not get claude to make an entire website instead? Early days I didn’t really give claude any specific information except what I wanted to build. My plan was as follows: > build out the UI skeleton first > populate with some dummy data > set up a content pipeline > tie up the ui with the data > fix bugs Claude decided to use react (Shocking). With just a couple of chats I was able to get the retro/hacker/matrix style UI down. (It’s going to be hard for frontend engineers to keep up with AI at this rate) I spent some time building and adding secrets/easter eggs to the website which no one has found yet lmaoooo. If/when all the easter eggs are found, I will open source the repository. Good hunting until then bros. Frustrations Things got really annoying, really fast. As soon as I wanted to build some sort of content pipeline, everything went to shit. Claude, no matter how smart of an AI, is not human. It did not think ahead. If I was going to build out this website I would not have started with a barebones react/vite app. I would have probably gone for a full stack framework like sveltekit instead. Generating content for the website was/is a nightmare. LLMs hallucinate, this is known, but did you also know how incredibly frustrating it is to get them to follow instructions? There were multiple instances where Claude (with all the 40% of project context of javascript code) generated a TypeScript interface and proceeded to spit TypeScript code. This project has 0 TypeScript???? The new edit in place nonsense was getting on my nerves. Half the time the output artifact wouldn’t even change, and the other half it would mess up the changes. I have to mention “please use a new artifact” if I wanted any real usable code. I was enthusiastic about building something with just Claude the first few days, then I started getting weary, and then eventually I wanted to take a sabbatical from using AI. When the project context grows, (think like 20% or more), Claude seems to have a hard time using that information. Often times I found myself hitting send, and Claude would spit some nonsense, I would hit stop and paste any relevant code directly and then get some useful code. Context is probably the biggest annoyance I’ve had with LLMs. Random thoughts Perplexity (with claude 3.5) is great (to an extent) because it’s essentially a RAG search so I was able to get somewhat up to date content for the resources and references sections from it. I feel like I was able to ship something of decent quality for sure, but I lost a lot of brain cells during the process. Do not take away from the programmer the only thing he wishes to do, program. Proompting I didn’t particularly do anything unique to get the best out of my prompts/chats. I did do everything in a Project on claude dot ai though, which let me set Project Instructions like so: BE ENTHUSIASTIC. WE ARE GOING TO CHANGE THE WAY THE WORLD LEARNS WITH THIS WEBSITE🚀🚀🚀🚀 LLMs seem to be much more open to doing anything you ask if you gaslight them, so go ahead and do it. Here are some prompts that I used while building the website: PROMPT ------ This is AWESOME: * For the loading screen add some text that says "you can learn anything in two weeks" * The ascii text is also weird, it says erain? is that supposed to mean something? * Where did all my placeholder topics go? * MOAR scrt scan lines * The matrix rain needs to fall vertically and it should be subtle. On mouse move it should get a bit brighter (the char under the mouse) What else do you think you could add? Surprise me One time I asked Claude to add something and it started changing the existing UI????? PROMPT: ------ OKAY, we have a beautiful boot sequence now. let's flesh out the main content screen. First let's fix some bugs: * The animations restart everytime i move my mouse instead of continue organically * What i mean is, when i move my mouse over the windows or around the matrix rain, the matrix rain restarts and so do the typewriter effect on the window title Work on these * [REDACTED] Easter eggs: * Fun Features: * "Power saving mode" that dims everything except what you're reading ....[REDACTED] So many times I ended up editing the prompt and adding lines like this: PLEASE DON'T CHANGE THE EXISTING UI. IT LOOKS GOOD BRO. I’ll just add some random prompts here PROMPT ------ Broski, we need to make the UI responsive and shit. Lot of people are reporting issues with it on mobile and smaller screens PROMPT ------ what sort of content do u think we should add? i was thinking things like formulae, charts (for phd), code blocks (for eng), research papers, youtube video links, blog posts etc. Let's think out loud how we're gonna do this before we proceed PROMPT ------ `pasted_code.jsx` Here is my current dashboard. Here is some sample concept content i have: `pasted_data.json` I need you to render this beautifully in the dashboard. Make any and all changes needed. It needs to look beautiful. And anything else u feel would be good. List out the things you're adding before adding it okay? Concluding thoughts This was a good experiment, a success. If you have a clear vision for what you want your product to be, then AI can help you achieve that vision quite well! However, I don’t think I will be using AI for the foreseeable future. I feel like my learning is stagnating the more i use AI and I want to write my slop code with my own two hands. I might use avante.nvim to quickly write some duplicated code here and there but by god, I miss coding. Actual coding.
