The title is not a rhetorical question, and I'm not going to bury an answer. I don't have an answer. This post is my exploration of the question, and why I think it is a question1. Important things up front: what's my relationship with LLMs today? I don't use any LLMs regularly. I do have access to GitHub Copilot through my employer. I have it available on a hotkey, I think, and I cannot remember how to trigger it since I do not use it. I've explored using LLMs in the past. I used to be a regular Copilot user, and I explored ChatGPT, Claude, etc. to see what their capabilities were. I have done trainings for my coworkers on how to use them effectively, though I would not feel comfortable doing so now. My employer's product uses LLMs. I don't want to link to my employer, but yeah, I guess my paycheck depends on being okay with integrating them in? It's complicated (a refrain). (This post is, obviously, my opinions and does not reflect my employer.) I don't think using or not using them is a moral failing. There is a lot of moralizing around LLM usage. I'm not doing any of that here. I have my own beliefs (or my own questions), but I don't think people using LLMs are immoral (or vice versa). So, you can see I have used them and I'm not absolutist, but I don't use them today. Why not? Why did I stop using them in spite of the advances, where they're more capable than ever? It's because of these questions and issues. Where I have undecided ethical questions, I lean toward the more conservative2 choice of not using them until I have clarity on the ethics. (Note: I am not inviting folks to email me with answers to this question.) Energy usage Another technology that uses a lot of energy is blockchains. I think using public blockchains is almost universally unethical since there are other, better, less harmful options. Part of the harm from blockchains is an absurd amount of energy usage. LLMs also use a lot of energy. This can be split into training and inference energy usage. These vary based on the model. Some models can run locally on Apple silicon, and those are lower energy usage—their upper bound is running your computer full tilt, and an M4 Mac mini's max power consumption is 65 watts. This is roughly equivalent to one incandescent light bulb, or 8 LED light bulbs. It's good to turn off unnecessary lights, but doing so isn't going to solve the climate crisis; we need bigger, more sweeping reforms. I don't think that local models are going to significantly alter the climate crisis in either direction. Other models are massive and run in data centers on lots of power-hungry GPUs3. These data centers also require construction, and that comes with its own environmental impact. An article from Tom's Guide last year showed that "a single query on ChatGPT-4 can use up to 3 bottles of water, that a year of queries uses enough electricity to power over nine houses". A lot of the cost comes from new data centers being built. The demand for LLMs has led to more demand for power generation and more demand for data centers. And this new power generation is coming from gas-fired plants instead of sustainable, clean energy sources, because that's all we can build fast enough. A lot of attention is given to the training side. The numbers for training are large and shocking: Llama 3 used 500 mWh and GPT-3 training used 1,287 mWh—even more if you include the cost of training failed models which preceded these, the experiments that made the models possible. The listed figures are high, and 500 mWh is about the cost of a large jet flying for 7 hours. But we do it once per foundational model, and then the cost is spread across all the remaining usage. I don't think that the training side is significantly shifting the equation on climate change. We'd have a much larger impact on improving the climate crisis by advocating for remote work—reducing vehicles on the road, making many flights unnecessary—than by not training models. Overall it feels to me like local models have a clearly acceptable impact, and data center models have higher energy usage but still probably do not change the situation very much. Training data The training data for LLMs has largely been lifted without the consent of the people who generated that data. This is a lot of writing, music, videos, visual art, all of it. There are some attempts at there at using licensed data only, but the majority of models, and the most popular ones, are unlicensed data. Now the question is: is this an ethical problem? I know there are opinions on both sides of this. Some say that this data is publicly visible on the internet (though some of the data was not on the internet), and so it's fair game. Others say that this use isn't one that people consented to, and it should require that consent. My thought experiment is this: If we made search engines today, would people have this same objection to a search engine using their data without their express consent? I think most people would ultimately support search engines. They are different than LLMs, because they (mostly) serve results that point you to the original source, rather than create new content for you to replace the original sources with. But maybe people would reject search engines. And maybe consistency between these two isn't necessary, or maybe it's a false consistency—there could be other differentiating details that lead to different answers. Where I come down is that I think we need a robust mechanism to opt out of use of data for training, but that it's probably fine to train with publicly available data on the internet. What you do with the trained model