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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.
Six years ago, I wrote Tech Lead Management roles are a trap. My argument then was that TLM roles present themselves as easier than moving into a full management role, but the tension between doing the software engineering and engineering management aspects of the role made being a TLM a much harder first management role than a pure engineering management role. I still agree with that post, and I have some additional bad news to share: middle management roles are mostly a trap as well, if you goal is to become an executive. The core aspects of middle management roles are: Balancing between top-down executive, lateral stakeholder, and bottom-up team pressure, e.g. keeping morale up as an organization deprioritizes last year’s big initiative Defining and operating an organization’s process, e.g. creating career ladders and interview loops Competing for a share of fixed organizational resources (e.g. budget) and allocating acquired resources These are all extremely important skills to be an effective executive, and they make up the bulk of The Engineering Executive’s Primer, but they are insufficient to make you a great executive. If you don’t have them, you will be a deeply flawed executive, but even if you’re an expert at them, you can still be a terrible executive. That’s because the most important skills of an effective executive are the same exact skills that make an excellent line manager: developing domain expertise, driving execution (including setting pace), and translating both of those into an organizational culture that extends beyond you (in any of innumerable different ways). All of them are more easily practiced and mastered as a line manager than as a middle manager. Most middle management roles make practicing those skills difficult, and sometimes negatively select against developing them. As a middle manager, if you drive execution too closely, you might get told off as a micromanager. As a middle manager, if you go too deep on domain expertise, you might get told that you’re not focusing enough on your internal stakeholders. That’s undoubtedly valid feedback in many middle management roles, but it’s the perfectly wrong feedback to someone who is trying to become an effective executive. As a result, I’ve come to believe that the filters for good middle managers inadvertently negatively select out the “challenging” line managers who actually have the best chance to be excellent executives. That’s not even necessarily irrational: most companies are developing middle managers to take on more complex middle management roles; very rarely do they worry about growing future executives from within. If you’re willing to embrace this fact that a little bit of time in middle management roles is important preparation to become an executive, but spending a great deal of time in middle management only prepares you for further middle management roles–and makes you less effective as a potential executive–then the AI-driven shift in managerial fads might be a threat to your current role, but it’s likely to improve your chances to succeed as an executive.
I’ve recently been thinking a lot about the concept of “soil horizons”, which is the idea that there are many distinct layers of soil, from topsoil all the way down to bedrock, which all combine into a soil horizon. Translating this idea into software, the ideal codebase would have a single uniform “code layer”, but a surprisingly large percentage of production software has numerous, distinct code layers as the leading architect shifted over time. I’ve found this particularly true for software in problem-spaces with high essential complexity and low scale complexity, where the purifying challenges of scaling never create enough pressure to compact disjoint layers into a unified layer. Codebases with the most code layers tend to be created by small teams working on complex domains over a long period of time. In many companies this might be an identity, permissions or payments team: stuff that’s permanently valuable, but usually not the central concern at any given time. On such teams, there is often only one architect who understands the nuances of the domain well enough to make tradeoffs. When that architect leaves, they are replaced by someone who aspires to operate in the same code layer, but simply cannot because they lack enough context to do so. As a result, that new replacement creates a new code layer, despite not intending to. If the team runs through a handful of folks as the new team leads struggle, it’s easy to end up with a complex code horizon very quickly. The problem of messy code horizons is not a new one, and the general approach to addressing them is the same one I wrote about seven years ago in Reclaim unreasonable software, but with the proliferation of coding and non-coding harnesses, lately I’m running into the problem of messy code horizons more frequently. Even more concerning, I’m seeing this problem expand from impacting code horizons into impacting how organizations make decisions outside of software, e.g. the company’s general reasoning horizons. When individuals or teams rely on LLMs to reason to conclusions, rather than using LLMs to explore or draft options, it’s possible for even the most important decisions to be built on top of flawed reasoning layers underneath. In the next section, I’ll develop the problem statement a bit about what I’m running into, and then in the final section I’ll lay out the approaches that I am finding (moderately) effective to navigate that problem. Messy reasoning horizons If you give three enthusiastic engineers a problem, a new codebase, a coding harness, and self-approval rights, it’s very easy to end up with three new soil horizons as their harnesses gleefully commit code. However, in engineering we have a number of techniques to derisk this problem. First, we have manual and automated code review, and second we increasingly have the ability for the harnesses to operate off sufficiently clear instructions that they write new code consistently with the existing code, even if the operator is unaware of what good looks like. This is also true for code review, where coding harnesses can drive consistency across pull requests even if the person (or harness) creating the pull requests is not operating off the same shared context as the wider team. Many codebases are not well-configured for this new reality, and those codebases are getting worse at an accelerating rate as more harness and agent contributions get added. Legacy codebases that reach a certain size before introducing these better practices are easier to fix than before, but still require a lot of work to fix. That said, I’m confident that coding harnesses are going to substantially improve the quality of code horizons over the next year or two as the way we configure harnesses improves. That’s not the problem I’m worried about. What I’m worried about is the application of harnesses to problems outside of writing software, where there’s no static typing, linting, or unit tests to validate the output. Let me provide a very recent example from my own work that highlights this problem: I wanted to understand how our incidents were trending over time. So I pulled data via an MCP, and the analysis was unintuitive to me, in particular I thought we were having more Data related incidents than the results reflected. I had to look at the incidents in Slack, then the results in our incident tool, and understand why the two conflicted. After a bit, I recognized the results in our incident tool were only showing incidents that properly tagged a team when the alert was triggered, so it was omitting about half the relevant incidents. After having the agent manually tag the incidents without team assignments, the data made a lot more sense. After recognizing the issue, it was trivial to fix. However, if I had simply accepted the initial analysis, I would have made the perfectly wrong conclusion about what was happening. On top of that wrong conclusion, I could have easily pushed the team to take on a project to solve an illusionary problem. What’s so pernicious about messy reasoning horizons, is once any reasoning layer is poisoned, it’s impossible to reason effectively on top of it. If you take the incident analysis example, it’s easy to imagine prioritizing the perfectly wrong set of remediations, which have the artifacts of solid strategic reasoning, but are nonetheless just wrong. It’s easy to imagine a team wasting a quarter of time building a solution to this sort of problem that never existed. It’s true that poor reasoning has always existed, long before harnesses, but my experience is that poor reasoning wearing well-formatted clothing is proliferating more widely than I’ve previously seen, and it is increasingly difficult to combat because certain social norms are – at least temporarily – collapsing around folks actually thinking. That collapse is largely driven by unprincipled adoption of AI techniques without paying attention to whether they work. Widespread adoption is, in my opinion, the fundamental risk for most companies at the moment, and something companies need to be doing, but many approaches inadvertently mix play (experimenting with something new in ways that are likely to fail!) with production (creating load-bearing work product!) in ways that erode social norms for quality. The norms are not uniformly collapsing by any means, they are generally intact, but even a small increase in the proliferation of low quality reasoning layers has a devastating effect on your ability to reason successfully. Especially true the further up the poor reasoning occurs (sloppy reasoning from senior leaders) or when senior leaders rely on layers of reasoning without inspection (leaders who aren’t sufficiently “in the details” to spot likely reasoning errors in reasoning layers). As a result, we now live in a world where accepting any part of the reasoning context before inspecting it might lead to making a catastrophic mistake. This is an exhausting way to live. Make no assumptions Accepting that this is the world we live in, I wanted to lay out the techniques that I am finding useful to deal with it. Some of these are novel, but many of them are the same techniques I was using before the LLM-advent: Make no assumptions. When new hires join my team or my company, the first thing I tell them is that it’s essential that they “make no assumptions.” This is difficult to do, and it goes against every instinct because it forces you to inspect each aspect of how the company works and thinks, but I do think it’s the necessary approach. It’s a bit like learning “internet-skepticism” at some point in your life, where you realize that everything on the internet is self-motivated in some way, and you have to maintain a strict filter on what ideas you accept. This is a hard change to make, but I genuinely believe this is the correct mindset for accepting new information in the current era. The combination of fewer management layers and more flawed reasoning layers means that the core job of leadership is inspecting the details. The author must be the first human in the loop for their output. The biggest cultural failure with harnesses is when you can tell that you–the recipient of a piece of work–are the first human in the loop reviewing it. You must set a cultural norm that the creator of a piece of content is always the first human in the loop before asking another human to review it. If you fail to set that cultural expectation, then you will quickly crush the remaining team with a high standard for quality reasoning, which will lead to a full destruction of your reasoning horizon. Prioritize reasonable software. Run the Reclaim unreasonable software playbook, recognizing that migrations are cheap in 2026, so it’s much faster to remediate gaps. The core idea here is that relying on convention doesn’t work, and instead you have to rely on deterministic decisioning for each approach. For humans this can feel overly prescriptive, but harnesses don’t care. Learn faster by separating play and production. Many folks trying to learn how to use harnesses and LLMs leap directly into using them in their most critical work. This is a slow way to learn, and can lead to substantial errors in your most critical work. It’s much faster to work by buffering small pockets of time to learn. For example, our head of data has spent time building an iOS app fully “hands off the keyboard” to get a better feel for the tools. This sort of experiment goes much faster and gives you more repetitions in less time. The very practical version of this is setting aside a day or two periodically for folks to experiment. Structure how you think with LLMs. In Crafting Engineering Strategy, I lay out a structured approach to reasoning through creating a strategy document, which aims to prevent the reasoning errors that folks make in their thinking. This applies equally in how we use LLMs, and I think you can substantially reduce the chance of introducing flawed reasoning layers by focusing LLM work on exploration (gathering information on internet and via various MCPs), refinement (presenting gathered information effectively), and a final formatting pass. That takes much of the work out of strategy creation while constraining the areas you have to avoid making any assumptions about its output. I’m certain there are more things! What are you trying?