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The Tetris effect is one of psychology’s most easy to reproduce experiments. Simply spend a bit of time playing the eponymous game every day for a few weeks. After a little while, you’ll start recognizing familiar Tetromino shapes in clouds, buildings, and everyday objects. You might even see them appear before your eyes when you start falling asleep. Tom Tang Attention hijacking There’s one lesson the Tetris effect teaches us: whatever you focus on long enough will end up shaping your thoughts. This can be a good thing since it’s how we learn new skills and discover new ideas. Sadly, less and less of our attention is focused intentionally. Instead of picking what we want to see we let other people decide what is supposed to be good for us. Do you want to watch a video? YouTube knows you like cooking and art streams. But why not also recommend a few clips about the stock market bubble, global warming, and the war in Iran. Doomscrolling will make you stay longer and click on a few more ads. Do you want to listen to music? Just open a Spotify playlist and let the algorithm figure out what you like. Please ignore the AI slop they will insert in between real songs to avoid paying royalties to real artists. Do you want to know how your colleagues are doing? Too bad, LinkedIn will bury any relevant career news between the opinion of complete strangers. It is surely just a coincidence that those strangers happen to be shilling whatever Microsoft is invested in at the moment. Do you want the opinion of strangers on a product? Well those Redditors you wanted to ask are probably just a bunch of LLMs talking to a bunch of Russian trolls now. I hope you didn’t value their opinion too much. If, like me and most people, you spend the major part of your day focused on your device, there’s no doubt it’s affecting you. And when you let someone else dictate what appears on your screen, it’s the same as giving them the key to your brain. New York Said Back to an intentional internet The internet wasn’t always like that. Before recommendation algorithms where a thing, you had to decide what you would be doing on the computer. You didn’t really have one big app that you could open and order it to entertain you. Instead, you had a few dozen of bookmarks to websites, each with a specific idea in mind. A site for video game news, that one website with lots of tutorials, a blog about anime that didn’t update often enough, a wiki about a TV show from the 90s… Of course awful things existed on the web. We had Encyclopedia Dramatica and Rotten.com, but you actually had to put the effort to go there if you wanted. Nobody was going to put pictures of dead kids and far-right propaganda as a suggestion after a pancake recipe or a cat video. The good thing is that this intentional internet is still around. It has just been a bit buried below the corporate web, but it’s not very hard to find. After all you’re on this blog, so you probably already have a good idea about it. The main difference between this time and now is you. When you want to get back to reading blogs, RSS feeds, and finish that tutorial instead of doomscrolling shorts, you have to get used to a slower internet. One where content is not infinite and doesn’t get updated every click. But like every habit, the only thing you have to do is to keep at it. And if you pay enough attention to it, something will click in your brain.
