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Third Stage Engineering

from Brendan Gregg's Blog [alt+shift+b] in programming

The real performance of any computer hardware in production is the result of the hardware, software, and tuning; the investment and sequence of these efforts can be pictured as a three-stage rocket: I recently presented this embarrassingly simple diagram to Intel's executive leadership, and at the time realized the value of sharing it publicly. The Internet is awash with comparisons about Intel (and other vendors’) product performance based on hardware performance alone, but the performance of software and then tuning can make a huge difference for your particular workload. You need all three stages to reach the greatest, and most competitive, performance. It's obvious why this is important for HW vendors to understand internally – they, like the Internet, can get overly focused on HW alone. But customers need to understand it as well. If a benchmark is comparing TensorFlow performance between HW vendors, was the Intel hardware tested using the Intel Extension for TensorFlow Software, and was it then tuned? The most accurate and realistic evaluation for HW involves selecting the best software and then tuning it, and doing this for all HW options. I spend a lot of time on the final stage, tuning – what I call third-stage engineering. It's composed of roughly four parts: People, training, tools, and capabilities. You need staff, you need them trained to understand performance methodologies and SW and HW internals, they need tools to analyze the system (both observational and experimentals), and finally they need capabilities to tune (tunable parameters, settings, config, code changes, etc.). I see too many HW evaluations that are trying to understand customer performance but are considering HW alone, which is like only testing the first stage of a rocket. This doesn't help vendors or customers. I hope that's what my simple diagram makes obvious: We need all three stages to reach the highest altitude.
16th Nov 2025

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More from Brendan Gregg's Blog

Why I joined OpenAI

The staggering and fast-growing cost of AI datacenters is a call for performance engineering like no other in history; it's not just about saving costs – it's about saving the planet. I have joined OpenAI to work on this challenge directly, with an initial focus on ChatGPT performance. The scale is extreme and the growth is mind-boggling. As a leader in datacenter performance, I've realized that performance engineering as we know it may not be enough – I'm thinking of new engineering methods so that we can find bigger optimizations than we have before, and find them faster. It's the opportunity of a lifetime and, unlike in mature environments of scale, it feels as if there are no obstacles – no areas considered too difficult to change. Do anything, do it at scale, and do it today. Why OpenAI exactly? I had talked to industry experts and friends who recommended several companies, especially OpenAI. However, I was still a bit cynical about AI adoption. Like everyone, I was being bombarded with ads by various companies to use AI, but I wondered: was anyone actually using it? Everyday people with everyday uses? One day during a busy period of interviewing, I realized I needed a haircut (as it happened, it was the day before I was due to speak with Sam Altman). Mia the hairstylist got to work, and casually asked what I do for a living. "I'm an Intel fellow, I work on datacenter performance." Silence. Maybe she didn't know what datacenters were or who Intel was. I followed up: "I'm interviewing for a new job to work on AI datacenters." Mia lit up: "Oh, I use ChatGPT all the time!" While she was cutting my hair – which takes a while – she told me about her many uses of ChatGPT. (I, of course, was a captive audience.) She described uses I hadn't thought of, and I realized how ChatGPT was becoming an essential tool for everyone. Just one example: She was worried about a friend who was travelling in a far-away city, with little timezone overlap when they could chat, but she could talk to ChatGPT anytime about what the city was like and what tourist activities her friend might be doing, which helped her feel connected. She liked the memory feature too, saying it was like talking to a person who was living there. I had previously chatted to other random people about AI, including a realtor, a tax accountant, and a part-time beekeeper. All told me enthusiastically about their uses of ChatGPT; the beekeeper, for example, uses it to help with small business paperwork. My wife was already a big user, and I was using it more and more, e.g. to sanity-check quotes from tradespeople. Now my hairstylist, who recognized ChatGPT as a brand more readily than she did Intel, was praising the technology and teaching me about it. I stood on the street after my haircut and let sink in how big this was, how this technology has become an essential aide for so many, how I could lead performance efforts and help save the planet. Joining OpenAI might be the biggest opportunity of my lifetime. It's nice to work on something big that many people recognize and appreciate. I felt this when working at Netflix, and I'd been missing that human connection when I changed jobs. But there are other factors to consider beyond a well-known product: what's my role, who am I doing it with, and what is the compensation? I ended up having 26 interviews and meetings (of course I kept a log) with various AI tech giants, so I learned a lot about the engineering work they are doing and the engineers who do it. The work itself reminds me of Netflix cloud engineering: huge scale, cloud computing challenges, fast-paced code changes, and freedom for engineers to make an impact. Lots of very interesting engineering problems across the stack. It's not just GPUs, it's everything. The engineers I met were impressive: the AI giants have been very selective, to the point that I wasn't totally sure I'd pass the interviews myself. Of the companies I talked to, OpenAI had the largest number of talented engineers I already knew, including former Netflix colleagues such as Vadim who was encouraging me to join. At Netflix, Vadim would bring me performance issues and watch over my shoulder as I debugged and fixed them. It's a big plus to have someone at a company who knows you well, knows the work, and thinks you'll be good at the work. Some people may be excited by what it means for OpenAI to hire me, a well known figure in computer performance, and of course I'd like to do great things. But to be fair on my fellow staff, there are many performance engineers already at OpenAI, including veterans I know from the industry, and they have been busy finding important wins. I'm not the first, I'm just the latest. Building Orac AI was also an early dream of mine. As a child I was a fan of British SciFi, including Blake's 7 (1978-1981) which featured a sarcastic, opinionated supercomputer named Orac. Characters could talk to Orac and ask it to do research tasks. Orac could communicate with all other computers in the universe, delegate work to them, and control them (this was very futuristic in 1978, pre-Internet as we know it). Orac was considered the most valuable thing in the Blake's 7 universe, and by the time I was a university engineering student I wanted to build Orac. So I started developing my own natural language processing software. I didn't get very far, though: main memory at the time wasn't large enough to store an entire dictionary plus metadata. I visited a PC vendor with my requirements and they laughed, telling me to buy a mainframe instead. I realized I needed it to distinguish hot versus cold data and leave cold data on disk, and maybe I should be using a database… and that was about where I left that project. Last year I started using ChatGPT, and wondered if it knew about Blake's 7 and Orac. So I asked: ChatGPT's response nails the character. I added it to Settings->Personalization->Custom Instructions, and now it always answers as Orac. I love it. (There's also surprising news for Blake's 7 fans: A reboot was just announced!) What's next for me I am now a Member of Technical Staff for OpenAI, working remotely from Sydney, Australia, and reporting to Justin Becker. The team I've joined is ChatGPT performance engineering, and I'll be working with the other performance engineering teams at the company. One of my first projects is a multi-org strategy for improving performance and reducing costs. There's so many interesting things to work on, things I have done before and things I haven't. I'm already using Codex for more than just coding. Will I be doing more eBPF, Ftrace, PMCs? I'm starting with OpenAI's needs and seeing where that takes me; but given those technologies are proven for finding datacenter performance wins, it seems likely -- I can lead the way. (And if everything I've described here sounds interesting to you, OpenAI is hiring.) I was at Linux Plumber's Conference in Toyko in December, just after I announced leaving Intel, and dozens of people wanted to know where I was going next and why. I thought I'd write this blog post to answer everyone at once. I also need to finish part 2 of hiring a performance engineering team (it was already drafted before I joined OpenAI). I haven't forgotten. It took months to wrap up my prior job and start at OpenAI, so I was due for another haircut. I thought it'd be neat to ask Mia about ChatGPT now that I work on it, then realized it had been months and she could have changed her mind. I asked nervously: "Still using ChatGPT?". Mia responded confidently: "twenty-four seven!" I checked with Mia, she was thrilled to be mentioned in my post. This is also a personal post: no one asked me to write this.

