Guillaume Paumier

Wizards, Metadata, and Memory

Guillaume Paumier — Mon, 21 Apr 2025 07:00:00 GMT

Stage design of the Enchanted Garden by Hermann Burghart for the 1886 premiere of Karl Goldmark's opera Merlin. (Herman Burghart on Wikimedia Commons // Public domain)

One in three pictures you see on Wikipedia was added through UploadWizard, a tool I designed in 2009. Over the past 15 years, 1.8 million unique volunteers have uploaded over 42 million files with UploadWizard to Commons, Wikipedia's media library. But unearthing those metrics turned out to be more complicated than I expected. What began as a straightforward question became a journey through over a decade of evolving logging practices, overwritten traces, and quietly deleted markers. The road of metadata archaeology is wild and wicked, winding through the wood. Follow me, my friend, to glory at the end.

The Tower, Reversed

UploadWizard has been used to upload tens of millions of files to Wikimedia Commons. It was designed to simplify the contribution process, especially for newcomers. I was both the designer and product manager of UploadWizard, and it was the first project I worked on after joining the Wikimedia Foundation in 2009. At the time, I was also a very active volunteer contributor on Commons, which made the project feel deeply personal. In many ways, UploadWizard felt like my "baby."

And yet, more than a decade after its introduction, one simple question turned out to be surprisingly hard to answer: how many files have been uploaded with UploadWizard?

In any corporate tech environment, this basic key performance indicator (KPI) would be displayed on an easily accessible metrics dashboard. But Wikimedia isn't a typical tech company. It started as a small, scrappy nonprofit, and it's always kept as little user data as possible to protect the privacy of its readers and contributors. And sometimes, the volunteer nature of Wikipedia and its sister sites makes it even more difficult to follow basic practices to measure a product's impact.

For you see, when it comes to UploadWizard, there is no central log, no consistent tag, no definitive metric. The metadata is incomplete and inconsistent. In a movement devoted to free knowledge and historical preservation, the impact of one of its most important tools has been left largely undocumented.

During the initial development of UploadWizard, we had included a tracking category called [[Category:Uploaded with UploadWizard]] as a basic product KPI. And for a while, it worked: this maintenance category, hidden from view, populated steadily with uploads from the new tool.

But years later, the volunteer community decided that the tracking category was unnecessary. I had managed to save it in 2012,[1] but in 2016 it was deleted,[2] and the software changed to no longer add it automatically (gerrit:315121), thus leaving no structured marker. UploadWizard was victim of its own success: volunteers deemed the category purposeless, because "UploadWizard is the default mode of upload." One volunteer likened the UploadWizard tracking category to "having an 'Articles created using the Edit Button' category on Wikipedia."

Recently, I set out to find other ways to measure the uploads. It wasn't just a matter of Wikimedian curiosity; it was about acknowledging an accomplishment and tracing the impact of work I had poured so much of myself into.

The metadata hadn't crumbled all at once. It had eroded quietly, over years of changes, edits, and well-meaning decisions. And yet, the traces were still there, winding out of time, if you knew how to look for them. This is the archaeological story of how I tried to reconstruct that lost history; not just by counting files, but by piecing together evidence across shifting metadata, deleted categories, forgotten patches, and overlapping logging mechanisms.

We're Off to See the Wizard

The question seemed deceptively simple: how many files had been uploaded using UploadWizard? At least 9 million as of August 2016, according to one comment in the community discussion that had led to the deletion of the category.[3] That was a start.

The tracking category had given us a built-in count, accessible to anyone and displayed prominently on the category's page. It was no longer available, but I knew of another way to identify the wizard's uploads: the files would all have the same "logging comment" for the upload, recorded as "User created page with UploadWizard." It would take a few database queries, but it was still easy enough. Or so I thought.

Screenshot of the file history for one of my uploads to Wikimedia Commons. The log entry shows the automated comment "User created page with UploadWizard."

The Quarry tool makes it possible to run queries on a copy of Commons' database. In October 2016, Steinsplitter used the log comment method on Quarry and counted 10,228,537 uploads (query/13031).

Screenshot of query/13031 run by Steinsplitter in October 2016 to count UploadWizard uploads using the log comment "User created page with UploadWizard." At the time, this query returned just over 10 million files. However, as I later discovered, this method excluded early uploads (before structured logging began in 2012).

This was a replicable, simple enough method. In October 2020, I used a similar approach to count UploadWizard files, although I had to adapt it due to changes in the structure of the database. My friend Reedy double-checked with his own query, and we counted 20,231,414 files. The number was growing over the years, which made sense.

But when I ran the same query again in March 2025, the result was almost exactly the same as in 2020: 20,231,573 files, so I knew something was wrong (query/42024).

Down, Down, Down the Road, Down the Wizard's Road

After some digging, I found out that Wikimedia's Multimedia team had changed the log comment in 2020 and replaced it with two possible patterns: "Uploaded own work with UploadWizard" for volunteers uploading their own pictures, and a more complex one for for uploads of works by others: "Uploaded a work by $1 from $2 with UploadWizard" where $1 is the copyright holder, and $2 the source.[4] The former used a fixed format and could be queried easily. The latter, though, would vary from upload to upload.

I looked up the identifier of the own-work pattern (query/91991) and queried the database, which returned 14,118,636 files (query/42025).

To look into the second pattern for uploads of third-party works, I had to use a regular expression: a pattern of text that matched comment uploads independently of each upload's details (query/92166). The query yielded 4,436,472 results, bringing the total to a minimum of 38,836,118 files uploaded with UploadWizard.

I was pretty happy with myself by that point, and decided to tabulate the results by year and month to visualize the growth over time. That's when I realized that the story was more complex, but it led me to eventually discover change tags.

Through Many Miles of Tricks and Trials

When I broke down the uploads by year for the original log comment (query/92164), I noticed that no files were listed for 2010 or 2011. (There were also some anomalies uploaded after 2020, which I investigated later.) Further research indicated that the original log comment replaced in 2020 had only been introduced in 2012 (gerrit:9714), leaving out two years of uploads.

Checking some of my own uploads from that period (December 2010, March 2012), I confirmed that they had no log comment or initial edit summary. Their page history did reassure me that I had uploaded them with UploadWizard, since the tracking category had been removed in 2016 (permalink/222387780), when the community had decided to get rid of it (that same unfortunate decision that had started me on this whole wikiarchaeology expedition in the first place).

I realized that the category removal might give me an indirect way to identify early uploads between 2010 and 2012: they would contain an entry in their edit history with the mention "Category:Uploaded with UploadWizard removed per community decision." I just needed to make sure I excluded files already counted using the log comment method.

I got the associated identifier (query/92177, query/92193) and counted an additional 529,936 files uploaded with UploadWizard in 2010−2012, including some false positives that I ruled out later (query/92202).

Winding Out of Time

Through my digging, I came across a few related tickets in Phabricator, Wikimedia's platform for tracking feature requests and bugs. One of them was a request to "Use an informative, custom edit summary for every file uploaded with UploadWizard" (T142687), which gave me a scare because it would have made it impossible to do any sort of counting in the future. But I also found a request to "Mark UploadWizard uploads with a change tag" (T121872).

In MediaWiki, change tags are annotations for certain types of edits, for example if it was made with the visual editor, or if it reverted the content to a previous version.[5] Matthias Mullie had added an uploadwizard tag to the software in May 2017 (gerrit:337566), as well as an uploadwizard-flickr tag for files from flickr. Unfortunately, the tags would only be applied to future uploads. Still, the tags provided a new, easy, and reliable method for counting post-2017 uploads, especially those after 2020 when the log message was split into two patterns.

In the end, I had identified five methods for counting uploads: 1. the removal of the original category, 2. the original log comment, 3. the two change tags, 4. the log comment for own works, and 5. the log comment for third-party works. I could safely ignore the latter two, but the remaining three methods still overlapped over many years, so I needed to figure out exact timestamps and boundaries to avoid double-counting.

Timeline of the five distinct detection methods used to identify uploads made with UploadWizard over its lifetime, illustrating the fragmented nature of its historical metadata. Each method corresponds to a different metadata marker introduced at different stages: removal of the original category (2010−2016), original log comment (2012−2020), the uploadwizard change tag (2017 onward), and the now-undeeded log comments introduced in 2020.

A few more queries later, I had identified the timestamps for the first upload to use the log comment in 2012 (2012-08-23T20:33:03Z, query/92207), and for the first one to use the change tag (2017-05-10T19:47:57Z, query/92206). The early uploads from 2010−2012 were trickier because the removal of the category was a more fragile detection method.

I went looking through the archives of the Server admin log, which documents software deployments and other system operations in the Wikimedia infrastructure. An entry by Roan Kattouw indicated that UploadWizard had been deployed to Commons on November 30, 2010 at 11:29. [6]

This gave me a strict boundary and it narrowed down the search for the first file uploaded with UploadWizard. I looked for pages created that day that were later edited to remove the UploadWizard category (query/92267), and found a photo of the TV Tower of East Berlin uploaded shortly after deployment by Neil Kandalgaonkar, the lead developer of UploadWizard. It is likely that Neil uploaded this file both as an initial test to verify that the tool had been successfully enabled on Commons, and as a fitting inauguration, making it a historically significant first use of the feature.

Pulling back the curtain

And so, at last, I had all the ingredients for my spell: I had three detection methods, each clearly bounded to avoid false positives and double-counting.

This timeline shows the precise the start and end dates for each metadata marker used to detect UploadWizard uploads, making it possible to measure usage across its full history without overlap.

Once I had assembled a methodology and carved out clean timestamp boundaries for each detection method, I was finally able to begin extracting numbers, and the stories they told.

As of April 21, 2025, 1,820,907 unique volunteers have uploaded a total of 42,596,080 media files to Commons with UploadWizard (query/92995, query/92994). The monthly breakdown in the following chart shows the growth rate over the past 15 years, as well as the yearly spikes corresponding to contribution campaigns and global contests like Wiki Loves Monuments (in September−October each year).

Monthly uploads to Wikimedia Commons using UploadWizard, from its launch in November 2010 through April 2025. The chart shows strong annual cycles, with peaks around September, coinciding with Wiki Loves Monuments. The gradual growth over time reflects UploadWizard's role as the primary contribution tool for media files.

To better understand contributor behavior, I ran a query to group UploadWizard users into buckets based on how many files they had uploaded over time (query/92997). The engagement distribution reveals a classic long-tail pattern: of the 1.8 million volunteers who used UploadWizard, nearly half uploaded only a single file, and another 40% contributed fewer than ten. These numbers are consistent with Commons’ role as an open platform, where many users participate sporadically, often to share a single image of personal or local relevance. These numbers were evidence of a tool doing the work it was designed to do: helping people contribute freely licensed media to the world.

Distribution of UploadWizard contributors by number of uploads. While the tool lowers barriers for newcomers (over half the users uploaded only once), it's also used by dedicated contributors: more than 4,300 users have uploaded over a thousand files each, highlighting the tool’s long-term utility and wide adoption.