From early 2014 through late 2020, I was working in hypergrowth environments, which are challenging, but also educational. The most valuable feature of hypergrowth is that your mistakes reveal themselves next month rather than next year, because things go wrong very loudly when you’re moving fast. I’ve been thinking a lot about hypergrowth recently, because Imprint’s business is growing quickly and we did a large batch of hiring last year, but also because the AI-tooling shift has changed the pace at which it’s possible to work. This post documents the new rules I’ve revised my approach to engineering leadership around, and then talks through the specific projects I’ve worked on over the past year that caused me to believe in these rules. Revised rules Migrations can be done by an individual rather than a team. Even complex, large changes can be 95% owned by the driving individual or team, and done in 10% of the time. As the initial cost of migrations goes down, the reward/penalty of each migration’s quality goes up: even small sharp edges will break your colleagues’ mental models about the software you co-maintain. The impact of individual judgment on your company has never been higher. While 1st-pass code is nearly free, the cost of working code depends on your development harness, and is not free. We’re in an era when many companies say that everyone should be writing code, however our experience is that writing code that works well, while avoiding messy edgecases, remains difficult. Just how difficult remains a factor of your development harness, e.g. your tests, CI/CD, validation environments, preview-ability of changes, and so on. While I personally don’t imagine it’s valuable for most folks at a company to be contributing code, I suspect that most disagreement about that topic is actually a miscommunication: even at a company where “everyone codes”, the marketing team isn’t reducing allocations in your servers, instead it’s about whether there is a safe boundary where they can participate. (Much like a SaaS product that allows customization by writing software.) The good news is that this means the things that were most valuable to speed up engineering two years ago are still the things that are most valuable to speed them up today. Optimize the base-case of process for agents. Most steps of most processes can be fully automated in most cases. With the right harnesses, the right controls, domain context, and good judgment in their designers, you can fully automate the base-case of most processes in modern technology companies. For example, the base case of code review from a human is slower and less effective than a good harness’ code review. Of course, the harness will miss things, but so will human reviewers, and most areas are relatively safe to make changes. Of course, there are some higher risk areas, where this doesn’t hold true. By effectively capturing these distinctions properly, we can go much faster without introducing risk. By failing to capture these distinctions, we’ll create innumerable problems for ourselves. As a corollary, I think most planning processes like weekly or bi-weekly sprints are operating at too low an altitude. Humans planning together still matters, but should be operating at a higher level. Durable, high-ownership teams with domain-context are even more important. One of my biggest lessons at Uber was that persistent, durable teams work magic by accumulating domain-context, building a sense of camaraderie, and feeling an increasingly strong sense of ownership over an area as they continue to work in it. Even in an era where specifically doing something is much cheaper, you still have to do the right thing, which has gotten a bit easier but not much easier, and structural improvements help address this. (As a recent example of that, we had an issue in production where the necessary data to optimize it simply wasn’t being captured at all, so the harness’ ideas to solve it were reasonable but wrong, since the only real path forward was instrumenting the missing information.) As a specific disagreement, there’s a prevailing idea that AI-first companies will be run by a small number of genius engineers who create perfect versions of things one by one, doing such a good job that there’s nothing to maintain. This is a very compelling vision, but I don’t see it happening. High judgment individuals can wander across a company doing remarkable things, but at some point they do get hemmed in by lack of domain context, which is why durable teams are the fundamental building block, even in this era. Quick, good, and durable decision-making is a prerequisite to meaningfully benefit from AI. Being able to replace a legal review with automation only works if Legal can commit to that change, which depends on designing the automation thoughtfully, and also the teams’ willingness to collaborate. Implementing a new feature is only valuable if you can decide to launch that feature. Your team and company can only benefit from this increased pace of execution if you can make durable decisions quickly, and those decisions are good. This is the primary reason, in my opinion, why the average CTO role has necessarily become substantially more technical and less bureaucratic than a year ago. In many cases, I am the only person who can make binding decisions when teams disagree on the path forward, and that means I am making decisions constantly in this new world in order to maintain the pace. (That’s not an argument that executives are better decision makers, just that binding executive decisions are uniquely powerful to the extent that the executives themselves are aligned enough to honor those decisions.) What have we done in practice So, I genuinely believe the above rules based on my experiences over the past year, and let me try to connect them to specific projects we’ve worked on that have convinced me of them: Migrations A year ago, we deployed manually, and deployed ~6 times a week, and now we deploy 200-400 times a week. Our engineering headcount has doubled, but even if we double the prior deploys, we’re still up 20-30x year-over-year. This is due to a complete overhaul of how we deploy and run migrations, and this migration was done over two months and done 90% by two folks on our infrastructure team. The first day of January, about 25% of folks on our team used Claude Code or Cursor every day. By the end of February, 100% did. We did this without any top-down mandate, just by making the tooling good and chatting with non-adopters to remove sources of friction. Pretty much every PR is written by harnesses now, at least in the first pass. We migrated from a large number of varied configuration mechanisms to two configuration mechanisms (one for client or server constants that rarely change, a second for product-specific or frequently changing values). This was a large series of changes, which were largely done as a series of isolated projects by individual engineers. First, one engineer cleaned up the architecture to support this approach. Then another engineer did a reference architecture on the new approach. Then several more engineers followed the reference architecture in other areas of our codebase. This might have been a years long project of many people in the prior world, but took less than a quarter to complete, including a new internal tool for managing these values across engineering and non-engineer teams. We unified a multi-repo frontend application architecture into a mono-repo frontend architecture over about a month. This was 95% driven by one frontend engineer. We now have a shared frontend development harness, can maintain libraries cheaply, and entirely moved off using npm for package hosting, which was a source of ongoing friction. We fully statically typed our frontend code, going from a place where the majority of our frontend code was not typed. This was done by one engineer, and a lot of tokens, over the course of a few weeks. We migrated from npm to pnpm for better security defaults and faster deploys. This took one engineer a few hours a day for a few days. Cost of working code depends on your development harness. Where we’ve tried to throw design documents and PRs “over the wall” to engineers on other teams, they’ve never gone anywhere. Slop pull requests and design documents are cheap, but are actively harmful. They not only have to be cleaned up and repaired, their context poisons the LLM, leading to worse outcomes than starting over. We’ve seen tremendous success in managers contributing software, as long as those managers are validating the work directly, looking at dashboards after their changes go out, and resolving any issues their changes cause. We’ve found no positive impact from folks attempting to make changes where they don’t do those things. Optimize