6th Feb 2026 • 1 votes
Leaving Intel

InnovatiON 2022 AI Flame Graphs GPU Flame Scope Harshad Sane SREcon APAC Cloud strategy Last day I've resigned from Intel and accepted a new opportunity. If you are an Intel employee, you might have seen my fairly long email that summarized what I did in my 3.5 years. Much of this is public: AI flame graphs and released them as open source GPU subsecond-offset heatmap Worked with Linux distros to enable stack walking Was interviewed by the WSJ about eBPF for security monitoring Provided leadership on the eBPF Technical Steering Committee (BSC) Co-chaired USENIX SREcon APAC 2023 Gave 6 conference keynotes It's still early days for AI flame graphs. Right now when I browse CPU performance case studies on the Internet, I'll often see a CPU flame graph as part of the analysis. We're a long way from that kind of adoption for GPUs (and it doesn't help that our open source version is Intel only), but I think as GPU code becomes more complex, with more layers, the need for AI flame graphs will keep increasing. I also supported cloud computing, participating in 110 customer meetings, and created a company-wide strategy to win back the cloud with 33 specific recommendations, in collaboration with others across 6 organizations. It is some of my best work and features a visual map of interactions between all 19 relevant teams, described by Intel long-timers as the first time they have ever seen such a cross-company map. (This strategy, summarized in a slide deck, is internal only.) I always wish I did more, in any job, but I'm glad to have contributed this much especially given the context: I overlapped with Intel's toughest 3 years in history, and I had a hiring freeze for my first 15 months. My fond memories from Intel include meeting Linus at an Intel event who said "everyone is using fleme graphs these days" (Finnish accent), meeting Pat Gelsinger who knew about my work and introduced me to everyone at an exec all hands, surfing lessons at an Intel Australia and HP offsite (mp4), and meeting Harshad Sane (Intel cloud support engineer) who helped me when I was at Netflix and now has joined Netflix himself -- we've swapped ends of the meeting table. I also enjoyed meeting Intel's hardware fellows and senior fellows who were happy to help me understand processor internals. (Unrelated to Intel, but if you're a Who fan like me, I recently met some other people as well!) My next few years at Intel would have focused on execution of those 33 recommendations, which Intel can continue to do in my absence. Most of my recommendations aren't easy, however, and require accepting change, ELT/CEO approval, and multiple quarters of investment. I won't be there to push them, but other employees can (my CloudTeams strategy is in the inbox of various ELT, and in a shared folder with all my presentations, code, and weekly status reports). This work will hopefully live on and keep making Intel stronger. Good luck.

