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More from Eric Bailey

GitHub repository landing pages now show an accessibility tab, if provided

My last official contribution to GitHub was something I’ve wanted for a long time: Writing the code to display an ACCESSIBILITY.md file’s contents on the repository landing page. This content lives in the same tab component that the README, License, Code of Conduct, Security, and important information is surfaced. For example, I’m using an ACESSIBILITY.md file located in ./github to communicate my accessibility statement on my a11y-webring.club repository. Here’s an image of it in action: The file only appears if it is supplied, so it is not a required part of creating or maintaining a repository. However, it is my hope that the act of providing one becomes more commonplace the same way providing those other special kinds of files are. Another broader hope I have is that this promotion of content helps to normalize accessibility as a consideration and practice in some small way—something that helps to send a signal of a mature software project. I’m excited to see how people use this new addition to the platform. I would also like to extend a huge thank you to Jan Maarten and Maria Lamardo for their help getting this effort across the finish line.

yesterday • 1 votes
CSS-Tricks could be a co-op

I owe a lot of my professional identity and success to CSS-Tricks. CSS-Tricks repeatedly gave me the opportunity to write for them. In doing so, they helped to both socialize and normalize accessibility as a mainstream frontend concern. I’m deeply thankful to them for this. The team was also a joy to work with, notably Geoff Graham. He’s a mensch, and one of the nicest people you can interact with in the frontend web space. If you have not been following the news about the site, Kevin Powell has a good video about the whole situation: Content skipped. I’m not speaking on behalf of Geoff, Chris, or others involved with running the current version of CSS-Tricks. I’ve got skin in the game as an author. This is my personal opinion, born of my feelings and beliefs. I think a lot of the web’s infrastructure should be co-ops, and CSS-Tricks is knowledge infrastructure. To that point, I should also point out that the website covers far more than just CSS. The corporate model of ownership can be a risk. If infrastructure is not part of a corporation’s core strategy, it is not a priority. As Kevin’s video touched on, it seems like promotion via owning the frontend content space isn’t part of Digital Ocean’s strategy anymore. It is not that CSS-Tricks does not have value. It is that Digital Ocean cannot see it. It is deeply, tragically ironic to me that Digital Ocean allowed this to transpire. This is because I know for a fact that the techniques and philosophies shared by CSS-Trick authors helped to shape iterations of their product’s UI. Some may be quick to point out that this knowledge now—illegally—exists inside of LLM training data, so the risk of the website going away is mitigated. To this, know that we should be striving to keep resources like CSS-Tricks going. Human creativity is the force that creates new techniques, strategies, and technologies. The web will calcify without voices sharing what they know, forever locking us into endless permutations of a fixed point in time. Unlike corporations, co-ops don’t have to be motivated by profit. By not needing to prioritize growth at all costs it means co-ops can instead prioritize and incentivise things like preservation and cultivation. It is also a successful model of operation, one that even already exists, and flourishes in the tech space. Collective ownership can also serve as checks and balances for, and protection against hierarchical decision-making. I only need to point to the chaotic and aberrant decisions many CEOs in the technology space have been making as of late to demonstrate the value of this approach. Paddy Srinivasan, if you somehow wind up reading this: Save some face and take a big swing. Give CSS-Tricks back to the people who love it.

2 weeks ago
Here’s yet another metaphor about tech debt

The city next to the town I grew up in had a shopping center that was built atop a sanitary landfill. For those unfamiliar, creating a sanitary landfill involves taking landfill waste, spreading it into thin layers, compacting it, then paving over it. It enjoyed popularity as a practice in the 60s and 70s. I’m all but sure this shopping center was built in the mid-to-late 70s. It was the 90s when I was a kid and started going to it. Of note was a Kmart that I’d frequent with my parents when running errands. At some point, the certain parts of the tile floor of the Kmart started to buckle and bubble—their edges tinged with an ominous yellowish brown color. The reason for this is that is that the landfill was sealed improperly—accumulating methane and carbon dioxide gasses was pushing decomposing waste upwards and outwards. The employees put up some warning signage around the worst of it, but that was pretty much it. They did not have the expertise, nor the incentives to do anything past indicating the problem. One day we tried to go to the Kmart, and it was closed for good. A few years later the entire shopping center had to be demolished and the landfill had to be dug up and disposed of.