But another story also lies in the deeper tiers: over 22,000 contributors uploaded between 101 and 1,000 files, and more than 4,300 users crossed the 1,000-file threshold. These power contributors (just 0.2% of all uploaders) account for a disproportionate share of Commons' visual knowledge. Their sustained participation underscores that UploadWizard isn't just a tool for newcomers. This highlights the importance of balancing ease of use with the advanced needs of experienced users. Designing for both ends of that spectrum is key to growing and sustaining Commons' media ecosystem.

No One Mourns the Wizard's metadata

Looking back, the amount of effort it took to reconstruct the history of UploadWizard's usage is perhaps the most ironic aspect of this excavation. Wikimedia is a movement obsessed with preservation: we document every edit, every template, every discussion. We track every page's revision history in minute detail. And yet, the historical record of one of the most significant tools used to contribute content to Commons was never formally maintained.

That's not to say it was malicious, or even careless. It was simply a mismatch of priorities. The category was seen by volunteers as clutter and removed, a reasonable decision made in good faith. But from a product perspective, such decisions can carry unintended consequences, like the loss of institutional memory.

Today, measuring the impact of a new tool is much more straightforward: improvements to the platform, like change tags, make measurement easier, and Wikimedia now has full-time product analytics staff involved at every step of the development process. A decade ago, categories and log comments were all we had. Tools like UploadWizard are still in use and central to the contributing experience, but measuring their impact takes more determination. Or, as a mustached orange fluffball would say, someone who cares a whole awful lot.

Like Merlin in The Once and Future King, I found myself living backwards in time,[7] remembering what the system once knew, even as its present structure forgot. Querying the past through metadata felt less like analysis and more like reconstruction: following traces not because they were meant to be followed, but because they hadn't yet disappeared.

This fragility, the slow disappearance of signals, is why this work felt more like archaeology than analysis. I wasn't pulling data from a dashboard; I was excavating buried layers, hoping that enough of the traces remained to reconstruct a timeline.

The Wizard and I

The last time I tried to count UploadWizard uploads was in 2020. Back then, I used the log comment method and came up with about 20 million files, which already felt staggering. What I didn't realize at the time was that this method missed the first two years of uploads entirely, and that the logging pattern was just about to change.

This time, I discovered twice that amount: a full third of all files on Commons. When I first moved to San Francisco and began working on UploadWizard, I couldn't have imagined that the tool would still be in use 15 years later, largely unchanged, and that the numbers would be so vast. It's humbling, a little surreal, and deeply gratifying.

What stayed with me most throughout this archaeological expedition wasn't just the technical puzzle; it was the reassembly, the emotional arc of uncovering it. The joy of finding Neil's first test upload. The frustration of queries that almost worked. The satisfaction of unlocking the Grimmerie and watching the story piece itself back together, one log comment and patch note at a time.

I didn't initially set out to revisit the most important project of my early career; I just wanted to answer a simple question. But as I sifted through missing metadata and fading fragments, I found myself face-to-face with something much more personal: the enduring presence of a tool I helped bring into the world.

This exploration wasn't merely about product analytics and KPIs. It was an act of stitching Commons' memory back together (and in the process, restitching my own) through the quiet, persistent work of following traces others had left behind. It was about memory, continuity, and the fragile threads that hold institutional knowledge together. I set out to measure a tool's impact; through some magic, I ended up unearthing my own legacy.

Bananas, Rubber Ducks, and AI

Guillaume Paumier — Wed, 09 Apr 2025 07:00:00 GMT

Krzysztof Golik on Wikimedia Commons // CC-BY-SA 4.0.

AI is changing everything. But for disabled and neurodivergent people, the changes aren't just interesting; they're personal. Generative, conversational, and agentic AI tools are beginning to fill long-standing gaps in accessibility at a time when inclusion is still more promise than practice. From planning and emotional regulation to co-thinking, AI is emerging as a cognitive ally for people whose needs have too often been ignored.

The things we dismiss too quickly

You've probably seen the photos: pre-peeled bananas[1] or oranges[2] in plastic wrap, sold in a supermarket. They appear in memes as examples of wasteful design, mocked for laziness or environmental harm. Then social media algorithms designed for engagement do their thing and outrage ensues.[3]

But here's the thing: that banana might be the only way someone with arthritis, chronic pain, or limited dexterity can eat fruit independently. What looks ridiculous to one person can be essential accessibility for another. Pre-packaged and easy-open foods are functional necessities that support autonomy for people with limited grip strength or joint pain.[4]

WHAT DO YOU MEAN YOU CAN'T PEEL A FRICKING BANANA YOURSELF. (Alejandro Bayer Tamayo on Wikimedia Commons // CC-BY-SA 2.0)

AI tools like ChatGPT are sometimes criticized for doing things people should just "do themselves." They're dismissed as cognitive shortcuts that encourage laziness or erode critical thinking. But for many neurodivergent people, composing a clear email or initiating a plan can feel as daunting as peeling an orange with numb fingers. It's not that the task is conceptually difficult, but rather that the brain's executive and social systems are overloaded, under-supported, or structurally misaligned with the task at hand.

As someone with an autistic and ADHD brain, I use generative, conversational, and agentic AI every day, not because I lack the ability to do things without them, but because it reduces cognitive load, lowers anxiety, and helps me stay focused on what matters most in a society that's actively hostile to people with my neurotype.

Understanding generative, conversational, and agentic AI

Before diving into specific examples, here's a quick explainer of what these terms mean:

Generative AI "creates" output: it can write, summarize, translate, brainstorm, or generate images and audio. Tools like ChatGPT, Gemini, and Claude fall into this category.
Conversational AI is generative AI with a back-and-forth format. It helps you think, clarify, and structure your ideas by engaging in dialogue.
Agentic AI takes generative AI a step further. It doesn't just respond. It can plan, take action, remember information, and coordinate across tools such as calendars, APIs, and project management systems.

In simplistic terms: Generative AI answers prompts. Conversational AI engages in dialogue. Agentic AI coordinates tasks. Each type (or mode) of AI supports different cognitive needs on their own; used together, they can transform how we think, plan, and communicate. Let's look at five specific examples of how AI can serve as meaningful accessibility tools.

I'm not ignoring the environmental impact of AI. When I brought it up to Monday (ChatGPT's "emo twin"), it called AI "the world's most advanced distraction machine" and went on to say that "the whole AI ecosystem is kind of like rearranging the deck chairs on the Titanic, except the deck chairs are TikToks and the iceberg is literal climate collapse."

AI can and should be used responsibly, especially for accessibility and productivity gains that may offset its environmental costs. Text-based interactions are much less computationally expensive[5][6] than video editing or generating Ghibli-style selfies that, in addition to melting OpenAI's servers,[7] really do feel like "the world’s most advanced distraction machine."

1. Getting started despite inertia

Executive dysfunction makes it difficult to begin or complete tasks, especially when the first step is unclear or emotionally loaded. Composing messages or planning a project can feel overwhelming. Social communication differences add another layer of stress; it's not just about writing, but worrying how the message will come across.

For many neurodivergent people, these challenges stem not from laziness or lack of skill, but from impaired self-regulation mechanisms tied to dopamine availability in the brain: "Dopamine is largely involved in motivation, and because our brains have less of it available at any given time, it's much harder for us to initiate tasks just because we 'should.' … The lower levels of dopamine mean that without interest, challenge, reward, or urgency, we will struggle to get started on tasks."[8]

This insight reframes inertia not as procrastination, but as a neurological bottleneck: "It's not that people with ADHD don't know what to do. It's that they struggle to do what they know—especially when a task doesn't provide immediate stimulation or feedback."[9] What appears from the outside as avoidance is, in fact, a tangle of internal friction: a cognitive landscape shaped by uncertainty, emotional intensity, and neurochemical imbalance. Tasks do not simply require time and effort; they require the right internal conditions, or external scaffolding, to even begin.

AI can help bridge those gaps. It can break vague goals into clear, immediate steps, help you clarify what you actually need to do, and generate a rough draft so you're not starting from scratch. When it comes to communication, it can draft messages from your notes or voice input, suggest tone-appropriate edits, and offer feedback to help ease anxiety about how you're coming across.

This interaction I had with ChatGPT is an illustration of how AI can support task initiation by providing structure without overwhelm. For neurodivergent people (especially those with ADHD), getting started on a task often feels like the hardest part due to executive dysfunction and dopamine-related inertia. By explicitly asking for one task at a time, I can avoid cognitive overload and create a doable entry point. Conversational AI tools can serve as low-pressure scaffolding that breaks down complex goals into gentle momentum-building steps.

This support is becoming even more seamless with the purposeful integration of AI tools directly in applications. Todoist, my favorite task management system, offers an "AI Assistant" extension that can make tasks more actionable or break a task down into sub-tasks, right there in the Todoist interface.[10] In Gmail, Gemini can proactively write and revise messages in context, turning a blank screen into a manageable jumping-off point instead of a wall.[11] And if the draft contains errors, it will likely provide additional motivation to the neurodivergent brain by triggering its need to fix incorrect information.

2. Text interpretation and emotional regulation

Social communication tasks, such as writing emails, add further complexity. For autistic individuals, communication is not just a transactional act; it's a cognitively demanding task involving prediction, translation, and masking. This anxiety compounds the difficulty of initiation, particularly when combined with executive dysfunction.

Interpreting messages is a major source of social anxiety for autistic and ADHD people: Did I upset them? Is this sarcastic? Those of us suffering from rejection sensitivity go through a dramatic emotional response of "intense panic and distress" to perceived or anticipated rejection, often stemming from past negative experiences.[12] This can make any kind of social interaction feel fraught with risk.

Assistance animals provide essential support for a wide range of disabilities, helping blind or low-vision individuals navigate the world, offering grounding and sensory regulation for autistic people, and providing safety cues or emotional reassurance for those living with PTSD. (Texas A&M University Libraries on Wikimedia Commons // CC-BY 2.0)

Even small misunderstandings or neutral feedback can trigger a cascade of shame, fear, or rumination. The cumulative emotional cost of this sensitivity can be enormous and lead to withdrawal, over-apologizing, and masking behaviors.[13] Emotional dysregulation makes it even more difficult to manage and recover from those emotional roller-coasters. Instead of being able to process and move on, the impact lingers for hours or days.

In these moments, AI offers a valuable buffer and can help regulate those emotional responses. When you're afraid to send a message, it can offer reassurance or help rephrase it. When you're confused about an incoming message, it can analyze its tone and meaning. When you're spiraling about whether you upset someone, it can offer objective feedback.

This screenshot of a chat I had with Gemini, Google's AI assistant, shows how conversational AI can help users interpret or validate their own social messages. By affirming tone and intention, AI can reduce anxiety, boost confidence, and make emotionally complex tasks (like texting a friend during a difficult time) feel more manageable for neurodivergent people.

Processing events and messages with an AI partner doesn't eliminate the deeper pain of rejection sensitivity or emotional dysregulation, but it can reduce the frequency and intensity of their daily impact. AI can do this in part because it's been trained on an enormous dataset of online and written communication. It can draw on subtle linguistic cues, patterns of phrasing, and statistical correlations to infer tone and intention, making it a surprisingly good mirror for emotional interpretation when you're too anxious to trust your own.