the base-case of process for agents. We triage all incoming issues from our customer operations team using a harness which knows our team, our open tickets, and has limited access to our data warehouse to size the impact of issues. This is complex, high-skill but not particularly interesting labor that we’re now doing better and faster with agents. Yes, there is still a human triage for the edgecases. Importantly, we’re also doing this without changing human workflows, it’s the same workflow, just with some steps automated. The first pass of code review is done by the same harness that implements the changes, cleared of the context used to write the change, allowing humans to focus on higher value feedback. We rolled out Claude Code and Cowork to all folks in the company last quarter, and have seen them also automate an increasingly large swath of their work as well. Our fraud team has been particularly ambitious in replacing manual workflows with a first-pass of automation–with attribution to the data itself–to do the initial investigation on potential attacks automatically. We’ve migrated to Linear, and off Jira, to better support this workflow with a more capable MCP and better Slack integration, making it possible for everyone internally to have better infrastructure for building these agent-first workflows. More on this later, but we’re almost done alpha-testing our internal harness pulling issues off Linear, and working to resolve them, automatically which is our biggest next step in this direction. Durable, high ownership teams with domain-context are even more important. When I joined, we had a number of areas supported by very talented folks who rotated through them quickly on a per-project basis. This worked, but it meant we were very reactive to issues. Now, we’ve been able to dedicate at least a small team to every important area of the company, where they are able to persistently invest. These teams are now wielding all the new techniques afforded by AI themselves. Without them, no one would be capturing these opportunities, because there is simply too much happening. We launched SierraAI, which is quite good, but since then the team has iterated on it relentlessly, getting it truly excellent. This is something we wouldn’t have been able to do without a dedicated, focused team. Quick, good and durable decision making is a prerequisite to benefit from AI. Changing how we do configuration was a controversial decision, and I’ve had to make repeated clarifications on the approach. This would have been very difficult to do bottom-up, because it impacts every team differently, and the benefit is only experienced at the ecosystem-level (allowing one person to configure all configuration across teams). Reworking our CI/CD pipeline was controversial, as it changed many folks’ mental models of how we deploy and release (e.g., it forced us to explicitly decouple deploy and release via feature flagging). This was a contentious decision, and would have been slow and difficult to make bottom-up. Unifying into a web mono-repo was also a controversial decision with varied opinions. It benefitted greatly from having a unified decision. Moving to SierraAI was a difficult discussion versus both various competitors, and also not doing it. It needed the executive stamp to finalize the cross-functional debate. These are just representative examples, we’ve done a lot more than these. The aperture of what’s possible has continued to expand every month this year, but the things holding us back haven’t changed all that much: organizational misalignment, lack of clarity, and poor technical architecture. It’s a wild time to be working in technology.
One of my gifts/curses is an endless fixation with how processes can be optimized. For a brief moment early in my career, that was focused on improving how humans collaborate, but that quickly switched to figuring out how we can minimize human involvement, and eliminate human-to-human handoffs as much as possible. Lately, every time I perform a recurring task–or see someone else perform one–I think about how we might eliminate the human’s involvement entirely by introducing agents. This both has worked well, but also worked poorly, and I wanted to highlight the pattern I’ve found useful. For a concrete example, a problem that all software companies have is patching security vulnerabilities. We have that problem too, and I check our security dashboards periodically to ensure nothing has gone awry. Sometimes when I check that dashboard, I’ll notice a finding that’s precariously close to our resolution SLAs, and either fix it myself or track down the appropriate team to fix it. However, this feels like a process that shouldn’t require me checking on it. Five to six months ago, I added Github Dependabot webhooks as an input into our internal agent framework. Then I set up an agent to handle those webhooks, including filtering incoming messages down to the highest priority issues. About a month ago, when I upgraded from GPT 4.1 to GPT 5.4 with high reasoning, I noticed that it got quite good at using the Github MCP to determine the appropriate owners for a given issue, using the same variety of techniques that a human would use: looking at Codeowners files where available, looking at recent commits on the repository, and so on. The alerts and owners were already getting piped into a Slack channel. So, this worked! However, it didn’t actually work that well, because despite repeated iteration on the prompt, including numerous CRITICAL: you must... statements, it simply could not reliably restrict itself to critical severity alerts. It would also include some high severity alerts, and even the occasional medium severity alert. This is a recurring issue with using agents as drop-in software replacement: they simply are not perfect, and interrupting your colleagues requires a level of near-perfection. If I’d hired someone on our Security team to notify teams about critical alerts, and they occasionally flagged non-critical alerts, eventually someone would pop into my DMs to ask me what was going wrong. That didn’t happen here, because the knowledge that those DMs would show up prevented me from rolling the notifications out more aggressively. Coding agents address this sort of issue by running tests, typechecking, or linting, but less structured tasks are either harder or more expensive to verify. For example, I could have added an eval verifying messages didn’t mention medium or high severity tasks before allowing it to send to Slack, but I found that somewhat unsatisfying despite knowing that it would work. Instead, after some procrastination on other tasks, I finally prompted Claude to update this agent to rely on a code-driven workflow where flow-control is managed by software by default, and only cedes control to an agent where ideal. That workflow looks like: A webhook comes in from Dependabot Script extracts the severity and action (e.g. is it a new issue versus a resolved issue), and filters out low priority or non-actionable webhooks The code packages the metadata into a list of issues and repositories The code passes each repository-scoped bundle to an agent with our internal ownership skill and the Github MCP to determine appropriate folks to notify for each issue The issues and ownership data are passed to a second agent that formats them as a Slack message This works 100% of the time, while still allowing us to rely on our internal ownership skill to determine the most likely teams or individuals to notify for a given problem. It’s now something I can rollout more aggressively. The immediate fast follow was a weekly follow-up ping for open critical issues, relying on the same split of deterministic and agentic behaviors. The next improvement will be automating the generation of the vulnerability fixes, such that the human involvement is just reviewing the change before it automatically deploys. (We already do this for Dependabot generated PRs, but in my experience Dependabot can solve a reasonable subset of identified issues, but far from all of them.) That is the pattern that I’ve found effective: Prototype with agent-driven workflow until I get a feel for the workflow and what’s difficult about it Refactor agent-driven control away, increasingly relying on code-driven workflow for more and more of the solution End with a version that narrowly relies on agents for their strengths (navigating ambiguous problems like identifying code owners) This has worked well for pretty much every problem I’ve encountered. The end-result is faster, cheaper, and more maintainable. It’s also a cheap transition, generally I can take logs of some recent runs, the agent’s prompt, and some brief instructions, throw them into Codex/Claude, and get a working replacement in a few minutes.