4th Aug 2026 • 1 votes
Announcing What Can’t I Press?

I made an app! What Can’t I Press? is an app that allows you to: Read, search, and filter through screen reader keyboard shortcuts. Scan open apps and add their keyboard shortcuts to the collection. Copy a keyboard shortcut to your clipboard, with modifier keys to format the keyboard shortcut in different ways. Export the list of keyboard shortcuts in its current state as JSON. It is intended to be both a reference and a discovery tool. Here’s a quick demo of it in action: Two open apps placed side-by-side on macOS: TextEdit and What Can't I Press? TextEdit is open to a blank document. What Can't I Press? shows a search input, an expand/collapse all toggle button, a list of 4 disclosures in a collapsed state, a notification message, a shortcut filter input, a download icon, and two buttons labeled "Scan all open apps" and "Scan last focused app". The disclosures are labeled, "JAWS", "Narrator", "NVDA", and "VoiceOver" and each has a badge tallying how many shortcuts are present. The notification message reads, "What Can't I Press cannot detect all possible keyboard shortcuts. Be sure to also check manually." The mouse cursor clicks the "Scan last focused app" button and a brief scan happens. The list of disclosures updates to show global keyboard shortcuts and shortcuts for TextEdit. All disclosures are set to an expanded state, and links to jump to the next section are also revealed. The mouse cursor then clicks on the search input, where the word "save" is entered. It then briefly scrolls through the list to show what apps have functionality related to saving. The search input is cleared by clicking a clear button, then focus is placed on the filter input, where Commandkbd> + s is entered. The list of keyboard shortcuts filters to show all shortcuts that incorporate these keystrokes. The cursor then clicks on the Commandkbd> + S save row button, and places focus on the TextEdit document. The content "⌘S" is inserted into the document via pasting. The app is also available as a web experience, and can be installed as a Progressive Web App. This form of the experience only allows viewing and filtering screen reader keyboard shortcuts, as scanning open apps is not possible using current web technologies. Why did I make it? App and webapp keyboard shortcuts are an often overlooked area of user experience, especially when it comes to working nicely with assistive technology. As much as possible we should not put the burden on the person using assistive technology to use workarounds. In other words: what is added as a convenience for someone who doesn’t require assistive technology should not impair someone who does require assistive technology functionality to get what they want or need. I made this app to help speed up and streamline the process of adding keyboard shortcuts in a safe and responsible way. For more background on this act, reference my post, How an accessibility designer adds keyboard shortcuts to a web app. Limitations What Can’t I Press? cannot detect all keyboard shortcuts used by an app. Consider this as a tool that helps as a first pass, but always be sure to also manually check. There are specific ways of declaring keyboard shortcuts in code that can be picked up by a scan, but not everyone uses those methods. Microsoft is an especially poor actor in this regard. The app is also currently unsigned. This means you’ll have to bypass operating system warnings about installing unsigned apps. All app source code is on GitHub and set to public, so you can review exactly what you’re signing up for. Getting the app signed is on the roadmap, so consider this more of a soft-launch announcement. Is it free? Yes. This kinda reads like you think all keyboard shortcuts should be rebindable Also yes. Looks like you used a LLM to make this A third yes. I honestly couldn’t have made this without it, and I have complicated feelings about that. Believe me, there was a ton of human intervention required. What’s with the icon? It’s a stovetop gas burner. You shouldn’t press it with your hand. I’m really funny, I know. And what about this anecdote that I couldn’t resist sharing? A friend mentioned one of their coworkers uses an app whose main mode of operation is text entry. For whatever reason, pressing Command + b toggles open a navigation drawer, and not, you know, apply bold formatting. This isn’t even an accessibility issue, it’s straight-up bad design. Is this app helpful? Let me know!