3. Supporting executive function and follow-through

Communication and emotional challenges don't exist in isolation. Emotional dysregulation, for example, can worsen executive dysfunction, and vice versa, making the bridge between emotion and action harder to cross. For people with ADHD in particular, getting started is only half the battle; staying engaged long enough to finish the task can be just as hard.

In this context, agentic AI can step in to support daily executive functioning. For example, with "Scheduled Tasks," you can tell ChatGPT "Remind me to follow up in three days" or "Check in tomorrow on this."[14]

Combined with Todoist and automation tools like IFTTT or Zapier, AI can orchestrate a productivity ecosystem that adapts to how your brain works. I use it to prioritize tasks and break them down, set up soft nudges and reminders, and automate routines so I don't rely on my own memory and processes alone. It's like having an assistant who never forgets and never judges.

This screenshot is an example of a ChatGPT check-in to stay on top of open loops across different projects. This kind of structured reflection, automatically triggered and customized, can help with executive function and avoiding overwhelm while maintaining momentum.

Agentic AI can also help people with ADHD stay focused by setting up a kind of virtual body doubling, a "strategy used to initiate and complete tasks" that involves the physical or virtual presence of "someone with whom one shares their goals, which makes it more likely to achieve them."[15] While AI can't fully replicate the effect of working alongside another actual human being, it can mimic the structure and accountability mechanism. It can set up time-boxed work sessions, check in on your progress, and reflect back your small wins, all key components of motivation and follow-through.

4. Organizing scattered thoughts and retrieving memories

While agentic AI helps manage day-to-day functioning, its potential goes further: it extends how we think. Especially when information is scattered across digital tools and systems, AI can support cognitive offloading, the practice of using external tools like notes, checklists, or digital files to reduce the load on our working memory and executive function.

This idea is at the heart of Clark & Chalmers's Extended Mind theory, which argues that thinking does not occur solely within the skull. Instead, cognition is distributed across tools, spaces, and social contexts: "If, as we confront some task, a part of the world functions as a process which, were it done in the head, we would have no hesitation in recognizing as part of the cognitive process, then that part of the world is … part of the cognitive process."[16]

I keep daily personal logs and document them on my home wiki. I asked ChatGPT to analyze my log entries from 2020 and to give me a high-level summary, plus a list of fun things I may have forgotten. This is one of my favorite examples of how AI can facilitate cognitive tasks: I can offload years of personal memories to an external system so I don't have to hold it all in my head, and use AI to help me retrieve meaning and moments that matter. This example of cognitive offloading and retrieval is exactly the kind of interaction described in Clark and Chalmers's Extended Mind theory.

AI is uniquely positioned to support cognitive offloading by bringing together information scattered across fragmented digital systems, particularly if they're in different or inconsistent formats. From this perspective, AI is not merely an assistant; it becomes part of the thinking system itself, helping us offload, externalize, and extend our minds in new ways.

This summary of my daily 2020 log entries helped me see the shape of my year: what I was working on, how my routines adapted, what I learned, and where my energy went. The AI processed my logs and gave back a clear narrative. It's not just memory support; it's cognitive synthesis.

I personally rely on a suite of tools that contain different types of information stored in various systems that can't be centralized: Todoist for tasks; a personal wiki for notes and journals; a book catalog on LibraryThing; PDF references on my hard drive; and online documents and spreadsheets, just to name a few. AI can act as a connective layer across these silos, helping you search, summarize, and integrate content that would otherwise remain scattered or mentally inaccessible. It can parse vast amounts of text, identify relevant patterns, and surface what matters most, whether that's a forgotten idea from a past journal entry or the next action step buried in a meeting transcript.

In a neurodivergent life where memory can be patchy or nonlinear, moments of joy are easy to lose track of. This list of fun things I did in 2020 made me laugh and feel proud, for example by reminding me of my 1,000 paper cranes. I'd forgotten most of these things until ChatGPT surfaced them.

Some AI agents now even include memories themselves. For example, "ChatGPT can now remember details between chats, allowing it to provide more relevant responses. As you chat with ChatGPT, it will become more helpful – remembering details and preferences from your conversations."[17] This memory system means AI can recall your preferred style of writing, project names, recurring goals, or even emotional cues over time. When used well, it allows for continuity across fragmented sessions: no need to re-explain your situation each time you return. For neurodivergent users, that kind of persistent context can ease cognitive strain, support long-term projects, and turn the AI into a partner that adapts rather than resets with every conversation.

5. AI as an attuned co-thinker

In addition to helping manage scattered information and ongoing tasks, AI can also serve a more interactive role, supporting how we think in real time, not just what we store. Talking through ideas out loud often helps clarify thinking, particularly for ADHD minds that process ideas verbally. When other people aren't available to listen, AI can fill that role.

This approach is similar to the classic programming trick: rubber duck debugging. You explain a code problem out loud to a rubber duck and suddenly you understand it better. AI is a rubber duck with benefits: it listens without judgment, helps you clarify and reflect, and can summarize or organize your thoughts after you've talked them out.

This duck I got at KDE Akademy 2010 introduced me to the concept of rubber duck debugging. It came with the advice to "Take the duck from your desk, look at your code and explain to the duck—line by line—what it does." While the duck has been lost, my desk now contains a whole panoply of small Lego characters I can talk to out loud, including WALL·E, Stitch, and Moana's Heihei.

Devon Price summarizes the autistic experience as processing the world "in a careful, systematic, bottom-up way." Autistic people "don't rely on knee-jerk assumptions or quick mental shortcuts to make our decisions. We process each element of our environment separately, and intentionally, taking very little for granted."[18] This mode of functioning of the autistic brain requires a lot more deliberate processing to make any single decision. Making multiple decisions in a row quickly becomes overwhelming due to the cumulative volume of factors to consider. AI can narrow down options based on preferences, ask clarifying questions to ease the process, and provide structure or defaults when your brain is in "nope" mode.

While taking the class Agentic AI and AI Agents for Leaders by Jules White, I built my own custom GPT as a proof of concept: the "Neurodivergent Thinking Companion." It's tailored to how my brain works: nonlinear, interdisciplinary, and often juggling multiple projects. It understands executive function challenges, breaks down complex writing or planning tasks, explores ideas without judgment, recognizes patterns in my behavior and thinking, and normalizes task-switching and creative tangents.

In a way, this mirrors how AI itself is built and refined, through a process called tuning, where developers iteratively adjust an AI's behavior by supplying examples, reinforcement signals, or fine-grained instructions. I did the same thing in miniature: tuning my GPT not with code, but with instructions that shape how to support my thinking.

Screenshot of the "Neurodivergent Thinking Companion," a custom GPT I created as a proof of concept to support autistic, ADHD, and nonlinear brains. The left panel shows its tailored instructions, emphasizing gentle structure, emotional validation, and flexible, affirming language. The right panel displays the response to a custom /panic command, offering calming prompts to help navigate moments of overwhelm with clarity and care.

Of course, the AI doesn't actually "think." But I gave it specific instructions to tailor its behavior and responses to both mirror how my brain works, and mitigate its limitations. Going back to Clark & Chalmers's Extended Mind theory, AI can go beyond just supporting cognitive offloading: by simulating human thinking and behaving like a thinking partner, it approximates socially distributed cognition.

In her chapter "Thinking with Peers," Annie Murphy Paul explains that "our brains evolved to think with people: to teach them, to argue with them, to exchange stories with them. Human thought is exquisitely sensitive to context, and one of the most powerful contexts of all is the presence of other people. As a consequence, when we think socially, we think differently — and often better — than when we think non-socially."[19]

For minds accustomed to non-linear or associative thinking, this kind of support can be transformative. When AI can search your notes, recall past conversations, and suggest next steps, all while taking into account your brain's particular modes of working, it's not just storing information for you or responding to prompts; it's thinking with you.

A game-changer, for some

Stepping back, what I'm sharing here isn't just about individual tools or clever hacks; it's about how we define access, design, and participation in a society that still centers the neurotypical by default. At first glance, the pre-peeled banana and the AI assistant might seem excessive, frivolous, or even wasteful. But they meet specific needs, and our reaction to them reveals our biases and preconceptions about what we consider "normal."

This perspective is grounded in the social model of disability introduced by Mike Oliver, which reframes disability not as an individual deficit, but as a mismatch between a person's needs and their environment.[20] As Lennard Davis put it, "the ‘problem' is not the person with disabilities; the problem is the way that normalcy is constructed to create the ‘problem' of the disabled person."[21] When we ascribe value with only the average person in mind, we build systems that exclude everyone else. Devon Price articulates the cost of that exclusion clearly: "Each of us has been repeatedly overlooked and excluded because society views our differences as shameful defects rather than basic human realities to accept."[22]

When a banana doesn't make sense to you, leading with curiosity is a better starting point than leading with outrage. (Alejandro Bayer Tamayo on Wikimedia Commons // CC-BY-SA 2.0)

AI doesn't erase disability, but it helps reframe it. Instead of asking people to conform to neurotypical expectations, these tools can adapt to individual needs and provide support. In doing so, AI becomes a medium for bridging the gap between disability and difference. It helps shift the focus from what someone "lacks" to how their environment can change to support them. As the social model of disability argues, disability isn't located in the person but in the barriers that society fails to accommodate. AI tools, when designed with inclusion in mind, can soften those barriers, making the world more cognitively equitable.

These tools don't replace our abilities. They extend them. They reduce friction. They help us thrive in a world not built with our brains in mind. They help unlock potential that's often been sidelined, hidden, or dismissed. And in doing so, they broaden the conversation about diversity and inclusion, not just in hiring or representation, but in how we build systems, tools, and cultures that truly accommodate the full range of human minds.

Famartin on Wikimedia Commons // CC-BY-SA 4.0.

None of this negates the real concerns around AI like hallucinations, bias, surveillance, deskilling, and environmental cost. But for disabled and neurodivergent people, the stakes are different. This isn't about convenience or novelty; it's about agency, participation, and independence. For us, AI isn't a "nice-to-have." It's a door that's been locked for decades finally swinging open. And yes, we should keep critiquing the systems behind these tools; but we should also recognize that for many disabled and neurodivergent people, this is a rare time when digital infrastructure is beginning to move in our direction, even if imperfectly. For once, we're not being asked to adapt ourselves to the system; it's the system that's beginning to adapt to us.

A revenue strategy for Wikimedia

Guillaume Paumier — Fri, 24 May 2019 07:00:00 GMT

Javier Allegue Barros on Unsplash

The importance of Revenue Strategy for the Wikimedia Foundation lies in two main factors: the uncertain future facing us, which might endanger existing revenue sources, and the impetus to grow, mandated by the ambitions of the Wikimedia movement. The following is a summary of my reflection on Revenue Strategy for the Foundation's Advancement department.

Challenges to the banner fundraising model

Since the creation of the Wikimedia Foundation, most of its revenue has been raised through fundraising banners: a small portion of readers of Wikipedia are prompted by banners to donate a small amount to support the continued existence of the site.

