Jonathan Warner's book More Than Words: How to Think about Writing in the Age of AI is a spirited defense about the value and humanity of writing without AI at a time when AI promises to replace many writing activities. Warner argues that writing involves thinking and feeling, and as we grapple with ways to identify, express, and articulate our ideas in writing, it's an experience that changes who we are.
Crossings: How Road Ecology is Shaping the Future of Our Planet, by Ben Goldfarb, is a richly descriptive work of investigative journalism exploring the topic of road ecology, which looks at how roads impact their surrounding environment. More specifically, road ecology is "'the study of how 'life change[s] for plants and animals with a road and traffic nearby'" (6).
I'm starting an book club called AI Book Club: A Human in the Loop. As you might expect, the book club involves reading and discussing books about AI on a regular basis. The book club will meet online, currently planned for the third Sunday of each month at 10 am Pacific Time (Seattle's time zone). There are some async chat options through Slack as well, and we'll record the meetings. Check it out at AI Book Club: A Human in the Loop. If you've been looking to read more (or get back into reading) while also increasing your understanding of AI, this club could be a good fit.
While AGI refers to performing tasks at a human level, superintelligence refers to performing tasks that exceed human capabilities. If tech writers want to survive the AI apocalypse, we'll have to go beyond mere AGI levels of competence and tread water within the superintelligent space.Reason being, AI will eventually replace most of what we do, making it such that when AGI is reached, job displacement for tech writers will be more common because AGI will perform the same tasks, only cheaper. But the likelihood of AGI progressing to Superintelligence seems less likely to me (in the same way that moving from assisted driving to fully autonomous driving is so much harder than anyone anticipated). Striving for superintelligent docs seems like the most logical counter-move against AI's encroachment on tech writer territory.
This post is a compilation of various thoughts and responses to articles I find interesting. I decided to compile these into a single post, similar to a News and notes style rather than separating them into individual articles.
Although code samples have long been a staple in API documentation, I'm not sure users need them that much. Many developers now use AI tools that can generate the same basic code samples that are commonly provided in documentation. If these same developers pass in either the source files or reference documentation, AI tools can generate the code samples they need in the language they want, and better yet, tailored to their project and business context.
This post includes a mix of various thoughts on AI, including fixing bugs without thinking, competitive pressures to adopt AI workflows, risks of atrophied critical thinking, recursive self improvement, and the shift toward more complex tech comm tasks. There's not necessarily an argument throughline here, just various thoughts and perspectives on AI topics in my tech comm world.
Divided Highways: Building the Interstate Highways, Transforming American Life, by Tom Lewis (2013), tells the history of how the US Interstate Highway System (IHS) was built. This monumental project, initiated in 1956 under President Eisenhower, spanned decades and multiple administrations before finally reaching completion in 1992. The project encompassed over 46,000 miles of roadways, a network longer than the circumference of the Earth. The final cost, adjusted for inflation, was more than $600 billion.
I gave a presentation to the Write the Docs Australia group on using AI to write release notes using file diffs, on Feb 16, 2025. Here's the recording, presentation description, and transcript.
This week's post on AI and tech comm includes a collection of related topics: Is AI eroding slow mode? Push-button solutions versus thought partners, and strategies for challenging writing tasks
To make a somewhat strange analogy, I think that issue tracking systems are like the intestines of an IT organization—through these channels, nearly all information flows: bugs, iterations, priorities, user issues, release blockers, needed information, and more. For this reason, this series on prompt engineering would be incomplete if I didn't examine whether and how AI techniques could be used as technical writers work within these channels.
It's that time of year again when we take to analyzing trends. If you know me, you're probably gearing up for a load of AI-optimistic predictions because, as I've noted in previous posts like Unpacking the issues from AI, I'm an AI optimist. However, my AI optimism isn't based on hype or the current tech zeitgeist. Rather, I'm an AI optimist because my daily experiences using AI for technical documentation, especially API docs, throughout 2024 has shown it to be invaluable.
I added a new post on my Prompt engineering series about using AI to mine log messages. This post describes how to use AI to identify important information from scripts that build your reference docs—information such as warnings about missing documentation or deviations from engineering style. These log messages often whiz by in the terminal during builds, even when those builds are successful. It would be tedious to try to manually read the extensive logs and find relevant messages. AI can help turn the logs into actionable information, identifying warnings about missing documentation.
After my post on Biohacking your glucose with AI, I had another thought: what if I didn't just log food + exercise + energy levels, etc., but also tasks, thoughts, observations, or other notes? I learned that this kind of all-in-one journal is often called a bullet journal—which is an officially trademarked term for Ryder Carroll's bullet journaling methodology, often shortened to Bujo. Carroll has detailed methodologies for organizing, tagging, and managing information in a journal that's far beyond the scope for how I'm using the term. So to avoid conflating my approach with bullet journaling, I'm calling my technique here an AI stream journal. Mainly, I just wanted a single place where I could jot down the stream of information flowing through my mind and have AI sort and organize it for me.
In this post, I stray slightly outside my normal tech comm focus to explore strategies for personal optimization, specifically with biohacking your glucose with AI. Some of these techniques could apply to tech comm, such as in analyzing user analytics, but that isn't something I've tackled yet. In short, I'm experimenting with using AI tools to adopt healthier habits so that I feel better and am more productive throughout the day. Specifically, I'm exploring ways to hack your glucose using AI to analyze logs.