4th Dec 2025 • 1 votes
When to Hire a Computer Performance Engineering Team (2025) part 1 of 2

As a leader in computer performance I've been asked by companies about how (and why) to form a performance engineering team, and as this is broadly useful I'll share my advice here. Large tech companies in the US hire performance engineers (under that or other titles) to ensure that infrastructure costs and service latency don't grow too high, and that their service is reliable under peak load. A new performance team can likely find enough optimizations to halve infrastructure spend in their first couple of years, even for companies that have been using commercial performance or observability tools. Performance engineers do much more than those tools, working with development teams and vendors to build, test, debug, tune, and adopt new performance solutions, and to find deep optimizations that those tools can miss. I previously worked on the performance engineering team for Netflix, a large tech consumer running on hundreds of thousands of AWS instances. I'm now doing similar work at Intel (a large tech vendor) for Intel and their customers. As a leader in this space I've also interacted with other performance teams and staff doing performance work at many companies. In this post I'll explain what these teams do and when you should consider forming one. In part 2 I'll provide sample job descriptions, specialties, advice, pitfalls, comments on AI, and what to do if you can't hire a performance team. It's easy for hardware vendors like Intel to justify hiring performance engineers, as the number one factor in sales is beating a competitor's performance. However, my focus in this post is on non-vendor tech-heavy companies who hire these staff to drive down costs and latency outliers (e.g., banks, telecomms, defense, AI, tech-based companies, and anyone else who is spending more than $1M/year on back-end compute and AI). What is the ROI of performance engineering? The main ROIs are infrastructure cost savings, latency reductions, improved scalability and reliability, and faster engineering. The cost savings alone can justify a performance team and can help calculate its size, and I'll explore that in depth, but the other ROIs are worth considering and may be more important to a company depending on its stage of growth. Infrastructure Cost Savings and Margin Improvements An appropriately-sized performance team should be targeting 5-10% cost savings per year through tuning and product adoptions. (I'll explain appropriate sizes in the When To Hire section.) For many large companies a 5% result would be considered "good" and a 10% would be "great." Achieving this in practice can mean finding large wins (15-80%) on parts of the infrastructure, which become 5-10% overall. Wins are cumulative, so a team hitting 5% savings each year will multiply to become 28% after their 5th year (like compound interest). Even a modest 2% per year will become significant over time. While these compounded numbers can become large, a team needs to continue finding new cost savings each year to justify long-term retention, and should always focused on the next 5-10%. Companies may invest in this work for more than just the cost savings: It can be about developing a competitive advantage in their area by providing a better cost/performance ratio, especially for companies with similar tech-based services that pass costs on to customers. For sites that haven't employed performance engineers before, there can be enough low-hanging fruit that the team can halve infrastructure costs in their first couple of years (50%). It all depends on the number of staff, their level of expertise, how much perf work other staff are already doing (senior developers, SREs), how much custom code is running, and how complex and volatile the stack is. It would great if we could publicly share specific results, which would look something like this: "This year we helped reduce our company's infrastructure spend by 5%, from $60M/year to $57M/year, however, since our user base also grew by 3%, we actually reduced cost-per-user by 8%, saving $5M/year in this and all future years." However, these numbers are usually considered financially sensitive as they can reveal company growth, financial health, confidential infrastructure discounts, etc. As a performance engineer I can talk publicly about percent wins on a back-end service, but I usually can't map it to dollar signs. That doesn't help other companies to understand the value of performance engineering. It's not so much of a problem in Silicon Valley, since staff change companies all the time and word spreads about the latest practices in tech. But in far away countries performance engineering doesn't really exist yet, even though there are companies with sufficiently large infrastructure spend. Continuing the above example, a typical 8% win could be composed of: 2%: direct optimizations. We looked across all the infrastructure and found on average 5% wins on 40% of the infrastructure (on the rest we found nothing, this year). 3%: developer/SRE enablement. We developed and adopted custom observability tools and helped developers use them, who in turn found some 15% wins across 20% of our infrastructure. 3%: vendor adoptions. We supported a major adoption project that replaced 10% of our infrastructure for a 30% win. With developer/SRE enablement and vendor adoptions, the performance team isn't finding the wins directly but is enabling other teams and vendors to do so. For example, when I worked at Netflix we built and maintained the flame graph "self-service" application, which developers used daily to find wins, and we worked on multiple product adoptions every year. This all needs to be considered as part of the performance team’s ROI. Latency Reductions Reducing the response time or latency of a service is a large part of performance engineering. This involves analyzing average latency, 99th percentile latency, and outlier latency; ensuring latency SLA/SLOs are met; and ensuring acceptable latency during perturbations or peak usage. Many of the cost optimizations described earlier will also reduce average latency, but latency variance or outliers can remain. For example, a once-every-5-minute system task may have negligible cost and CPU footprint, but it may briefly perturb the application and cause latency outliers. These are debugged differently, often using monitoring, logs, distributed tracing, system-level tracing, packet logs, and custom ad-hoc tools. Sometimes the high latency is caused by the request type itself (the system is fine, but the end-user has requested a slow thing) or is an expected consequence of load (queueing theory, tail latency). Other times it can be from complex interactions across multiple layers of the software stack or from interactions across multiple network endpoints. As a related