is another question entirely. When you try to replace people making original works instead of creating an entirely new function, that's where you get questions. "Replacing" people There's a lot of LLM usage that is just trying to replace people in entire jobs. I mean, it feels like all of it. We see LLMs that are meant to replace writers and editors, artists and illustrators, musicians and songwriters. It doesn't say that directly—it says you're empowered to create things yourself. But what it means is that people should be able to press a few buttons and, with no artist involved, get out a beautiful artwork. Sounds like replacing to me. This is something we've long done with technology. We make technology that puts people out of jobs, and that was the whole industrial revolution. That's what happened with shipping from the containerization of ports, putting many dockworkers out of jobs. Replacing people in the abstract is not unethical. The problem is if we fail to deal with the harm created from replacing people. And people will be harmed, because losing your job or the value of it going down has a serious impact on quality of life. When we put people out of work, we—both society and technologists—have an ethical responsibility to ensure there's a plan to mitigate the harm from that. Maybe that means grants for living expenses while people switch fields, if put out of work in an LLM-heavy industry. Maybe that means a universal basic income. But it certainly doesn't mean doing nothing. Incorrect information and bias One of the major well-known problems of LLMs is a tendency to "hallucinate," or to confidently state facts that are made up from whole cloth. They also have an unknown amount of bias, with unknown mitigations in place, due to being closed systems. This is a big problem! We're not good at seeing what information is incorrect in something that's generated to look like it's the most likely string of tokens. If there is incorrect information in there, we'll just miss it. This means that people can make poor decisions on the basis of what an LLM tells them. They can have lost income due to its mistakes. And the bias? That's a huge problem, because it means that we don't know if this system will reinforce existing problematic norms4. We don't know what it will reinforce, because the training data is closed and there's not a lot of public evaluation on bias. So ultimately, we're left with an unknown harm of unknown magnitude to an unknown population5. Concentrating power (with the wealthy elite) A big ethical concern for me is also what this will do to our entire society. Many technologies are heralded as "democratizing" things. Spotify "democratized" music by making it so that anyone can get listens—but, y'all, it ended up flattening the tail and making the popular artists more money while making small artists less money. Will LLMs do the same? We know that the big models need large data centers to run their training and inference. And even small models need beefy hardware to run inference, let alone training! We have some access to models which can run locally, which is a good step. But the problem is that we can only run other people's models. They'll have those people's decisions baked in, decisions on which data to include or to exclude, decisions on how to approach questions of bias and abuse. And when the hardware to run the biggest models is only accessible to a few companies, that means that those companies really get a lot more powerful. OpenAI and Anthropic and Google and Meta all have the ability to run really large models. I certainly don't, though. This means that a technology that many are heralding as making things more accessible to everyone is controlled by a small handful of people. A small handful of people can decide how the models are trained, and set policies on how they're used. In a time when the US government is trying to get any paper retracted that mentioned queer people, and erasing trans people from the Stonewall monument, it feels self-evident that letting a small group of people control this technology imperils the future of many people. * * * Ultimately, I want robots to do the things I don't want to do. I want them to do my dishes and my laundry. I don't want them to play music instead of me, write code instead of me, write words instead of me. I am not sure whether or not using LLMs is unethical. There are certainly ways of using them which are unquestionably unethical—as is true with every technology. And there are ways of developing LLMs which are unethical—as is true with every technology. But the problems with them are large. I think it is unethical to use them without addressing the ethical questions above. If you're not working on mitigating the harms from LLMs (which do exist), then you might be doing something unethical. 1 I've been in the interesting situation of having anti-LLM people think I'm pro-LLM and vice versa. It's a very weird feeling, and makes me a little nervous of posting this! But this is an important question and an important conversation. 2 Footnoting out of an abundance of caution: I don't meant conservative-like-Republicans, because, no, I'd like to keep my rights thank you very much. I just mean in terms of minimizing risk. Let's please stop attacking trans rights, immigrants, Palestinians, and you know, everyone else that I've forgotten because the whole world seems to be on fire. 3 If we ever achieve artificial sentience, this sentence may acquire a second, more sinister, meaning. 4 It will. 5 My guess is a large harm to all underrepresented groups, but who asked the trans woman?