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Download Now View on pouet.net Intro In the late 80s, I spent an embarrassing number of hours hunched over my ZX Spectrum, typing in Sinclair BASIC programs and rudimentary assembler routines copied from magazines, trying to re-create the Amiga crack intros I’d seen round at a friend’s house. Obviously, they never came even slightly close - but now, many decades later, I’ve finally managed it! I’m now the proud author of a hand-rolled retro demoscene intro that runs on a stock 128Kb Spectrum. It features all those late 80s tropes: A smooth scrolltext (with ‘leet custom fonts!), a three-channel AY soundtrack, bouncing VU meters and the essential parallax starfield. It’s currently 10287 bytes of code, or 19552 bytes as a complete tape image - which, given I’ve been working on this since December 13th last year, means my average rate of code-production is a paltry ~ 455 bytes per week! This blog post is a technical overview of how I got there, some cool tricks I picked up, and everything I had to learn (and re-learn) about the Speccy and Z80 assembly along the way. It simultaneously rates as one of my favourite side-projects ever, and also the one that most often nearly triggered a foot put through my monitor in frustration. I’ll reflect on all that at the end, but for now if you just want to see the finished product, the download button is at the top of the article and the included .tap cassette tape image should load into any emulator. There’s also a YouTube capture, links to the production on pouet.net, and you can run it from within a web browser by visiting my TNFS site portal - check option 4 for recent uploads. Be Kind, Rewind But let’s skip back briefly to the late 80s. Coming from the world of an 8-bit, cassette tape loading computer, seeing an Amiga for the first time was like encountering something from outer space. Like everyone who saw one back in the day, I was blown away by the amazing graphics and sound on display, not to mention the library of ground-breaking games. But I found myself even more captivated by the mysterious (and totally badass) “intros” displayed at the beginning of pirated games. Created by the software piracy groups that had cracked the copy protection and distributed the floppy-disk based games, they were mini works of art in their own right - music, graphics and usually some cool-looking effect to show off the group’s prowess. There’s a great collection of these intros (or “cracktros”) archived on YouTube. Watch a few of those videos and you’ll quickly notice the “design” tropes that almost every cracktro shares: a cool, edgy logo at the top of the screen (bonus points if it’s dripping blood or in any way “metal”), some boastful text displayed on top of an effect of some sort - usually a scrolling parallax starfield, or rotating 3d object - and a further scroll-text full of shout outs, greetings to fellow pseudonymous crackers and adverts for illegal BBS systems galore. Needless to say, to a kid in the 80s/early 90s this was possibly the coolest thing ever. I thought this one was the absolute bomb when I first saw it. Sine-scroller, pirate BBS adverts, cool logo with a sword - and of course, a starfield. There’s something for everyone here. The music from this is burned into my brain. I think pretty much everyone of my classmates knew this intro given the popularity of Lemmings in the early 90s. Skid Row as a group are still active to this day and this is probably the quintessential Amiga cracktro of the period. Endless Piracy. Edgy. Another cool logo, sine-wave scroller, starfield and boasting text. The classics never go out of style. Another great combination of all the essential ingredients. Cool logo, even cooler text font, scrolling starfield and a scroll-text running along the bottom. Vintage stuff. Logo. Text. Scroller. But no starfield - otherwise a solid effort from Anthrox here :) The demoscene grew out of these intros. The cracking eventually faded into the background, and coders, “graphicians” and musicians all contributed to feature-length productions that showcased their abilities, competed with other groups and pushed the machine to its limits. In amazing feats of ingenuity and pure “code as an art form”, the scene lives on today, with groups producing breathtaking productions like this and this - bear in mind, both of those are running on a 7Mhz home computer equipped with a floppy disk, 1Mb of memory and a sound chip from 1985. But much as I admire the sheer skill and dedication that takes, it’s the early “single screen” scrolly-text-and-starfield cracktros I’ve always found captivating. They took on a sort of mystical quality for me, hinting at a scene and a culture I could only ever experience from the outside - which is exactly why I spent all those hours in Sinclair BASIC, trying in vain to re-create them, and dreaming of the day I’d eventually have an Amiga of my own. The Jump Back I eventually did trade up to a range of Amiga systems, joined a few “scene” groups myself and threw myself into a variety of activities until exams and the end of my teenage years beckoned. I dabbled with Z80 code back then, and at one point knew enough 68k assembly to bluff my way through - partly from the Amiga’s heyday, partly from a few “embedded systems” modules at University where I had to program a set of traffic lights or lift-controllers. Given there’s not much call for that sort of thing at my $DAYJOB, though, it had all pretty much faded from my memory. I have kept working on a bunch of projects in my spare time, and one of my favourites is my TNFS site - sort of an old-school BBS system or interactive “website” for the Spectrum, which grew out of a fun experiment to see how far I could push Sinclair BASIC. It relies on a modern network card fitted to the Spectrum, which is how I came to be chatting to “Desertkun”, another TNFS-protocol enthusiast. He’s now producing a great new WiFi network card for the Speccy, and was kind enough to send me a few of the prototypes to test out for him. I was massively inspired by his hardware and low-level coding chops, and during one of our video calls where we’d been comparing our development setups, he asked why I didn’t write any Z80 assembly myself. A throw-away comment, but the challenge was set! Now, I couldn’t face re-writing the whole site in asm, but I figured I could produce a self-contained intro to promote it in the style of the early “BBStros”, and finally be one of the cool kids with a scene production of my own :) I mean, how hard could it be ? (ooooh - foreshadowing!) Dev Environment My development environment through this was pretty straightforward, and is the same stack I use for developing my TNFS site. I just used a Makefile-driven build system which ran my code through the Pasmo assembler, my fork of zmakebas to build BASIC files, and the FUSE emulator for testing. For final testing on the real hardware, I uploaded my .tap files to my local TNFS server where I could run them on my +3. A few additional tools I have to call out because they ended up making a big difference - one is the amazing web-based “asm-editor” I discovered while looking up Z80 instructions. Not only does this site have a great reference of all the Z80 instructions, but it also has an editor where you can run example code and see how the memory and/or various registers change state. Take for example, the djnz instruction - you can build it and step through the example loop construct one instruction at a time. Or try my example of an add instruction in the screenshot to see how the carry flag works when a bit gets shifted out of a register. Although it uses a slightly different syntax to Pasmo (so I couldn’t drop my code directly in it), it massively helped when manually stepping through things. And lastly, probably the most significant debugging tools I used on this project - a notepad and pen, and my two dogs (say “Hi!” to Biscuit & Pebble…) I’ll write some more about this in the wrap-up, but this really was a highly unusual project for me, coming from decades of much higher-level languages and constructs. Most of my z80 code I only ever really understood after drawing out the bytes and bitmasks, keeping a table of register flags and drawing chunks of screen memory out by hand. And I found my evening walks with the two dogs provided ideal rubber duck exercises, although I’m not sure what they both made of my protracted mutterings about the hl register pair. Either way, debugging-by-dog is highly recommended! The Starting Point So, let’s get started. I followed a great “Hello, world!” example from futurice.com to validate my build environment. Beyond that, the first thing I wanted to do was load in my logo and background image I’d use as a “stage” for the remainder of the planned intro. I wasn’t really sure what I was going to do at this point, just that I wanted my logo at the top, and a scrolltext along the bottom row of the screen. As my TNFS site header already looked kinda demoscene-y, I made a quick edit putting in place two blue guide-bars inside which I’d put further content. Memory map courtesy of breakintoprogram.co.uk A Spectrum SCREEN$ file (usually displayed as loading screens) is just a dump of screen memory, starting from byte 16384, and running for 6912 bytes. The weird layout of this is very relevant later, but in order to display it all you need to do is load your file into that memory location. After a few hour’s work of reviewing Speccy internals and following various guides, I had a very simple starting point for my demo that served as the basis for everything else that came after. org $8000 screen EQU $4000 ; address of the first byte of screen memory, 16384 in dec scrlen EQU 6912 ; total length of the screen memory inc. attributes ula EQU $FE ; I/O port for the ULA to control the border black EQU 0 ; Spectrum colour number, the exact same ones BASIC uses start: ld a,black ; Set the border colour to black using the ULA out (ula),a ; Copy my logo header picture to screen memory ld hl,header ; HL = location of my screen file in this code ld de,screen ; DE = start of screen memory ld bc,scrlen ; BC = 6912 bytes to copy ldir ; block copy, see https://jnz.dk/z80/ldir.html ; busy loop - rest of intro goes here :P hold: halt ; sleep until the next interrupt (1/50 second) jr hold ; then jump back and sleep again, for ever header: incbin "assets/header.scr" ; my logo header/background end start Not much to look at now, but this simple starting point introduced me to important concepts like interfacing with the Spectrum’s hardware (ULA chip to start with, and AY sound chip later) via the out instruction, how to pull in binary files using Pasmo’s INCBIN directive, using the Z80’s block copy ldir instruction, the use of register pairs for 16-bit addressing, and simple branching to labels. Hello World The next step was to print something on the screen and explore some of the Speccy’s built-in ROM routines, which are somewhat analagous to syscalls on modern operating systems. There are plenty of disassemblies and guides to the Spectrum ROM available on the internet, one of the best commented and linked one is at https://skoolkid.github.io/rom/. Using this as a reference alongside the various “Hello, world!” tutorials showed me how this sort of thing is built up: at EQU 22 ; "at" control character used to position the cursor row EQU 11 ; coordinates where to print the message col EQU 9 prstring EQU $203C ; where the pr_string routine lives in ROM ld de,msg ; DE = address of the first character ld bc,msglen ; BC = how many characters to print call prstring ; print the lot ; busy loop is the same... msg: db at,row,col ; equivalent of "PRINT AT 11,9" in BASIC db "Hello, world!" msglen EQU $-msg ; 16 bytes in all, pasmo shortcut, $=program counter I ended up mostly not using the ROM routines in my code as they dragged in all sorts of things I didn’t need, but it was massively useful to understand how they worked as they introduced me to yet more commonly-used constructs. For example, the PR_STRING routine defined here shows how you can build a loop over each character. If you’re just starting out, I highly recommend going over the 8 lines in that routine with a notepad and pen, working out the contents of the registers and following the loop constructs. I also made a version of it in asm-editor you can try. If you click that link, you’ll see a version of the code loaded into the web editor. Click “Build”, and then step through the code using the “Step” button - although no text will be actually displayed (I skipped over the call to the Spectrum ROM “restart” vector that does this), you’ll see the various states of the registers and for example the BC register counting down as each byte of the string is processed. Adding Some Style Problem is, the Speccy’s built-in font is not exactly dripping with demoscene credibility. I wanted to use some stylish fonts and this turned out to be super easy - a quick search turned up Jim Blimey’s awesome font collection, which included a ready-to-use set of assembler files and some instructions which soon led me to these additions in my code: ; CHARS is the address the ROM's printing routine reads to find the font. ; Picking our own font is simply a matter of over-writing this chars EQU $5C36 ; from Jim's docs - POKE 23607=5C36 in hex ld hl,myfont-256 ; HL = font address - 256 as per docs ld (chars),hl ; from now on the ROM draws with it! ; rest of code is the same myfont: include "textfont.asm" ; "Cartoon" font from jimblimey.com There we go! Now that’s looking a little more elite. Sorry, I mean 31337. Strap in folks, we’re leaving lamer city… So at this point, a month or so in to my dabblings and I’d started to get a reasonable understanding of common idioms and was starting to feel like this was a project that might actually go somewhere. The problem started when I moved on to trying to implement actual demo effects: It became very apparent, very quickly, just how much I didn’t know about how this all worked. Not to mention the fact that at this level, you’re really closely exposed to the physical hardware intricacies, including the design decisions and trade-offs that the hardware designers had to make when working with the extremely limited specs of the early 1980s. Cursed Screen Layout The first such problem - and the cause of probably 90% of my pain - is the Speccy’s extremely weird screen layout. Rather than standard bitmapped display you might expect on more modern hardware, it is instead split into 3 sections, each with a set of interleaved rows (so if you keep adding 1 to a screen address, eventually instead of reaching the next row, you’ll instead end up 8 rows down, and so on until you hit the bottom of the section and then loop back to the row underneath where you started!) followed by a block of “attribute” bytes which determine the colours of each 8x8 block. The attribute blocks are the reason why each 8x8 character cell can only have two colours (a PAPER and INK attribute) which led to the garish “colour-clash” of spectrum games and distinctive style where artists try to keep inside 8x8 blocks as much as possible. But it was the layout of the bitmap portion that is such a mind twister. Download the video You can see this illustrated whenever a game reads in a loading screen from tape. You can see each section built up with the interleaved rows one at a time, and then finally the colour and other attributes (like flashing ). There’s a lot of detail at the fantastic Break Into Program site. In the defense of the designers, they were working with extremely limited hardware and I don’t think the Speccy was ever imagined as a games machine. When you start shuffling bytes around, you realise that this approach is really optimised for displaying text which is neatly arranged into 8x8 character boundaries. For example, using this “scrambled” bit order, if you have a screen address in the hl register pair, you can quickly get to the next pixel line of a given address by simply incrementing the “high” byte. There’s a bunch of other “cheap moves” you can make by simply shifting bits around: Move Instruction Right 8 pixels inc l Left 8 pixels dec l Down 1 pixel line inc h Up 1 pixel line dec h Down 1 character row add 32 to l It was a reasonable choice for a machine that would probably spend a lot of it’s time displaying BASIC program listings - it’s only when you start trying to write demo or sprite routines that it starts to feel really weird. This also means you’ll pretty quickly need a routine to convert X,Y addresses to the relevant memory addresses. Again, L Break Into Program has a useful routine and if you disassemble more or less any spectrum game or demo, you’ll find a version of this procedure somewhere. And yes, this all became horribly relevant later on… The Scroller Download the video Admittedly, not the most interesting demonstration - but this was the first time I had made something move on the Spectrum’s screen with assembly! I watched this with pride more times than I care to admit :) I had a snazzy logo and “Hello World” text, but that doesn’t make for a very interesting intro! The first effect I wanted was the scrolltext - without that, it’s not really an intro. Now, the Amiga had all kinds of hardware sprites, blitter operations, custom chips but the Spectrum had none of that. I was going to have to work out how to do things the hard way - “banging on the metal”, literally by shifting bits around the screen memory by hand and having the CPU do all the work. This is where understanding the Speccy’s weird screen and character cell layout is so important. It turns out there’s a neat trick as long as I stay inside one character row - there’s a RL instruction, which takes a byte and “rotates” it left. It inserts whatever is in the carry flag into the rightmost bit, and whatever gets shifted out ends up in the carry bit again. That means if you string a bunch of them together, you can move a set of pixels across the screen - the moment they “fall off” one character cell, they end up in the carry flag and then the next rl instruction inserts them at the beginning of the next character cell to the left. Repeat 32 times, and you’ve just moved a pixel (or set of pixels) across a line of the screen: linestart EQU $4880 ; leftmost byte of the line 12 rows down lineend EQU linestart+31 ; rightmost byte column ($489F) linelen EQU 32 ; one whole line is 32 bytes (character cells) across dot EQU %00000001 ; the dot "shape", just one pixel lit at the far right end newdot: ld a,dot ; load the dot shape (only rightmost pixel lit) into a ld (lineend),a ; store it in the rightmost byte of the line, pixel is now lit frame: halt ; wait for the next frame, otherwise it whizzes across too fast to see ld hl,lineend ; hl = last (rightmost) byte of the line ld b,linelen ; loop counter, 32 bytes to work through or a ; trick to clear the carry flag, or against itself leaves a register ; unchanged but clears various bits including the carry flag ; see https://jnz.dk/z80/or_r.html shift: rl (hl) ; rotate this byte left (00000001 -> 00000010) dec hl ; point hl at the next byte to the left djnz shift ; loop construct: b=b-1 and then jump back to shift label until b=0 jr c,newdot ; now we've shifted the whole line, so if the carry flag is set now it means ; the dot has just left the screen, so we jump back to the start jr frame ; otherwise it is still somewhere on the screen, so keep shifting! Download the video Hell yeah. It’s almost a proper scrolltext now! Wrapping this in another loop that increments h each time (remember - that gets us moving down a scanline) gets us moving an entire character cell. Here you can see it moving an entire row of my “Hello world” message. There was however a lot more work needed to make a proper scrolltext, with each letter appearing smoothly. I started off by just printing an entire character at a time on the right-hand edge of the screen - it worked, but was pretty ugly as you saw each new character appear at once in the right-hand side of the screen, scrolling 8 pixels over and then the next character flashed up. Instead, I wanted to make each character appear pixel by pixel and that required a bit more