aside: One performance anti-pattern is when a company, to debug one performance problem, installs a monitoring tool that periodically does work and causes application latency outliers. Now the company has two problems. Tip: try turning off all monitoring agents and see if the problem goes away. While latency is the main consideration to improve end user experience, others include throughput and parallelism. Improved Scalability and Reliability Systems under load can respond with exponential latency or a cascading failure, causing disruptions or a service outage. Performance engineers can test resource scalability with custom load generators and benchmarks, and use analysis tools to study all parts of the system to find and solve bottlenecks. A performance engineer will not just measure scalability limits, but should also explain what the limiting factors are and how to address them to scale further. A stable and performant service will also earn trust in your company, and can help you grow customers more quickly. It may be a requirement for satisfying enterprise SLA/SLOs. I'll share a scalability story from my time at Sun Microsystems (a vendor). My goal was to achieve the number one throughput in the industry for a storage system, which would require exceeding 1M IOPS. The expected bottleneck was the rotational disks. I developed my own load generators and analysis tools and concluded that the real bottleneck was, surprisingly, the CPU interconnect. The interconnect was AMD HyperTransport 1, so AMD sent me a new systemboard with HT3 and faster CPUs. I installed it and…performance was identical. I was upset with myself for getting it wrong, until I discovered that AMD had sent me a HT1 board by mistake. They then sent me a real HT3 board and the performance increased by up to 75%! The CPU interconnect (when present) is just one of many components that companies typically don't check, and commercial observability tools don't check either. Faster Engineering Performance engineers can take care of components outside of a developer's code base so the developers can stay focused, and also provide them with a greater performance budget so that they can adopt expensive features earlier. For some early stage companies this ROI may be their most important (and is sometimes called engineering velocity). In detail: Eliminate outside performance distractions: A development team can be coding at one hundred miles an hour in their codebase, but then run into a performance problem in a library, kernel, hypervisor, or hardware component, and need to slow down to learn some new thing and its analysis tools. Or, it could be some new performance technology that someone saw on hackernews and the development team wonder if they should stop to evaluate it. These performance activities can be offloaded to the perf team, so the developers stay focused on their code and at full speed. This is the same as how OS, deployment, and reliability issues can be offloaded to SRE and DevOps teams. Bypass expensive project failures: Good performance engineers know the internals of the software and hardware stack (including the Linux kernel). Many developers don't and most of the time they don't need to. But this can lead to developers proposing ideas based on incorrect assumptions. (This actually happens a lot.) Having a one hour meeting with a performance engineering team can save months of engineering effort: A developer talks about their ideas A and B, and the perf team says "A won't work and this is why, but B sounds good and we can help." Months saved in one hour. At one large tech company many years ago I analyzed an engineering proposal to adopt a new event-based coding framework on the belief it would improve performance by ten fold. My analysis showed the real gain would have been less than 10%. The project was abandoned. This saved having all the engineers rewrite all the company's code, something we were expecting would take a year. This is one of the biggest performance wins of my career, not measured as a percentage but rather as engineering hours saved. Develop accelerator tools: As mentioned earlier, performance teams can develop custom observability tools that developers use, finding issues sooner and accelerating development. For example, I've been publishing here about AI flame graphs, which involved kernel and hardware internals to build. Faster feature adoption: Reducing costs and latency can enable developers to do more with a limited infrastructure and latency budget, offering more capabilities for their service or allowing more processing to be crammed in per request. Some companies have cost limits for adding features, so optimization work can mean a feature is now approved. All this work can mean a company can outpace their competitors with feature adoption, while maintaining a reliable, low-latency, cost/performance competitive service. What do performance engineers do? For non-vendor tech companies, in summary: A. Test, debug, and tune new software and hardware products to find performance improvements, and drives company-wide adoption. Examples: New cloud instance types, language runtimes, JVM versions, JVM subsystems (new GC algorithms or compilers: Graal vs c2), system libraries (glibc vs tcmalloc etc.), kernels (Linux vs BSD) and versions, compilers (gcc, llvm, icc), processor features (AVX, QAT, etc.), hardware accelerators, and so on. It can take months to debug, fix, and patch everything so the latest thing delivers its performance claim. B. Develop in-house performance solutions, such as custom analysis tools, that other teams use to find performance wins. Examples: Custom monitoring using Prometheus and Grafana, one-click flame graphs, and analysis tools using eBPF: All of this is open-source based, but someone has to get it working locally, integrate them with existing local tools, teach other teams how to use them, and maintain them. C. Does deep-dive analysis to identify and reduce workload bottleneck(s) and latency outliers. Examples: Using code profilers (CPU flame graphs), distributed tracers (OpenTelemetry and products), application logs, system counters (Linux: sysstat), system tracers (Linux: eBPF, Ftrace, perf), static and dynamic instrumentation (Linux: kprobes, uprobes), debuggers (gdb, etc.), hardware counters (Linux: perf), and on rare occasions hardware instruction tracing. A lot of hands-on live debugging over an SSH session, following methodologies to efficiently find the root-cause(s), which can require the development of custom tools (mini load generators, observability tools, etc.). D. Optimize software and hardware via tunable parameters and configuration choices. Examples: System tunables (Linux: sysctls), network tunables (socket options, qdiscs), device tunables, runtime tunables (Java -XX:*), library settings, environment variables, etc. As with (C), the team needs SSH access to do