Working on my cursed MIDI project, I needed a way to store the most recent messages without risking an unbounded amount of memory usage. I turned to the trusty ring buffer for this! I started by writing a very simple one myself in Rust, then I looked and what do you know, of course the standard library has an implementation. While I was working on this project, I was pairing with a friend. She asked me about ring buffers, and that's where this post came from! What's a ring buffer? Ring buffers are known by a few names. Circular queues and circular buffers are the other two I hear the most. But this name just has a nice ring to it. It's an array, basically: a buffer, or a queue, or a list of items stacked up against each other. You can put things into it and take things out of it the same as any buffer. But the front of it connects to the back, like any good ring1. Instead of adding on the end and popping from the end it, like a stack, you can add to one end and remove from the start, like a queue. And as you add or remove things, where the start and end of the list move around. Your data eats its own tail. This lets us keep a fixed number of elements in the buffer without running into reallocation. In a regular old buffer, if you use it as a queue—add to the end, remove from the front—then you'll eventually need to either reallocate the entire thing or shift all the elements over. Instead, a ring buffer lets you just keep adding from either end and removing from either end and you never have to reallocate! Uses for a ring buffer My MIDI program is one example of where you'd want a ring buffer, to keep the most recent items. There are some general situations where you'll run into this: You want a fixed number of the most recent things, like the last 50 items seen You want a queue, especially with a fixed maximum number of queued elements2 You want a buffer for data coming in with an upper bound, like with streaming audio, and you want to overwrite old data if the consumer can't keep up for a bit A lot of it comes down to performance, and streaming data. Something is producing data, something else is consuming it, and you want to make sure insertions and removals are fast. That was exactly my use: a MIDI device produces messages, my UI consumes them, but I don't want to fill up all my memory, forever, with more of them. How ring buffers work So how do they work? This is a normal, non-circular buffer: When you add something, you put it on the end. If you're using it as a stack, then you can pop things off the end. But if you're using it as a queue, you pop things off the front... And then you have to shuffle everything to the left. That's why we have ring buffers! I'll draw it as a straight line here, to show how it's laid out in memory. But the end loops back to the front logically, and we'll see how it wraps around. We start with an empty buffer, and start and end both point at the first element. When start == end, we know the buffer is empty. If we insert an element, we move end forward. And it gets repeated as we insert multiple times. If we remove an element, we move start forward. We can also start the buffer at any point, and it crosses over the end gracefully. Ring buffers are pretty simple when you get into their details, with just a few things to wrap your head around. It's an incredibly useful data structure! Rust options If you want to use one in Rust, what are your options? There's the standard library, which includes VecDeque. This implements a ring buffer, and the name comes from "Vec" (Rust's growable array type) combined with "Deque" (a double-ended queue). As this is in the standard library, this is quite accessible from most code, and it's a growable ring buffer. This means that the pop/push operations will always be efficient, but if your buffer is full it will result in a resize operation. The amortized running time will still be O(1) for insertion, but you incur the cost all at once when a resize happens, rather than at each insertion. You can enforce size limits in your code, if you want to avoid resize operations. Here's an example of how you could do that. You check if it's full first, and if so, you remove something to make space for the new element. let buffer: VecDeque<u32> = VecDeque::with_capacity(10); for thing in 0..15 { // if the buffer is already full, remove the first element to make space if buffer.len() == 10 { buffer.pop_front(); } buffer.push_back(thing); } There are also libraries that can enforce this for you! For example, circular-buffer implements a ring buffer. It has a fixed max capacity and won't resize on you, instead overwriting elements when you run out of space. The size is set at compile time, though, which can be great or can be a constraint that's hard to meet. There is also ringbuffer, which gives you a fixed size buffer that's heap allocated at runtime. That buys you some flexibility, with the drawback of being heap-based instead of stack-based. I'm definitely reaching for a library when I need a non-growable ring buffer in Rust now. There are some good choices, and it saves me the trouble of having to manually enforce size limits. 1 One of my favorite rings represents the sun and the moon, with the sun nestling inside this crescent moon shape. Unfortunately, the ring is open there. It makes for a very nice visual effect, but it kept getting snagged on things and bending. So it's not a very good ring for living an active hands-on life. 2 These can also be resizable! The Rust standard library one is. This comes with reallocation cost, which amortizes to a low cost but can be expensive in individual moments.