In recent years, a few trends have emerged in internet usage and challenged the banner model. The increasing use of mobile devices has translated into fewer donations compared to desktop, only compensated by improvements and optimization of banner effectiveness, as well as the growth of our email program to past donors. A stagnation in overall readership has also threatened the banner model, with internet users accessing content from Wikipedia through intermediaries and not visiting Wikipedia directly.

The Foundation's email fundraising program is one of the existing efforts undertaken to address those challenges. Banners are the main point of entry for first-time donors; once they have donated, they can be solicited for support over email in later fundraising campaigns. Initial work in recurring donations also aims to reduce the friction of donating and build sustainability. Cultivating major donors through event and direct outreach is another way to bring in larger donations that don't rely on direct interaction with the website.

Building on those initiatives is the first step towards longer-term financial sustainability, but it won't be enough. As knowledge becomes more granular, remixed by others, and served through interfaces out of our control, it will become increasingly difficult to maintain a direct line of communication with people willing to support our mission. The whole concept of "readers" might become obsolete, and banner fundraising might not prove to remain a major source of revenue by 2030.

Thinking about long-term revenue strategy in this context requires thinking about strategy in broader terms, and in particular identifying our unique strengths, both today and tomorrow.

Sustainable differentiators

The scenarios for 2031 explore possible futures for the human ecosystem and Wikimedia's place in it. Without purposeful, drastic change, the outlook is bleak: deep adaptation is necessary, based on our differentiators of today and tomorrow. The Wikimedia societies and brand offer a path through the possible stormy futures ahead, if we are bold enough to change.

Today, Wikipedia is mainly seen as a website: one of the top-ten websites in the world, containing millions of encyclopedia articles that are mostly considered trustworthy. However, none of those characteristics can be relied upon as stable, unique strengths.

Wikipedia is a popular website. But that popularity has grown primarily out of its ranking in search engines; as search interfaces get more clever, they have an incentive to provide immediate answers that make use of Wikipedia content but don't actually send their users to Wikipedia.

Wikipedia is one of the most famous encyclopedias. But it is not the only one; the mere concept of the long-form, Enlightenment-style encyclopedia is even becoming obsolete as online usage and populations evolve.

Wikipedia is a source of reliable content. To date, content has been the cornerstone of our relationship with the rest of the knowledge ecosystem. Other actors, whether partners, allies, or foes, are primarily interested in using, expanding, protecting, or influencing Wikipedia's content. But content is already no longer a unique strength of Wikipedia: for one thing, its free license not only allows, but encourages its use in a variety of contexts and interfaces. For another, technological progress leans towards a decreasing reliance on Wikipedia's humans to generate and curate knowledge.

We can't rely on Wikipedia's position as a popular website, as a famous encyclopedia, or as a source of reliable content. What are our sustainable unique strengths, then?

One current differentiator is Wikimedia societies. While there are many online communities, those comprising the Wikimedia movement have a unique affinity and talent for collecting and curating free, reliable knowledge. Over the past 18 years, they have developed policies, processes, and social structures that are largely responsible for the reputation of Wikipedia. The uniqueness of Wikimedia communities resides in the culture and institutions that define them as societies, independent of the websites they work on and artifacts they produce.

Another current differentiator is the Wikipedia brand. Beyond just a name, the brand encompasses a complex set of components that define our relationship to the public. The brand includes the trust established through the hard work and integrity of Wikimedia contributors. The brand includes the love that donors express when they support us. The brand includes the principles that we have stood up for and we demonstrate every day.

Both the communities and the brand are likely to become stronger differentiators in the future. In a world swimming in data and overwhelmed with information, discernment and sensemaking are paramount. To use Hal Varian's vocabulary, data and information are increasingly abundant inputs; trust and human judgment on information (determining what is accurate and important) are a scarce and therefore valuable complement to those inputs. It is where the Wikimedia movement brings unique value, and the brand is the vehicle of that value.

Building our future on the foundations of communities and brand helps us explore a future where Wikimedia societies may become the world's foremost sensemaking engine. Where machines and algorithms may collect and assemble facts, but where Wikipedians organize, weigh, and nuance them. Where information may be omnipresent, but the Wikipedia brand is a sought-after indicator of trust, regardless of the medium or interface.

Implications for Revenue strategy

Elements of a revenue strategy for the Wikimedia Foundation have emerged organically. Some are evident: continuing to refine and improve the effectiveness of banner fundraising for as long as possible; continuing to develop email fundraising and major giving as complementary sources of donations that might one day overtake banner fundraising; encouraging recurring and seamless donation processes that reduce friction and increase predictability.

Another element of revenue strategy is to take a longer view, and build on today's financial stability to plan for tomorrow. This includes a model of engagement with donors that takes into account the various stages of their life and interactions with Wikipedia. It includes investing today in a solid planned giving program that will only benefit us in several decades. It includes building out the Wikimedia Endowment today to mitigate uncertainties in future revenue, and provide the resources for experimentation as we transition to other funding models.

Those new funding models might not look like what we have become familiar with. The fully donative model doesn't appear sufficient for long-term sustainability. Exploring revenue beyond donations means experimenting with models like fee-for-service, joint for-profit ventures, and other "earned income" options that must be allowed to take risks and fail, so long as we learn from them. Profitability and ease of launch must not be the only criteria: new ventures must also align with the larger mission and strengthen the brand, sustainability, and relevance in our ecosystem.

The last element of revenue strategy is one that goes beyond the Wikimedia Foundation. Increasing revenue to the level required by the strategic direction will require the whole movement to commit to its funding, taking advantage of its global reach and local presence. The Movement strategy working group on Revenue Streams is expected to offer recommendations for revenue strategy on a global scale.

All those elements combine to form a revenue strategy that is inextricably intertwined with the larger decisions we have to make as an organization and a movement. Even if revenue could flourish alongside a failing product, which it likely can't, it wouldn't be enough: money can't buy readers. Money can't buy relevance. Money can't buy trust. At least not in the long run.

How much revenue we can access to fulfill our mission will depend on the story we can tell about who we are and who we have become. It can be the story of a once-popular website whose struggle to adapt became a struggle to survive. The story of knowledge communities whose decreasing relevance didn't provide a continued incentive for public support. The story of a brand that slowly faded from the public's memory. The story of a social movement starved of the resources it needed to advance.

Or it can be the story of a community that evolved to adapt to the world around it, and built on its unique strengths. The story of a society of trusted sensemakers that provides such value to the public and partners that it is in their vital interest to support it. The story of a brand that the public trusts, loves, and supports wherever they encounter it. The story of a vibrant social movement with the financial might to match its ambitious vision.

There are many elements of the story that we can't control. The question is what we do with those we can. The story of our survival tomorrow is the story we choose to write today.

The Stakes of Knowledge

Guillaume Paumier — Sat, 18 May 2019 01:57:00 GMT

Fred Kearney on Unsplash.

Knowledge has real consequences on human lives, both for individuals and for societies. As I reckoned with the privilege of growing up surrounded by books, I deepened my understanding of knowledge and the real power it holds, both as an instrument of liberation, and as a vital component of our collective survival.

The privilege of knowledge

Janko Ferlič on Unsplash.

It is easy to take knowledge for granted when you grow up surrounded by it, and have immediate access to readily-available, trustworthy information at your fingertips.

My childhood home was filled with books, and my mom even opened a volunteer-run library in our small village. I had access to education, and a fifteen-volume encyclopedia at home that I would browse at random. I would collect magazines and create little dossiers on topics of interest, putting together endearing reports for no other reason than finding it fun.

When I started editing Wikipedia, it was mostly out of curiosity and fascination. Curiosity about an encyclopedia that anyone could edit, and about the strange people who would devote their time to sharing knowledge for free. Fascination about an opportunity to record the sum of all knowledge, which appealed to my knowledge-hoarder mind. I had found my people.

But thinking of knowledge as only an abstract concept is a privilege. Thinking of Wikipedia as merely a hobby for people with too much time on their hands ignores the opportunity it represents for people who did not have the opportunity to grow up surrounded by books.

Lifelong learning & opportunity

R. Martinez on Unsplash.

Knowledge is a manifestation of power: withheld, it reinforces control and prevents change; liberated, it erodes structures of oppression and opens up opportunities.

Today, learning, and therefore, quality of life, are very much a matter of privilege. Your age, geography, gender, and wealth determine your level of access to quality education, if any. Affordable formal education, only available to a few, is neither inclusive nor scalable to all who need it.

Elites and other gatekeepers of knowledge in positions of power have a vested interest in preserving the status quo, whether that takes the form of denying education to girls, imprisoning journalists, or censoring the internet.

Museums Victoria on Unsplash.

Fencing knowledge in causes a structural hoarding of opportunities: opportunities for self-determination, for better work, for healthier lives, and for toppling the self-reinforcing systems of durable inequality and exploitation.

Tomorrow, the challenges may look different, but the underlying structures of power are poised to perpetuate. The accelerated pace of industrial and technological change is rendering obsolete not just the concept of the decades-long career, but also the paradigm of retraining for career change: the skills acquired by those in the most precarious work situations become outdated before they can lead to individual prosperity.

Sydelle Willow Smith on Wikimedia Commons // CC-BY-SA 3.0.

Denied lifelong learning opportunities, many are being forced into the modern serfdom of the “gig economy.” Millions will be displaced as they see their jobs being automated and replaced by machines, which will cause mass unemployment and further concentrate wealth and power. Millions more will die as they are deprived of life-saving knowledge, while others barely survive, deprived of the means and opportunity to lift themselves out of poverty.

Knowledge can change a life. It can be an instrument of equity, and help the arc of history bend towards justice. It can be an instrument of liberation and self-empowerment for people who have been left out. But knowledge can do none of those things if it stays in the hands of the few.

Disinformation & solving problems together

Elijah O'Donnell on Unsplash.

Democracy relies on an informed society; oppressive regimes and fascism thrive on a disinformed one. But it is no less than the survival of humanity that is at stake if we cannot learn to tackle crises together.

Today, too many lives, particularly of infants and women, are still lost to the lack of access to trustworthy information. Disinformation goes even further: it consists of coordinated and relentless campaigns by bad actors, unscrupulous companies, and oppressive regimes, acting in the pursuit of ideology, profit, and power, against the interest of humankind.

Every day brings ample evidence of the consequences of disinformation. Doctored videos cause lynchings. Corruption and egregious partisanship cause climate change denial. Fake news cause stolen elections. Vaccines don't cause autism, but ignorance spreads measles.

Parker Coffman on Unsplash.

The issue is not just that of private interests mortgaging the future, spreading disinformation to cover their unsustainable extraction of resources and destruction of the planet. It is also that of the newer actors of surveillance capitalism: algorithms designed for "engagement," short-term profit, and shareholder satisfaction are abused by bad actors to pervert democracy. Unaccountable giants position themselves as helpful assistants in navigating the flood of information deluged onto us, all the while denying their own responsibility in epidemics of viral disinformation.

Patrick Hendry on Unsplash.

Tomorrow, the challenges facing humankind will be even greater, as will the temptation to fear and blame each other for them. The climate emergency does not just bring extreme weather; it is also the harbinger of famine, plague, conflict, unbreathable air, loss of land, death of oceans, economic collapse, and mass displacement of climate refugees.