bit juggling. The routine I ended up with effectively streams each character in, line by line, pixel by pixel using the font data directly instead of calling the ROM print character routine. It does this by looking up the ASCII code for a character, and then fetching the corresponding font representation into a 8-byte buffer, one byte for each line of data. The font data starts with the space character (character code 32), so a lookup is simply font code base address + (ascii code - 32) * 8 to get to the bits for that letter. Arranged into an 8x8 grid, the bits for the letter H in the font data look like this. For each row of bits, I shift the line left using a neat trick. The byte is loaded into the a register, and then by adding itself to itself - add a,a we shift the value left (in binary, multiplying by 2 moves everything over one bit) so that the pixel I want to draw ends up dropping into the carry flag. This is an idiomatic way of doing it in Z80 code as it’s faster and smaller than the sla instruction. And every cycle quickly adds up when you’re re-drawing 256 bytes every frame! The character buffer then looks like this, with the top line shifted. Now, with the hl register pair pointing at the correct location on the screen, I can then use the rl trick discussed above. This shifts a scanline from the bottom character row of the screen left by one pixel and draws my pixel in the rightmost position by moving it in from the carry flag. Looping 32 times over this moves the entire row by 1 pixel with my new pixel inserted at the right. I then repeat this step again using inc h to move down one scan line until the entire character column has been drawn. At this point, the character buffer looks like this, and all 8 lines of the bottom screen row have all been shifted left by one bit by looping over all 32 character columns. The whole thing then repeats another 7 times until the entire character has been smoothly scrolled onto the screen. So just to re-state this because I think it’s worth the emphasis - just to scroll a message across the bottom of the screen, we’re having to loop over and re-draw 256 pixels every frame! It shows how much faster assembly is than the BASIC interpreter built into the spectrum’s ROM. I found it amazing to see my code finally working and imagining all the bits flying around and registers flapping away. It really makes me appreciate just how much effort went into creating the games I played back then. Music The original Amiga module I converted. A proper 1980s ear-worm! Of course, any good intro needs some music. On the Amiga, the standard was the “tracker” module format, which has a long and involved history of different forks, versions and tweaks. A song is built up from a series of patterns, each made up of rows of instructions. Each instruction usually includes note or pitch information, which sample to play and various parameters such as volume, effects - or in the case of some instructions, which position in the song to jump to. In many ways, it’s almost like a programming language for music. Using ProTracker as an example, a typical note cell is 4 bytes, shown on-screen as something like C-2 01 C20. That breaks down into three fields: the note and octave (C-2), the sample number (01), and the effect and its parameter (C20 - in this case, “set the volume to $20”, which is half of the maximum $40). A close-up of Protracker playing a tune. Note the VUmeters - another classic demo effect I would pinch later on! For an impressive demonstration of what’s possible with a tracker and to see what the note data looks like on-screen, check out the work of h0ffman, and in particular his winning “Professional Tracker” entry from the Revision 2014 competition. He’s an undisputed master of the science (or art!) of tracking, and his “Secrets of Protracker” talk gives a great overview of what’s possible. The Amiga’s sound hardware far eclipsed anything else on the market at the time, and the ZX Spectrum is paltry by comparison. The original 48K models only offered a rudimentary “beeper”, but the later 128K models included a 3-channel AY-3-8912 sound chip. This was very popular in 8-bit micros of the time, and a more-or-less identical variant was also used in the Atari ST. While it only offers basic waveforms and sound processing, it does enable multi-channel “chip music”, and so it became the mainstay of the ZX Spectrum demoscene. It also spawned a variety of SoundTracker clones on the Spectrum itself, although most musicians these days prefer to work in modern tools like Vortex Tracker II, which runs natively under Windows. The Conversion Pex looking back on his demos - this starts at the point where he features my letter and plays a bit of my cover version! For my intro, I wanted to port an Amiga module - and I had the perfect one in mind. One of my favourite Amiga music disks was “Sounds Of Gnome”, released in 1989 by the Swedish duo “Mahoney & Kaktus”. Mahoney also wrote NoiseTracker, one of the first really widely used trackers on the Amiga, and the program that served as my teenage introduction to making computer music of my own. Back in 2018 I made a rock cover of one of the other tunes on that disk, and wrote to Mahoney (real name “Pex”) about it. He ended up showing my “music video” at a demo conference, which I am eternally grateful for! So it seemed only fitting that I mangle another song from that collection with a Spectrum conversion… Sorry, guys! I picked the track “Milkman” which was written by “Glue Master”, an associate of Mahoney’s who had a few of his tracks featured on the compilation. It’s an ideal track for converting: It’s short (only 7 patterns in total), most of those patterns share the same chord sequence, and it looked fairly easy to squeeze the original 4 channels down into the Spectrum’s 3. Plus, it’s an incredibly catchy tune and I knew it well - I’ve been listening to it for 35+ years and often jam along to it on guitar or bass. The conversion started off pretty easily: all I really had to do was copy the patterns note-for-note from the Amiga mod, which I had open in OpenMPT as my Windows tracker. Below is a session with both trackers open on the same pattern - the original Amiga mod on the left, and my 3-channel Spectrum conversion on the right. Note the 11.7 in the 3rd channel of the Spectrum version. This is a “pitch slide down” effect in Vortex Tracker syntax, which drops the pitch by about a quarter-tone. It’s repeated on the next line, so the overall effect is a slide down from the A# that leads in to the next bass note. The original Amiga mod does the same thing using its own syntax - those are the 202s you can see on the same notes, at the bottom of the 4th track. Using just a single “beep” sound for everything was enough to check everything was working, and after a few weeks of hacking on it and re-arranging things in the evenings, I had a basic version which sounded surprisingly good for a first draft: Download the MP3 instead. I have to give a special mention to this forum post which was a massive help getting me started. It’s probably the best introduction to Vortex Tracker I’ve found - many, many thanks to the original author for taking the time to write it all up! The real challenge was making it sound as close to the original as possible. That meant building up a library of better-sounding samples for the different parts, and making extensive use of Vortex Tracker’s “ornaments”. Vortex Tracker comes with a great selection of sounds, and fortunately I’m also a weirdo who likes to listen to chiptunes in the background while I work! So whenever I found something particularly enticing, I filed it away for later pilfering inspiration. A Vortex Tracker sample isn’t really a “sample” in the Amiga sense of a recorded sound - it’s a definition of how a note sounds, one line per frame. Each line can mix in a tone or the white noise generator (useful for snare effects and suchlike), along with various other parameters. Ornaments, on the other hand, give you extra control over what pitch a note plays - and this enables really useful tricks like “arpeggio chords”. For example, an ornament of 0,4 makes the channel switch rapidly (25 times a second) between the note being played and the one 4 semitones above it. It happens so quickly that it sounds much like two notes played at the same time, which means you can build up full-sounding triad chords using only two channels. Take the opening chord in my fully converted version, Cm: It plays C in the first channel and the minor 3rd (D#) in the second, then I apply an ornament to the second channel to bring it up 4 semitones to the 5th (G). That gives all three notes of the chord, while keeping the 3rd channel free for other parts. As well as making much more efficient use of the limited 3 channels, this also gives Spectrum music that characteristic “warbling” sound, which I find really appealing in a retro kind of way! Between tricks like this and samples that mix tones with white noise (to make a combined “bass-snare” sound, for example), I managed to condense the full track down to 3 channels. Many months later, I have to say I’m really pleased with the final result. Playback The original site that was home to Vortex Tracker, documentation and other resources appears to be offline at the moment. I managed to grab a copy of everything I needed from archive.org - you’ll want the PTxTools.7z file for the playback routines discussed here. The playback was simple in theory: As this overview notes, you basically just pull in the player routine, include your module file and call the playback routine once per frame in your main loop. The catch is, none of the source code versions supplied in the archive worked with my Pasmo assembler. Fortunately, there is a pre-compiled binary object shipped alongside the source (VTII10bG in the PTxTools/PT3Play directory). This just has to be loaded in at address $C000 - this cannot be changed, as it has lots of absolute addresses baked in. My code to handle module playback now looks something like this, with the binary module loaded in place: ; Define the start address of