this and likely superuser privileges. E. Work with development teams (internal and external) to catch non-scalable solutions early in development, and to suggest or test later performance improvements. Examples: Identifying communication layer will flood network links when horizontally scaled; A developer has a good optimization idea but can't get it to work and needs some help; There's a performance-related pull request on some software the company uses but the request is two years old and needs someone to fix code conflicts, test it, and advocate for merging it. F. Develop proof-of-concept demonstrations of new performance technologies. Examples: Linux eBPF and io_uring can provide significant performance improvements when developed into hot-path kernel-based accelerators, but someone needs to at least build a POC to show it would work for the company. These are typically too esoteric for developers to try on their own. G. Develop performance improvements directly for internal and external code. Examples: Performance engineers get a lot done by asking the right people, but sometimes no one has the time to code that Linux/runtime/database performance fix the company needs, so a perf engineer takes it on. We aren't as quick as full-time developers since we are hopping between different languages all the time, and as a new code base committer will typically come under extra (and time-consuming) scrutiny. H. Capacity planning activities: purchase guidance, choosing metrics to monitor, and bottleneck forecasting. Examples: Modeling and performance characterization for hardware purchases, resource utilization monitoring to forecast capacity issues (nowadays often done by developers and SREs using monitoring tools); propose the best metrics to be watched in those monitoring tools for alert generation and auto-scaling rules; work with business side of the company to help define practical SLA/SLOs. I. Perform knowledge sharing to uplift engineering. Examples: Performance education to help developers produce more efficient software; act as a conduit to share performance learnings between teams (that may otherwise be siloed) to avoid rework and rediscovery. J. Provide in-house expertise to guide purchasing performance solutions. Examples: Providing in-house expertise for performance topics like observability, telemetry, and eBPF can help the company choose better commercial products by evaluating their capabilities and overhead costs, and can recognize which are just Prometheus and Grafana, or my open source eBPF tools, in a suit. Without expertise you're vulnerable to being ripped off, or may adopt a tool that increases infrastructure costs more than the gains it provides (I've seen some that have overhead exceeding 10%). To elaborate on (A), the testing of new products: Other staff will try a technology by configuring it based on the README, run a load test, and then share the result with management. Some companies hire dedicated staff for this called "performance testers." Performance engineers get more out of the same technology by running analyzers during the test to understand its limiter ("active benchmarking"), and will tune the technology to get an extra 5%, 50%, or more performance. They may also discover that the limiter is an unintended target (e.g., accidentally testing a caching layer instead). Any performance test should be accompanied by an explanation of the limiting factor, since no explanation will reveal the test wasn't analyzed and the result may be bogus. You can simply ask "why is the result not double?". As an aside: "CPU bound" isn't an explanation. Do you mean (a) clockspeed, (b) thread pool size, (c) core count, (d) memory bus (which kernels misleadingly include in %CPU counters), or something else (like power, thermal, CPU subsystem bottleneck)? Each of those leads to a different actionable item for the company (E.g.: (a) faster processors; (b) more threads; (c) more cores; (d) faster memory, bigger caches, less NUMA, or software techniques like zero copy). That's just the generic stuff. The code behind any CPU bound workload will also be analyzed to look for inefficiencies, and sometimes their instructions as well. Day to day, a performance engineer can spend a lot of time fixing broken builds and configuring workloads, because you're the first person testing new patches and bleeding-edge software versions. What I've described here is for companies that consume tech. For vendors that sell it, performance engineering includes design modeling, analysis of prototype and in-development software and hardware, competitive benchmarking, non-regression testing of new product releases, and pre- and post-sales performance analysis support. (I explain this in more detail in Systems Performance 2nd edition, chapter 1.) When to Hire a Performance Team and How Many Most companies I've encountered are already doing some kind of performance work scattered across projects and individuals, but they don't yet have a central performance engineering team looking deeply at everything. This leaves their attention spotty, ok in some areas, poor to absent in others. A central performance team looks at everything and prioritizes work based on the potential ROI. Here are a few rough rules to determine when you should start forming a company-wide performance engineering team and how to size it (see caveats at the end): (A) One engineer at $1M/year infrastructure spend, then one per $10M to $20M/year That first engineer finds some of the low-hanging fruit, and should be cost effective as your company grows past $1M/year. I'd then consider another performance engineer for every $10M to $20M, and maintain a 3:1 junior:senior ratio. The values you use depend on your performance engineer's skill and the complexity of your environment, and how aggressively you wish to improve performance. At a $20M spend, 5% yearly wins means $1M in savings per staff member (minus their cost); whereas for a $10M spend you'd need to hit 10% wins yearly for $1M in savings. Consider that as your spend keeps growing you will keep adding more staff, which makes their job harder as there is less low-hanging fruit to find. However, your site will also be growing in scale and complexity, and developing new performance issues for the growing team to solve. Also, smaller percent wins become more impactful at large scale, so I expect such a growing perf team to remain cost effective. (To a point: the largest teams I've seen stop at around 150 staff.) (B) Staff spend should equal or exceed observability monitoring spend If you're spending $1M/year on an observability product, you can spend $1M/year on a performance engineering team: e.g., 3 to 4 good staff. If you're only spending $50k/year on an observability product, you can't hire a performance engineer at that price, but you can bring in a consultant or pay for performance training and conference