Historically exploited and disenfranchised people are, as always, the most vulnerable. Not only to disasters and other deadly consequences of status and geography, but also to fear and blame: we fear what we do not know, and we cannot understand those whose stories we have erased.

Agustín Lautaro on Unsplash.

The inevitability of global heating requires deep adaptation; it will take many ingenious minds to solve those seemingly intractable conundrums. Without mutual understanding and empathy, there can be no trust. Without widespread knowledge of the issues confronting us, there can be no awareness, no agreement, and no collective action.

The world of tomorrow will be forged in the fiery crucible of the climate crisis and its myriad consequences on humankind. Unless we reach a shared understanding of each other and of the challenges facing us, we cannot hope to survive the Anthropocene, let alone to advance as a global civilization.

It is the year 2031.

Guillaume Paumier — Thu, 21 Mar 2019 07:00:00 GMT

The Ladies' home journal (1889) // Public domain.

As part of my work on revenue strategy for Wikimedia, I imagined three scenarios about the world in 2031 and the organization's place in it. I used these stories to spark discussions and shift conversations from day-to-day concerns and busywork to longer-term strategic thinking.

The scenarios all contained both favorable and unfavorable story elements, to ensure that people engaging with them wouldn't be tempted to pick one as the future they favored. The actual future that would come to pass was likely to be a combination of elements from all these stories. These stories were the basis of the revenue strategy I devised in 2019.

2031: The War of the Words

Frederick Burr Opper on Wikimedia Commons.

Misinformation, disinformation, testudo formation.

It is the year 2031, and Wikipedia is celebrating its 30th birthday. In a world of unrest and division, Wikipedia seems strangely united. Environmental disasters due to global warming, compounded by socioeconomic crises and identity politics, have birthed a world where distrust and fear of the other are the new normal. Bad actors have never been more active online, whether they are state-funded or attempting to destabilize nations. The violence in the streets is fueled by institutional censorship, organized disinformation, and trolling campaigns that make the online atmosphere even more polluted and toxic than the actual atmosphere.

Wikipedia has fared relatively well and looks, from afar, like a surprisingly quiet haven, whose content is stable and safe from harm. In trolls, censors, and purveyors of lies, Wikipedians have found a common enemy to unite them; in tech giants, they have found unexpected allies. Big Tech companies have invested heavily in technologies to combat bad actors: they have developed tools to circumvent censorship, to make sure that they still have access to their markets. They have devised ways to identify and derank information that is not from established reputable sources, to protect the content on which their business model relies from being poisoned. They have created shared reputation scores for internet users based on all the data they collect (which are now being tested by insurance companies as well), to determine who is trustworthy online.

Faced with a deluge of disinformation and editors acting in bad faith, most Wikipedia communities have erected social and technical dams. Entire countries are blocked from editing due to sustained cyberattacks. Administrators have access to checkuser information on all accounts except each other's. Pages on all Wikipedias are now Trust-Protected: only Trusted editors can directly edit pages and approve edits from Untrusted users. Untrusted edits are held in a review queue for seven days before being automatically discarded, which happens to 92% of them; the review backlog had been growing exponentially before the automated flush was implemented. All the product improvements developed in the past twenty years to make it easier for newcomers to contribute have been counter-balanced by more stringent policies and heavy moderation tools developed in the last ten.

This War On Lies has not been without casualties. The knowledge and communities that had been systematically left out are excluded more than ever. Historical structures of power and privilege have been reinforced by algorithms and defensiveness. More people care about guarding the temple of knowledge than about asking whose knowledge it is. The discussion on expanding the definition of reliable sources was originally intended to find ways to bring more content to Wikipedia on topics not traditionally referenced in Western academic publications. The debate ended before it even started: in a world of fake newspapers and fabricated audiobooks, oral citations are a non-starter.

The reputation system built by tech companies suffers from systemic bias, notably because it was developed quickly based on the data available to them at the time. A recent effort to measure and address the reputation gap for Internet users from Africa unexpectedly resulted in an increase of the reputation scores of most of Europe's population, which had apparently been consistently lower than those of North American citizens. Executives have promised to take another look at the reputation scores for African users in the future.

Wikipedia's financial sustainability isn't a huge concern at the moment. Although readership has declined dramatically, and content is now accessed more atomically through a multitude of unbranded interfaces, tech giants have stepped in to partly make up for the decrease in donations from readers. They can't win this fight without Wikipedia's armies of humans, who have proved to be critical in the arms race between bad actors and tech corporations. Most affiliates have disappeared, some due to lack of funding, and most due to changes in local legal landscapes that made any relation to Wikipedia dangerous for the individuals involved.

It is unclear how, when, or if the fight will end, but it has taken an undeniable toll on the health of communities: while they were originally energized by the righteousness of their mission to defend Wikipedia against the hordes, more and more of them are retiring from the wikis due to burnout, doxxing, or simply lack of enjoyment in contributing any more.

2031: Success into oblivion

Reinhold Möller on Wikimedia Commons // CC BY-SA 4.0

More money than we had hoped, and the cost of success.

It is the year 2031, and Wikipedia is celebrating its 30th birthday. Hundreds of parties are happening around the world and on all continents. The Wikipedia Consortium can be proud of its achievements. We did it; we achieved the ambitious goals dreamed in the 2030 strategic direction. Looking back, our success can be attributed to a series of good decisions and external events that played in our favor, for better or for worse.

A series of earthquakes, tsunamis, and violent storms in Asia, combined with civil unrest due to economic downturn, created an extended shortage of computer chips, and a prolonged increase in the price of all technological hardware that weakened tech giants worldwide. Regulators seized this opportunity to strengthen antitrust laws and break down most of the largest tech corporations, in an effort to reassert state control and power that had been eroded in the previous two decades.

The weakening of the tech giants appeared as a blessing for the civil sector, and in particular the Wikimedia movement. Public distrust of large profit-driven corporations strengthened charities and other institutional actors that were appealing to civic-minded populations. Fundraising from small-dollar donors soared and unlocked major infrastructural investments to make Wikimedia content more accessible and embeddable into all the major devices and experiences that have emerged in the past ten years. As knowledge became more structured, bite-sized, and media-driven, the concept of the encyclopedia quickly disappeared. All Wikimedia projects were folded into the Wikipedia brand in 2024, and only the grumpiest old-timers insist on still calling it Wikimedia.

This opulence also empowered Wikimedia organizations to dramatically expand globally and set up a local presence in every region, except for Asia. The movement diversified into an array of foundations, chapters, for-profit subsidiaries, affiliates, and partners, all forming the Consortium. The fundraising manna and strategic alignment happened to combine with a societal desire for social justice and equity, fueled by the rising social, political, and economic power of Millennials and Gen-Zers. This convergence started a Golden Age of Equitable Collaboration, and a multitude of successful programs to include knowledge and communities that had been historically left out by structures of power and privilege.

Yet, some members of the Consortium are raising concerns about the future. Wikipedia has become the essential infrastructure of the ecosystem of free knowledge, and anyone who shares our vision would be able to join us—if only they knew that we exist.

As the margins of big tech corporations waned, so did their scruples and seemingly benevolent intentions. Their smaller size made them both more desperate and less subject to scrutiny: when faced with a choice between not being evil and not being at all, survival comes first. Content from Wikipedia is regularly remixed and presented through better experiences than what Wikipedia can offer, provided by companies free of ideological constraints of universality and privacy. Tech corporations are aggressively exploiting the digital commons without any desire to invest in its sustainability, in hopes of regaining ground in their quest for control and profit.

When we transformed into a platform that serves open knowledge to the world across interfaces and communities, we paved the way for our own disappearing act. Knowledge has been commodified and disintermediation is now total: hardly anyone visits Wikipedia sites directly any more. 94% of fact pulls are from automated programs. (Fact pulls replaced page views as the primary access metric in 2025.) With so few humans on the sites, and no way to contribute content from third parties, content growth has fallen to pre-2003 levels, which has seemingly solved most issues of community health. The glacial pace of contribution is only sustained by expensive outreach and contribution programs; incidentally, contributions from Latin America and Africa have surpassed those from Northern America and Europe, where no such programs were initially deemed necessary.

The global expansion of the Consortium has been costly and has committed most resources to illiquid assets. For-profit ventures, initially intended to serve as a mission-aligned way to generate revenue, are barely turning any profit: there is always someone else to make the same business model more profitable. Maintaining the human and technical infrastructure of the Consortium is putting a serious toll on the Money Bin accumulated through previous fundraising, and the financial reserves are running low. As the money hose dries up, long-standing squabbles of internal governance resurface, made worse by the Consortium's sluggish bureaucracy.

As the celebrations wind down, optimism is widespread but the future is uncertain. The Consortium was a success for a while, but is it still?

2031: Human obsolescence

Pixabay on Wikimedia Commons // CC0 1.0

The robot revolution will not be advertised.

It is the year 2031, and Wikipedia is celebrating its 30th birthday. Banners and celebratory logos have been chosen through community contests, but they saw little participation. No one is really in the mood for celebrating: last month, Wikipedia was acquired by a large media group. And even though the new owners have promised editorial independence, the few remaining editors expect the giant to kill off the site in the next few years. How did we not see this coming?

The opening of the Northern Sea Route and Northwest passage in the 2020s, following the melting of the ice caps due to global warming, caused tensions between Arctic powers. With defense spending eating more and more of national budgets, governments have increasingly relied on large corporations to take on social services and infrastructure projects. Facing pressure from their constituents for more efficiency, regulators caved to the Big Tech lobby: artificial intelligence, connected devices, and smart everything appeared as modern solutions to do more with less government money and bureaucracy. The fact that the same companies were also some of the largest defense contractors, providing digital warfare and intelligence services, was not a coincidence.

Free of regulatory shackles and fueled by generous defense contracts, Big Tech made giant leaps in machine learning, instant translation, natural language processing, and general sensemaking engines. Similarly to technologies developed during the Space Race, these digital advances made their way into many everyday commercial products and further profited tech corporations.

All the while, the Wikimedia movement slowly made progress on its 2030 strategic direction, not realizing it had already slid into irrelevance: in a bloodless and silent coup, the machines had not only risen; they had already won.

While humans were slowly sifting through books to reference facts, machines were reading and making sense of millions of pages and integrating that knowledge into their databases. While humans were struggling to keep up with current events and news, machines were combing through millions of social media posts, data from devices and wearables, and assembling information that was more relevant, more local, and more timely. While humans were writing encyclopedia articles on the same topics in dozens of languages, machines were combining all of them into a structured, language-agnostic corpus that was then served to customers in their preferred tongue, through their interface of the moment, at the level of detail they needed. Any advances made by humans were quickly integrated into digital brains.

The machines and their powerful, wealthy human masters only needed to collaborate with humans until they had learned enough from them. We thought the threat was disintermediation: tech corporations appropriating knowledge from Wikimedia websites and serving it directly to their customers, cutting Wikimedia as the intermediary. Instead, the threat was that of human obsolescence: there is no need to cut the intermediary if you can assemble the knowledge yourself in the first place.