the player routine and functions we need player EQU $C000 ; hard-coded org address of binary player plinit EQU player+3 ; init function in docs, module address in hl plplay EQU player+5 ; play and move on one frame ; from the docs - this "analyzes PT3 module header and prepares ; corresponding note and volume tables" ld hl,moduleimg call plinit ; main loop hold: halt call plplay call scroll ; other effects / routines go here... jr hold moduleimg: incbin "assets/music.pt3" Starfield Download the video Well, that didn’t work as hoped. TNFStro - tortoise edition. This brings me to the main event. No self-respecting cracktro is complete without a starfield! My first, naive approach to the classic starfield effect was to scatter random stars across the screen, then move down each scan-line applying the same rl trick I’d used for the scroll-text. I figured I could get parallax by shifting each row a different amount: One pixel for the first row, two for the second, three for the third, then back to one again, all the way down the screen to give the impression of several “fields” of stars moving at different speeds. It turned out there were two major problems with that approach. Firstly, shifting an entire row smeared whatever else was on it along with the stars - which I’d expected, and had filed away as a problem for future me. The bigger issue was speed: by the time I’d finished the first three rows I’d already performed hundreds of shift operations, and it was nowhere near fast enough to fit inside a single frame. Everything crawled across the screen, and the music playback slowed down so much that you could actually hear the arpeggio ornaments warbling away as separate notes. Clearly I was going to have to keep track of the individual stars instead. I found this routine, which was more-or-less what I had in mind and started me off with the idea of a structure to hold the state of each star. It also uses a screen address lookup much like the one from “Break Into Program”. Download the video Dammit. Back with another funeral dirge rendition :( That got things moving, but the co-ordinate lookup and tracking proved expensive to do (it has to run for every star, every frame as well as keep track of the old position for erasing) and I’d been ignoring the bigger question how to display stuff on top of the starfield. My original idea was simply to draw the stars and then paint text over them each frame, which blew the frame budget just as comprehensively as my first attempt. If I wanted something more interesting than an empty starfield, I’d have to keep track of which cells were occupied by text and avoid drawing stars in them in the first place, rather than over-painting them every time. After lots of scribbling in my notepad and visualising rows of bits, I eventually solved both challenges by “cheating” fairly liberally. So now, I don’t generate a random starfield at all. Instead, I built a table of pre-calculated Y and X position pairs: stdata: defb 109, 25, 88,182, 42, 96, 155,226 defb 59,254, 137,169, 160, 84, 125,193 ; ... and so on, for 32 stars ... This saves faffing about with an RNG, and once I have a layout that looks good I know it’ll always look good. I can also “fudge” things and ensure no two stars share the same y co-ordinate which sidesteps a whole class of problems like stars sharing a scanline, and having to deal with them overtaking each other. The “random” layout is generated by a simple Ruby script: #!/usr/bin/env ruby PAIRS = 32 PER_LINE = 4 # pairs per line Y_RANGE = 40..175 # exclude rows taken up by my logo & bars X_RANGE = 0..255 raise "y range too small" if Y_RANGE.size < PAIRS # no duplicate y values ys = Y_RANGE.to_a.sample(PAIRS) pairs = ys.map { |y| [y, rand(X_RANGE)] } pairs.each_slice(PER_LINE) do |row| body = row.map { |y, x| format('%3d,%3d,', y, x) }.join(' ') puts "defb #{body.chomp(',')}" end When the routine initialises, I convert those co-ordinate pairs into a table that tracks the state of each star. So rather than storing a position and translating it to a screen address every frame, each of my star records now looks like this (offsets from the base address): STSCR EQU 0 ; screen address of the byte the star is in, e.g. $4123 STOCC EQU 2 ; a pointer to the "occupancy check" table cell STMASK EQU 4 ; the star's bit pattern, e.g. %00010000 STSTEP EQU 5 ; "speed" - num. of pixels this star travels per frame The key thing is that there are no X/Y co-ordinates at all. For each star I store the screen byte it sits in and the bit mask within that byte, so it’s already in the form the screen writes need. That means no address conversion per frame, just simple bit shifting and rotation. Moving a star is then dead simple. I rotate its mask left with rlca, and when the bit rotates out of bit 7, it lands back in bit 0 with the carry flag set. That’s the signal I need to move the star one character cell to the left, which is one of the “cheap moves” from earlier. Because I’m tracking which cell each star is in, the collision check comes almost for free. That’s what the second pointer is for: STOCC (Star Occupancy) moves in step with STSCR through a table holding one byte per character cell. The “typewriter” routine sets a byte to 1 for every cell containing a text character or the cursor, so checking whether a star is hidden behind text is a single or instruction. If the cell is occupied, I skip drawing the star but carry on rotating and stepping it along until it reaches a cell where it can be displayed: ld l,(ix+STOCC) ld h,(ix+STOCC+1) ld a,(hl) ; A = the occupancy check for this cell or a ; test A against zero jr nz,stmove ; it's occupied, so leave the screen alone Parallax is still handled by moving each star a different number of pixels per frame, but the cost is a lot lower this time as I’m only touching the cells the stars actually occupy, rather than every byte of every row. Download the video That’s looking more like it! The typewriter routine is a simple copy of the font data from memory, and is very cheap as we can use the “cheap moves” to quickly fetch the 8x8 block. I also skip drawing any space character so it leaves the “occupancy cell” unmarked. This means the stars keep drifting through the gaps in the text - you can see here the odd star passing in between the words. I switched to ALL CAPS writing for the final version which helps the effect even more as the font is quite blocky, so you don’t really notice stars skipping the spaces inside a glyph at all. Misc Notes A few odds and ends that didn’t warrant a section of their own… VU Meters Some sort of graphic display bouncing along with the audio playback was another common early demo trope. I did originally consider a real-time graphic equaliser, but that would have been far too complex, probably in terms of spare cycles per frame, and definitely in terms of my limited mental capacity at this point! So I settled for a simple set of bouncing meter bars instead. It’s worth a mention mostly because it gives me an excuse to link to the awesome chibiakumas website! I just love the late 90s web design on display - one can argue that readability and accessibility take a hit, but it’s just so much fun to encounter a site that still looks like this. It’s a clear labour of love and there’s a wealth of content there as well, if your eyeballs are up to it. Their guide on Making Sound with the AY-3-8910 was what showed me how to get the data I needed to draw the meter bars. The examples write a register number to $FFFD to select a register, and I realised that once a register is selected, I could use the in instruction to read its value back out instead of writing a new one. As the page says, R8, R9 and R10 hold the channel amplitudes, so they feed nicely into the height parameter for drawing a bar. Getting the amplitude for channel A is then as simple as: ld a,8 ; Channel A register call ayread ; ... rest of code here ... ayread: ld bc,$FFFD ; Register select port out (c),a in a,(c) ret Twinkle, Twinkle, Little Star This is a simple effect that makes the header logo a little more interesting. I’m not sure where I originally got the idea - I’ve definitely seen something like it on a few late 80s/early 90s demos, though. All I wanted was to pick random star dots in the logo background and animate them. Here’s the header image loaded in ZXPaintbrush with the character cell grid showing. By happy chance, there are four stars which each sit neatly inside a single character cell with enough surrounding space, so they can be used to give an animated “twinkle” effect by drawing a cross out from the centre point and back in again. These dots map to the following co-ordinates, which target the top scanline of each fully-grown star (two pixels up from the centre): twkdata: defb 1,5 ; char row 0, col 5, centre y=3 ink 5 defb 1,20 ; char row 0, col 20, centre y=3 ink 1 bright defb 26,7 ; char row 3, col 7, centre y=4 ink 4 defb 25,22 ; char row 3, col 22, centre y=3 ink 6 bright The “twinkle” routine itself is simple enough: it adds a pixel on either axis to grow the star, waits a few frames, then shrinks it back down again. The interesting bit was how I made the twinkle order pseudo-random. Remember that my starfield was entirely pre-calculated from a data table generated by a Ruby script, which meant I’d never actually needed a random number generator in assembly. From Jonathan Cauldwell’s “How To Write Spectrum Games” e-book, I found another great “hack” which simply walks through the start of the Spectrum’s ROM, picking out random-looking bytes as it goes. As he says, he uses it in most of his games, and it does the job in a really simple way - as long as you stay below the block of structured, repeating data that sits 8Kb into the ROM. twkrand: push hl ld hl,(twkseed) ld a,h and 31 ; keep it within the first 8k of ROM ld h,a ld a,(hl) ; the "random" byte inc hl ld (twkseed),hl pop hl ret Out Of Space About halfway through writing the intro, I hit a fairly predictable problem: I had run out of memory. In an attempt to keep everything self-contained in one block of code, I was pulling all of the assets (the screen background, the