Last year, my coworker Suzanne and I got a case study accepted into an ethnography conference! She's an anthropologist by training, and I'm a software engineer. How'd we wind up there? The short version is I asked her what I thought was an interesting question, and she thought it was an interesting question, and we wound up doing research. We have a lot more to go on the original question—we're studying the relationship between the values we hold as engineers and our documentation practices—and this case study is the first step in that direction. The title of the case study is a mouthful: "Foundations of Practice: An Ethnographic Exploration of Software Documentation and its Cultural Implications for an Agile Startup". I mean, it's intended for a more academic audience, and in a field that's not software engineering. But what we studied is relevant for software engineers, so let's talk about it here in more familiar language! If you want to read the full case study, it's open access. I'll start with the recommendations we provided, share a little bit about our methodology, and finish us out with a little about where we can go from here. Our recommendations The case study concluded with recommendations. This is an area I'd like us to do a lot more work in, because recommendations based on a case study are based on one example, and they'd obviously be much stronger based on more data! But for the conference submission, they were a necessary component. I think that the specific recommendations we gave hold up based on my experience (I'm biased, so take this as it is). These were our core recommendations. Start with high-level documentation. If you don't have any docs today, or all your docs are just in a bad way, the biggest bang-for-buck is with your high-level systems docs and process documentation. Don't have a system architecture diagram? Make it! Don't have a CI process diagram? Make it! These help you get your bearings on everything quickly, and they're also usually much faster to make than detailed documentation of more focused pieces. Use design reviews! Here, "design review" means putting your architecture changes—or any proposal for a major code change—through a review process analogous to a code review. This is one of the things we implemented at my company over four years ago, and it's incredibly useful. It makes us think more deeply on our designs, so we make fewer errors in the design phase of any given feature. And it makes the design visible to the whole team, so we get much better knowledge transfer. If I could drop these into every software company, I would. They're that useful. Think about your audience. This is just plain good writing advice, and it bears repeating. Not every kind of documentation has the same audience, and if you don't take that into account you'll write bad docs that your end user won't be able to use effectively. You may use too much jargon for the lay user, or too little jargon for efficient communication with the engineers working on a feature. Maintain the docs, updating them as part of your workflow. I think we all know that docs should be updated. The recommendation here is that updating docs should be part of your workflow. If you run into something that's out of date, go ahead and fix it. If someone asks you a question because it wasn't in a given doc, go ahead and add it there. Document test plans. QA processes are woefully underappreciated in software engineering. I don't quite understand why QA engineering is so looked down upon, because y'all, that stuff is hard. At any rate, testing? That's something you should document! Assuming you run tests before you release (you do, don't you?) then you need to have some organization and documentation around what those tests are, why they're there, etc. This needs to be shared between both QA and product engineers since if the test fails, it's probably, you know, the product engineer's change that broke it—so the better those docs are, the faster they can find the issue. Make onboarding docs. We know that good onboarding is important for bringing new team members up to productive contribution quickly. And we know that documentation is a big part of that. So focus on your documentation as part of your onboarding strategy. Run post-mortems and retrospectives. Mistakes will happen, and when they do? It's imperative that we go back through why they happened and what changes (if any) are merited to our processes as a result. Maybe there's work to be done to prevent similar incidents, or there's something we could do to detect this quicker in the future. Whatever you find, also update your docs: point to the post-mortems in the design docs that led to these decisions so that if someone reads those, they'll also see the longer-term impacts of those decisions. Encourage collaboration on documentation. You can do this by involving multiple team members in creating and reviewing design docs. This is a tool for mentoring and professional development. It also improves all the documentation quality. If you involve team members at all levels (not just your senior engineers!), you'll be able to ensure the docs and processes meet everyone's actual needs. Prioritize critical, complex areas for docs. You can't document everything. We mentioned earlier that you want to start with a high-level documentation overview, but you also want to focus your efforts on things which are critical or particularly complex. This will pay off by reducing the difficulty of understanding these areas, reducing rates of defects and speeding up development. Eliminate ineffective docs, focus on friction points. If your team says that documentation