attendance. As I'd expect staff to halve infrastructure costs over time, just the savings on monitoring alone (which typically scale with instance/server count) will pay for the new staff. Because these new engineers are actively reducing infrastructure spend, the total savings are much greater. (C) When latency or reliability is prohibitive to growth I’ve heard some small companies and startups say they spend more money on coffee than they do back-end compute, and don't want to waste limited developer time on negligible cost reductions. However, when a wave of new customers arrive they may hit scalability issues and start losing customers because latency is too high or reliability is too inconsistent. That's usually a good time for small companies to start investing in performance engineering. Caveats for A-C You likely already have some perf engineers under different titles, such as senior developers or SREs who are focusing on perf work, leaving less work for the central performance team. Account for these focused staff when you are determining how many performance engineers to hire. There will be a backpressure effect: If a new team halves your infrastructure, do you then halve the team? Up to you. But first think about what a perf engineer does: They have to work on anything, any operating system, language, or hardware the company needs. So you have these extra staff that can go deep on anything. Just some personal examples: There was an 18 month stretch when Netflix needed more core SREs on rotation, so myself and other perf engineers were temporarily assigned to do SRE work; Netflix would also have me do kernel crash dump analysis and application core dump analysis, since I was already using debuggers at that level. You ideally have more performance engineers than technologies so staff members can specialize. E.g.: If your stack is AWS, Intel, Linux kernel, Ubuntu OS, containers, lambda, gRPC, Java, golang, Python, Cassandra, and TensorFlow, that's 12 performance engineers. Plus at least one for locally developed code. Want to go multi-cloud? Add another engineer for each CSP. Remember that performance wins are cumulative for future years. A novice team could struggle to get up to speed with both performance engineering and your complex environment, and could also discover that you already had senior staff find all the low-hanging fruit. They might therefore only deliver 2% cost savings in each of their first three years. But that still combines to be 6% going forward. Companies and Global Staff Here are some example articles about performance engineering work at non-vendor companies: Netflix: Cloud Performance Root Cause Analysis [me, 2018] Meta: Strobelight: A profiling service built on open source technology [Jordan Rome, 2025] Pinterest: Debugging the One-in-a-Million Failure: Migrating Pinterest’s Search Infrastructure to Kubernetes [Samson Hu, et al. 2025] LinkedIn: Who moved my 99th percentile latency? [Richard Hsu, Cuong Tran, 2015] eBay: Speed by a Thousand Cuts [Senthil Padmanabhan, 2020] Twitter: #ExpandTheEdge: Making Twitter Faster [Todd Segal, Anthony Roberts, 2019] Salesforce: How Salesforce makes performance engineering work at Enterprise Scale [Archana Sethuraman] Uber: PerfInsights: Detecting Performance Optimization Opportunities in Go Code using Generative AI [Lavanya Verma, Ryan Hang, Sung Whang, Joseph Wang] Twitch: Go’s march to low-latency GC [Rhys Hiltner, 2016] Airbnb: Creating Airbnb’s Page Performance Score [Andrew Scheuermann, 2021] Stripe: Using ML to detect and respond to performance degradations in slices of Stripe payments [Lakshmi Narayan, Lakshmi Narayan, 2025] DoorDash: How DoorDash Standardized and Improved Microservices Caching [Lev Neiman, Jason Fan, 2023] Roblox: How We Redesigned a Highly Scaled Data Store to Lower 99th Percentile Latency 100x [Andrew Korytko, 2020] Capital One: Driving cloud value through cost optimization & efficiency [Jerzy Grzywinski, Brent Segner, 2024] I would like to add Bank of America, Wells Fargo, JPMorgan Chase, and CitiGroup to this list since they have many staff with the title "performance engineer" (as you can find on LinkedIn) but it's hard to find public articles about their work. I'd also like a canonical list of central performance engineering teams, but such org chart data can also be hard to find online, and staff don't always call themselves "performance engineers." Other keywords to look out for are: insights, monitoring, and observability; some are just called "support engineers". Note that there is also a lot of performance engineering done at hardware, software, and cloud vendors (Intel, AMD, NVIDIA, Apple, Microsoft, Google, Amazon, etc.) not listed here, as well as at performance solution companies. In this post I just wanted to focus on non-vendor companies. Global Staff I've never seen concrete data on how many people are employed worldwide in performance engineering. Here are my guesses: Staff identifying as performance engineers at non-vendor companies: <1,000. Staff identifying as performance engineers at SW/HW vendors: >10,000. Staff who have become focused on performance engineering work under other titles (developer, SRE, support): >100,000. Staff who occasionally do performance engineering work: I'd guess most developers. It's possible LinkedIn can provide better estimates if you have enterprise access. Conclusion There are many reasons to hire a performance engineering team, such as infrastructure cost savings, latency reductions, improved scalability and reliability, and faster engineering. Cost savings alone can justify hiring a team, because a team should be targeting a cost reduction of 5-10% every year, which over the years adds up to become significantly larger: 28%-61% savings after 5 years. In this post I explained what performance engineers do and provided some suggested rules on hiring: A) One engineer at >$1M infrastructure spend, then another for every $10-20M. Note that you likely already have some senior developers or SREs who are focusing on perf work, reducing the number of new performance engineers you need. I've met people who would like to work as performance engineers but their employer has no such roles (other than performance testing: not the same thing) despite spending millions per year on infrastructure. I hope this blog post helps companies understand the value of performance engineering and understand when and how many staff to hire. Hiring good performance engineers isn't easy as it's a specialized area with a limited talent pool. In part 2 I'll discuss how to hire or train a performance engineering team and provide sample job descriptions and tips, and what to do if you can't hire a performance team. Thanks Thanks for the feedback and suggestions: Vadim Filanovsky (OpenAI), Jason Koch (Netflix), Ambud Sharma (Pinterest), Harshad Sane (Netflix), Ed Hunter, Deirdre Straughan.