player binary, the music file etc.) into the program itself, and then using the ldir block copy instruction to move each one into its correct location. Since that meant effectively storing every block twice, and the player routine had a hard-coded start address of $C000, I quickly ran out of room and my code started crashing into the playback routine. The simplest option was to split things out into separate code blocks, load each one directly to the address it needed to live at, and wrap the lot in a multi-stage BASIC tape loader: 10 CLEAR 32767 20 BORDER 0: PAPER 0: INK 0: CLS 30 LOAD "" CODE 40 LOAD "" CODE 50 LOAD "" CODE 60 RANDOMIZE USR 32768 This loads the main program, then the player routine directly into $C000, and finally the background screen straight into screen memory. It does mean the background starts appearing before the demo has fully loaded, but as I imagine most people will be seeing this running in an emulator or on my TNFS site, it doesn’t really matter. Plus, at this point I was in danger of disappearing down yet more rabbit holes (I still really want to work out how to do multi-colour border effects!) and I’d never have got this thing released. The Zen Of Z80 This whole project is a really weird one to look back at. On the one hand, I’ve fulfilled a childhood ambition and have something I’m very proud of. It’s also pretty unusual these days to end up with a binary where you can say pretty much what every single byte is doing and the story of how it got there. But on the other hand - I now have an answer for why I don’t code in assembly: It’s because I value (what’s left) of my sanity! The thing is, in order to write code like this you have to start thinking like the machine itself. You have to visualise the registers and the status flags flapping away, graphics as lines of bits slotted into strange orders, loops and conditionals as binary logic and so on. Getting this close to the hardware definitely brought about a greater sense of attachment to the hardware of my childhood, and it’s easy to see why assembly coders and sceners get so passionate about it. That said, I’m in no hurry to start re-writing my body of work in assembly! The projects I really enjoy are those that give me an excuse to learn something for the sheer hell of it, and to slow down a little from the ever-increasing velocity and demands of the modern work world. Going back to Z80 code is probably about the furthest I could have taken this, short of going into hardware hacking. I found it at times an intriguing puzzle, often a frustrating exercise that nearly had me throwing in the towel at several stages. This project has certainly taken the longest to go from the “idea” stage of any given feature to any kind of meaningful output. True, I didn’t spend every day of the last year working on it - my actual coding time was probably only a few hours over a couple of evenings a week. But I’ve certainly been thinking about it and pondering it most days (as my dogs will attest). The biggest breakthroughs and “aha!” moments usually didn’t come from behind the keyboard; they came from scribbles in my notebook and thinking about processor states while doing something unrelated. It has however given me massive appreciation and respect for the coders who pull this stuff off and make it look easy. And that goes double for those kids and bedroom coding superstars who managed all of this back in the 80s, and had to work it all out by experiment themselves. I only managed to get this far because of numerous books, online tutorials, GitHub repositories with sample code, YouTube videos… I can’t imagine how much longer this would have all taken me if the only resources I had were my Spectrum, a cassette tape recorder and a notepad. So, as I draw a line under this project - for now! - I raise a glass, and in the words of h0ffman: “Shout out to the hackers, graphicians and protrackers.”
A decade ago, a little bit of history was made. I didn't realize it, but a colleague made a great point, one of those real mind-changing points that seem too obvious to admit same-day. But, the next day, calver.org was born. At the time my team maintained the Python infrastructure for eBay and PayPal, and we were stuck deciding whether we were really ready for a "major" 1.0 release. Semantic Versioning was the only game in town and "major" means "big", right?! Thankfully, a wiser colleague mentioned: Ubuntu and Twisted don't struggle with version number debates. They slap a date on it and keep shipping. In fact, their date-based versions were even better because you always knew where it stood, in terms of updatedness and support. The only problem is that no one really knew about it. Somehow, this problem solving versioning alternative, arguably as old as history itself, had gone nameless for millenia, conspiring to make me feel foolish in an office meeting. Never again! Ten years of adoption Fast forward 10 years, we've seen CalVer adopted by Apple, Nvidia, JetBrains, and countless others. (We have a timeline!) The site may have more inbound links than any other project of mine. Apple made the biggest jump, at WWDC 2025: iOS went from 18 to 26 macOS from 15 to 26 watchOS from 11 to 26 and visionOS from 2 to 26 All landing on one, consistent number like a car's model year. I still remember the texts from the Venn diagram fanbase of my friends who love Apple and reasonable versioning. No such texts from when NVIDIA announced calendar versions across the GPU Operator, RAPIDS, and its monthly NGC containers, but still very cool. Open source, too: Home Assistant, pip, CockroachDB, and yt-dlp all ship on dates, with plenty more on the users page. The conversation even reached the language core; PEP 2026 proposed versioning CPython as 3.YY, and it almost happened, too. And it's never too late, time marches on! Fixing the notation But I don't think I got every detail right from day 1. That's the main motivator for CalVer 26. It's high time to start righting a couple idiosyncratic token design choices, starting with some additions: Meaning Before 26.0 26.0 Full year YYYY YYYY Short year (6, 16) YY YY Zero-padded year (06, 16) 0Y 0Y Short month (1 ... 12) MM M Zero-padded month (01 ... 12) 0M 0M Short week (1 ... 52) WW W Zero-padded week (01 ... 52) 0W 0W Short day (1 ... 31) DD D Zero-padded day (01 ... 31) 0D 0D Seeing double First, the doubled letters. From the first version (16.6), MM and DD meant the unpadded month and day, which reads backwards to anyone who knows date formats (ISO 8601's YYYY-MM-DD, Java, moment.js, day.js), as some community members correctly pointed out. I was ready to flip them, until I checked what people actually use: most projects with a YY.MM.MICRO badge (conda, Twisted, Ansible's tooling) don't pad, and more than a dozen other version management tools (like bumpver and bump-my-version) implement the old meaning. So, it's too late to flip MM's meaning. Instead, 26.0 deprecates it and offers a more explicit and hopefully clearer option: M is the short month, 0M the padded one, and MM is a technically-retired synonym for M. In case you're wondering, the explicit 0M was me being overinspired by Ubuntu's approach, perhaps: 6.06 pads its month but not its year, and YY.0M says exactly that. To pad or not to pad I think it's worth a detour into why padding is even a thing anyways. It's become important now that new ecosystems have emerged that enforced SemVer formatting semantics, and I wanted clear guidance about on the spec site. SemVer forbids leading zeros outright, so Cargo rejects 26.04.0 and Go modules reject v26.04.0. Even Python's packaging spec normalizes leading zeros away, so you can tag 2026.08.19 if you want, but PyPI will still show 2026.8.19. NVIDIA's GPU Operator docs put it this way: "Zero padding is omitted for month to be still compatible with semantic versioning." CalVer was always intended to drop in where SemVer was used. So 26.0 recommends unpadded (YYYY.M.D) as a sane "pure" default for software libraries. But libraries are not the only objects of versioning schemes. The exception is a version that becomes a filename, an image tag, or an object-store key that gets listed lexically. There, padding keeps 26.10 sorted after 26.09, which is why Ubuntu, NixOS, and NVIDIA's own NGC containers pad. More evidence of teams designing their versions. We love to see it. Our FAQ has a longer discussion of the padding issue, as well. Optional segments There was never any rule against them, but 26.0 makes optional trailing segments more explicit with square brackets. Now, yt-dlp's scheme can finally be written down: YYYY.0M.0D[.MICRO]. For the CalVer badges I could find on GitHub, they all stay valid for now. I've got a note on the deprecated spellings and a new copy-paste badge section for new ones. What else is new? It's always a great time to add more citations to the site. A spec changelog; the spec now versions itself: 16.6, 19.7, 26.0. A FAQ: breaking changes, same-day releases, and padding. The users page, rebuilt by category, with past users of note (schemes change; that's fine) and tooling. Case studies: Apple and NVIDIA in, yt-dlp replacing youtube-dl. Much of the thinking behind these changes happened in the GitHub issue tracker over the years, and 19 or so issues close with this release. That's where ideas for CalVer should go, so by all means, open an issue, and we'll get it sorted! In due time, of course. In closing, I can't believe I still love belaboring these numbers so much. Thanks to all (but especially Mark, Glyph, Hugo, issue reporters, translators, and maintainers) for the discussion, and ultimately making the most timely versioning system a timeless classic. See also 2016 announcement Designing a version My Yap on Why CalVer beats Semver
Four sincere attempts at OKRs, four failures, and every time: "you didn't do it right." Maybe the framework doesn't fit you -- so how do you find one that does?
Multiple concurrent writers. Multiple processes. Same SQLite. No modifications.
Brilliant jerks, ZIRP-era managers, and how psychological safety lost the plot. Part 3 of my conversation with Dr. Cat Hicks.
