Developing a long-term illness, whether chronic or acute, is like being dropped into a country completely unfamiliar to you. You don’t know the language, the customs, the cuisine, the people. You feel alone, isolated, and totally out of your depth. Eventually, you start to learn the language, the customs. You find community, fellow travelers, people who can help you understand your new life better. It doesn’t stop being hard, but the learning curve becomes less steep and the isolation less intense. 

However, unlike when you’re immersed in a new country and culture, you’re falling into this new place and experiencing that painfully slow acculturation while you’re trying to still live your regular life in parallel. You’re expected to be a good parent, partner, family member, friend, employee, housekeeper, bill-payer, etc. But you’re living in two different realities now and because of that, it’s easy to feel alienated from your regular life, especially if you don’t feel like you can bring that other part of yourself to your interactions at work, at home, or out with friends. The cognitive dissonance can be jarring.

It’s hard enough to live that double life, but adding in the vagaries of seeking out a diagnosis and often not being believed, not to mention coping with the symptoms themselves, can make life feel completely untenable. Before my autoimmune diagnosis, I spent more than a year seeing medical professionals who didn’t believe there was anything wrong with me other than the normal discomforts of aging. I kept asking doctors if they thought my symptoms could be autoimmune and was told “no” over and over again, though that felt wrong to me. One PA suggested that some of my symptoms might stem from anxiety since I have a history of anxiety (like I wouldn’t know at this point what anxiety feels like). Within five minutes of talking with the first rheumatologist I saw, after waiting five months for the appointment, he said to me “this doesn’t sound rheumatological at all.” Luckily he still did all the standard testing which showed that he was very wrong. But not being believed by so many doctors for so long stays with you. It leaves a scar. Every time I see a doctor now, I feel like I’m going to court and I’m ready to be cross-examined, to be picked apart. I’m a bundle of nerves.

And my experience is painfully common, especially for women, as poet Meghan O’Rourke writes in her amazing book about chronic illness, The Invisible Kingdom (a meditation on and journalistic exploration of chronic illness and how it is positioned in our social fabric):

And so it is a truth universally acknowledged among the chronically ill that a young woman in possession of vague symptoms like fatigue and pain will be in search of a doctor who believes she is actually sick. More than 45 percent of autoimmune disease patients, a survey by the Autoimmune Association found, “have been labeled hypochondriacs in the earliest stages of their illness.” Of the nearly one hundred women I interviewed, all of whom were eventually diagnosed with an autoimmune disease or another concrete illness, more than 90 percent had been encouraged to seek treatment for anxiety or depression by doctors who told them nothing physical was wrong with them. (p. 103)

Once I got on meds for my condition that finally started working (most first-line meds for autoimmune conditions take three months on average to produce any effects on the immune system), I thought I was past the worst of it. Other than occasional much smaller flares, I was essentially in remission. I learned my limits. I protected my spoons with my life. I kept my stress low. I felt like I had it figured out. I felt good even. And then I got sicker with new symptoms that have stolen so much from me, including my sleep. The past 10 months have honestly been a nightmare with a carousel of doctors who all have completely different theories of what is going on and with a condition that is constantly evolving so what they see in the moment isn’t the full picture. And each doctor I’ve seen hyperfocuses on something different and ignores every other aspect of my case. It’s all making me feel like I’m going crazy. 

Each wrong diagnosis brought me to another country, another reality, another identity, that I lived in for a short while. And in each of these countries, I spent countless hours learning, learning, learning all I could, going down research and subreddit rabbit holes, and spending way too much money on products that did nothing because none of those diagnoses were correct. The dermatologist I see who specializes in autoimmune conditions at a research university seems to have given up even trying to diagnose me and she’s basically my last best hope in the state. She wouldn’t even give me differential diagnoses last time beyond “it’s clearly autoimmune given your systemic symptoms.” She’s the one who has put me on a serious immunosuppressant with debilitating side effects, which I guess at least shows she’s taking it seriously, but if she doesn’t know what she’s treating me for, how does she know that this drug that is making me feel terrible is even going to help? Atul Gawande said of doctors that “nothing is more threatening to who you think you are than a patient with a problem you cannot solve” (quoted in The Invisible Kingdom, page 209) and I feel that in how I’m treated at every appointment I go to. With the exception of the charlatan immunologist who was desperate to diagnose me with MCAS though there was no evidence in support of it, not one doctor has seemed at all interested in figuring out what this is – it’s felt more like a game of “not it.”

Of a similar liminal moment in her own illness, O’Rourke wrote, “in my illness I was moored in an unreachable northern realm, exiled to an invisible kingdom, and it made me angry. I wanted to rejoin the throngs. In dark moments I continued to wonder if the wrongness was me” (99). And of course people will feel like that wrongness is them when we live in a culture that views chronic illness as some sort of weakness or something we caused through through our own bad habits. Like O’Rourke, I feel both exiled from my world and forced to be in it at the same time, which is a unique form of torture. Going to work, pretending things are ok, doing my job, meeting deadlines, helping students, smiling, all the while my body is attacking itself, I’m barely sleeping, I’m spontaneously bleeding from my skin and under my skin, and I’m so itchy I sometimes have to wear gloves to bed or I’ll scratch myself raw in my sleep. You feel like you’re play-acting being yourself, being a person in the world, because you’re not really there anymore. And when you’re suffering, and don’t know what your illness is, and you feel abandoned, it’s easy to go down rabbit holes of self-loathing along with those rabbit holes of fruitless research that make you feel like an unhinged obsessive with a murder board and yarn. As Meghan O’Rourke wrote, “your sense of story is disrupted” (p. 259) and you feel like a stranger to yourself.

I started writing about slow librarianship long before I got really sick, but even then, I knew the importance of fostering a work culture where you can be a whole person. I knew how it felt to have a child and feel like you couldn’t prioritize family obligations over work ever (though working during family time? Totally ok, right?). In a workplace that encourages people to be whole people, workers feel like they can prioritize the things in their lives outside of work that are important – their caregiving responsibilities, their health, the people they love, etc. They feel like they can talk about these things – that they don’t make them liabilities. They can be vulnerable and real. And feeling like you can be vulnerable and real about who you are and how you’re doing means that you can also be vulnerable and real in your work, which makes us better employees who are energized to try new things.

I think a lot of people in positions of power might even want a culture like this, but very few actively create it. They might think that saying “take what time you need” when someone is facing illness is enough. But I think two pieces are missing from this. First, managers need to not only say “take what time you need” but work with their direct reports to address the work that would otherwise pile up. If you say “take all the time you need” but all the work with its stressors and deadlines is still there, you’re not really giving people space. Can you take good care of yourself while you watch the work pile up and up and up? How many of us have come back to work while still not fully recovered from an illness because of the work that was piling up or a class they needed to teach? 

Also, managers need to model vulnerability, transparency, and being whole people themselves. If they put up a false front of strength, if they’re not willing to be vulnerable and human and real themselves, if they do not model transparency, there’s no way that others will feel safe doing so. I was lucky to have a boss in my first academic library job who was deeply human in her interactions with her employees, so I got to see what that looked like. And it was her humanity that engendered fierce loyalty in her employees – we all thought the world of her. Even when she made decisions that people didn’t like (which was rare as she really did take our insights to heart), she explained her thinking in a transparent way. Given my later experiences, her way of being feels vanishingly rare. I think a lot of managers feel like they need to project strength, not explain their decisions, not let their direct reports really know them as people with full lives, but I don’t think that’s true. A lot of managers operate out of a place of fear or insecurity, but my first academic library director was confident enough to be her full self, flaws and all. 

In a culture where we don’t feel like we can bring ourselves fully to work, I don’t feel like I can talk about my illness. I feel selfish and weak for even considering it. Like, we all have shit going on, right? The world is pretty awful right now. People’s lives are complicated and messy and there’s probably a lot of suffering I know nothing about happening all around me. If they don’t talk about it, who am I to talk about it? I’m not special. While I’ve mentioned being sick at work in the context of being immunocompromised and needing to protect myself and not participate in large, crowded events, even that has felt really uncomfortable. Everyone should feel like they are important enough to their places of work and valued enough to bring up these things without feeling embarrassed or like they’re asking for “special treatment.” I read recently (can’t remember the source) that close to 60% of people with chronic illnesses have not told people at work about it. Imagine hiding something that is such a pivotal and ineluctable piece of many people’s identities and think about the double life that forces them to live.

At work I feel a lot of shame about being sick and I work more than I should given how I’ve been feeling (this is common). I feel like I need to mask how I’m really doing, that people don’t want to hear it. And it’s true that most don’t. There are three people at work I can talk to about my illness, but others seem so incredibly uncomfortable when I mention it, so I’ve learned to just pretend it’s all ok or say nothing. I know that some of my reticence and shame comes from my own internalized ableism as it’s the water we all swim in, but when I worked in that library where the culture encouraged vulnerability, humanity, and care, I remember how different it felt. How much less distance there was between the person I really was and the person I was at work.  

In a meeting last year, our Dean was talking about making the next all-library meeting in-person only. Previously, they had always offered them hybrid, but she didn’t like that most people were choosing not to come in-person. And I totally get it, even though it sucks to always be the outlier. She wants it to be a team-building experience and that’s really hard to do when most people are participating from home with their cameras off. At that meeting, for the first time, I disclosed my illness in front of a bunch of people and talked about how important it is to always offer an online option for folks who are medically vulnerable or at least find ways to make indoor spaces safer for those who can’t afford to get sick. My boss then asked to meet with me to talk about how to make our spaces safer. I talked about airflow, encouraging and providing masks, and, during temperate months, having the meetings at places where windows and doors can remain open or even holding them outside (we’d had one meeting at a park a few years ago which had been the best one ever from both a health and team-building perspective). I suggested that we make the Winter meeting fully virtual since it’s the height of flu season and you can usually get people participating more in a fully virtual meeting than a hybrid one, where the online people feel like weird lurkers. I was thanked for my feedback and didn’t hear anything after that. This September, the all-library meeting was held in our campus library where the windows don’t open (albeit with a couple of HEPA filters scattered around, but I know we do have large college spaces where doors and windows can be opened because I went to an all-day union meeting in one where they did just that and we’d used that space for library meetings in the past) and our February meeting is going to be held in-person during the worst flu season in decades. Obviously, none of this was personal or intended to cause harm, but, at the same time, how should I feel under the circumstances? Clearly speaking out in that meeting, something that I had to really steel myself to do, had been pointless. Why would I ever bring it up or ask for anything again?

When we come back from winter break, people inevitably ask each other how it was, but do they really want to know? I think they want to hear “good,” “fun,” “restful,” etc. How do you talk about a “vacation” in which you spent most of it doubled over in pain after eating anything thanks to the toxic meds you have to take and your father-in-law was in the hospital dying the entire time? I wish that I felt I could share what an absolute shitshow it’s been, to feel like I could be a full person at work, but when you know no one really wants to hear it, when it just makes people uncomfortable, it becomes so much easier to smile and say “it was good!” and move on. Who wants to be a buzzkill? 

Slow librarianship puts worker well-being over productivity and deadlines, allows workers to be whole people at work, and supports a culture of care. While a radical idea, this even makes good business sense because depleted and burned out workers have been shown to be a major drain on the organization and negatively impact the culture. If you’re a manager and you’re not actively fostering a culture where people can bring their whole selves to work, then you are fostering a culture where people do not feel safe being vulnerable and having needs. Workers, if you know a colleague is struggling with something (an illness, losing a loved one, a difficult caregiving situation, etc.) and you don’t check in to see how things have been going for them, you’re sending the message that you don’t want to hear about these things, that they make you uncomfortable, that they’re not appropriate to take to work. I think we’ve all probably been guilty of this at some point in our lives and maybe we even thought that not asking was the right thing to do. I can imagine some people think that asking is invasive or reminds the person that they are sick, but it is an expression of care. As Philip Hoover writes in his excellent Sick Times article entitled “You know someone with Long COVID. They need you to ask about it genuinely” 

Approach us with empathy and curiosity. Ask questions that show a sincere desire to understand. How are you feeling this week? works because it acknowledges the chronic, fluctuating nature of my health. Or a version of Nunez’s question, and one I’ve longed to be asked since I’ve been ill: What has this been like for you? 

If our response is tough to hear, try not to smother it in optimism. And tread lightly. While some long-haulers may appear okay in public, much of our suffering occurs in private, shade-drawn rooms, across lonely afternoons, stuck in bed. When in doubt, remember that the act of asking never hurts — but never being asked certainly does.