isn't serving a useful purpose, trust them and get rid of it. Change what you do to do something more effective. Focus your documentation efforts on resolving friction points between teams or in the development process. The methodology Our research comprised two main pieces: a literature review and interviews with our engineering team. We did the literature review first, to get our bearings. And, honestly, I was hoping to find answers to what we ultimately were setting out to answer. I'd rather not have to do this research to get our answers! The existing papers gave us a good grounding. I won't repeat too much here—you can read the section in the case study if you're interested. The bulk of the new research we did was through interviews, and analysis of those interviews1. We interviewed nine of our software engineers who were present at a critical transition in the company's history, when we implemented a major new documentation process as a result of transitioning to remote work. Each interview was recorded with the consent of the participants. We then transcribed them and did qualitative coding to extract themes. We were looking for some pre-defined themes (collaboration, learning, and knowledge sharing) and we were also interested in emergent themes. From there, we were able to extract out five values that are represented by documentation practices at the company. These values (collaboration, learning, continuous improvement, accountability, and technical excellence) are exhibited through our documentation practices. They are also reinforced by the documentation practices, so it's not wholly a causal relationship one way or the other. At this point we were able to take the results of the interviews and combine that with the literature review to figure out recommendations based on our case study. These are, of course, not universal. And they're to some extent conjecture: by the nature of a case study, we're not able to ground this in data across multiple companies. But they're a launching off point for teams in need and for future research. What's next? Ultimately, we want to drive toward answering the bigger questions we have around software engineering and documentation. One of those is: a lot of software engineers say their documentation is bad, and we also say that we value good documentation, so why do we not make the documentation better? Why are we not able to live our what we value there? Or do we not value documentation? To answer these questions to a satisfying end, we'll need to do a larger study. That means we'll need to recruit participants across many companies, and we'll need to design a study that can cut to the heart of these issues. I'm not sure when we'll have the time or energy to approach that, but I know we've both been completely sucked in by these questions. So we'll get there eventually! 1 One of the most interesting questions Suzanne got when presenting our research was: how did you get permission to do this? The question surprised me, because I hadn't thought that other companies would make this really hard, but her peers at the conference certainly had a hard time doing similar projects! The answer to this is a few things: we didn't ask for budget (nothing needs approval there), our engineers volunteered their time for it (they were interested, too!), and I'm on the leadership team and was able to just talk to the other company leaders to clear it.

It's kind of dark times right now. And I'm definitely only talking about the days being short. It's pretty dark out right now, since it's the winter in the northern hemisphere. Every year, I start to realize somewhere around January that I'm tired, really tired, and don't want to do anything. I'm not sure that this is seasonal affective disorder, but I did start to explore it and found that bright light makes me feel a lot better in the winter. The problem is, I go through this discovery every year, it seems. My earlier attempts After I first learned that bright light can actually help you feel better in the winter, I got a Happy Light. That's the actual branding on it, and it might have helped, maybe? But it was really inconvenient to use. I got one of the more affordable ones, which meant a small panel I had to sit uncomfortably close to. And I was supposed to use it almost first thing in the morning for 30 minutes. That's... really not going to fit in my routine, especially now that I have two young kids who are raring to go right at 7am, and they do not want to deal with mom sitting in front of a lamp that early. So I just kind of stopped using it, and would poke at it in the drawer occasionally but not really do anything with it. Instead, I got a few more lamps in my office. These seemed to help, a little, but that might just be that I like my spaces well lit1. Going as bright as possible Somewhere along the line I saw a post talking about winter energy and light. And the author suggested that indoor lights just have to be bright. They recommended some high-watt LED bulbs. I went ahead and ordered some and... these are very bright. They're 100 watts of LEDs each, and I have two of them. I plugged them in to these plug-in fixtures that have inline switches. These seemed to really help me out a lot. On days I used them, I was more energetic and felt better. But I had a lot of days when I didn't use them, because using them was inconvenient. The main problems were that I had to turn on two switches manually, and they were too bright to look at. Turning them on doesn't sound like a lot, but doing it every day when you're already struggling can be just enough friction to avoid doing it! The brightness was