The jury is still out on systemic bias. The reliance on technology has in a way served as a Great Equalizer: knowledge is available to all, regardless of culture, region, or language. And ever since general sensemaking engines started being able to understand and organize local social data, knowledge and news from historically disenfranchised populations have entered the global knowledge corpus. However, long-standing structures of power and privilege can still be discerned by whoever cares enough to look: the machines and algorithms are still Children of Profit, and their creators have little incentive to make them auditable and accountable.

There might have been a future for Wikimedia if the movement had figured out its unique advantage over the machines and adapted in time, but by the time we realized what was happening, it was too late. Deprived of readers, and therefore of donors and contributors, the options for survival were few. Swallowing our pride, we were the ones who went to the media giant asking for help; they agreed to host us out of pity more than interest. The new owner isn't even planning to serve ads on Wikipedia: the low number of readers (and therefore the meager revenue from ads) isn't worth the trouble.

Beyond the scenarios

Black swan (cygnus atratus) by Rikiwiki2 on Wikimedia Commons // CC-By-SA 4.0.

The point of this exercise was not to choose a scenario over another: we can't choose what the future will look like, just like we can't change the past. Our temporal agency is limited to the decisions we make in the present, based on our understanding of the past and the future. The goal was to provoke thinking, devise strategies, and guide decisions that would help us adapt to the variety of possible futures.

Workshop & Chaotic Story

During a department retreat, I organized a workshop with Advancement staff, using full-page cards representing story elements of the scenarios, to encourage long-term thinking while tapping into the participants' own expertise and imagination. The cards provided the framework for the discussion, but let the participants weave them together in new ways.

Projecting ourselves into the future (for example, with scenarios and unexpected events) is a helpful way to chart a course for the future, and become antifragile in an uncertain world.

By fragmenting the scenarios into individual story cards, I inadvertently applied a powerful game design technique known as the chaotic story. Jane McGonigal describes this approach as breaking a story "into thousands of pieces like a jigsaw puzzle," compelling players to "actively make sense of the game content for themselves and for each other …. Until players put a chaotic story together, it doesn’t really exist—it's just a web of evidence, the raw materials for a story. It's up to the players to do the actual final storytelling."[1]

Black swans & building adaptability

If all you've ever known is white swans, you think black ones can't exist.

Halfway through the activity, I introduced "Black swan" cards,[2] picked at random among a few options. The goal was to prompt the participants to contend with unpredictable, wild card events and consider how their draft strategies would fare in those new circumstances.

Those unexpected disruptions further amplified the effectiveness of the chaotic story technique, demanding real-time adaptation and fostering a playful, creative problem-solving atmosphere. Instead of passively receiving predefined futures, participants engaged in dynamic interpretation and collaboration, significantly enhancing their ability to navigate ambiguity.

This experience underscored the value of incorporating game mechanics into strategic thinking, not only to enhance engagement but also to build adaptability. Effective scenario planning is less about predicting specific outcomes and more about equipping teams with the agility, creativity, and collaborative skills needed to thrive in uncertain and rapidly changing environments.

The workshop was a high point of the retreat, and I've held similar sessions for Advancement and Wikimedia staff over the years, all the way up to the executive team and the Board of Trustees. Future-oriented thinking helps build resilience by shifting the perspective of the organization's leaders to the long view, and leading them to imagine the future consequences of current events and choices they make today.

My life as an autistic Wikipedian

Guillaume Paumier — Thu, 30 Jul 2015 00:14:00 GMT

Two years ago, I discovered that I was on the autism spectrum. As I learned more about myself and the way my brain worked, I started to look at past experiences through the lens of this newly-found aspect. In this essay, I share some of what I've learned along the way about my successes, my failures, and many things that confused me in the past, notably in my experiences in the Wikimedia movement.

Introduction

This is a picture of me taken when I was 4, in nursery school, the French equivalent of Kindergarten.

I don't have many memories about that time, but my parents remember that, while I wasn't usually enthused about going to school during the week, I would often ask to go on Saturdays, because most of the other kids weren't there.

It wasn't that I didn't like them; it was because the school was much quieter than during weekdays, and I had all the toys to myself. I didn't have to interact with other children, or share the pencils, or the room. I could do whatever I wanted without worrying about the other kids.

I didn't know it at the time, but it would take me nearly 30 years to look back at this story and understand how it made complete sense.

Today

I'm now 32 years old, and a lot has changed. Two years ago, after some difficulties at work, my partner decided to share his suspicions that I might be on the autism spectrum. I knew little about it at the time, but it was a hypothesis that seemed to explain a lot, and seemed worth exploring.

Sure, the subject had come up before a few times, but it was always as a joke, an exaggeration of my behavior. I never thought that label applied to me. One problem is that autism is usually represented in a very uniform manner in popular culture. Movies like Rain Man feature autistic savants who, although they have extraordinary abilities, live in a completely different world, and sometimes aren't verbal. The autism spectrum is much more diverse than those stereotypical examples.

After I started researching the topic, and reading books on autism or autobiographies by autistic people, I realized how much of it applied to me.

It took a bit longer (and a few tests) to get a confirmation from experts, and when it came, many people still had doubts. The question that came up the most often was "But how was this never detected before?" Autism is generally noticed at a much younger age, and it seemed that for most of my life, I had managed to disguise myself as "neurotypical," meaning someone whose brain works similarly to most people.

The current prevailing hypothesis to explain this, based on an IQ test taken as part of the evaluation process, is that I am privileged to have higher-than-average intellectual capacities, which have allowed me to partly compensate for the different wiring of my brain. One way to illustrate this is to use a computer analogy: in a way, my CPU runs at a higher frequency, which has allowed me to emulate with software the hardware that I'm missing. What this also means is that it can be exhausting to run this software all the time, so sometimes I need to be by myself.

As you can imagine, realizing at 31 that you are on the autism spectrum changes your perception dramatically; everything suddenly starts to make sense. I've learned a lot over the past two years, and this increased metacognition has allowed me to look at past events through a new lens.

In this essay, I want to share with you some of what I've learned, and share my current understanding of how my brain works, notably through my experience as a Wikimedian.

One caveat I want to start with is that autism is a spectrum. There's a popular saying among online autistic communities that says: "You've met an autistic, you've met one autistic." Just keep this in mind: What I'm presenting here is based on my personal experience, and isn't going to apply equally to all autistic people.

"Taipei Wm2007 Guillaume.jpg," by Cary Bass, under CC-By-SA 3.0 Unported, from Wikimedia Commons.

The picture above was taken during Wikimania 2007 in Taipei. I was exploring the city with Cary Bass (User:Bastique) and a few other people. Looking back at this picture now, there are a few things I notice today:

I'm wearing simple clothes, because I have absolutely no sense of fashion, and those are "safe" colors.
I'm carrying two bags (a backpack and a photo bag), because I always want to be prepared for almost anything, so I carry a lot of stuff around.
I'm sitting down to change a lens on my camera, because it's a more stable position to avoid dropping and breaking expensive gear. I've learned that this habit of using very stable positions is actually a mitigating strategy that I developed over the years without realizing it, to compensate for problems with balance and motor coordination.

Spock

A good analogy to help understand what it's like to be autistic in a neurotypical society is to look at Mr. Spock, in the Star Trek Original Series. The son of a Vulcan father and a human mother, Spock is technically half-human, but it is his Vulcan side that shows the most in its interactions with the crew of the Enterprise.

Some of the funniest moments of the show are his arguments with the irascible Dr. McCoy, who calls him an "unfeeling automaton" and "the most cold-blooded man [he's] ever known." To which Spock responds: "Why, thank you, Doctor." [1]

As a Vulcan, Spock's life is ruled by logic. Although he does feel emotions, they are deeply repressed. His speech pattern is very detached, almost clinical. Because of his logical and utilitarian perspective, Spock often appears dismissive, cold-hearted, or just plain rude to his fellow shipmates.

In many ways, Spock's traits are similar to autism, and many autistic people identify with him. For example, in her book Thinking in Pictures, Temple Grandin, a renowned autistic scientist and author, recounts how she related to Spock from a young age:

Many people with autism are fans of the television show Star Trek. [...] I strongly identified with the logical Mr. Spock, since I completely related to his way of thinking.

I vividly remember one old episode because it portrayed a conflict between logic and emotion in a manner I could understand. A monster was attempting to smash the shuttle craft with rocks. A crew member had been killed. Logical Mr. Spock wanted to take off and escape before the monster wrecked the craft. The other crew members refused to leave until they had retrieved the body of the dead crew member. [...]

I agreed with Spock, but I learned that emotions will often overpower logical thinking, even if these decisions prove hazardous.

—Temple Grandin [2]

In this example, and in many others, Spock's perception filter prevents him from understanding human decisions mainly driven by emotion. Those actions appear foolish or nonsensical, because Spock interprets them through his own lens of logic. He lacks the cultural background, social norms and unspoken assumptions unconsciously shared by humans.

The reverse is also true: Whenever humans are puzzled or annoyed by Spock, it is because they expect him to behave like a human; they are often confronted with a harsher truth than they would like. Humans interpret Spock's behavior through their own emotional perception filter. They often misunderstand his motives, assume malice and superimpose intents that change the meaning of his original words and actions.

Autism

You're probably familiar with the conceptual models of communication. In many of those models, communication is represented as the transmission of a message between a sender and a receiver.

In a basic communication model, the sender formulates the message, and transmits it to the receiver, who interprets it. The receiver also provides some feedback.

An oral discussion involves a lot more signals from nonverbal communication, like tone of voice, facial expressions and body language.

If you apply this model to an oral conversation, you quickly see all the opportunities for miscommunication: From what the sender means, to what they actually say, to what the receiver hears, to what they understand, information can change radically, especially when you consider nonverbal communication. It's like a 2-person variation of the telephone game. In the words of psychologist Tony Attwood:

Every day people make intuitive guesses regarding what someone may be thinking or feeling. Most of the time we are right but the system is not faultless. We are not perfect mind readers. Social interactions would be so much easier if typical people said exactly what they mean with no assumptions or ambiguity.

—Tony Attwood [3]

If this is the case for neurotypical people, meaning people with a "typical" brain, imagine how challenging it can be for autistics like me. A great analogy is given in the movie The Imitation Game, inspired by the life of Alan Turing, who is portrayed in the film as being on the autism spectrum.

Historical accuracy aside, one of my favorite moments in the movie is when a young Alan is talking to his friend Christopher about coded messages. Christopher explains cryptography as "messages that anyone can see, but no one knows what they mean, unless you have the key."

A very puzzled Alan replies:

How is that different from talking? [...] When people talk to each other, they never say what they mean, they say something else. And you're expected to just know what they mean. Only I never do.

Autistic people are characterized by many different traits, but one of the most prevalent is social blindness: We have trouble reading the emotions of others. We lack the "Theory of mind" used by neurotypical people to attribute mental states (like beliefs and intents) to others. We often take things literally because we're missing the subtext: it's difficult for us to read between the lines.

Liane Holliday Willey, an autistic author and speaker, once summarized it this way:

You wouldn't need a Theory of Mind if everyone spoke their mind.

—Liane Holliday Willey [4]

How are you?

Many languages have a common phrase to ask someone how they're doing, whether it's the French Comment ça va ?, the English How are you? or the German Wie geht's?