While I don’t have Long COVID, this piece so perfectly encapsulates how I feel as someone with a mostly invisible chronic illness who would just love to be asked “how are you feeling this week?” instead of feeling like I have to pretend I’m okay. I told my manager at the start of Fall term that my autoimmune disease had become significantly worse and that I didn’t know what my capacity might look like going forward. She expressed sympathy, told me to take what time I needed, and never checked in with me after that. That September, I was taking over a very important committee chair role that was made enormously more time-consuming and onerous by the departure of the three colleagues most involved in supporting this work (two of whom had more than 20 years of institutional knowledge locked up in their heads). I didn’t feel like I had the leeway or support to let things drop as more and more kept piling up on my plate related to my chair role and it became clear that I was expected to do a lot of onboarding for the new people in this role even though I was new to my role and was no one’s manager. While I know my boss is extraordinarily busy, the message that not checking in with how I was doing sent me was very different from what I assume she’d wanted to convey. Checking in with a colleague or direct report seems like such a small thing, and it is in terms of the effort it requires, but the impact it can have in making someone feel cared for and less like they have to live a painful double life is enormous.

These are some things I’ve wandered across on the web this week.

Congratulations to All three Dr. J's from the @NirdsLab Dr. Bathsheba Jackson @sheissheba , Dr. Gavindya Jayawardena @Gavindya2 , and Dr. Yasith Jayawardana. @yasithdev. @WebSciDL PhD Crush board updated, just waiting in the border of alumni cloud until the paperwork is done! pic.twitter.com/dwEMMzOpy5

— Sampath Jayarathna (@OpenMaze) July 10, 2024

After the defense, @lesley_elis continued @WebSciDL's tradition of students sharing a hometown meal with us. Lesley's Philly roots showed up in the cheesesteaks (made authentic with Cheez Whiz), @Tastykake, birch beer soda, and @GrittyNHL decorations. Congrats again, Lesley! 🎉🎓 pic.twitter.com/WP8Ny1d2BY

— Michele Weigle (@weiglemc) July 29, 2024

I’m glad we’ve reached a new Public Domain Day, and that the works I’ve been featuring in my #PublicDomainDayCountdown, and many more, are now free to copy and reuse. I’ve been posting about works joining the public domain in the United States, which include sound recordings published in 1925, and other works published in 1930 that had maintained their copyrights. (Numerous works from 1930, and later, that had to renew their copyrights, and did not, were already in the public domain, though many of the best-known works did renew copyrights as required.) This is the eighth straight year Americans have seen a year’s worth of works join the public domain, after a 20-year freeze following a 1998 copyright extension.

I intend my countdown not just to celebrate the works joining the public domain, but also to celebrate what people have done with those works. In some posts, I note later creations based on those works. In nearly all my posts, I link to things that people have written about those works. Like the works themselves, those responses may have flaws or quirks, but I value them as human reactions to human creations. Whether they’re reviews, personal blog posts, professionally written essays, scholarly analyses, or Wikipedia articles, they’re created by people who encountered an interesting work and cared about it enough to craft a response to it and share it with the world. Those shared responses in turn pique my interest in the writers and the works.

It wasn’t always easy for me to find such responses online. Sometimes I’d go searching for responses to a promising-sounding work, and only find sales listings on e-commerce sites, social media posts not easily linkable or displayable without logging into a commercial platform, paywalled articles that many of my readers can’t view, or generic-sounding pages that read like they were generated by a large language model or a content farm, but not by anyone who I could clearly tell cared about or even read the work in question. Some works I initially hoped to feature got left off my countdown, replaced by other works where I could more readily link to an interesting response.

The people publishing the responses I link to are often swimming against a strong current online. Many online writing systems– including the one I’ve been writing these posts on— are now urging their users to “improve” their posts by letting “AI” write them. Some writers may be tempted to allow it, when facing an impending deadline or writer’s block or anxiety, even when the costs can include muffling one’s own voice, signing onto falsehoods confidently stated by a stochastic text generator, or abusively exploiting existing content and services. Other writers may feel pushed to put their work behind paywalls or other access controls that makes them less likely to be plagiarized or aggressively crawled by those same “AI” systems. And most writers, myself included, find it easy to dash off a quick short take on a social media platform, be quickly gratified by some “like”s, and then have it forgotten. It’s harder to take the time to craft something longer or more thought-out that will be readable for years, and that might take much longer for us to hear appreciated. The easy alternatives can discourage people from devoting their time to better, more lasting creations.

As I’ve noted before, both copyright and the public domain serve important purposes in encouraging the creation, dissemination, sharing, and reuse of literature and art. One reason I write my public domain posts is to promote a better balance between them, particularly in encouraging shorter copyright lengths to benefit both original creators and the public. Similarly, as I’ve noted in another recent post, I value both human creation and automated processes, but I increasingly see a need to improve the balance between those as well, especially as some corporations aggressively push “generative AI”. While I appreciate many ways in which automation can help us create and manage our work, I treasure the humanity that people thoughtfully put into the creation of literature and art of all kinds, and the human responses that those creations elicit.

Today I’m thankful for all of the people, most no longer with us, who made the works that are joining the public domain today. I’m thankful for the new opportunities we have to share and build on those works now that they’re public domain. I’m thankful to all the people who have responded to those works, whether as brief reactions or as new works as ambitious as the works they respond to. And I hope you’ll keeping making and sharing those responses with the world when you can. I look forward to reading them, and perhaps linking to them in future posts.

Personal Note, January 1 2026: I have a new job: Executive Director of the Project Gutenberg Literary Archive Foundation. Here's what I wrote for PG's January Newsletter.

Greetings from the new Executive Director

On October 21, Project Gutenberg lost its longtime leader, Greg Newby, to pancreatic cancer. I had agreed to step up as Acting Executive Director so that Project Gutenberg could continue the mission that had become Greg's life work: to serve and preserve public domain books so that all of us can use and enjoy them without restrictions. Although I've been doing development work for Project Gutenberg for the past 8 years, I did not really understand what Greg's job entailed, or how many tasks he had been juggling. Three months in, I'm still discovering mysterious-to-me aspects of the organization. I've also been amazed at the dedication and talent of the many volunteers behind Project Gutenberg and our sister organization, Distributed Proofreaders. And at the large number of donors who make the organization financially viable and sustainable. So as of 2026, with your support, I'm continuing as Executive Director.

In the past three months Project Gutenberg has proven to be resilient; we took a heavy blow and managed to keep going. My top priority going forward is to make Project Gutenberg even more sustainable as well as resilient. In other words, my job is be one runner in a relay race: take the baton and make sure I get it to the next runner. That's what we all have to do with public domain books, too. We want them to still be there in 50 years! Whether you're already a volunteer or booster, an avid reader, or just someone curious about what we do, I hope you'll help us pass that baton.

Source

showed that (i) a successful block-reverting attack does not necessarily require ... a majority of the hash power; (ii) obtaining a majority of the hash power ... costs roughly 6.77 billion ... and (iii) Bitcoin derivatives, i.e. options and futures, imperil Bitcoin’s security by creating an incentive for a block-reverting/majority attack.

Source

90% of transaction volume on the Bitcoin blockchain is not tied to economically meaningful activities but is the byproduct of the Bitcoin protocol design as well as the preference of many participants for anonymity ... exchanges play a central role in the Bitcoin system. They explain 75% of real Bitcoin volume.

The Attack

The key idea behind this strategy, called Selfish Mining, is for a pool to keep its discovered blocks private, thereby intentionally forking the chain. The honest nodes continue to mine on the public chain, while the pool mines on its own private branch. If the pool discovers more blocks, it develops a longer lead on the public chain, and continues to keep these new blocks private. When the public branch approaches the pool's private branch in length, the selfish miners reveal blocks from their private chain to the public.
...
We further show that the Bitcoin mining protocol will never be safe against attacks by a selfish mining pool that commands more than 1/3 of the total mining power of the network. Such a pool will always be able to collect mining rewards that exceed its proportion of mining power, even if it loses every single block race in the network. The resulting bound of 2/3 for the fraction of Bitcoin mining power that needs to follow the honest protocol to ensure that the protocol remains resistant to being gamed is substantially lower than the 50% figure currently assumed, and difficult to achieve in practice.

Given that the rule of thumb followed by most practitioners is to wait for 6 confirmations, a fork that goes 6 levels deep can very likely diminish the public’s trust in Bitcoin and cause a crash in its market price. It is also widely accepted that a prolonged majority attack (if it happens) would be catastrophic to the cryptocurrency and can cause its downfall.

The conventional wisdom in the blockchain community is to assume that such block-reverting attacks are highly unlikely to happen. The reasoning goes as follows:

1. Reverting multiple blocks and specifically double-spending a transaction that has 6 confirmations requires control of a majority of the mining power;
2. Having a majority of the mining power is prohibitively expensive and requires an outlandish investment in hardware;
3. Even if a miner, mining pool or group of pools does control a majority of the mining power, they have no incentive to act dishonestly and revert the blockchain, as that would crash the price of Bitcoin, which is ultimately not in their favor, since they rely on mining rewards denominated in BTC for their income.

Source

In short, an attacker can first use the Bitcoin derivatives market to short Bitcoin by purchasing a sufficient amount of put options or other equivalent financial instruments. She can then invest any of the amounts calculated above, depending on the timeline of the attack, to obtain the necessary hardware and hash power to perform the attack. If the attacker chooses to obtain a majority of the hash power, her success is guaranteed and she can revert the blocks as deeply as she wishes. However, she also has the option of a smaller upfront investment in hardware in exchange for longer wait times to achieve a high probability of success. In any case, as long as her earnings from shorting Bitcoin and then causing an intentional price crash outweighs her investments in hardware, there is a clear financial incentive to perform such an attack. The numbers above show that the annual trade volume in Bitcoin derivatives is more than three orders of magnitude larger than the required investment in hardware. Thus, it is possible and profitable to perform such an attack.

Assumptions

• We only consider the cost of hardware at the time of writing. We assume the attacker is buying the hardware, rather than renting it and do not consider potential discounts on bulk orders.
• We ignore electricity costs as they vary widely based on location.

The justification for the first assumption is that it keeps our analysis sound, i.e. we can only over-approximate the cost by making this assumption. As for the second assumption, we note that electricity costs are often negligible in comparison to hardware costs and that our main argument, i.e. the vulnerability of Bitcoin to majority attacks and block-reverting attacks, remains intact even if the estimates we obtain here are doubled. Indeed, as we will soon see, the trade volume of Bitcoin derivatives is more than three orders of magnitude larger than the numbers obtained here.

Goal

Attacker

• Outsiders: someone who has to acquire or rent sufficient hash power.
• Insiders: someone or some mining pool who already controls sufficient hash power.

• Obtaining and maintaining for the duration of the attack sufficient hash power without detection.
• Obtaining and maintaining for the duration of the attack a sufficient short position in Bitcoin without detection.

Hash Power

Outsider Attack

• Mining rigs.
• Power to run the rigs.
• Data center space to hold the rigs.

Mining rigs

• Could they acquire mining rigs sufficient to provide 30% of the combined insider and outsider hash power, or ~43% of the pre-attack hash power?
• How long would it take to acquire the rigs?
• Would their acquisition of the rigs be detected?

Source

Power

Data centers

Hyperion

Operational cost

Insider Attack

Source

Short Position

• When the attacker's chain is within one block of being the longest, they have ten minutes to purchase the shorts. There is unlikely to be enough liquidity in the market to accommodate this sudden demand, which in any case would greatly increase the price of the shorts. I will ignore this possibility in what follows.
• At the start of the attack the attacker gradually accumulates sufficient shorts. Even assuming there were enough liquidity, and that the purchases didn't increase the price, the attacker has to bear both the cost of maintaining the shorts for the duration of the attack, and the risk of the market moving up enough to cause the position to be liquidated.

At the time of writing, the open interest of BTC options is a bit more than 20 billion USD. Thus, a malicious party performing the attack mentioned in this work would need to obtain a considerable amount of the available put contracts. This may lead to market disruptions whose analysis is beyond the scope of this work. This being said, if the derivatives market continues to grow and becomes much larger than it currently is, purchasing this amount of contracts might not even be detected.

• Regulated exchanges such as the CME offering options on Bitcoin and stock exchanges with Bitcoin ETFs and Bitcoin treasury companies such as Strategy.
• Unregulated exchanges such as Binance offering "perpetual futures" (perps) on Bitcoin.

Unregulated Exchanges

Instead of all of a particular futures vintage settling on the same day, perps settle multiple times a day for a particular market on a particular exchange. The mechanism for this is the funding rate. At a high level: winners get paid by losers every e.g. 4 hours and then the game continues, unless you’ve been blown out due to becoming overleveraged or for other reasons (discussed in a moment).

Consider a toy example: a retail user buys 0.1 Bitcoin via a perp. The price on their screen, which they understand to be for Bitcoin, might be $86,000 each, and so they might pay $8,600 cash. Should the price rise to $90,000 before the next settlement, they will get +/- $400 of winnings credited to their account, and their account will continue to reflect exposure to 0.1 units of Bitcoin via the perp. They might choose to sell their future at this point (or any other). They’ll have paid one commission (and a spread) to buy, one (of each) to sell, and perhaps they’ll leave the casino with their winnings, or perhaps they’ll play another game.