One of the interesting challenges of the AI ecosystem in 2026 is that new, effective patterns emerge faster than I can adopt them. I’ll find a handful, get back to work, and realize a month later that I’d missed four or five more. The adoption cycle for Imprint this year has been something like: January: get every engineer onto Claude Code every single day March: ok, let’s also get everyone else onto Claude Code or Claude Cowork every single day April: local development is bottlenecked on checkout and worktree model, instead create ~10 local workspaces which each have an independent checkout of every repository, and operate at the workspace level, not at the repository level, so it can generate cross-repository pull requests across frontend, backend, infrastructure and data monorepos June: oh boy, agent-driven development is heavily constrained by lack of a common task management system with higher visibility and less permission complexity than Jira, so let’s migrate the entire company over to Linear and hard stop on Jira July: yikes, now we have visibility into all these tickets, many of them are trivial but managing them through local development isn’t scaling, let’s roll out an orchestrated harness which internally we call “Agent Fleet”, along the lines of Stripe’s Minions The most recent question for me has been figuring out how to adopt the software factory pattern. (After some light research, the specific AI-context origin of this term is slightly messy to attribute, but I think it might be Justin McCarthy in February 2026’s Software Factories And The Agentic Moment.) The software factory pattern is looping on a broad goal, and then relying on the harness to drive progress towards that goal. Our first pass at implementation is fairly basic: An agent skill /linear-project-loop which reads in a Linear project and starts by auditing that project’s goal definition on these dimensions: An RFC in Notion that describes the project’s goals, how those goals are measured, and the general approach A Datadog dashboard or Snowflake queries that measure progress against those goals If those are missing, or the Linear project is missing in its entirety, it iterates with you on creating those missing tools. Then it reviews the state of the metrics and issues for the project. If new work is identified, it adds those issues to the project. It updates the state of issues that have moved. It works on the non-blocked tasks based on the project’s current state. This is often writing a pull request, updating a pull request, pinging for review, asking a clarifying question, etc. When a task completes, if the project description is fresh, it takes on the next task. If the description hasn’t been updated in a while, it reruns the loop starting with the first step. Right now I am running this locally in a local harness, but it’s working well enough that I anticipate moving the behavior to be driven by the same orchestrated harness that we assign one-off tasks to. What I particularly like about the factory pattern is that it parallels very closely how I’ve been working locally, while forcing me to recognize the places where I was accidentally hording parts of the state for myself regarding the goals of the project. I was already asking agents to iterate on specific Linear projects, but they didn’t have the ability to evaluate if they were going in the right direction, or if it was missing necessary tasks. Now it does. The other place this has been extremely helpful for me is checking in on projects post release. For example, I shipped our passkeys implementation earlier this year, but some months go by without my checking in on how it’s going. If we saw adoption spike, or error rates start to turn, I might miss it, but running the factory in a less frequent post-release mode would catch it immediately. The final thought that’s been interesting to me is how much all of the pieces here compound only to the extent that you have the other pieces. For example, this factory pattern depends on having Datadog MCP and Snowflake access available to manage goal-tracking, but it also depends on Linear being the single source of state for the company’s work, and an orchestrated harness that can perform work independently from your laptop. Keeping up with this many migrations is a fascinating industry moment.
I owe a lot of my professional identity and success to CSS-Tricks. CSS-Tricks repeatedly gave me the opportunity to write for them. In doing so, they helped to both socialize and normalize accessibility as a mainstream frontend concern. I’m deeply thankful to them for this. The team was also a joy to work with, notably Geoff Graham. He’s a mensch, and one of the nicest people you can interact with in the frontend web space. If you have not been following the news about the site, Kevin Powell has a good video about the whole situation: Content skipped. I’m not speaking on behalf of Geoff, Chris, or others involved with running the current version of CSS-Tricks. I’ve got skin in the game as an author. This is my personal opinion, born of my feelings and beliefs. I think a lot of the web’s infrastructure should be co-ops, and CSS-Tricks is knowledge infrastructure. To that point, I should also point out that the website covers far more than just CSS. The corporate model of ownership can be a risk. If infrastructure is not part of a corporation’s core strategy, it is not a priority. As Kevin’s video touched on, it seems like promotion via owning the frontend content space isn’t part of Digital Ocean’s strategy anymore. It is not that CSS-Tricks does not have value. It is that Digital Ocean cannot see it. It is deeply, tragically ironic to me that Digital Ocean allowed this to transpire. This is because I know for a fact that the techniques and philosophies shared by CSS-Trick authors helped to shape iterations of their product’s UI. Some may be quick to point out that this knowledge now—illegally—exists inside of LLM training data, so the risk of the website going away is mitigated. To this, know that we should be striving to keep resources like CSS-Tricks going. Human creativity is the force that creates new techniques, strategies, and technologies. The web will calcify without voices sharing what they know, forever locking us into endless permutations of a fixed point in time. Unlike corporations, co-ops don’t have to be motivated by profit. By not needing to prioritize growth at all costs it means co-ops can instead prioritize and incentivise things like preservation and cultivation. It is also a successful model of operation, one that even already exists, and flourishes in the tech space. Collective ownership can also serve as checks and balances for, and protection against hierarchical decision-making. I only need to point to the chaotic and aberrant decisions many CEOs in the technology space have been making as of late to demonstrate the value of this approach. Paddy Srinivasan, if you somehow wind up reading this: Save some face and take a big swing. Give CSS-Tricks back to the people who love it.