15th Jun 2026 • 1 votes
Evolved antennas, LLM-generated code, and a potential antifuture

I think about evolved antennas a lot. If you’re not familiar, an evolved antenna is created when you set an evolutionary algorithm on the task of producing a structure that is as efficient at its intended function as possible. Past that there’s no human input, the algorithm just does its thing. It: Modifies elements of the initial design, Evaluates how well the design would perform according to a set of requirements, Adds or discards the changes accordingly, then Repeats until a success threshold has been achieved via iteration. Some evolved antenna practices also create a series of designs in parallel. They then pit the outputs against each other as a final layer of proving out efficacy. This process mimics natural selection. Here, mutations that are beneficial to surviving in an environment are passed through generations. It also creates absolutely wild, alien outputs: Source: Human-Competitive Results: Evolved Antennas for Deployment on NASA’s Space Technology 5 Misson - SlideServe. An evolved antenna looks the way it does because the algorithm that creates it is focused on the efficiency of the artifact it produces. It has no concept of what antennas are “supposed to look like”. That’s a bias that is inherent to us humans. Structural limitations I also have a light fascination with early programming. Specifically, the creativity involved with how programmers accommodated limitations inherent with the medium. Early mainframe programmers were constrained to the point where there would be a limit on the number of characters you could use, which led to cryptic names that oftentimes required a physical dictionary to look up the human-friendly meaning of something. There was also a long tail of this practice even after the hardware-imposed restrictions of the mainframe era. This was likely built on muscle memory, “best practice”, and a DRY mindset taken too far. Fortunately, contemporary practice—where computational power is far more luxurious—favors clarity over brevity. Profusion and proliferation Nowadays function and variable names are long and luxurious. We’re inundated with text. Flooded by it, even. In fact, we’ve gotten so good at using text that we built software to produce it for us, on demand and at volume. There have been some good things to come out of this development if you make digital experiences for a living. Concerns such as documentation, design systems, styleguides, and other vital-yet-neglected areas of the practice are suddenly critical for operations. It turns out that written words that help to explain things and create consistency are extremely important. Who knew! Here, I must confess: As someone who has long-favored these downplayed and underfunded areas of the trade, I have found myself ugly laughing at this tragicomic phenomenon. I also know that being able to feel this smug ruefulness is on borrowed time. Returning to this sudden explosion of text content—as well as other factors—we now have words for everyone who wants, or may eventually need them. Well, anyone who can pay. Decoupling and system gaming Many contemporary LLM-providing companies operate under a model where you pay them proportionate to how much computational power you utilize. This is sold via tokens, which is an abstract representation of a segment of content to be processed. Money is something people are reluctant to part ways with. This creates incentives to get creative. I’ve read all sorts of clever techniques, tricks, optimizations, and hacks people have created to lower token expenditure. The one that stands out to me most is the caveman prompt. It promises to “[cut] 65% of tokens by talking like caveman”. Of note is its ability to specify the level of “grunt”: Futurecasting The caveman prompt is a signal. Right now we live in a space where full, complete words and sentences are still desirable for us humans, and LLMs work with it. This space is in competition with a few intractable realities: LLM companies are continuing to operate at a loss. The real cost of using their services may manifest sooner than later, as promises of profitability need to be kept. Human language is an inefficient medium for transmitting data, and largely lacks the precision needed to describe the logic that powers contemporary software. Consider concepts like Robot Interaction Language and ggwave as ways we’ve attempted to address this fact. LLM-based product development is focused more on what is produced compared to how it is made. Given these considerations, one can imagine a future where human-friendly language is a liability. The forces of cost and efficiency are difficult to overcome, especially if results can be generated with minimal effort. One can also imagine that code returns to its infant state of being terse, cryptic, and near-impenetrable due to efforts to optimize how LLMs operate. Here, human language sets the parameters and LLMs act as the dictionary. This LLM optimization might even go a step further. In the pursuit of efficiency its code output becomes a language completely decoupled from a human’s ability to follow. In the torrential downpour that is everyone saying the quiet part out loud, the CEO of OpenAI’s horrifying comment about selling our intelligence back to us is a raindrop worth paying attention to. This possible outcome may mean that this time around there is no dictionary to reference. Or there is a dictionary, but it is sold back to you at a premium. It’s easy to foresee a proprietary language that only a LLM vendor can compile, decode, and otherwise change—it’s not like there’s