Where did the money come from? Someone else was symmetrically short exposure to Bitcoin via a perp. It is, with some very important caveats incoming, a closed system: since no good or service is being produced except the speculation, winning money means someone else lost.

Perp funding rates also embed an interest rate component. This might get quoted as 3 bps a day, or 1 bps every eight hours, or similar. However, because of the impact of leverage, gamblers are paying more than you might expect: at 10X leverage that’s 30 bps a day.

In a standard U.S. brokerage account, Regulation T has, for almost 100 years now, set maximum leverage limits (by setting minimums for margins). These are 2X at position opening time and 4X “maintenance” (before one closes out the position). Your brokerage would be obligated to forcibly close your position if volatility causes you to exceed those limits.

Binance allows up to 125x leverage on BTC.

One reason perps are structurally better for exchanges and market makers is that they simplify the business of blowing out leveraged traders. The exact mechanics depend on the exchange, the amount, etc, but generally speaking you can either force the customer to enter a closing trade or you can assign their position to someone willing to bear the risk in return for a discount.

Blowing out losing traders is lucrative for exchanges except when it catastrophically isn’t. It is a priced service in many places. The price is quoted to be low (“a nominal fee of 0.5%” is one way Binance describes it) but, since it is calculated from the amount at risk, it can be a large portion of the money lost. If the account’s negative balance is less than the liquidation fee, wonderful, thanks for playing and the exchange / “the insurance fund” keeps the rest, as a tip.

In the case where the amount an account is negative by is more than the fee, that “insurance fund” can choose to pay the winners on behalf of the liquidated user, at management’s discretion. Management will usually decide to do this, because a casino with a reputation for not paying winners will not long remain a casino.

But tail risk is a real thing. The capital efficiency has a price: there physically does not exist enough money in the system to pay all winners given sufficiently dramatic price moves. Forced liquidations happen. Sophisticated participants withdraw liquidity (for reasons we’ll soon discuss) or the exchange becomes overwhelmed technically / operationally. The forced liquidations eat through the diminished / unreplenished liquidity in the book, and the magnitude of the move increases.

Risk in perps has to be symmetric: if (accounting for leverage) there are 100,000 units of Somecoin exposure long, then there are 100,000 units of Somecoin exposure short. This does not imply that the shorts or longs are sufficiently capitalized to actually pay for all the exposure in all instances.

In cases where management deems paying winners from the insurance fund would be too costly and/or impossible, they automatically deleverage some winners.

So perhaps you understood, prior to a 20% move, that you were 4X leveraged. You just earned 80%, right? Ah, except you were only 2X leveraged, so you earned 40%. Why were you retroactively only 2X? That’s what automatic deleveraging means. Why couldn’t you get the other 40% you feel entitled to? Because the collective group of losers doesn’t have enough to pay you your winnings and the insurance fund was insufficient or deemed insufficient by management.

In theory, this can happen to the upside or the downside. In practice in crypto, this seems to usually happen after sharp decreases in prices, not sharp increases. For example, October 2025 saw widespread ADLing as (more than) $19 billion of liquidations happened, across a variety of assets.

Source

Source

Regulated Exchanges

Conclusions

Outsider Attack

• They could not acquire 43% or more of the hash power.
• Even if they could it would take so long as to make detection inevitable.
• Even if they could and they were not detected, the high cost of the rigs makes the necessary shorts large relative to the open interest, and expensive to maintain.
• These large shorts would need to be leveraged perpetual futures, bringing significant risks of loss of collateral through liquidation, and of the potential payoff being reduced through automatic de-leveraging.
• The attacker would need more than the peak aggregate open interest in Bitcoin futures over the past year.

Insider Attack

Source

Source

mining-company stocks are still flying, even with cryptocurrency prices in retreat. That's because these firms have something in common with the hottest investment theme on the planet: the massive, electricity-hungry data centers expected to power the artificial-intelligence boom. Some companies are figuring out how to remake themselves as vital suppliers to Alphabet, Amazon, Meta, Microsoft and other "hyperscalers" bent on AI dominance.
...
Miners often have to build new, specialized facilities, because running AI requires more-advanced cooling and network systems, as well as replacing bitcoin-mining computers with AI-focused graphics processing units. But signing deals with miners allows AI giants to expand faster and cheaper than starting new facilities from scratch.
...
Shares of Core Scientific quadrupled in 2024 after the company signed its first AI contract that February. The stock has gained 10% this year. The company now expects to exit bitcoin mining entirely by 2028.

Counter-measures

Source

• The 6-block rule is just a convention, there is no dial that can be turned.
• Much of the access to the Bitcoin blockchain is via APIs that typically have the 6-block rule hard-codded in.
• Many, typically low-value, transactions do not wait for even a single confirmation.
• Even it were possible, changing from a one-hour to a four-hour confirmation would have significant negative impacts on the Bitcoin ecosystem.

Acknowledgements

Happy New Year

Blackeye peas and collard greens for us.

I don’t think any of us went into 2025 thinking this would be an easy year, and boy wasn’t it. Rather than restate our collective challenges, I’m going to stick to my own little life and keep it short with bullet points and pictures. I’ll also list as many positives as I can, because even in the darkest of times, there are sources of joy and reasons to be grateful.

Real life, in no particular order

• (OK, there’s some order, let’s do the hard one first) My mom passed away in late October – our relationship was complicated, and so are my feelings, but I’m working through it all with a therapist
• (Bittersweet) I adopted Mom’s green cheek conure, Tutu (which we jokingly spell “Teauxtu,” thanks to my brother), who bonded to me quickly and thoroughly, but who is currently plucking all of his feathers anyway — we’re working on it with his vet, and I’m hopeful we’ll get him past this (though I’ll love him anyway, even if he’s a naked bird forever; I cannot overstate how sweet a little guy he is!)
• My brother got married to a really nice lady, and I’m so happy for them!
• In early October, we moved from Midcoast Maine to Western NY, where there is a larger and more active covid-conscious community – because the economy was so unstable, it took months to sell our house in Maine, but we did finally succeed, the day before Christmas
• We joined the mask bloc here for a D&D night and had a lot of fun – looking forward to getting involved in mask distribution and attending more events in 2026
• I finally read all of the Murderbot Diaries series by Martha Wells, spurred on by the release of the TV show – then I listened to the audiobooks, and it became my comfort listen for the whole latter part of the year
• I ran an accessible birding event here in Rochester, and I’m hoping to run more next year
• I took American Sign Language 101 and 102, which I’ve really enjoyed. I’ll be retaking 102 in early 2026 (it was a rough autumn/winter), and then I hope to move on to 103 and 104.
• I learned to darn (as in, mending damaged knitted items), and I’m working on expanding my mending skills repertoire – this is well-timed, since, besides his own feathers and our hair, Tutu loves to bite on shirts until they have holes in them
• We saw the most vivid aurora we’ve ever seen, including during our time in Alaska
• My aunt sent me her scone recipe, and I made so. many. scones. this year

Bird Buddies! (Outdoor birds captured with our feeder cameras.)

The three baby bluebirds in the video below brought us so much joy this spring! Here, they appear to have a little conference, to decide whether it’s time to fly away or not, before a starling shows up.

Bird Buddies who live indoors

Miscellaney!

Work, in no particular order

• I ran the first part of a major, potentially multi-year LibGuides accessibility remediation project (“potentially” because there’s a task force considering how important LibGuides are to us and our patrons, and maybe we will shift focus away from them or have a single accessibility remediator or … something I haven’t thought of, who knows?)
• I co-ran a task force focused on accessibility training for library staff
• We survived a university-wide “Cybersecurity Alignment” (or the first part of one?)
• I learned the developer side of Figma (kind of, I still find parts of it baffling)
• I learned a ridiculous amount about authentication to electronic resources (which would be really satisfying if I didn’t keep running into things I still don’t know)

Coming up in 2026, an incomplete list

• I’ll take more ASL classes
• I’ll mend some things
• I’ll ride my bike more, because there are more safe places to do so where I live!
• I’m hoping to work through more herbal studies and practice more of those skills – it may become vital in the near future
• There will be an IT “alignment” at work mid-year – it could mean anything from “nothing” to “now I report to Central IT (who doesn’t care about my MLIS and might actually consider it a negative) instead of the Library (who doesn’t require an MLIS for my position, but knows it’s a strength, and also my team is so good, I don’t want to lose them, OK, yes, I am very stressed about this possibility)”
• Our landlord will be selling our house, something I think (hope) he didn’t realize he’d be doing when we started renting here – we’ll get first right of refusal on buying it, and he’s willing to write us a lease for as long as we want one if we choose not to buy (NY law means the new owner will have to honor it), so while it’s a stressful situation, it’s not as bad as it could be
• A valued colleague (everyone in my department is valued, truly, but his institutional knowledge is unmatched) will be retiring, and because of budget constraints, that will probably mean some shuffling of responsibilities semi-permanently
• If we decide we aren’t moving (so, we decide to buy this house or to sign a longer lease), I’ll set up some garden beds and grow some things
• I want this to be a thing, but financially, it may not: I really very much want to build or buy a small (like, Class B, or C at the highest) camper so that I can travel again

Ninety-five (or 100) years is a very long time for copyrights to last. But Olaf Stapledon saw a much longer future for us in Last and First Men (reviewed here and here). His book tells a story of successive human species over the next 2 billion years.

Stapledon died in 1950, and his work is already public domain most places outside the US. Tomorrow a copy in Australia will be among the first books to be relisted in my new books listing, finally free for all. #PublicDomainDayCountdown

In January 2021, I began a journey that would span nearly five years, three children, countless late nights, and a singular focus: teaching machines to extract data from complex scientific tables with confidence—and to know when they're uncertain. On October 29, 2025, I successfully defended my dissertation titled "SCITEUQ: Toward Uncertainty-Aware Complex Scientific Table Data Extraction and Understanding" at Old Dominion University. This milestone represents not just the culmination of intensive research but a testament to perseverance, family support, and the power of focused determination.

Finding My Path at LAMP-SYS

When I joined the Lab for Applied Machine Learning and Natural Language Processing Systems (LAMP-SYS), part of ODU's Web Science and Digital Libraries Research Group (WSDL) under the guidance of Dr. Jian Wu, I knew exactly what problem I wanted to solve: making scientific table data extraction both accurate and trustworthy through uncertainty quantification.

Scientific tables are ubiquitous in research papers, containing critical experimental data, statistical results, and research findings. Yet extracting this data automatically from PDF documents remains surprisingly difficult. Unlike the simple, well-structured tables you might see on Wikipedia, scientific tables are complex beasts—featuring multi-level headers, merged cells, irregular layouts, and domain-specific notations that confound even state-of-the-art machine learning models.

But here's the real problem: existing methods don't tell you when they're wrong. They extract data with the same confidence whether they're processing a simple table or struggling with a complex one. For scientific applications where accuracy is paramount, this means researchers must manually verify every single extracted cell—a task that doesn't scale when you're dealing with thousands of tables.

The Research Challenge

My research addressed a fundamental question: How can we build systems that not only extract data from complex scientific tables but also quantify their uncertainty, allowing us to focus human verification effort only where it's needed?

To tackle this challenge, I formulated four research questions:

RQ1: What is the status of reproducibility and replicability of existing TSR models?

Before building something new, I needed to understand what already existed. I conducted the first systematic reproducibility and replicability study of 16 state-of-the-art Table Structure Recognition (TSR) methods. The results were sobering: only 8 of 16 papers made their code and data publicly available, and merely 5 had executable code. When I tested these methods on my newly created GenTSR dataset (386 tables from six scientific domains), none of the methods replicated their original performance. This highlighted a critical gap in the field. This work was published at ICDAR 2023: "A Study on Reproducibility and Replicability of Table Structure Recognition Methods."

RQ2: How do we quantify the uncertainties of TSR results?

To address this, I developed TTA-m, a novel uncertainty quantification pipeline that adapts Test-Time Augmentation specifically for TSR. Unlike vanilla TTA, my approach fine-tunes pre-trained models on augmented table images and employs ensemble-based methods to generate cell-level confidence scores. On the GenTSR dataset, TTA-m achieved an F1-score of 0.798, with over 80% accuracy for high-confidence predictions—enabling reliable automatic detection of extraction errors. This work was published at IEEE IRI 2024: "Uncertainty Quantification in Table Structure Recognition."

RQ3: How can we integrate uncertainties from TSR and OCR for holistic table data extraction?