3rd Aug 2025 • 1 votes
3 Years of Extremely Remote Work

In the last 3 years I've attended 77 meetings that began between 1am and 6am, roughly once every two weeks, followed by my usual 7am start, Monday to Saturday. I'm working remotely from Australia for a US firm (Intel) who does not have a local office here. I'm not complaining. I work weird hours, but I don't think I work too many. I'm writing this post because there are some misconceptions and assumptions about the lives of remote workers, and I thought I'd share my own details as an anecdote, along with some tips for others in a similar situation (US job from Asia). Most early meetings were 1 hour, but some were longer, for a total of 102 hours awake. As a histogram: I've never been a morning person, but I can manage a 7am start. What about a 2am meeting? Also doable. They sound ok in isolation, but consider that every 2-3am is followed by a 7am start, which means a 6:30am alarm after going back to sleep at 3:30am, if you're lucky. It's not easy working this timezone difference, and I'd guess others have it even worse than I do (more meetings). Like other remote staff I work with, I have a dedicated office at home where I can close the door and work uninterrupted for hours (it's changed quite a bit since it was filmed in the eBPF documentary): That's a Samson Meteor mic on a desktop suspension boom, and I have another suspension boom clamped onto a bookcase holding a Logitech BRIO camera (when I had it sitting on my monitor it'd shake as I typed). It's important to have the best sound and video when that's how you're interacting with people all day. Miscellaneous notes and tips about remote work: Count out-of-hours meetings. Sometimes people hesitate to book me at 2am, when there's no other time that would work, and it takes a few emails to convince them that it's ok. I've found it saves time to log these meetings, count them, and share stats: "I've had 76 meetings between 1am and 6am, if you need to add another, that's ok, it'll be my 77th." Never complain about the hours. There may be someone in management who doesn't support remote work, who could use such complains to argue against it. Sometimes, when I'm searching to find the right words to say at 4-something-am, I've confessed that I'm a bit tired, but I really try to never mention it. Staying motivated. I found keeping a daily log of what I accomplished works best (I've done this for over a decade). If one day my entry looks soft, I try to do more on the next. I summarize each week for my manager. People assume it's a problem when it isn't. Brendan didn't accept the meeting, oh, it's because it's 2am for him and he'll be asleep. Wrong! It's because I have a clash with another 2am meeting. I try to communicate this with the meeting host, but there can be others in the meeting that don't get the message, and they never consider this possibility. If I were back in California I'd still miss the meeting, but people would assume it's due to a clash. Here, they assume I'm asleep and not making the effort, when in fact I am. It means people are assuming that remote work is a problem when it isn't. Some meetings get cancelled or silently recorded. The 77 count I mentioned earlier doesn't include the several meetings that were cancelled at the last minute, but I woke up for anyway, or those that weren't supposed to be recorded but I woke up to find they were. If you have remote staff, please try to cancel meetings before they go to bed, and be clear on which meetings are recorded for later viewing. Upset stomaches. One early meeting every two weeks doesn't sound too bad. The worst problem is that it can leave me with an upset stomach that can last for days. I'm still working fine, it's just uncomfortable. I don't know if other people suffer this or why it happens. Maybe it's just the extra coffee. Fewer sick days. I don't normally take many sick days, so I can't say my data is very significant. FWIW: In my last in-person job I averaged 1.5 sick days/year (9 in 6 years), and now it's 0.33 (1 in 3 years). Northen/Southern hemisphere times get weird. Daylight savings moves in different directions and on different dates, so from Sydney depending on the time of year I have 3, 4, or 5 hours of overlap with 9-5pm on the US west coast. To make it easy for people I setup my calendar with my work hours highlighted. Saturday? Saturday morning is Friday afternoon in the US. For a while I tried working Tuesday to Saturday, but it's better for family time if I finish early on Saturday (at US 5pm) and work Monday to make up the difference. Phone alarms Career limiting I've experienced it to be career limiting, and I've heard the saying "out of sight, out of mind." Opportunities can be given to local workers despite the remote worker being more qualified, or even number one in the world in that area. The remote worker is then expected to teach the local worker in weekly or monthly meetings. It's not enough time and there's a power imbalance: the local worker is in charge and doesn't have to listen. This reduces company competitiveness. It's also fixable: try giving remote workers the chance they are best qualified to do. Compared to office work. It depends on the job and meeting load. In my last years as an in-person office worker I was on a team where we typically worked solo on different projects, with headphones on, and primarily communicated over chatrooms with few in-person meetings. The only regular time we had human interaction was lunch. Remote work has not been a big difference to that kind of job: similar work, similar chatrooms. (The thing I miss the most about the office is playing cricket with other staff after work.) It wouldn't make a big difference to my current job either, as the people I work with are scattered. The one thing it would solve is "out of sight" problem, but that's not the only way to solve it. Remote work success stories. Linux development is a great example of engineers around the world working together. Note that Linux engineers do try to meet at least once a year at one of the Linux conferences, something remote office workers can do as well at company get-togethers. My books are another example: they are out-of-hours projects where I worked with the reviewers online, some of whom I still haven't met. And the world's first AI Flame Graphs is the work of a fully-remote team. I was in a video meeting recently with US staff where I mentioned the "77 meetings between 1 and 6am" statistic, and I could see the look of shock in their faces. Did they assume I was just working 9-5 and not making an effort to accommodate other timezones? I know some companies are discussing ending remote-work: are the deciders also making such assumptions about the lives of remote workers? It may not do much, but I can at least share my own details here as an example anecdote. Update: Some comments about this post have pointed out that these hours can be unhealthy and shouldn't be encouraged, and I don't disagree. I hope to have fewer early meetings in the years ahead, myself. This post is just to show that remote workers can be more accomodating than people may assume, and I hope that's taken into consideration when changing remote work policies. When some people hear I'm working from Australia, they may think of beaches and surfboards and sunny vacations, but my reality is a full time job across weird hours, lots of coffee, and being overlooked for opportunities. That said, every job has its pros and cons, and I'm still grateful that some companies make fully remote work an option.