the biggest issue, because they were blinding to look at and cast this reverse shadow out of my office. The brightness was the point, so now do I deal with that? Shading it Turning on the switches was easy. I put them on some smart outlets, set a schedule, and now they turn on and off at my predetermined times! Then I had to make a shade for the lamps. My main goal here is to diffuse the light a bit, so I was thinking of a small wooden frame wrapped in a light white fabric. That would spread the light out and avoid having any spots that are too bright to look at. I took measurements for where it was going to fit, then headed out to my workshop. Then I ripped a scrap board into 0.75"x0.75" stock. I ended up with just enough for the design. This is what I ended up with, after it was glued up. Here's a bonus detail shot of the extremely unnecessary joinery here. But hey, this joinery was incredibly satisfying, and the whole thing went together without any nails or screws. Joy makes it worthwhile. And then with the fabric attached2, it ended looking pretty nice. In its intended home, you can see that it does its job! It's now not painful to look straight at the lamp. You wouldn't want to do it, but it won't hurt you at least! Waiting for summer Of course, we need these bright lights during the winter, during the dark days. But summer will come again. Not all of us will see the long days. We'll lose some to the winter. But summer will come again, and we'll be able to go outside freely. Soak in the sun. Be ourselves. Dance in sunshowers. It's hard to see that in the darkest of times. I'm writing this for myself as much as I'm writing it for you. But believe me: you are strong, and I am strong, and together, we will survive. It seems like this winter is going to last a long time. And it probably will. But I'm not just talking about the winter, and the dark times are going to end, eventually. 1 Most places are very poorly lit and don't have enough lamps. You should have a lot of lamps in every room to make it as bright and cheery as possible! I refuse to believe otherwise, but my wife, a gremlin of the darkness3, does like rooms quite dim. 2 My wife helped with this step. She works with fabric a lot, and this was our first project combining our respective crafts. Certainly not our last! 3 She approved this phrasing.

Recently, a reader wrote to me and asked about my writing process and burnout. They had an image in their head that I could sit down at a computer and type up a full post on a given topic, but were unsure if that's the right approach when they start blogging. And they were concerned about how to keep things sustainable for themselves, too. I started to write back to them, but decided to ship my answer for everyone else, as well. Now, to be clear: this is my writing process. There are many other very valid approaches, such as what Gabriella wrote. But I think outlining my process here will at least help break some of the mystique around writing, because I certainly do not just sit down and plunk out a post. Well, it often looks like that if you're an outside observer1. But that's only what's visible. How I write The very first piece of any blog post for me is an idea, some inspiration, something I want to share. These ideas come from a variety of places. Some of them are from conversations with friends or coworkers. Others come from problems I'm solving in my own code. Or they come from things I'm reading. Very rarely, they come from just thinking about things, but those are other sources with the connection hidden by the passage of time. After an idea comes to me, I try to capture it quickly. If I don't, it's probably gone! I store these ideas in a note in Obsidian, which currently has 162 ideas in it2. I format this file as a bulleted list, with minimal categorization so that the friction for adding something new is as low as possible. When I revisit this list to plan things to write (I do this every few weeks), I'll remove things which are either completed or which I'm confident I am not interested in anymore. Ideas which are promoted from "want to write" to "definitely going to write" then get moved into my primary task tracking system, LunaTask. When I start typing an article, I move it from "later" or "next" to "in progress". But in reality, I start writing an article far before I start typing it. See, the thing is, I'm almost always thinking about topics I'm going to write about. Maybe this is an element of having ADHD, or maybe it's from my obsessive interests and my deep diving into things I'm curious about. But it started happening more when I started writing on a schedule. By writing on a schedule, having to publish weekly, I've gotten in the habit of thinking about things so that I can hit the ground running when I sit down to type. Once I've picked out an idea, then it sits in the back of my head and some background processing happens. Usually I'm not crystal clear on what I want to say on a topic, so that background process churns through it. After figuring out what I want to say, I move onto a little more active thought on how I want to say it and structure it. This happens while I'm on a run or a walk, while I'm on coffee breaks, between exercises I'm working on for my music lessons. There's a lot of background processing between snatches of active effort. Piece by piece, it crystallizes in my mind and I figure out what I want to say and how to say it. Then, finally, I sit at the keyboard. And this is where it really looks like I just sit down and write posts in one quick sitting. That's because by then, the ideas are fully formed and it's a matter of writing the words