I designed and implemented the TSR-OCR-UQ framework, which integrates table structure recognition (using TATR), optical character recognition (using PaddleOCR), and conformal prediction-based uncertainty quantification into a unified pipeline. The results were compelling: the accuracy improved from 53-71% to 83-97% for different complexity levels, with the system achieving 69% precision in flagging incorrect extractions and reducing manual verification labor by 53%. This work was published at ICDAR 2025: "Uncertainty-Aware Complex Scientific Table Data Extraction."

RQ4: How well do LLMs answer questions about complex scientific tables?

To evaluate the QA capability of Large Language Models on scientific tables, I created SciTableQA, a benchmark dataset containing 8,700 question-answer pairs across 320 complex scientific tables from multiple domains. My evaluation revealed that while GPT-3.5 achieved 79% accuracy on cell selection TableQA tasks, performance dropped to 49% on arithmetic reasoning TableQA tasks—highlighting significant limitations of current LLMs when dealing with complex table structures and numerical reasoning. This work was published at TPDL 2025: "SciTableQA: A Question-Answering Benchmark for Complex Scientific Tables."

The SCITEUQ Framework

Putting it all together, SCITEUQ (Scientific Table Extraction with Uncertainty Quantification) represents a comprehensive solution to uncertainty-aware scientific table data extraction. The framework achieves the state-of-the-art performance while providing essential uncertainty quantification capabilities that enable efficient human-in-the-loop verification.

Each component contributes to a more reliable approach:

• GenTSR provides rigorous cross-domain evaluation
• TTA-m quantifies uncertainties in structure recognition
• TSR-OCR-UQ integrates structure and content extraction with uncertainty maps
• SciTableQA enables systematic evaluation of reasoning capabilities

Publications and Research Impact

My research resulted in five first-author publications at top-tier conferences and journals:

• ICDAR 2025: "Uncertainty-Aware Complex Scientific Table Data Extraction."
• TPDL 2025: "SciTableQA: A Question-Answering Benchmark for Complex Scientific Tables."
• IEEE IRI 2024: "Uncertainty Quantification in Table Structure Recognition."
• Nature Scientific Data 2023: "DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding."
• ICDAR 2023: "A Study on Reproducibility and Replicability of Table Structure Recognition Methods."

Each of these papers faced initial rejection before ultimately being accepted. This taught me an invaluable lesson: rejection is not failure; it's an opportunity to refine and improve your work.

Industry Experience: From Azure to Alexa to Microsoft AI

While my research focused on scientific tables, my internships at Microsoft and Amazon broadened my perspective on applying machine learning at scale.

Microsoft (Summers 2022, 2023, 2025)

My first two summers at Microsoft were with the Azure team, where I worked on infrastructure optimization problems far from my research area. I developed an AI-human hybrid LLM-based multi-agent system for AKS Cluster configuration, reducing cluster type generation time from 2 weeks to 1 hour (link to blog post). I also designed ML anomaly detection systems on Azure Synapse that reduced hardware maintenance costs by over 20% and formulated new metrics for characterizing node interruption rates that decreased hardware downtime by 25% (link to blog post).

In Summer 2025, I joined the Microsoft AI team under the Bing organization, working on problems at the intersection of large-scale search and AI—which is what I'll be doing when I return to Microsoft full-time in January 2026.

Amazon (Summer 2024)

At Amazon, I worked with the Alexa Certification Technology team in California, where I drove 10% customer growth by designing LLM-based RAG systems with advanced prompt engineering techniques and increased the revenue by over 5% by developing LLM Agents on AWS to improve Alexa-enabled applications (link to blog post).

These internships, while not directly related to my dissertation research, taught me how to apply ML thinking to diverse industrial problems and to work effectively in large, complex organizations.

Balancing PhD Life with Family

Perhaps the most challenging aspect of my PhD journey had nothing to do with research—it was combining my studies with raising three young children. My youngest son, Daniel, was born just six month after I was enrolled in the PhD program. Managing research deadlines, experimental runs, paper submissions, and the demands of parenting three boys (Paul, David, and Daniel) required discipline and sacrifice.

I developed a strict routine: work from 9 AM to 3 PM every day at my research lab, then pick up my kids from school and be fully present for them. This meant no late nights in the lab, no weekend marathons of coding—just consistent, focused work during designated hours. It wasn't always easy. Conference deadlines sometimes meant asking my wife, Olabisi, to take on even more, or my mother, Beatrice, to provide extra support. But this routine kept me grounded and taught me that quality of work matters more than quantity of hours.

The Defense

On October 27, 2025, I defended my dissertation before my committee:

• Dr. Jian Wu (Director) - Old Dominion University
• Dr. Michael L. Nelson - Old Dominion University
• Dr. Michele C. Weigle - Old Dominion University
• Dr. Sampath Jayarathna - Old Dominion University
• Dr. Yi He (Co-advisor) - College of William & Mary

Their thoughtful feedback, probing questions, and constructive critiques throughout my PhD journey were instrumental in refining my research and pushing me to think deeper about the implications and limitations of my work.

Lessons Learned

Looking back on nearly five years of doctoral work, several lessons stand out:

1. Embrace Rejection as Refinement

Four of my papers were initially rejected. Each rejection stung, but each one ultimately led to a stronger paper. The review process, while sometimes frustrating, forced me to clarify my arguments, strengthen my experiments, and address weaknesses I hadn't noticed. My TPDL 2025 paper on SciTableQA went through two rounds of revisions, but the final version is significantly better than the original submission.

2. Establish Non-Negotiable Boundaries

My 9 AM to 3 PM schedule wasn't just convenient—it was essential for maintaining my sanity and my family relationships. While some might argue that PhD students need to work 80-hour weeks, I proved that focused, disciplined work during reasonable hours can produce quality research. Those boundaries also made me more efficient: when you only have six hours a day, you learn to prioritize ruthlessly.

3. Build for Reproducibility from Day One

My systematic study on TSR reproducibility taught me the hard way how difficult it is to reproduce other people's work. This experience shaped how I approached my own research. Every framework I built—TTA-m, TSR-OCR-UQ, SciTableQA—comes with comprehensive documentation, publicly available code, and clear instructions for replication. Future researchers shouldn't struggle to build upon my work the way I struggled with others'.

4. Choose Problems That Matter to You

I entered my PhD knowing I wanted to work on table extraction with uncertainty quantification, and I never wavered from that focus. This singular vision helped me navigate the inevitable setbacks and distractions that come with doctoral research. When experiments failed or papers got rejected, I could always return to the core question: How do we make scientific data extraction both accurate and trustworthy?

5. Internships Broaden Your Perspective

While my Microsoft and Amazon internships didn't directly contribute to my dissertation, they fundamentally shaped how I think about research. Working on production systems with millions of users taught me to think about scalability, robustness, and real-world constraints in ways that academic research rarely emphasizes. These experiences make me a better researcher because I can now evaluate my work not just on benchmark performance, but on whether it could actually be deployed at scale.

Looking Forward

In January 2026, I'll be joining Microsoft as a Data Scientist 2 with the Microsoft AI team at the Redmond campus in Washington state. My family and I are excited about this new chapter—moving from Norfolk, Virginia, to the Pacific Northwest, and transitioning from academic research to industry applications.

While I'll be working on different problems at Microsoft, the skills and mindset I developed during my PhD—rigorous experimentation, systematic evaluation, uncertainty quantification, and reproducible research—will continue to guide my work. I'm particularly excited about the opportunity to apply research-driven thinking to real-world problems at a scale that can impact millions of users.

Acknowledgments

This journey would have been impossible without extraordinary support:

To Dr. Jian Wu, my advisor, mentor, and guide—thank you for believing in my research vision, for pushing me to think bigger, and for your patience during the inevitable frustrations of doctoral research. Your mentorship has not only shaped my research but also my approach to solving complex problems.

To Dr. Yi He, my co-advisor at William & Mary, your expertise and thoughtful feedback greatly enriched this research. Thank you for your guidance and support throughout this journey.

To my dissertation committee—Drs. Michael Nelson, Michele Weigle, and Sampath Jayarathna—your constructive critiques and expert insights were essential in refining my ideas and strengthening this work.

To my colleagues in WSDL and LAMP-SYS, the collaborative environment, intellectual exchanges, and camaraderie made this journey both enriching and memorable.

To my wife, Olabisi—you walked beside me every step of this journey with unwavering devotion and love. Your patience during the long hours, your understanding through the challenges, and your constant encouragement when the path seemed difficult made this achievement possible. This accomplishment is as much yours as it is mine.

To my sons—Paul, David, and Daniel—you are my greatest blessings and my constant source of joy and motivation. I hope this work serves as an example that with dedication and faith, you too can achieve your dreams.

To God Almighty, who is the source of all wisdom and strength, I give thanks and praise.

-Kehinde Ajayi (@KennyAJ)

Recorded in a Ohio service center somewhere along I-80.

The muted sound of cars and trucks passing at high speed about 200 feet away.

This is a snippet cut and pasted a few times in REAPER:

Yale’s 1905 commencement ceremonies included a honorary doctorate for British composer Edward Elgar, and a portion of his first “Pomp and Circumstance” march. It’s been a staple of graduation processions ever since. The full suite of five marches that Elgar finished takes about 30 minutes to play, but took nearly three decades to complete. The first march has long been in the US public domain; the last, published in 1930, joins it there in two days. #PublicDomainDayCountdown

David Seubert writes that more than 2500 records from 1925 digitized by the UC Santa Barbara Library will soon be freely downloadable there. A full listing of these recordings is online (though note that recordings made in 1925 but not released that year won’t be public domain yet).

A highlight of the collection is “St. Louis Blues”, sung by Bessie Smith with Louis Armstrong on cornet. One of the top selling records of 1925, it will be public domain in 3 days. #PublicDomainDayCountdown

Marlene Dietrich enjoyed success on stage and screen in 1920s Berlin, but became an international star in 1930. That year she came to the United States to star in Morocco alongside Gary Cooper. Her performance was nominated for an Academy Award. So was the direction by Josef von Sternberg, who also directed her in The Blue Angel and several other films. Morocco was inducted into the National Film Registry in 1992, and will be inducted into the public domain in 4 days. #PublicDomainDayCountdown

Source

A gauge of risk on Oracle Corp.’s (ORCL) debt reached a three-year high in November, and things are only going to get worse in 2026 unless the database giant is able to assuage investor anxiety about a massive artificial intelligence spending spree, according to Morgan Stanley.

A funding gap, swelling balance sheet and obsolescence risk are just some of the hazards Oracle is facing, according to Lindsay Tyler and David Hamburger, credit analysts at the brokerage. The cost of insuring Oracle Corp.’s debt against default over the next five years rose to 1.25 percentage point a year on Tuesday, according to ICE Data Services.

The company borrowed $18 billion in the US high-grade market in September. Then in early November, a group of about 20 banks arranged a roughly $18 billion project finance loan to construct a data center campus in New Mexico, which Oracle will take over as tenant.

Banks are also providing a separate $38 billion loan package to help finance the construction of data centers in Texas and Wisconsin developed by Vantage Data Centers,

Source

When you run using the latest, say, an Nvidia chip for training a model, those things are being run flat out, 24 hours a day, seven days a week, which is why they're liquid-cooled, they're inside of these giant centers where one of your primary problems is keeping them all cool. It's like saying ‘I bought a used car and I don't care what it was used for.’ Well, if it turns out it was used by someone who was doing like Le Mans 24 hours of endurance with it, that's very different even if the mileage is the same as someone who only drove to church on Sundays.

These are very different consequences with respect to what's called the thermal degradation of the chip. The chip's been run hot and flat out, so probably its useful lifespan might be on the order of two years, maybe even 18 months. There's a huge difference in terms of how the chip was used, leaving aside whether or not there's a new generation of what's come along. So it takes us back to these depreciation schedules.

73% of Ethereum miners have just given up: “About 10.6 million RTX 3070 equivalents have stopped mining since the merge.”

We strongly recommend that you do not hit eBay for a cheap video card, despite the listings reassuring you that this card was only used by a little old lady to play Minecraft on Sundays and totally not for crypto mining, and that you should ignore the burnt odor and the charred RAM. Unless you’re poor, and the card’s so incredibly cheap that you’re willing to play NVidia Roulette.

How well do miners treat their precious babies? “GPU crypto miners in Vietnam appear to be jet washing their old mining kit before putting the components up for sale.” There are real cleaning methods that involve doing something like this with liquid fluorocarbons — but the crypto miners seem to be using just water.

Conduit debt financing is a structure where an intermediary entity (the “conduit”) issues debt securities to investors and passes the proceeds through to an end borrower. The key feature distinguishing conduit debt from regular corporate bonds is that the conduit issuer has no substantial operations or assets beyond the financing transaction itself. The conduit is purely a pass-through vehicle, the debt repayment relies entirely on revenues or assets from the ultimate borrower.