When I first moved to the US, every time someone asked me "How are you?," I would pause to consider the question. Now, I've learned that it's a greeting, not an actual question, and I've mostly automated the response to the expected "Great, how are you?." It only takes a few milliseconds to switch to that path and short-circuit the question-answering process. But if people deviate from that usual greeting, then that mental shortcut doesn't work any more.

A few weeks ago, someone in the Wikimedia Foundation office asked me "How is your world?," and I froze for a few seconds. In order to answer that question, my brain was reviewing everything that was happening in "my world" (and "my world" is big!), before I realized that I just needed to say "Great! Thanks!."

"Small talk" by Randall Munroe, under CC-BY-NC 2.5, from xkcd.com.

Privilege and pointed ears

This is only one of the challenges faced by autistic people, and I would now like to talk about neurotypical privilege. I'm a cis white male, and I was raised in a loving middle-class family in an industrialized country. By many standards, I'm very privileged. But, despite my superpowers, being autistic in a predominantly neurotypical society does bring its lot of challenges.

The most common consequence I've noticed in my experience, and in accounts from other autistic people, is a feeling of profound isolation. The lack of Theory of mind and the constant risk of miscommunication make it difficult to build relationships. It's not anyone's fault in particular; it's due to a general lack of awareness.

Imagine that you're talking to me face to face. You don't really know me, but I seem nice so you start making small talk. I'm not saying much, and you need to carry the discussion over those awkward silences. When I do speak, it's in a very monotone manner, like I don't really care. You try harder, and ask me questions, but I hesitate, I struggle to maintain eye contact, and I keep looking away, as if I'm making stuff up as I go.

Now this is what's happening from my perspective: I'm talking to someone I don't really know well, but you seem nice. I don't know what to talk about, so I keep quiet at first. Silences aren't a problem: I'm just happy to be in your company. I don't have very strong feelings about what we're talking about, so I'm speaking very calmly. You're asking me questions, and of course it takes a while to think about the correct answer. All this "eye contact" thing that I learned in school is taking a lot of mental resources that would be better used to compute the answer to your question, so I sometimes need to look away to better focus.

This illustrates one of many situations in which each person's perception filter caused a complete disconnect between how the situation was perceived on each side.

There are also many professional hurdles associated with being on the autism spectrum, and autistics are more affected by unemployment than neurotypicals [5]. I'm privileged in that I've been able to find an environment in which I'm able to work, but many autistics aren't so lucky. It's been well documented that people in higher-up positions aren't necessarily the best performers, but often people with the best social skills.

With that in mind, imagine what the career opportunities (or lack thereof) can be for someone who is a terrible liar, who has a lot of interest in doing great work, but less interest in taking credit for it, who doesn't understand office politics, who not only makes social missteps and angers their colleagues, but doesn't even know about it, someone who's unable to make small talk around the office. Imagine that person, and what kind of a career they can have even if they're very good at their job.

Casual relationships with colleagues and acquaintances are usually superficial; the stakes of the water cooler discussions are low, so people are more inclined to forgive missteps. However, friendship is another matter, and for most of my life, I have hardly had any friends, unless you use Facebook's definition of the term. Awkwardness is generally tolerated, but rarely sought after. It's not "cool."

Most of those issues arise because you don't have a way of knowing that the person in front of you is different. At least Spock had his pointed ears to signal that he wasn't human. His acceptance by the crew of the Enterprise was in large part due to the relationships he was able to develop with his shipmates. Those relationships would arguably not have been possible if they had not known how he was different.

Computer-mediated communication

Let me go back to that conceptual model of face-to-face communication. Now imagine how this model changes if you're communicating online, by email, on wiki, or on IRC. All those communication channels, that Wikimedians are all too familiar with, are based on text, and most of them are asynchronous. For many neurotypicals, these are frustrating modes of communication, because they're losing most of their usual nonverbal signals like tone, facial expressions, and body language.

In online discussions, most of the nonverbal communication disappears, leaving only words. This can frustrate neurotypicals, but is much closer to the native communication model of autistic people.

However, this model of computer-mediated communication is much closer to the communication model of autistics like me. There is no nonverbal communication to decrypt; less interaction and social anxiety; and usually, no unfamiliar environment either. There are much fewer signals, and those that remain are just words; their meaning still varies, but it's much more codified and reliable than nonverbal signals.

What there is online, instead, is plenty of time, time that we can use to collect our thoughts and formulate a carefully crafted answer. Whereas voice is synchronous and mostly irreversible, text can be edited, crafted, deleted, reworded, or rewritten until it's exactly what we want it to be; then we can send it. This is true of asynchronous channels like email and wikis, but it also extends to semi-synchronous tools like instant messaging or IRC.

It's not all rainbows and unicorns, though. For example, autistics like me are still very much clueless about politics and reading between the lines. We tend to be radically honest, which doesn't fly very well, whether online or offline. autistics are also more susceptible to trolling, and may not always realize that the way people act online isn't the same as the way they act in the physical world. The Internet medium tends to desensitize people, and autistics might emulate behavior that isn't actually acceptable, regardless of the venue.

Autism in the Wikimedia community

Of course, one major example of wide-scale online communication is the Wikimedia movement. And at first glance, Wikimedia sites, and Wikipedia in particular, offer a platform where one can meticulously compile facts about their favorite obsession, or methodically fix the same grammatical error over and over, all of that with limited human interaction; if this sounds like a great place for autistics (and a perfect honey trap) well, it is to some extent.

For example, my first edit ten years ago was to fix a spelling error. My second edit was to fix a conjugation error. My third edit was to fix both a spelling and a conjugation error. That's how my journey as a Wikipedian started ten years ago.

Wikipedians are obsessed with citations, references, and verifiability; fact is king, and interpretation is taboo. As long as you stay in the main namespace, that is. As soon as you step out of article pages and venture into talk pages and community spaces like the "Village Pump," those high standards don't apply any more. There are plenty of unsourced, exaggerated and biased statements in Wikipedia discussions.

That's in addition to the problems I mentioned earlier. As an autistic, it can be hard to let go of arguments about things or people you care about. It's often said that autistic people lack empathy, which basically makes us look like cold-hearted robots. However, there is a distinction between being able to read the feelings of other people, and feeling compassion for other people.

Neurotypical people have mirror neurons that make you feel what the person in front of you is feeling; autistic people have a lot fewer of those, which means they need to scrutinize your signals and try to understand what you're feeling. But they're still people with feelings.

If you're interested in learning more about autism in the Wikimedia community, there's a great essay on the English Wikipedia, which I highly recommend. One thing it does really well is avoiding the pathologization of autism, and instead insisting on neurodiversity, meaning autism as a difference, not a disease.

Conclusion

Steve Silberman, who wrote a book on the history of autism, presented it this way:

One way to understand neurodiversity is to think in terms of human operating systems: Just because a PC is not running Windows doesn't mean that it's broken.

By autistic standards, the normal human brain is easily distractible, obsessively social, and suffers from a deficit of attention to detail.

—Steve Silberman [6]

But still, neurodiversity has a cost. Sometimes, you'll be offended; sometimes, you'll be frustrated; and sometimes, you'll think "Wow, I would never have thought of that in a million years."

As I mentioned earlier, I believe Spock was only able to build those relationships over time because people were aware of his difference, and learned to understand and embrace it. Spock also learned a lot from humans along the way.

My goals here were to raise awareness of this difference that exists in our community, to encourage us to discuss our differences more openly, and to improve our understanding of each other.

There is a lot I didn't get into in this essay, and I might expand on specific points later. In the meantime, I'm available if you're interested in continuing this discussion, and you should feel free to reach out to me, whether in person or online.

Live long and prosper. 🖖

"ISS-42 Samantha Cristoforetti Leonard Nimoy tribute," by NASA, in the Public domain, from Wikimedia Commons.

2014 in review, and the year ahead

Guillaume Paumier — Mon, 23 Feb 2015 07:24:15 GMT

In 2014, I posted a few photos, I continued to work on technical communications at Wikimedia before a role change, I learned more about myself, I moved to California, and I hiked a lot.

2014 in failures

Let's begin with what didn't work and get it out of the way. In January 2014, I started posting some of my photos on this site. I have accumulated tens of thousands of photos over the past eight years, but published only a small fraction of them. By starting to publish a selection of them here, my goal was to create a momentum that would encourage me to process my backlog and publish my collections here and on Wikimedia Commons.

The photos didn't last, but they might come back.

The momentum didn't really last, though, and I ended up stopping after posting only seven photo articles. In retrospect, I think the issue wasn't really the photos themselves, but rather the accompanying texts. I've acknowledged this failure, and recently decided to retire the “Photo” section of this website. The photos are still online, but I've removed the navigation shortcut to that section.

I may resume posting photos in the future, although it's not a priority at the moment. If I do, I might change the format of the posts and only feature the photos with a very short text, if any.

2014 in work

During most of 2014, I continued to work as Technical Communication Manager at the Wikimedia Foundation, the nonprofit that operates Wikipedia.

Part of this work involved reviewing technical posts for the Wikimedia blog; I notably edited and published a series of candid essays written by students who participated in the Google Code-in program. In their “discovery reports”, they outlined their first steps as members of the Wikimedia technical community, and provided a newcomer's perspective on tools and processes regularly used by experienced contributors.

I also continued to assemble monthly engineering reports, and to put together the weekly technical newsletter, which celebrated its first anniversary in May. I also worked with André on the Project management tools evaluation, which eventually led the Wikimedia technical community to migrate to Phabricator.

I attended the Zürich hackathon, as well as Wikimania, the annual Wikimedia conference, whose 2014 edition was in London. At Wikimania, I presented on Tech News and put together a poster so that attendees could learn about it even if they couldn't attend the presentation.

In September, my role at the Wikimedia Foundation changed, and I started working on other projects, most notably the File metadata cleanup drive. The drive is an initiative to decrease the number of files (on Wikimedia sites) whose information can't be read by programs.

In September, my role at the Wikimedia Foundation changed, and I started to work on other projects, like the File metadata cleanup drive.

2014 in self-discovery

2013 had been a turning point for me, in that I had discovered that I was likely on the autistic spectrum. In 2014, a few experts officially confirmed that hypothesis. When asked why this had not been detected earlier in my life, the prevailing hypothesis was that I had unknowingly compensated this social blindness by a higher potential, as suggested by tests performed in 2013. I like to think of it as having my my own emulated emotion chip.

I feel like I deserve a membership card or something.

Throughout 2014, I continued to research and read on this topic. Doing so, I've continued to better understand my blind spots, and explored what I now refer to as my “super-powers”, a fancy way of characterizing the unique way in which my brain works.

Notably, I started reading on a variety of specialized topics I was not familiar with but intrigued me. Doing so, I discovered that I was very fast at picking up and understand new concepts and disciplines. I had had a feeling that that was the case for a long time, but experimenting with this skill was particularly fun and rewarding (I've recently been reading about Civil engineering and Human spaceflight).

2014 in transatlantic move

The biggest change in 2014 was our emigration from France to the US. As part of my role change at the Wikimedia Foundation, I relocated to the San Francisco Bay Area (again). The relocation process was easier this second time around, in part because my partner was able to relocate with me this time, and also because we decided to get organized.