a lack of prior art. Preventative measures Science fiction author Vernor Vinge wrote a brilliant book titled Rainbow’s End. In it, a character named Robert Gu is cured of his Alzheimer’s and brought into a post-singularity world. Gu—a former English professor and avowed technophile—struggles to adapt to a radically changed world. In one notable scene from the book, Gu rages when dismantling a device. He finds all its internal components are sealed and labeled, informing him that there are “no user-serviceable parts within”. Code may be poised to follow this direction. Imagine a world where attempting to open up any piece of software reveals a dense forest of evolved antennas, each constructed using inscrutable, black box language. Gu's outburst of anger is valid. It stems from understanding that intentionally preventing someone from making modifications is a method of enacting control. Another notable part of the future Rainbow’s End posits is the mass-proliferation of augmented reality. Here, people can access different “layers” of perception, with paid tiers of information. Looking with your naked eyes is free, but the more information you want about something the more you need to spend. Craft and cost As someone who cares about craft, I am deeply worried about this potential future. How something is built is just as important as the results it produces—reference LLMs' bias towards producing inaccessible code as an immediate concern. Inaccessible output by default is a case of implicit and unwitting control, in that it shapes who can—and cannot—use the web. The companies that provide these models could hypothetically be compelled to address that. However, the much-needed regulation needed to prevent this systemic digital exclusion is unlikely to happen with the current status quo. Or more realistically, will happen in a way that is more in-line with the regressive path the United States is currently traveling down. Some here may argue that these systems are performing better than how human developers have, and that will increase as LLM-generated code becomes more prevalent. Here, know that: As of now, homepage accessibility errors and complexity are both on the rise, and This increase in inaccessible code will continue to be enshrined, codified, and amplified as the source code is re-scraped and fed back into model data training efforts. Websites are being built via LLM-generated code and populated LLM-generated content at an exponential pace. This, in turn, raises more concerns: Overcoming this ever-increasing inaccessible default becomes progressively more resource-intensive. This makes it something only larger organizations can afford to take on, yet will be unwilling to due to a proportionately scaling cost. Inaccessible outputs become increasingly more normalized. This makes it ever more unlikely to be questioned as something that needs intervention. Command and control More distant—yet more relevant—fears center around explicit control. We're already seeing this manifest with refusals to communicate facts that may be inconvenient for LLM providers’ success. More abstractly, this entire model of operation is antithetical to one of the most radical and equalizing forces humans have ever invented. And subversion of this model is deliberate. You must imagine Sam Altman holding a knife to Tim Berners-Lee's throat. Anil Dash The public internet has been available for 35 years, and open source for 28. Both are revolutionary ideas that upended traditional ways of how knowledge is distributed. The lifespan of these ideas are also tiny blips compared to the centuries of traditional power structures and systems that came before them. It’s also enough time for said structures and systems to figure out how to address these existential threats and return things to how they prefer to operate. Being robbed of the profound openess and transparency we have become accustomed to means that we place control of reality in the hands of closed, unaccountable organizations. Here, we need to think deeper than code. Antifuture There is a potential future where we are lead down a path that works against our own self-interests, amnesic to how we wound up where we are and without the language to communicate it. Dominant players are contemptuously bypassing the standards process. We are already seeing attempts to manipulate LLM output to serve political agendas. Even further, this manipulation can occur with the providers themselves. Also recall that the more unaccountable LLM output is normalized the less we will question its outputs. Now be the villain and imagine what is possible if these agendas feel threatened. Before the internet there was Usenet. Before Usenet there was ARPANET. Through this lens, the browser is just another place information is stored. Here, we should be worried about the wolf-in-sheep’s-clothing threat of monopolistic vertical interoperability—interfacing what came before as a method to subsume it. You may be reading this post as a paranoid fear response. I should point out that these fears are cultivated. Here, know that I am not feeling fear as much as anger and a desire to preserve what is boring and works. You can open Notepad and type whatever you want into it. You can open Paint and draw whatever you can imagine. You can publish whatever you want online with a small degree of technical know-how. This all costs nothing past the hardware, software, and connection fee. We take this for granted, to our own detriment. Being open and transparent is a radical strength, not a weakness. We should not throw this away in the pursuit of a convenient perceived inevitability.