Think of it this way: Company A wants to borrow money but doesn’t want that debt appearing on its balance sheet or affecting its credit rating. So it works with a conduit entity, Company B, which issues bonds to investors. Company B takes that capital and uses it to build infrastructure or acquire assets that Company A needs. Company A then enters into long-term lease or service agreements with Company B, and those payments service the debt. On paper, Company A is just a customer making payments, not a debtor owing bondholders.

The structure creates separation. The conduit issuer’s creditworthiness depends on the revenue stream from the end user, not on the conduit’s own balance sheet (because there isn’t really one). This is why conduit debt is often referred to as “pass-through” financing, the economics flow through the conduit structure to reach the underlying obligor.

Legal risks when things break: Substantive consolidation (court merges conduit with sponsor), recharacterization (lease treated as secured financing), and fraudulent transfer challenges. The structures haven’t been stress-tested yet because hyperscalers are wildly profitable. But if AI monetization disappoints or custom silicon undercuts demand, we’ll discover whether bondholders have secured claims on essential infrastructure or are functionally unsecured creditors of overleveraged single-purpose entities.

Why would Meta finance this via the project finance markets? And why does it cost $6.5 billion more?

That’s how much more Meta is paying to finance this new AI data center using the project finance market versus what they could have paid had they used traditional corporate debt. So why on earth is this being called a win? And even crazier, why are other AI giants like Oracle and xAI looking to copy it?

Meta data center

Construction on the project was well under way when Meta announced a new financing deal last month. Meta moved the project, called Hyperion, off its books into a new joint venture with investment manager Blue Owl Capital. Meta owns 20%, and funds managed by Blue Owl own the other 80%. Last month, a holding company called Beignet Investor, which owns the Blue Owl portion, sold a then-record $27.3 billion of bonds to investors, mostly to Pimco.

Meta said it won’t be consolidating the joint venture, meaning the venture’s assets and liabilities will remain off Meta’s balance sheet. Instead Meta will rent the data center for as long as 20 years, beginning in 2029. But it will start with a four-year lease term, with options to renew every four years.

This lease structure minimizes the lease liabilities and related assets Meta will recognize, and enables Meta to use “operating lease,” rather than “finance lease,” treatment. If Meta used the latter, it would look more like Meta owns the asset and is financing it with debt.

The joint venture is what is known in accounting parlance as a variable interest entity, or VIE for short. That term means the ownership doesn’t necessarily reflect which company controls it or has the most economic exposure. If Meta is the venture’s “primary beneficiary”—which is another accounting term of art—Meta is required to consolidate it.

Under the accounting rules, Meta is the primary beneficiary if two things are true. First, it must have “the power to direct the activities that most significantly impact the VIE’s economic performance.” Second, it must have the obligation to absorb significant losses of the VIE, or the right to receive significant benefits from it.

Blue Owl has control over the venture’s board. But voting rights and legal form aren’t determinative for these purposes. What counts under the accounting rules is Meta’s substantive power and economic influence. Meta in its disclosures said “we do not direct the activities that most significantly impact the venture’s economic performance.” But the test under the accounting rules is whether Meta has the power to do so.

The second test—whether Meta has skin in the game economically—has an even clearer answer. Meta has operational control over the data center and its construction. It bears the risks of cost overruns and construction delays. Meta also has provided what is called a residual-value guarantee to cover bondholders for the full amount owed if Meta doesn’t renew its lease or terminates early.

Today, Nvidia's rack systems are hovering around 140kW in compute capacity. But we've yet to reach a limit. By 2027, Nvidia plans to launch 600kW racks which pack 576 GPU dies into the space one occupied by just 32.

To get ahead of this trend toward denser AI deployments, Digital Realty announced a research center in collaboration with Nvidia in October.

The facility, located in Manassas, Virginia, aims to develop a new kind of datacenter, which Nvidia CEO Jensen Haung has taken to calling AI factories, that consumes power and churn out tokens in return.

Another judgment call: Under the accounting rules, Meta would have to include the residual-value guarantee in its lease liabilities if the payments owed are “probable.” That could be in tension with Meta’s assumption that the lease renewal isn’t “reasonably certain.”

If renewal is uncertain, the guarantee is more likely to be triggered. But if the guarantee is triggered, Meta would have to recognize the liability.

Ultimately, the fact pattern Meta relies on to meet its conflicting objectives strains credibility. To believe Meta’s books, one must accept that Meta lacks the power to call the shots that matter most, that there’s reasonable doubt it will stay beyond four years, and that it probably won’t have to honor its guarantee—all at the same time.

David Sacks Nov 6

OpenAI explicitly requested federal loan guarantees for AI infrastructure in an October 27 letter to the White House—which kindly refused the offer, with AI czar David Sacks saying that at least 5 other companies could take OpenAI’s place—directly contradicting CEO Sam Altman's public statements claiming the company doesn't want government support.

The 11-page letter, submitted to the Office of Science and Technology Policy, called for expanding tax credits and deploying "grants, cost-sharing agreements, loans, or loan guarantees to expand industrial base capacity" for AI data centers and grid components. The letter detailed how "direct funding could also help shorten lead times for critical grid components—transformers, HVDC converters, switchgear, and cables—from years to months."

Trump unveiled the “Genesis Mission” as part of an executive order he signed Monday that directs the Department of Energy and national labs to build a digital platform to concentrate the nation’s scientific data in one place.

It solicits private sector and university partners to use their AI capability to help the government solve engineering, energy and national security problems, including streamlining the nation’s electric grid, according to White House officials who spoke to reporters on condition of anonymity to describe the order before it was signed.

• Mr. Sacks has offered astonishing White House access to his tech industry compatriots and pushed to eliminate government obstacles facing A.I. companies. That has set up giants like Nvidia to reap an estimate of as much as $200 billion in new sales.
• Mr. Sacks has recommended A.I. policies that have sometimes run counter to national security recommendations, alarming some of his White House colleagues and raising questions about his priorities.
• Mr. Sacks has positioned himself to personally benefit. He has 708 tech investments, including at least 449 stakes in companies with ties to artificial intelligence that could be aided directly or indirectly by his policies, according to a New York Times analysis of his financial disclosures.
• His public filings designate 438 of his tech investments as software or hardware companies, even though the firms promote themselves as A.I. enterprises, offer A.I. services or have A.I. in their names, The Times found.
• Mr. Sacks has raised the profile of his weekly podcast, “All-In,” through his government role, and expanded its business.

Steve Bannon, a former adviser to Mr. Trump and a critic of Silicon Valley billionaires, said Mr. Sacks was a quintessential example of ethical conflicts in an administration where “the tech bros are out of control.”

“They are leading the White House down the road to perdition with this ascendant technocratic oligarchy,” he said.

David Sacks Nov 24

“The way this works”, said an investor friend to me this morning: “is that when Nvidia is about to miss their quarter, Jen Hsun calls David Sacks, who then gets this government initiative to place a giant order for chips that go into a warehouse.”

I obviously can’t confirm or deny that actually happened. My friend might or might not have been kidding. But either way the White House’s new Science and AI program, Genesis, announced by Executive Order on Monday, does seem to involve the government buying a lot of chips from a lot of AI companies, many of which are losing money.

And David Sack’s turnaround from “read my lips, no AI bailout” (November 6) to “we can’t afford to [let this all crash]” tweet (November 24) came just hours before the Genesis announcement.

• OpenAI, Anthropic and xAI, none of whom have a viable business model.
• Meta, Google and Microsoft, all of whom are pouring the cash from their viable business models into this non-viable business,

Update: 28^th December 2025

Source

If I were advising Xi, I’d counsel him to go for the jugular by engaging in AI-dumping, a repeat of their aughts steel-dumping playbook. It’s already underway — and working. Eighty percent of a16z startups use open-source Chinese models. Same story at Airbnb. China is registering similar or better performance as the American LLM leaders, but with a fraction of the capex. Flooding the market with competitive, less-expensive AI models will put pressure on the margins and pricing power of the Mag 7, taking down a frighteningly concentrated S&P and likely sending the U.S., possibly the globe, into recession.

Inspired by Tom Whitwell's 52 things I learned in 2022, I started my own list of things I learned in 2023 and repeated it last year. Reaching the end of another year, it is time for Things I Learned In 2025. Part way through the year I had the brilliant idea of putting a learning at the bottom of my weekly newsletter, and that worked well until the middle of the year when I stopped publishing newsletter issues. So here is a half year of learnings.

What did you learn this year? Let me know on Mastodon or Bluesky.

In Ethiopia, time follows the sun like nowhere else

This could have easily gone in the Thursday Threads on time standards. There are 12 hours of daylight, numbered 1 through 12. Then 12 hours of night, numbered 1 through 12. What could be easier?

From Thursday Threads issue 104 on Long Term Digital Storage.

A biographer embedded with the Manhattan Project influenced what we think about the atomic bomb

The quote above comes from the transcript of this podcast episode. I've thought about this a lot in the past week as the Trump administration's flood-the-zone strategy overwhelms the senses. In a valiant effort to cover everything that is news, I can't help but wonder about the lost perspective of what isn't being covered. And I wonder where I can look to find that perspective.

From Thursday Threads issue 105 on Facial Recognition.

The origin of the computer term "mainframe" comes from "main frame" — the 1952 name of an IBM computer's central processing section

"Mainframe" is such a common word in my lexicon that it didn't occur to me that its origins was from "main frame" — as in the primary frame in which everything else connected. I've heard "frame" used to describe a rack of telecommunications equipment as well, but a quick Kagi search couldn't find the origins of the word "frame" from a telecom perspective.

From Thursday Threads issue 106 on How much do you know about the credit card industry?.

It takes nearly 3¢ to make a penny, but almost 14¢ to make a nickel

I knew pennies cost the U.S. mint more than one cent to make, but I didn't realize that the cost of nickels is so much more out of whack. I also learned a new word: seigniorage — the difference between the face value of money and the cost to produce it.

From Thursday Threads issue 107 on the humble battery. Also this year, the U.S. mint stopped pressing pennies in November.

It is much harder to get to the Sun than it is to Mars

I suppose it that headline above needs some nuance. It is easy to get to the Sun...just escape Earth's gravity and point yourself there. It is hard to get to the Sun in a controlled way that means you won't burn up along the way.

From Thursday Threads issue 108 on Educational Technology.

There are now 23 Dark Sky Sanctuaries in the World

The pictures that accompany this article from the New York Times are stunning (gift link). And to think that there are only 23 places in the world that have reached this level of commitment to the environment.

From Thursday Threads issue 109 on Generative AI in Libraries.

Mexico has only one gun store for the entire country

And not only is there one gun store, the single store in Mexico is located on an army base and is run by soldiers, according to an article in the Associated Press from 2016.

From Thursday Threads issue 110 on Research into Generative AI.

Plants reproduce by spreading little plant-like things

From Thursday Threads issue 111 on End-to-end Encryption.

Most plastic in the ocean isn't from littering, and recycling will not save us

Contrary to popular belief, most plastic in the Great Pacific Garbage Patch stems from the fishing industry, with only a small fraction linked to consumer waste. The video highlights that mismanaged waste, rather than individual littering, is the primary contributor to plastic pollution, with 82% of macroplastic leakage resulting from this issue. It emphasizes the ineffectiveness of recycling as a solution, noting that less than 10% of plastics are currently recycled, and the industry has perpetuated the myth that recycling can resolve the plastic crisis. Microplastics, which are increasingly recognized as a major problem, originate from various sources, including tires and paint, with new data suggesting that paint is a significant contributor.

From Thursday Threads issue 112 on Social Media Research.

"But where is everybody?!?" — the origins of Fermi's Paradox

This retelling of the Fermi Paradox coms from this story about why, despite the vastness of the universe, we have yet to encounter evidence of extraterrestrial civilizations. Enrico Fermi famously posed the question, "Where is everybody?" suggesting a disconnect between the expectation of abundant intelligent life and the lack of observable evidence. With this comes the Great Filter notion...proposing that there may be significant barriers preventing intelligent life from becoming spacefaring. The article goes on to speculate where we are relative to the "Great Filter" — are we past it, or is it yet in front of us? In other words, have we survived the filter or is our biggest challenge ahead of us?

From Thursday Threads issue 113 on Copyright and Foundational AI Models.

The pronoun "I" was capitalized to distinguish it from similarly typset letters

I'm not buying the opinion author's underlying premise (capitalizing “they” in writing when it refers to a nonbinary person), but the origins of why we capitalize "I" and not other pronouns are fascinating.

From Thursday Threads issue 114 on Digital Privacy.

The word "scapegoat" originated in a 1530 bible translation

From Thursday Threads issue 115 on Public and Private Camera Networks.