How can something that “just works” be so annoying? situation We live in Cambridge off a little road down a drive in shared ownership between us and our neighbouring houses. All the utilities are buried under this drive, including the phone line. anticipation Over the last few years we have been canvassed repeatedly by CityFibre saying that they can deliver fibre all way to our house. I saw them digging trenches and leaving tails of purple fibre cladding along nearby roads, ready to hook up all the houses. I thought they would need to do something similar to deliver fibre to us. So when they turned up and knocked on our door, I talked to their salesbods and walked them up and down the drive and pointed out where the existing BT line goes. Then they gave up trying to sell to us. This happened about three times. disaffection We were not eager enough for an upgrade to deal with these impediments. notification A few months ago we were told that CityFibre would soon come and do the upgrade, since there’s a nationwide deadline for turning off the copper phone network at the end of the year. We expected that this would force them to actually plan some digging works, so we talked to our neighbours about it. We were all ready for some huge faff to follow the next visit by the CityFibre bods. installation CityFibre turned up on the promised morning bright and early. To our enormous surprise, a brown fibre housing was already poking out of the ground next to our copper phone line. It had been fed through 50 metres of 5cm duct without us being aware they were even working on the street. Within a couple of hours, the technicians had drilled through our wall, installed the ONT, blown fibre through the unexpected pipe, plugged in the CPE (superficially identical to the old one), and left telling us to anticipate that it might not work properly until tomorrow. activation Around lunch time, the copper phone line stopped working completely. Some faff ensued, switching all our devices over to the new WiFi network. For a while we thought this was the death of our land line, but in the course of debugging other issues, I realised that the router has a built-in VoIP adapter (I don’t think we were told it has a built-in VoIP adapter) so I plugged the phone in and it Just Worked: they had ported our phone number across and everything. Flawless. I was seriously impressed. rumination It has been a few weeks since the switchover, and apart from a couple of horrible Clown-afflicted IoT devices, it has been fairly smooth. What prompted me to write this up was realising that we delayed this upgrade for years because the sales people were not given enough technical information about how the installation process works: the fact that houses typically have a 5cm duct containing the copper lines (probably standard for the last 40 years) and the fact that fibre can be shoved through a few tens of metres without difficulty. And worse, the sales people didn’t have an esclation path for difficult cases: they just gave up instead. From a technical point of view, the installation was impeccable. (I guess the loose 24 hour window for the cutover time was because OpenReach and CityFibre don’t have tight requirements on ISP reconfiguration schedules.) From the sales point of view, it was crap. Maybe it would have gone faster if we offered to switch early without asking if the drive would be a problem? But I guess the difference between “yes!” and “yes, but will this be a problem?” is too much to expect from a minimum-wage door-to-door salesbod whose employer didn’t give them enough information or any escalation path.