11th May 2026 • 1 votes

More in programming

Clip of me singing Despard in Ruddigore in 2013

A clip of me singing a funny song from Gilbert and Sullivan’s Ruddigore back in 2013

8 hours ago • 1 votes
How and Why fork() Uses Copy-on-Write

In this video, we look at why fork() needs copy-on-write, how it works inside the kernel, and a memory usage problem that Instagram encountered with Python.

14 hours ago • 1 votes
What we lost when we lost comments

Comments require commitment, but they’re worth it.

20 hours ago • 1 votes
Two-Stack Sliding-Window Aggregation

An aggregation is some kind of summary of a set of data. This can be the sum, length, minimum, etc. It is quite common to want to calculate such a summary repeatedly, e.g. “the maximum noise level in dB for the past 30 seconds” for a nuisance detector. In such a case we say there is a sliding window over our data, and we want to aggregate over our window. If our aggregation is a binary operator with an inverse, like integer sums, there is a very easy solution using a double-ended queue: from collections import deque class SlidingWindowSum: def __init__(self): self.sum = 0 self.elems = deque() def push(self, x): self.sum += x self.elems.append(x) def pop(self): self.sum -= self.elems.popleft() def eval(self): return self.sum But what if our operator has no inverse? This is actually the case for most interesting summaries such as minimum, quantile, approximate unique count (for example using HyperLogLog), etc. In fact, even something as simple as a floating-point sum suffers from the fact that floating-point addition is not invertible. For example, if you ever have a NaN in your input data with the above naive algorithm your sum will forever remain NaN, even long after the bad value has left your window. Six years ago I came up with an algorithm for maintaining just the minimum/maximum in a sliding window and posted it to cs.stackexchange. I now consider this algorithm pointless, because it turns out there is a simple and efficient algorithm that solves this problem for a very wide class of aggregations. I’m writing this blog post to spread the word, because I feel it should be more widely known. Folklore I came across this algorithm while reading a far more advanced paper, Low-Latency Sliding-Window Aggregation in Worst-Case Constant Time by Tangwongsan et al. Why is this paper titled low-latency? Because it does the same as what I’m about to describe, but in O(1) time for each step. However, in it they also described a “two-stack” algorithm, which does it in amortized O(1), and is far, far simpler. Amortized O(1) means that across many operations the total amount of work per element is constant, but an individual operation can take much longer. This is almost always fine, unless you absolutely need a low upper bound on latency. Funnily enough that paper attributes this algorithm to “adamax” from a 2011 Stack Overflow post. They in turn credit a 2001 lecture note by D. Sleator for the inspiration. However, this lecture note does not describe a sliding window aggregate, it describes the classical two-stack algorithm for implementing a FIFO queue and does amortized analysis on it. Ultimately I would not be surprised to find that this algorithm was already described in an obscure paper from the 1970s, seeing how simple and brilliant it is. Two stacks Like the authors of the paper, I will generalize the two-stack algorithm to arbitrary associative aggregation functions. By abstracting the aggregation as a set of functions, empty(), unit(x), combine(x, y) and finalize(x), you can describe many possible aggregations, for example a mean: empty = lambda: (0, 0) unit = lambda x: (x, 1) combine = lambda x, y: (x[0] + y[0], x[1] + y[1]) finalize = lambda x: x[0] / x[1] if x[1] else None I’d like to note here that these functions have the following signatures: fn empty() -> Agg; fn unit(x: Value) -> Agg; fn combine(x: Agg, y: Agg) -> Agg; fn finalize(x: Agg) -> Out; I’m making a distinction here between Value, Agg and Out because while they seem superficially similar for something