"Leeroy Jenkins!!!!" was staged

First posted on May 10, 2005, this year marks the 20th anniversary of this bit of internet folklore. I remember when this first came out, and I totally believed it was real until earlier this year.

From Thursday Threads issue 116 on Government Surveillance.

Ammonium chloride may be the 6th basic taste

From Thursday Threads issue 117 on Local Government Surveillance.

Struggle over academic freedom in Texas state universities has a long history. Today it’s often over race and gender; in the 1940s, it was over things like John Dos Passos’s USA trilogy. When the University of Texas Board of Regents banned it from classrooms, university president Homer Price Rainey objected to their interference. After they fired him, thousands protested on campus. The first part of the USA trilogy, The 42nd Parallel, joins the public domain in 5 days. #PublicDomainDayCountdown

The Penn Libraries, where I work, has first editions of many of the works featured in my #PublicDomainDayCountdown . From today through Public Domain Day, the Libraries social media will feature photos of some distinctive books from 1930.

One of featured photos, also shown here, is of the 1930 edition of W. H. Auden‘s Poems, which made his work known to readers worldwide. Glynn Young writes about the “brilliant collection” of 30 poems, and one verse drama, joining the public domain in 6 days.

In 1925 the Associated Glee Clubs of America put on a concert like no other. 15 choral groups, with over 850 singers in all, came together in New York’s Metropolitan Opera House to sing a program broadcast on radio across America. Portions were electrically recorded, including “Adeste Fideles”, where the audience of 4000 joined in the carol. Lloyd Winstead writes about the record, which is now in the National Recording Registry, and joins the public domain in 7 days. #PublicDomainDayCountdown

Moss Hart wrote the first draft of Once in a Lifetime, a comedy about Hollywood’s transition to “talkies”, as a 25-year-old unknown. Established playwright George S. Kaufman helped revise it into a Broadway hit. Steve Vineberg calls it the “the finest comedy ever written by Americans”, and discusses how it began a long successful collaboration between the two writers. Still making theatergoers laugh in the 21st century, the 1930 play joins the public domain in 8 days. #PublicDomainDayCountdown

Given how much “Dick and Jane” have been used sardonically, one might think Zerna Sharp’s schoolbook characters were already public domain. But you can’t copyright names, expressive style, or stock situations, and fair use allows limited copying for purposes like criticism, analysis, and parody.

In 9 days, the 1930 Elson Basic Readers introducing Dick and Jane join the public domain, and their stories’ full texts and original artwork can be reused without limit. #PublicDomainDayCountdown

Four writers get credit on the 1930 copyright registration for “Body and Soul”: composer Johnny Green, and lyricists Robert Sour, Edward Heyman, and Frank Eyton. But many more artists shaped the perennial jazz standard we know today. Among its more than 1700 recorded interpretations and variations are covers by Coleman Hawkins, Frank Sinatra, Billie Holiday, Ella Fitzgerald, Tony Bennett and Amy Winehouse. The song surrenders itself to the public domain in 10 days. #PublicDomainDayCountdown

Michael Gold’s bestselling novel Jews Without Money depicts the plight of poor East European immigrants in New York. It resonated with readers in 1930 facing not-yet-fully-acknowledged impacts of the Depression. Gold hoped it would counter antisemitic propaganda, but many readers grew repelled by his own Communist agitprop, and a later edition deleted his ending calling the workers’ revolution the “true Messiah”. Gold’s uncut original goes public domain in 11 days. #PublicDomainDayCountdown

These are some things I’ve wandered across on the web this week.

Norm Ferguson joined Disney’s animation team in 1929, and quickly had a major influence on the studio’s style. In The Chain Gang, the bloodhounds he designed to chase Mickey Mouse impressed Disney with their expressiveness. Ferguson went on to animate Minnie’s dog Rover with a variant of that design in The Picnic. The two cartoons, joining the public domain in 12 days, show a clear development into the dog who Ferguson would also animate in later cartoons named Pluto. #PublicDomainDayCountdown

The Internet Archive has multiple events in January for Public Domain Day, including a contest for film transformations of public domain works.

Those works themselves often involved transformation. Four songs that joined the public domain in 2024 were written for the stage musical Animal Crackers, which went public domain in 2025. The film adaptation, joining the public domain in 13 days, drops all four, but adds Groucho Marx’s signature song “Hello, I Must Be Going”. #PublicDomainDayCountdown

HathiTrust has created a 1930 Publications Collection for Public Domain Day. It already has over 20,000 items opened, many by their Copyright Review Program, which finds works without copyright renewals.

Over 50,000 more items will open in 14 days, including the book version of The Maltese Falcon. The serial version was in last year’s #PublicDomainDayCountdown, but I haven’t to date found its now-rare magazine issues. I thank HathiTrust and all others bringing the public domain to the public!

Annual Report, New Partnerships, Digital Public Goods, CKAN and Open Data Editor, Open Science and more.

The post 🚀 Trustworthy AI for the Open Data Ecosystem – OKFN Newsletter End-of-Year 2025 first appeared on Open Knowledge Blog.

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Source

Duke’s Center for the Public Domain has its Public Domain Day post out, listing many works joining the public domain in 2026, and explaining the complicated factual and legal determinations sometimes needed to verify their status.

Among the listed artworks researcher Jason Rosenberg found will join the public domain in 15 days is the Jules Rimet cup, created for the FIFA World Cup in 1930. The original trophy is now missing, but anyone can soon freely make their own. #PublicDomainDayCountdown

Motivation

Bar charts are a widely used method for presenting categorical data in digital content, education, and professional reports. While previous research has examined how sighted users and blind screen reader users interact with these charts, little is known about the experiences of low-vision users who rely on partial sight and screen magnifiers. This group faces unique visual challenges, including reduced acuity, contrast sensitivity issues, zoom-related blurring, and the need for constant panning. These factors can increase cognitive effort and reduce accuracy when interpreting visual information. In particular, the presence of stacked bars, misaligned elements, or irrelevant distractor bars can significantly impair perception. Without a clear understanding of these barriers, designers lack the evidence needed to create accessible visualizations. This study addresses that gap by analyzing how low-vision users perceive different bar chart layouts and identifying design factors that contribute to perceptual errors.

Background and Related Work

Bar charts are a widely used form of data visualization due to their simplicity and effectiveness in presenting categorical comparisons. Decades of research, starting with Cleveland and McGill, have examined how visual features such as alignment, spacing, and bar length influence accuracy in graphical perception. These findings have informed design guidelines for improving chart readability for sighted users. Later studies have built on this work by exploring graphical perception in different populations, including children and domain experts.

Research on accessibility for users with visual impairments has primarily focused on blind individuals using screen readers. These efforts have led to the development of alternative text descriptions and sonification tools for accessing chart content. However, low-vision users who retain partial sight and often use screen magnifiers have not received the same level of attention in visualization research.

Some studies have explored how low-vision users read text under different visual conditions, such as changes in font, color, or contrast. While informative, these findings do not address the unique challenges involved in interpreting graphical data. Issues like zoom-induced blurring, difficulty in scanning across the chart, and increased visual clutter from distractors can significantly impact comprehension. Prior work has not systematically examined these effects in chart reading tasks. This study addresses that gap by analyzing the visual perception of bar charts among low-vision users and identifying design factors that influence error rates.

Methodology

Participants

The study involved 25 low-vision participants (13 male, 12 female), aged between 17 and 37, all of whom regularly use screen magnifiers and have prior familiarity with bar charts. Participants were recruited via mailing lists and word of mouth. Eligibility criteria included proficiency with screen magnifier software and the ability to perform visual tasks involving bar charts.

Apparatus

A custom web-based chart viewer was developed to simulate interaction with bar charts under magnification. The system incorporated a built-in screen magnifier modeled after ZoomText to ensure user familiarity and eliminate learning overhead. The application logged all user interactions, including zoom and pan activity, in real time for detailed analysis. Participants viewed the interface on a 1440x1024 resolution monitor.

Experimental Design

Participants performed a percentage estimation task on various types of bar charts. The charts included simple bar charts, aligned and unaligned stacked bar charts, and divided single-column bar charts. Annotated bars were accompanied by distractor bars of varying heights, either short or tall. The goal was to estimate the percentage height of a shorter bar relative to a taller reference bar. Differences in bar height and separation distance were controlled based on prior graphical perception literature.

Procedure

After providing informed consent, participants completed a 20-minute practice session to become familiar with the interface. Each participant was then asked to perform the estimation task on a randomized set of charts. A time constraint of 15 seconds per chart was enforced to focus on perceptual rather than cognitive processing. Participants responded verbally, and a think-aloud protocol was used to capture their reasoning. An exit interview was conducted to gather qualitative feedback on strategies and challenges.

Data Analysis

Participant responses were compared against ground-truth values, and the log of absolute error was used to quantify perceptual discrepancies. Data points with a Pearson correlation below 0.7 were excluded to remove outliers, such as those resulting from fatigue or guessing. Both quantitative error rates and qualitative insights from verbal feedback and interviews were used to evaluate perception patterns and identify contributing factors.

Results

Evaluation

Standard Bar Charts

Most participants (90%) preferred having both bars visible within the same screen viewport. When bars were separated, the average log absolute error was 0.87 (median 0.91). For adjacent bars, the error dropped to 0.65 (median 0.63). The presence of tall distractors significantly increased errors. Charts with short distractors had an average error of 0.78, while those with tall distractors averaged 0.96. For separated bars with tall distractors, the error averaged 0.95. For adjacent bars with tall distractors, it averaged 0.72. However, for 12 participants, this pattern was reversed. In those cases, adjacent bars produced more errors (average 0.89) than separated bars (average 0.73).

Multi-Column Stacked Bar Charts

Stacked bar charts resulted in a higher average error (0.83) compared to unstacked bars (0.74). Within stacked charts, aligned bars had a lower error (0.64) than unaligned ones (0.77), indicating that alignment helped reduce estimation difficulty.

Single-Column Stacked Bar Charts (Divided Bars)

Divided bars showed the highest error rate, averaging 0.91. Participants struggled with separation and distractors. In some cases, adjacent segments were perceived as a single visual unit. Error rates were slightly higher for adjacent bars than for separated ones, with distractors contributing approximately 0.3 additional points of error.

Analyses

Separation Effect

In charts without distractors, separated bars were harder to compare due to the need for zooming and panning. Users often had to move back and forth to estimate bar heights, leading to increased confusion and error. In charts with distractors, participants tried to avoid panning by fitting both task bars into one view. This strategy increased blur, which resulted in higher error rates. For some participants, tall distractors in adjacent bars led to higher errors than in separated bars.

Influence of Distractors

Tall distractors affected perception more than short ones. They introduced visual clutter and blur, particularly for adjacent bar comparisons. Participants tended to focus on the tallest elements in the chart, which pulled attention away from relevant bars.

Blurring Effect

Most participants (80%) described difficulty estimating bar heights due to blurred edges at high zoom. For example, a bar that is 50 pixels high may appear to be anywhere between 40 and 60 pixels. Participants often needed to magnify and inspect each bar individually before comparing, which increased cognitive effort.

Unalignment Effect

Unaligned bars in stacked charts led to higher errors, consistent with prior findings. Participants had difficulty comparing bars without a common baseline. Some errors were also attributed to the "Parallel Line Illusion," where the length of a bar appears distorted when placed next to a much taller or shorter bar. 

Discussion

The study highlights several perceptual barriers low-vision users face when interpreting bar charts. One major finding is that strategies effective for sighted users do not always transfer to low-vision settings. For example, adjacent bars typically improve comparison accuracy for sighted users, but in our study, adjacent bars combined with tall distractors caused more errors for some participants. This effect was linked to increased blurring at high zoom levels and difficulty distinguishing task bars from clutter.

Distractor bars played a central role in shaping perception. Tall distractors consistently drew visual attention away from the task-relevant bars, even when users attempted to compensate by adjusting magnification. Participants also experienced fatigue from repeated panning and zooming, especially during longer sessions with complex chart layouts.

Stacked and divided bar charts introduced additional challenges. Unaligned stacked bars resulted in greater estimation errors, consistent with prior research. In divided bars, participants sometimes interpreted adjacent segments as a single visual unit, particularly when separator lines were faint or obscured by magnification. These results emphasize the importance of alignment and segment clarity for accessible chart design.

Participants relied heavily on screen magnifiers but noted that these tools were not well suited for structured visual tasks like chart reading. Continuous adjustments, frequent re-orientation, and loss of context were recurring concerns.

Limitations

• The annotation dot on bars was placed near the lower end rather than centered, which may have affected bar identification and height estimation.
• Charts were shown in a fixed color and contrast scheme, without allowing users to adjust visual settings based on their preferences or needs.
• The participant pool, while diverse in age and diagnosis, did not capture the full range of low-vision profiles, especially with respect to contrast sensitivity and field loss.
• We did not collect data on participants’ visual field characteristics, which could have provided further insight into perception strategies.
• Think-aloud and interview data provided valuable qualitative insights but lacked the precision that could be obtained through eye-tracking or gaze-based methods.