out—and after a few years of that, it's something I got pretty quick at. Take this blog post for example. I received the initial email on January 1st, and it sat in my inbox for a few days before I could read it and digest it. I started to write a reply, but realized it would work as a blog post. So it ended up in background processing for a week, then I started actively thinking about what I wanted to write. At that point, I created a blog post file with section headings as an outline. This post ended up being delayed a bit, since I had to practice content-driven development for a project that has a self-imposed deadline. But the core writing (though not the typing) was done in the week after opening the original email. And then when I finally sat down to write it, it was two 20-minute sessions of typing out the words3. Editing and publishing After the first draft of a post is written, there's a lot of detail work remaining. This draft is usually in the approximate shape of the final post, but it may be missing sections or have too much in some. I'll take this time to do a quick read over it by myself. Here I'm not checking for spelling errors, but checking if the structure is good and if it needs major work, or just copy edits. If I'm unsure of some aspects of a post, I ask for feedback. I'll reach out to one or two friends who I think would have useful feedback and would enjoy reading it early, and ask them if they can read it. This request comes with specific feedback requests, since those are easier to fulfill then a broad "what do you think?" These are something along the lines of "was it clear? what parts stuck out to you positively or negatively? is there anything you found unclear or inaccurate?" Once people get me feedback, I add an acknowledgement for them (with explicit consent for if/how to share their name and where to link). After I'm more sure of the structure and content of a post, I'll go through and do copy edits. I read through it, with a spellchecker enabled4, and fix any misspellings and improve the wording of sentences. Then it's just the final details. I write up the blurb that goes in my newsletter and on social media (Mastodon, Bluesky, and LinkedIn, currently). I make sure the date is correct, the title is a decent one, and the slug mostly matches the title. And I make sure I did do things like acknowledgements and spellchecking. Here's the full checklist I use for every post before publishing. - [ ] Edited? - [ ] Newsletter blurb written? - [ ] Spellchecked? - [ ] Is the date correct? - [ ] Is the title updated? - [ ] Does the slug match the title? - [ ] Are tags set? - [ ] Draft setting removed? - [ ] Are acknowledgements added? Once that checklist is complete, it's ready to go! That means that on Monday morning, I just have to run make deploy and it'll build and push out the article. Making it sustainable I've been writing at least one post a week since September 2022—over two years at this point, and about 200,000 words. I've avoided burning out on it, so far. That's an interesting thing, since I've burned out on personal projects in the past. What makes this different? One aspect is that it's something that's very enjoyable for me. I like writing, and the process of it. I've always liked writing, and having an outlet for it is highly motivating for me. I thrive in structure, so the self-imposed structure of publishing every week is helpful. This structure makes me abandon perfection and find smaller pieces that I can publish in a shorter amount of time, instead of clinging to a big idea and shipping all of it at once, or never. This is where content-driven development comes in for me, and the consistent schedule also leads me to create inspiration. I also don't put pressure on myself besides the deadline. That's the only immovable thing. Everything else—length, content, even quality5—is flexible. Some of my earliest posts from 2022 are of the form "here's what I did this week!" which got me into a consistent rhythm. The most important thing for me is to keep it fun. If I'm not finding something fun, I just move on to a different post or a different topic. I have some posts that would be good but have sat in my backlog for over a year, waiting to be written. Whenever I sit down to write those, my brain decides to find a bunch of other, better, posts to write instead! So far, it's been fun and motivating to write weekly. If that ever stops, and it becomes a job and something I dread, I'll change the format to be something that works better for me instead! 1 If this is what it looks like then hey, stop peeking through my window. 2 I keep them in a bulleted list, each starting with -, so I can find the count via: grep "\s*-" 'Blog post ideas.md' | wc -l 3 Drafting documents is where I feel like my typing speed is actually a real benefit. On a typing test this evening, I got 99% accuracy at 120wpm. This lets me get through a lot of words quickly. This is a big plus for me both as a writer and as a principal engineer. 4 I leave my spellchecker disabled the vast majority of the time, since the squiggles are distracting! It's not a big deal to misspell things most of the time, and I'd rather be able to focus on getting words out than noticing each mistake as I make it—talk about discouraging! 5 That said, I think it's hard to judge your own quality in the moment. As writers, we can be very critical of ourselves. We think we wrote something boring but others find it interesting. Or something we think is quite clever, others find uninspiring. Sometimes, when I push out something I think is lazy and quick and low-quality, I'm surprised to find a very eager reception.