21st May 2025 • 1 votes

More in programming

Organising my cross-stitch patterns

I have a lot of digital cross-stitch patterns and until recently they were a mess. My “collection” was a series of several hundred PDFs, disorganised and spread across my disk. Recently, I was looking for a pattern and it took me several minutes to find it. I had a brief moment of panic, thinking that I’d lost it, and the website this pattern came from is long since gone. Fortunately it did turn up, but it was clear I had to organise my pattern collection. Some people would use apps like Pattern Keeper or Flosshaven, but I just created another static website on my media drive. This is what it looks like: The patterns are arranged in a masonry layout with a preview thumbnail for each pattern, which is normally the first page of the PDF. If a pattern has multiple files – for example, different files for colour and black-and-white printing – there’s a list of links below the thumbnail. I can also add a source link to where I bought the pattern, if I want to find more patterns by the same author. Internally, it’s structured like my other static websites. The metadata for the patterns is stored in a JavaScript file, and then I have an HTML page which renders that metadata in this visual design. My pattern collection doesn’t change very often, so I’m adding new files by hand. There are no sorting or filtering options, because my collection is small enough that they’re unnecessary. I can scroll through the entire list in less than a minute and find whatever I need. I’d like to start making my own clothes soon, and I expect to copy this design to store clothing patterns. This collection now sits alongside my saved bookmarks, podcasts, TV shows, and more. I can keep all my patterns organised, and I won’t lose them in the depths of my hard drive. Technical tricks There are a couple of technical pieces I’m quite pleased with in this process: Masonry layout. I love the cascading grid style of a masonry layout, and I think they’re great when you have variable-sized content – like my pattern cards. I’ve been using them for over a decade; screenshots of my early web apps show my experiments with David Desandro’s Masonry.js library. Masonry layouts are now a native feature of CSS called “Grid Lanes”, which is simpler and more performant than the JavaScript approach. I’m excited to use these layouts in even more places. Thumbnails of PDFs. Most digital postage patterns are shipped as PDFs, and I’m creating the thumbnail images with my create_thumbnail tool. I had to extend my tool to support PDFs, which turned out to be straightforward with the pdf_oxide crate. I’d not come across Oxide before, but it’s a cross-language library for working with PDFs, and I can imagine using it in future. A coloured stripe for the palette. In the metadata for each project, I have the complete list of DMC thread colours and the pattern dimensions. I have a lookup table that converts DMC thread colours to hex codes, and then I’m able to create a linear gradient in CSS to render a coloured stripe of the pattern colours. Here’s an example from a pattern with four colours: border-bottom: 12px solid transparent; border-image: linear-gradient( to right, #7a451f 0%, #7a451f 25%, #985e33 25%, #985e33 50%, #e4bb8e 50%, #e4bb8e 75%, #361f0e 75%, #361f0e 100% ) 1; Building the HTML, CSS, and JavaScript was very quick – I have notes on how I build my media sites, and I’ve done it plenty of times before. I threw together the initial prototype in less than 15 minutes. The longer part was finding all the PDFs scattered across my disk – but now that’s done, and I know exactly where to find my patterns. [If the formatting of this post looks odd in your feed reader, visit the original article]

17 hours ago • 1 votes
Writing a Demoscene Intro for the ZX Spectrum

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.”

yesterday • 1 votes
CalVer 26.0: 10th Anniversary Edition

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

2 days ago • 1 votes
"You didn't do it right"

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?

2 days ago • 1 votes
We solved SQLite's single-writer limitation

Multiple concurrent writers. Multiple processes. Same SQLite. No modifications.

4 days ago • 1 votes
📚 BoredReading

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