Conclusion

This study examined how low-vision users perceive bar charts using screen magnifiers, revealing key factors that affect visual accuracy, including bar alignment, spacing, and the presence of distractors. Results from a lab study with 25 participants showed that tall distractors, unaligned bars, and divided stacked layouts significantly increased estimation errors, often due to blurring and visual overload during magnified viewing. Unlike prior research focused on sighted or blind users, this work highlights the distinct challenges low-vision users face in interpreting visual data. These insights can guide the design of more accessible chart layouts and inform future development of adaptive tools, such as intelligent magnifiers with automated panning. Further research will include eye-tracking to better understand visual attention patterns and support more effective accessibility solutions.

References

- AKSHAY KOLGAR NAYAK (@AkshayKNayak7) 

Motivation

Data charts are a powerful way to communicate trends, comparisons, and summaries, and they are now commonly found across news articles, financial tools, health dashboards, and social media platforms. For people with low vision, interacting with these charts on smartphones remains a significant challenge. While screen magnifiers allow users to enlarge content, doing so often hides important visual context, such as axis labels or relationships between data points. As a result, users are forced to pan repeatedly, remember individual values, and mentally piece together information, which can be both frustrating and cognitively demanding. Most existing accessibility solutions focus on blind screen reader users and do not address the specific needs of low-vision magnifier users. To bridge this gap, we introduce GraphLite, a mobile assistive technology that converts static charts into interactive and customizable visualizations tailored for low-vision users.

Background and Related Work

Research on chart accessibility has primarily focused on blind users, offering solutions such as textual descriptions (e.g., Alt-Text generation), sonification (e.g., audio-based data exploration), and table conversions (e.g., replacing charts with structured tables). These approaches are designed for non-visual interaction and do not support the needs of users who depend on residual vision and prefer visual formats.

For low-vision users, who typically rely on screen magnifiers, the challenges are different. Magnification can obscure spatial relationships, disconnect data points from axis labels, and require constant panning, which increases cognitive effort. Prior studies have proposed general techniques like space-preserving magnifiers (e.g., SteeringWheel) and responsive visualization frameworks for small screens (e.g., MobileVisFixer, Dupo). While these techniques reduce layout disruption, they are not optimized for analytical tasks like comparing distant values or identifying trends in visual data.

These gaps highlight the need for solutions that not only preserve visual context during magnification but also support selective focus, customization, and seamless navigation across chart elements. Our work builds on this insight by designing an approach that directly targets these unmet needs in mobile chart interaction for low-vision users.

Uncovering Usability Issues with Chart Interaction

To better understand the challenges low-vision users face when interacting with data charts on smartphones, we conducted a semi-structured interview study with 14 participants who regularly use screen magnifiers. Participants had a variety of eye conditions and reported different levels of visual acuity. The interviews explored participants’ everyday encounters with charts, typical usage contexts, and the specific difficulties they experience when using magnification to interpret chart content. All sessions were conducted remotely and recorded with consent. We applied open and axial coding to extract recurring themes that directly shaped our design decisions.

Participants described three core challenges. First, they found it difficult to associate data points with axis labels when zoomed in, often losing context due to the limited field of view. Second, making comparisons between distant values in a chart was described as mentally exhausting, requiring repeated panning and memorization. Lastly, many resorted to memorizing individual values and mentally reconstructing comparisons, especially when dealing with larger datasets. These insights revealed a clear need for features that minimize panning, support selective data access, and preserve visual relationships between chart elements. This feedback directly informed the design principles behind our solution.

GraphLite Design

Key Features

GraphLite introduces three core features designed to address the chart interaction challenges identified in our user study: selective data viewing, personalized visual styling, and simplified gesture-based navigation.

Selective Data Viewing

Participants expressed difficulty in comparing distant data points within magnified views. To support targeted analysis, GraphLite allows users to selectively choose specific bars or line segments for focused viewing. These selections are grouped into distinct views, enabling users to compare relevant data without the need to pan across the entire chart. Users can switch between these views using simple swipe gestures, reducing cognitive load and helping retain context during visual comparisons.

Personalized Visual Styling

To accommodate varying visual preferences and improve clarity, GraphLite provides customization controls for color, contrast, font size, and background themes. Users can tailor the appearance of charts to match their visual comfort, which was particularly important for those who found high contrast or dark themes more readable. Styling adjustments are applied directly to the interactive chart interface, allowing for more legible and less fatiguing interaction.

Simplified Gesture-Based Navigation

Instead of relying on complex multi-finger gestures often required by default magnifiers, GraphLite supports one-finger tap, swipe, and long-press interactions for common tasks such as selecting data, switching views, or opening configuration menus. This design reduces the need for precision input and lowers the physical effort involved in exploring chart content on small screens.

GraphLite Architecture

The design of GraphLite was shaped by insights from our interview study and prior research on low-vision interaction. The system architecture supports automatic chart detection, data extraction, and personalized rendering within a mobile browser interface. Below, we outline the key components of the system.

Design Considerations and Requirements

GraphLite was designed to support selective focus, reduce cognitive load, and preserve spatial context during magnified interaction. Our goal was to enable users to control both the content and appearance of charts. To support these needs, we focused on three design principles: enabling selective data views to minimize unnecessary information, allowing visual customization to improve clarity, and offering simplified gestures to reduce physical effort. These principles guided both the system’s architecture and interface behavior.

Overview

When a user opens a webpage in the GraphLite browser, the system identifies and processes chart images on the page. A trained classifier recognizes whether an image is a data chart and determines its type, such as bar or line. Once detected, the chart is transformed into a structured, interactive version that replaces the original static image. The user can tap a chart to activate GraphLite’s proxy interface. From here, they can select specific data points, customize appearance settings, and create multiple views that group relevant values together. Navigation between views is performed using simple swipe gestures. This structure lets users focus on comparing small subsets of data without needing to pan across the entire chart.

Chart Data Extraction

GraphLite uses ChartOCR, a hybrid deep learning model, to extract data from chart images. For bar charts, the system detects the corners of each bar and calculates height values. For line charts, it clusters key pivot points and reconstructs the full line segments. OCR is performed using AWS-Rekognition to extract axis labels, titles, and legends. The final output is a structured JSON representation containing all chart elements, which is used to render the interactive proxy.

Proxy Interface Design

Figure 3 Prakash et al.: (1) Magnifying a portion of the chart. (2) Panning downward to view labels. (3) Panning upward to follow the data bar. (4) Panning horizontally to explore chart values.

The proxy interface is built for screen magnifier users and avoids reliance on multi-finger gestures. Users interact with charts using tap, swipe, and long-press actions. A swipe up gesture opens the theme picker, allowing users to adjust color, font, and contrast settings. A long press on the chart opens a list of x-axis labels as checkboxes, enabling selection of specific data points to include in a focused view. Users can create multiple such views and move between them with left or right swipes. To reduce panning, GraphLite applies space compaction, optimizing layout spacing while preserving interpretability.

Video 2 Prakash et al.: Real-time demonstration of GraphLite’s end-to-end pipeline, including chart detection, type classification, data extraction, proxy interface generation, and interactive user navigation.

Implementation Details

GraphLite is implemented as an Android mobile browser using the Flutter framework. The app extracts page content and sends it to a back-end server for chart processing. Image classification is handled by a custom-trained Inception-V3 model, and chart data extraction is performed using the ChartOCR pipeline. The server communicates with the app using JSON-based APIs. For rendering charts in the proxy interface, we use the Syncfusion Flutter charting library, which supports full customization and interaction. All components are modular, allowing future upgrades to chart models or OCR engines without rewriting the interface logic.

Evaluation

To assess the effectiveness of GraphLite, we conducted a comprehensive user study comparing it against three baseline methods: the default screen magnifier, a table-based chart conversion tool, and a space compaction-only interface. The study aimed to evaluate GraphLite’s impact on task performance, usability, workload, and user satisfaction when interacting with data charts on smartphones.

Table 1 Prakash et al.: Definitions of all abbreviations and placeholders used throughout the system and study.

Participants and Study Design

We recruited 26 low-vision participants who regularly use screen magnifiers on mobile devices. The participant pool included individuals with varying eye conditions and levels of visual acuity. The study followed a within-subjects design, where each participant completed chart-based tasks across five conditions: screen magnifier (SM), tabular representation (TBL), space compaction only (SC), space compaction with customization (SCC), and the full GraphLite system (SCCF). Tasks included pairwise comparisons, selective filtering, trend prediction, and trend comparison, using both bar and line charts.

Procedure: Data Collection and Analysis

Participants performed 12 tasks across the five study conditions. Tasks were designed to simulate realistic data interaction scenarios, such as comparing stock values or filtering sales figures. We measured task completion time, task success rate, and accuracy. After each condition, participants completed the System Usability Scale (SUS) and NASA-TLX questionnaires, followed by a semi-structured interview to gather qualitative feedback. Quantitative data was analyzed using non-parametric statistical tests due to the non-normal distribution of some measures. Interview responses were coded thematically to identify common experiences and suggestions.

Figure 4 Prakash et al.: Task completion times in seconds across all conditions and tasks. For the MBF task, only TBL and SCCF results are shown, as participants could not complete the task in the SM, SC, and SCC conditions within the allotted time.

Task Performance

GraphLite’s full system (SCCF) consistently led to faster completion times across all tasks. For example, participants completed pairwise comparison tasks nearly twice as fast with SCCF compared to the default magnifier. In more complex filtering and trend analysis tasks, SCCF outperformed both the tabular baseline and partial GraphLite conditions, especially when multiple data points had to be evaluated. Task success rates with SCCF reached 100 percent, compared to 61.5 percent under the default magnifier.

Usability and Workload Scores

SCCF received the highest SUS scores (average = 85.8), indicating strong perceived usability. By contrast, the screen magnifier condition received the lowest average score (51.3). NASA-TLX scores showed a significant reduction in cognitive load with SCCF (average = 22.7), compared to high workload ratings in the SM condition (average = 70.1). The TBL condition showed moderate performance, with usability and workload scores improving for simpler tasks but degrading as task complexity increased.

Error Patterns and Observations

In the screen magnifier condition, participants often lost track of axis labels or misestimated bar heights due to limited field of view. Errors were also common in the SC and SCC conditions, especially when users attempted to manually compare distant data points. In the TBL condition, errors were typically due to mental overload while scanning long tables. The SCCF condition showed the fewest errors, aided by selective data views and tooltip support.

Qualitative Feedback

Participants described GraphLite as significantly easier to use compared to other methods. Many appreciated the ability to focus on a subset of data without panning, and the option to adjust contrast and font size. Users noted that they felt more in control when they could select and compare relevant values directly. Several participants mentioned that it was the first time they could analyze trends visually without assistance. Suggestions for future improvement included adding vertical compaction, auto-focus gestures, and quick-access tooltips.

Discussion

This study surfaces a core gap in how accessibility tools support low-vision users. Many systems rely on magnification as a catch-all solution, assuming that increased size alone will make content usable. Our findings suggest that this approach is inadequate. Magnification often introduces new problems such as excessive panning, broken spatial relationships, and cognitive fatigue. 

These are not minor inconveniences but real obstacles to effective data comprehension. A more meaningful approach centers on structural adaptability. Instead of simply scaling visuals, interfaces need to adapt the organization of information to match user goals. GraphLite’s success points to the value of giving users control over which parts of a chart to focus on and how that content is presented. This form of selective interaction allowed participants to reduce visual effort while improving accuracy. Simplifying input is not just about convenience. Many participants struggled with precision-based gestures in baseline conditions. A single-tap or swipe model made chart navigation more manageable, especially for tasks that involved comparing multiple values or scanning for trends. This highlights the importance of minimizing fine motor demands in mobile accessibility design.

Finally, the study underscores how underserved this population remains. While screen reader users benefit from a mature ecosystem of accessibility tools, low-vision users often have to improvise with general-purpose features. There is a clear need for purpose-built solutions that recognize the distinct ways low-vision users interact with visual content, particularly on mobile platforms.

Limitations

• GraphLite currently supports only bar and line charts, which limits its applicability to other common formats like pie, scatter, or stacked charts.
• The system assumes clean and well-aligned input images. Charts with low resolution, visual clutter, or unusual layouts may lead to extraction errors.
• Our study was conducted on a controlled device, not on users’ personal smartphones, which may affect the generalizability of usage behavior.
• While GraphLite supports visual customization, it does not currently integrate audio feedback, screen reader compatibility, or other multimodal access features.