like an integer sum, for an approximate unique count on strings you would have (Value, Agg, Out) = (String, HyperLogLogSketch, u64), three wildly different types. Without further ado, the algorithm: class TwoStackAgg: def __init__(self): self.values = [] self.values_agg = empty() self.cum_aggs = [] def push(self, x): self.values.append(x) self.values_agg = combine(self.values_agg, unit(x)) def pop(self): if not self.cum_aggs: cum_agg = empty() while self.values: cum_agg = combine(unit(self.values.pop()), cum_agg) self.cum_aggs.append(cum_agg) self.values_agg = empty() self.cum_aggs.pop() def eval(self): return finalize( combine(self.cum_aggs[-1], self.values_agg) if self.cum_aggs else self.values_agg ) That’s it, the entire algorithm. There’s two stacks (values and cum_aggs) and one more aggregate, values_agg. At any point in time values_agg holds the aggregate of values, and cum_aggs contains the cumulative aggregates of all values in our window that aren’t in values, in reverse order. From this we can get the aggregate over our entire window in constant time by by combining the last value of cum_aggs with values_agg. The neat part is that (assuming w is our window size) every wth operation we drain all of values and maintain a running aggregate while pushing the partial cumulative aggregates onto cum_aggs. This is what makes it amortized O(1), doing O(w) internal operations every wth pop bounds the total amount of work per element to O(1), even though a singular operation might not be constant time. I think this is best visualized. Suppose we sum [1, 2, ..., 10] with a fixed-size sliding window of four elements, then the state on each eval() call would look like this (values_agg not shown as it is simply the aggregate of the values): cum_aggs values out [] [] = 0 [] [1] = 1 [] [1, 2] = 1 + 2 [] [1, 2, 3] = 1 + 2 + 3 [] [1, 2, 3, 4] = 1 + 2 + 3 + 4 [4, 3 + 4, 2 + 3 + 4] [5] = 2 + 3 + 4 + 5 [4, 3 + 4] [5, 6] = 3 + 4 + 5 + 6 [4] [5, 6, 7] = 4 + 5 + 6 + 7 [] [5, 6, 7, 8] = 5 + 6 + 7 + 8 [8, 7 + 8, 6 + 7 + 8] [9] = 6 + 7 + 8 + 9 [8, 7 + 8] [9, 10] = 7 + 8 + 9 + 10 [8] [9, 10] = 8 + 9 + 10 [] [9, 10] = 9 + 10 [10] [] = 10 [] [] = 0 In total the memory usage is O(w), where w is your maximum window size. Note that for simplicity of analysis and the example I assumed a fixed-size window w, but there is nothing about the two-stack algorithm that requires this. You can call push(x) and pop() as many times as you’d like between each eval(), growing and shrinking the window size as needed. Floating-point non-associativity Note that we required above that our aggregate combine is associative, meaning: combine(combine(x, y), z) = combine(x, combine(y, z)) Technically speaking, floating-point addition doesn’t respect this. Nevertheless, the above algorithm is still very useful because the results closely match the expected outcome, even more so if you use a compensated summation algorithm like Kahan summation. Another neat thing about the two-stack algorithm is that it doesn’t require commutativity, if you follow the above implementation precisely. The order of operands is maintained, which can matter for things like string concatenation. However, there is a second very useful property of the above algorithm. Each aggregate is strictly a combination of the elements in the window, and none outside the window. This means if your window contains a NaN or infinity (or some other outlier), that value only poisons the windows that contain it rather than the rest of your computation. But even without NaN or infinity it is useful, due to not propagating errors endlessly. E.g. if your sliding window starts with [1e20, 1], this is what would happen with a naive rolling sum: >>> 1e20 + 1 - 1e20 - 1 -1.0 Compensated summation will reduce these effects, but not making your result depend on values outside of the window will eliminate long-term error accumulation entirely.

20 hours ago • 1 votes
Lighthouse map

Lovely global map with animated lights sweeping the waters

22 hours ago • 1 votes
📚 BoredReading

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