Future Work

One promising direction is to enhance data comprehension through details-on-demand (DoD) interactions. These techniques allow users to request specific information as needed, keeping the interface clean while still providing access to deeper insights. Future implementations could explore how selection-based and zoom-based DoD methods can reduce the need for constant panning and support a more efficient workflow for low-vision users. A controlled study that simulates various DoD techniques could help identify which specific interactions are most beneficial for tasks like trend recognition or value comparison, and how they can be optimized for mobile magnifier users.

Another avenue for future research is predictive magnification, which involves automatically guiding the user’s focus to salient regions of a chart. Participants in our study expressed a desire for such behavior, especially to minimize the number of manual gestures. Building on prior work in content saliency and gaze tracking, future systems could use predictive models to pan toward important chart elements like axis labels or data peaks. This could reduce navigation effort and cognitive load during analysis. Integrating predictive magnification with user-driven control mechanisms may lead to a more fluid and supportive chart navigation experience for low-vision users.

Conclusion

GraphLite demonstrates how mobile-first, visualization-specific accessibility tools can improve data comprehension for low-vision users. By transforming static charts into interactive, customizable, and compact interfaces, the system supports faster, more accurate, and less effortful interaction. User studies confirm significant improvements over screen magnifiers and tabular alternatives, validating the importance of selective focus and visual personalization. While current limitations include chart type support and generalizability to real-world images, GraphLite lays the groundwork for future research in predictive navigation and adaptive visualization design. It represents a step toward more inclusive visual interfaces, where accessibility is embedded as a foundational element rather than an add-on.

References

Prakash, Y., Khan, P. A., Nayak, A. K., Jayarathna, S., Lee, H. N., & Ashok, V. (2024). Towards Enhancing Low Vision Usability of Data Charts on Smartphones. IEEE Transactions on Visualization and Computer Graphics. DOI: https://doi.org/10.1109/TVCG.2024.3456348;

Code for GraphLite is available in the following Github link: https://github.com/accessodu/GraphLite.git

- AKSHAY KOLGAR NAYAK (@AkshayKNayak7) 

In Civilization and its Discontents, Sigmund Freud turned his psychoanalytic attention from the troubles of the individual to those of the world. The book, joining the US public domain in 16 days in both German and English, diagnoses inherent conflicts between individual drives for happiness and society’s needs to tame those drives for survival. Kristin Dorsey suggests ways to read Freud’s work in the context of its time, and in dialogue with works on similar questions. #PublicDomainDayCountdown

Pigs in Spaaaace!

To buy into SpaceX’s audacious $1.5 trillion valuation in a listing next year, investors will need to have faith in Elon Musk’s equally galactic vision for his rocket and satellite maker, from orbital data centers to lunar factories to human settlements on Mars.

In early November Google announced Project Suncatcher, which aims to launch solar-powered satellite constellations carrying its specialty AI chips, with a demonstration mission planned for 2027. Around the same time, the start-up Starcloud celebrated the launch of a 60-kilogram satellite with an NVIDIA H100 GPU as a prelude to an orbital data center that is expected to require five gigawatts of electric power by 2035.

In the interests of clarity, I am a former NASA engineer/scientist with a PhD in space electronics. I also worked at Google for 10 years, in various parts of the company including YouTube and the bit of Cloud responsible for deploying AI capacity, so I'm quite well placed to have an opinion here.

The short version: this is an absolutely terrible idea, and really makes zero sense whatsoever. There are multiple reasons for this, but they all amount to saying that the kind of electronics needed to make a datacenter work, particularly a datacenter deploying AI capacity in the form of GPUs and TPUs, is exactly the opposite of what works in space. If you've not worked specifically in this area before, I'll caution against making gut assumptions, because the reality of making space hardware actually function in space is not necessarily intuitively obvious.

K2 is designing two classes of satellites—Mega and Giga .... The company’s first “Mega Class” satellite is named Gravitas. Gravitas will also deploy twin solar arrays capable of generating 20 kilowatts of power.
...
K2 says Gravitas is “on par with the largest satellites that have ever been produced.” But K2 won’t stop there. The firm’s next satellite iteration, known as Giga, .... Underpinning Giga is its ability to generate up to 100 kilowatts of power per satellite ... Examples of missions Giga can support include AI computing

Today, Nvidia's rack systems are hovering around 140kW in compute capacity. But we've yet to reach a limit. By 2027, Nvidia plans to launch 600kW racks which pack 576 GPU dies into the space once occupied by just 32.

It is not uncommon to see data centers of 1 GW with liquid-cooled racks with densities over 100 kW.

Source

For longer duration missions, which would be the case with space based datacenters because they would be so expensive that they would have to fly for a long time in order to be economically viable, it's also necessary to consider total dose effects. Over time, the performance of chips in space degrades, because repeated particle impacts make the tiny field-effect transistors switch more slowly and turn on and off less completely. In practice, this causes maximum viable clock rates to decay over time, and for power consumption to increase.

Thiele et al Fig. 2

Here we propose a new metric, the CRASH Clock, that measures such stress in terms of the time it takes for a catastrophic collision to occur if there are no collision avoidance manoeuvres or there is a severe loss in situational awareness. Our calculations show the CRASH Clock is currently 2.8 days, which suggests there is now little time to recover from a wide-spread disruptive event, such as a solar storm. This is in stark contrast to the pre-megaconstellation era: in 2018, the CRASH Clock was 121 days.

One of the scariest parts of this project was learning more about Starlink's orbital operations. I had always assumed they had some kind of clever configuration of the satellites in the orbital shell that minimized conjunctions, and we would see the number of conjunctions grow over time in our simulations. But no! It's just random! There's no magic here, it's just avoiding collisions by moving a Starlink satellite every 2 minutes. This is bad.

At the Open Knowledge Foundation (OKFN), we are excited and grateful to announce an important initiative for the next year, with the generous support of the Patrick J. McGovern Foundation (PJMF). With over two decades of transforming digital infrastructure to drive impact worldwide, we have identified two emerging problems over the last few years:  The...

The post Announcement of Strategic Funding to Enhance Multilingual, Sector-specific AI Literacy and Develop Trustworthy AI for Open Data first appeared on Open Knowledge Blog.

It’s December, and we’re hosting our second annual Winter Holiday Hunt!

This hunt is meant to celebrate the season of light, and the holidays it brings. We wish all our members a Merry Christmas, Happy Hanukkah, and an entertaining hunt!

We’ve scattered a stand of evergreen trees around the site. You’ll solve the clues to find the trees and gather them all together.

• Decipher the clues and visit the corresponding LibraryThing pages to find some evergreen trees. Each clue points to a specific page on LibraryThing. Remember, they are not necessarily work pages!
• If there’s an evergreen tree on a page, you’ll see a banner at the top of the page.
• You have a little more than three weeks to find all the trees (until 11:59pm EST, Tuesday January 6th).
• Come brag about your stand of evergreen trees (and get hints) on Talk.

Win prizes:

• Any member who finds at least two evergreen trees will be
  awarded an evergreen tree Badge ().
• Members who find all 15 evergreen trees will be entered into a drawing for one of five LibraryThing (or TinyCat) prizes. We’ll announce winners at the end of the hunt.

P.S. Thanks to conceptDawg for the European goldfinch illustration! ConceptDawg has made all of our treasure hunt graphics in the last couple of years. We like them, and hope you do, too!

Franz Kafka‘s work is now known around the world, but it couldn’t be read in English until after he died, and there’s still limited access to good English translations of much of his work. The English Kafka books I list are copyrighted translations generously shared online by David Wyllie and Ian Johnston.

Soon the first English Kafka books will enter the US public domain. The Castle, one of several Kafka works translated by Willa and Edwin Muir, gets there in 17 days. #PublicDomainDayCountdown

LibraryThing is pleased to sit down this month with Minnesota-based author Loretta Ellsworth, whose published work includes books for both juvenile and adult audiences. A former middle grade Spanish teacher, Ellsworth received her MFA in Writing for Children and Young Adults from Hamline University, and made her authorial debut in 2002 with the young adult novel The Shrouding Woman. She has had three additional young adult novels published, as well as a picture book for younger children, Tangle-Knot, in 2023. These books have won many accolades, including being named as ALA and IRA Notables, and being nominated for prizes such as the Rebecca Caudill Young Readers’ Award. Ellsworth published her first work for adults, the historical novel Stars Over Clear Lake, in 2017, followed in 2024 by The French Winemaker’s Daughter. Her third historical novel for adult readers, The Jilted Countess, which follows the story of a Hungarian countess who makes her way to Minnesota following World War II, in pursuit of her American GI fiancé, is due out from HarperCollins this coming January. Ellsworth sat down with Abigail this month to discuss the book.

The Jilted Countess was apparently inspired by a true story of a Hungarian countess who emigrated to Minnesota after the Second World War. Tell us a little bit about that original story. How did you discover it, and what made you feel you needed to retell it?

In 1948, a penniless Hungarian countess came to Minnesota to marry the GI fiancé she’d met abroad, only to find out he’d recently married someone else. Determined to stay in the U.S., she appealed to newspaperman Cedric Adams to help her find a husband before she’d be deported in two weeks back to Hungary, which was under Communist control. He agreed, using a fake name for her, and putting her picture in the newspaper, citing her circumstances. She received almost 1800 offers of marriage! And in two weeks she narrowed it down, went on a few dates, chose a husband, and was never heard from again. Fast forward to 2015, when someone found an old copy of that article in their attic and asked columnist Curt Brown if he knew what had happened to her. Curt Brown wrote a short article asking if anyone could provide an answer. Unfortunately, no one could. But that article made me wonder how a Hungarian countess could disappear like that, and I also wondered if she ever encountered her former fiancé again. She was, after all, the first Bachelorette, before the show was even a concept.

Did you do any kind of research, historical or cultural, in order to write the book? What were some of the most interesting things you learned?

I spent an exorbitant amount of time at the Minnesota History Center researching old microfiche articles to find anything I could about her. I examined marriage records for Minneapolis and St. Paul for any Hungarian-sounding names, and I searched for clues as to her whereabouts. Without a name, though, it was very difficult, and I never found her. I also had to research Hungary during and after the war, and the life of aristocrats, which I knew little about.

Contemporary readers might be surprised at the idea of a “Bachelorette” dating program taking place in the 1940s. How do you think Roza’s experience would tally with and differ from that of contemporary women seeking a spouse in this way?

After her marriage, she was approached by Look Magazine and other outlets for interviews, all of which she turned down as she wanted a private life. With social media today, there’s no way Roza would have been able to disappear like she did in 1948. And most likely her search would have taken place on social media rather than through the newspaper and mail.

World War II stories remain perennially popular with readers, despite the passage of the years. Why is that? What is it about this period that continues to speak to us?

I think it was such a pivotal time in the world, and one we’re still struggling to understand. And there are so many hidden stories that we’re constantly discovering about that time period that continue to speak to us. Also, the last of WWII veterans are disappearing, and their stories will be gone as well.

Tell us about your writing process. Do you write in a particular place, have a specific schedule you keep to, or any rituals that help you? Do you outline your stories, or discover them as you go along?

Because I worked as a teacher and had four children of my own, I had to learn to write in short intervals and adapt my writing schedule to be flexible. I wrote everywhere: at soccer practices and coffee shops and the library. Now that I no longer teach and my children are grown, I have a more disciplined schedule and usually write in the mornings in my home office, sometimes stretching into the afternoon. I also have learned to outline, whereas I used to write from the seat of my pants before. It’s helped to save me from a great deal of revision, although I still revise, just not as much as before.

What’s next for you? Will you be writing more historical novels for adults, or perhaps returning to the world of young adult books?

I am working on a young adult novel as well as another historical novel, so I hope to keep my foot in both genres as long as I’m able to. I enjoy both and read both.

Tell us about your library. What’s on your own shelves?

I have one full shelf of books on the craft of writing–I’m still drawn to how others write and am curious about their process. I have a mix of memoir, middle-grade, YA, and a lot of historical fiction. I still buy physical books, and my shelves are always overflowing. I donate a lot of books to our local Friends of the Library group for their annual book sale. And I have so many signed copies of books that I can’t part with. But that’s a good problem to have, isn’t it?

What have you been reading lately, and what would you recommend to other readers?

I read a great deal–I just finished reading the first two books of the Westfallen series by Ann and Ben Brashares with my grandson, and I’m reading The Correspondent by Virginia Evans, The Ivory City by Emily Bain Murphy, and The Gospel of Salome by Kaethe Schwehn. And I just finished James by Percival Everett. There are so many good books out there!

We announce the call for proposals for a future special issue on how Generative AI could transform our professional landscape, and summarizing the content of the December 2025 issue.

From the Field column for December issue

Bridging the disciplinary gaps between library and information science and communication/cultural studies, this column utilizes LIS's interest in generative AI to frame a critical discussion regarding neutrality towards technology within librarianship.