Transitioning from a completely-remote environment to a tech open-space has required some adjustments, but overall we're very happy to have relocated.

2014 in physical activity

I do go outside sometimes, and as someone intrigued by the concept of Quantified Self, I try to keep metrics about my life whenever possible. Physical activity is one of the easiest things to track thanks to dedicated mobile apps.

I love to hike and I occasionally run. In 2014, I knew we were going to relocate to sunny California, so I decided to take advantage of the snowy Alps while we were still in France.

It had been years since I had skied downhill, but after a couple of days it all came back and I enjoyed it a lot. I also started snowshoeing, which was a really nice complementary activity. Where downhill skiing involves sprints and adrenalin, snowshoeing involves endurance and beautiful lesser-used forest trails.

Activity	Distance (km)	Distance (mi)
Running	178 km	110 miles
Hiking (inc. snowshoeing)	163 km	101 miles
Downhill skiing	105 km	65 miles
Cycling	49 km	30 miles
Cross-country skiing	21 km	13 miles

The year ahead

2015 is already well underway, but it's not too late to mention what I'm planning to do this year.

Regarding my work at the Wikimedia Foundation, I'm continuing to lead the File metadata cleanup drive, and I'm hoping to continue to drive down the number of files missing machine-readable metadata. I also have a few smaller projects in the pipeline, notably the Template taxonomy.

Regarding personal work and recreation, I've started to learn Spanish again. My goal is to be able to handle basic communication by Summer, when I may visit Mexico City. Hopefully, by then, I'll be able to say more than “¡Hola!”, “Soy una tortuga” and “El elefante come la manzana”.

I've also decided to learn to play the piano; we'll see how far I can go in one year. Considering that I'm a total beginner, I can only make progress!

This year, I'm starting (from scratch) to learn to play the piano.

Last, I intend to continue to populate this site with historical and new content. My current priority at the moment is finishing to write about past projects before embarking on new ones, but I do think there will be room to post new content before next year's “year in review” post.

Transatlantic move II

Guillaume Paumier — Sat, 21 Feb 2015 00:25:18 GMT

Where our heroes embark on their second move a quarter of a world away, but not before planning it thoroughly.

In previous episodes

I lived in San Francisco for a year in 2009–2010, when I first started working for the Wikimedia Foundation on the Multimedia Usability Project. Unfortunately, I had to move back to France because my partner couldn't get a visa at the time.

When I first came to the US, I didn't have a bank account, a social security number, or a credit history. My knowledge and understanding of American culture was limited to what could be gleaned from movies and TV series.

In 2009, we spent the first few nights after we arrived in a hotel, and our reservation ended before we could find a place. We managed to find temporary accommodation for two more weeks, which gave us enough time to find the apartment where I lived for a year.

I remember this period being a very stressful time because of all the uncertainty. Last year, I was offered the opportunity to transition to a new position at the Wikimedia Foundation, and as a consequence to relocate (back) to San Francisco. When we made the decision to come live in the Bay Area again, we also decided to do things differently this time around.

A staged process

Any move requires planning. Moving to a new country 9,400 km away (5,800 miles) requires a lot of planning.

A critical issue when moving is usually that, during the same time period, you need to leave your place, and find a new one, and do all that while packing your belongings.

Instead, we decided to decouple the “leaving Toulouse” part from the “arriving in San Francisco” part. Our visas wouldn't be ready until September, but we decided to leave Toulouse in July. The plan was to stay at my father's house in Normandy during the transition period.

Having this buffer period turned out to be a great decision. It allowed us to focus on leaving our place in Toulouse without having to worry about finding a place just yet, and it gave us a lot of flexibility with regard to shipping our belongings and flying to San Francisco.

My father's house in Normandy was going to be mostly unused during that period, and living in Normandy for a few months would also be an opportunity to catch up with the rest of the family.

Leaving Toulouse

Leaving our old apartment in Toulouse was the easy part. We gave notice to our landlord and scheduled our milestones based on when we'd be leaving. Because we didn't have to worry about finding a new place just yet, or choosing what to take with us to the US, it made things a lot simpler.

Our two-part move meant that we had two opportunities to go through our belongings and decide what to bring, and what to leave behind.

There were few parking spaces in the street of our apartment building, so we needed to park the moving van directly on the street. We contacted the city services in advance and they allowed us to temporarily block traffic in the street while we loaded everything.

One thing we didn't expect, though, was that “having permission to park on the street” didn't actually mean “being able to park there”. It turned out that work on the utility lines was happening in our street at the same time, and on moving day the whole street was filled with trucks, vans and other heavy equipment.

In the end, we managed to squeeze in our van during the workers' lunch break. We loaded it in a record one hour, thanks to half a dozen friends of friends who came to give us a hand at the last minute.

A few months in the Normandy countryside

We arrived in Normandy the next day and proceeded to set up our living quarters at my father's. It took a little skill to essentially merge two homes into one, so some of the boxes lasted a bit longer than expected, but we managed to set up our own space without disrupting the house's equilibrium too much.

I set up a makeshift office in a hallway where I worked for the first few weeks.

As we settled in, we also made a few improvements, notably to organize the house a bit more.

This is how a guest room gets Stolmened.

We had never stayed in this house more than a few days at a time, mostly for the holidays. This time, we were able to take advantage of the beautiful Summer weather to explore the area on the week-ends.

Living in Normandy for a few months became an opportunity to explore the area during beautiful Summer week-ends.

I also went through some of my old stuff to clear out boxes, notes from college, and generally put my affairs in order before leaving, since I knew we wouldn't be able to come back very often (or very long).

My Air Tech Claw Rig (8868) and beloved Mission Commander (6986) are now stored away, patiently waiting to be built again the next time I visit.

As October approached, we started focusing on preparing the second leg of our move, and finding a place in the San Francisco bay area. We quickly realized that the pace at which Craigslist ads were being posted and taken down meant that we'd have to be on site to do any kind of proper house-hunting. The good news was that we could leave Normandy at any time, since we didn't have to give notice.

Arriving and settling in in the Bay Area

Knowing from experience that we'd need a few weeks to find a home, we looked for temporary accommodation on Airbnb from France, and stayed in Berkeley when we arrived.

This time around, we didn't have to worry about setting up a bank account and getting a social security number, so that made things a bit easier. We still didn't have a credit history, though, so many Craigslist postings were out of our reach.

We eventually found a great place in the North Bay that was both affordable and in a great setting, and we've been living there since.

We now live in the North Bay, farther from the office, but in a much quieter setting.

Cultural acclimation has been smoother this time, partly due to having lived here before. The excellent “Life in the USA” site has been of great help as well. Touted as “the complete web guide to American life for immigrants and Americans”, it has provided us with incredible insight into the American culture, and I recommend it to anyone who's immigrating to the US. Also, this time my partner was able to get a visa (thank you, United States v. Windsor) so this has obviously made the transition much less stressful.

Living in the North Bay involves a longer commute than when I was living downtown, but this hasn't been a deal-breaker so far. I would obviously like to be able to live closer to work, but I'm currently willing to have a long commute if that means living in a quiet area and in a more affordable home. (The rent of the one-bedroom I used to rent in San Francisco has doubled since 2010.)

The longer commute provides time to get ready for work in the morning, and a welcome break in the evening.

It's now been four months since we moved, and we're mostly settled. We've had to adapt our routines to a new set of constraints, but we're glad we moved. We hope to explore the area a bit more as Spring and Summer approach, and roam the beautiful parks and trails of northern California.

42,812 photos

Guillaume Paumier — Mon, 06 Jan 2014 15:56:17 GMT

42,812. That's the number of files currently residing in my “photos” folder. They span seven years of photography, three continents, and an evolving mix of taste, experience and equipment. This mosaic marks the inauguration of the gallery section of this site, where I'll be sharing the pictures I like the most.

To be fair, many of the photos sitting in my hard drive are similar to each other. I often take series of nearly-identical pictures to increase the likelihood of getting at least one good shot out of the lot. This can be, for example, when taking photos in low light without stabilization, or when photographing a moving subject like a flying plane, a running squirrel or a babbling politician. There's also, in the lot, quite a bit of actual duplicates that exist in different formats.

Nonetheless, that's a fair amount of photos, and I'm slowly sorting, rating, tagging, describing, geolocating and uploading them all (mostly to Wikimedia Commons). One thing I've noticed that helps me process my photos is to have a routine, like dedicating a few hours every week to that activity. It can be a pretty intense and exhausting task, though, and I'm probably more likely to stick to it if I do it for shorter periods of time but more regularly.

Therefore, I've opened a dedicated gallery section on this site, where I'll be posting some of my photos a few times a week (ideally, once a day, but I'll start with a less ambitious goal).

To inaugurate this gallery, it seemed fitting to start with a representative sample of my body of work so far. I've therefore created a photographic mosaic, i.e. a photograph made of thousands of smaller photographs, picked for their color and composition, and assembled to reconstruct the pattern and color of a larger picture.

The mosaic above shows the "Painted Ladies": famous Victorian houses near Alamo Square, in San Francisco. It's composed of 22,059 of my other pictures, automatically picked by a program from the pool of 42,812. I used the excellent (and open-source) Metapixel tool to prepare the images and assemble the mosaic. Below is the original photo I took in June 2010.

Original picture, taken with a point-and-shoot camera.

The original photo is quite ordinary, and yet I really like the mosaic that was generated from it. The mosaic almost transforms the picture into a pointillism painting, which makes it much more interesting. The mosaic's colors appear washed out compared to some of the saturated colors of the original, but it actually contributes to the painting appearance.

It's my first time making a photo mosaic, so I've experimented a bit with the settings but didn't dive into them too much. For example, the small pictures composing the mosaic are all squares, regardless of their original aspect ratio (usually 3:2 or 2:3, or 4:3 for my older photos), which means that nearly all of them are squeezed. It's not too much of a problem in this case, but I'll want to retry assembling mosaics while conserving the images' ratio. There's a minimal distance of 50 between identical images, but several items of a series may be closer. I've also slightly favored chrominance over luminance in the color matching algorithm.

I've experimented with different pattern images, i.e. the source image whose pattern is reconstructed from the smaller ones. I like some of the other resulting mosaics, so I've included them below. I have a few others, but today I'll stick to the San Francisco theme.

Below is a mosaic showing the famous Golden Gate Bridge, seen from San Francisco's Presidio. It's composed of 11,094 pictures. The original photo actually made it to Wikimedia Commons a mere five weeks after it was taken.

The results aren't as good on images with gradients, but this one is still pretty nice.

This mosaic is a bit more vibrant than the one of the Painted Ladies, because the original image is more saturated. I like the original because it has strong red-green-blue components, but the strong blue mostly disappears in the mosaic. The original also contains more color gradients, which don't look so nice in the mosaic; the whole sky is pretty noisy, and the artistic vignetting is exacerbated, particularly in the lower right corner.

Part of the sky's noise is due to the minimal distance of 50 images between two identical ones: this causes groups of images to be repeated in waves that become noticeable from afar. It's possible to increase the minimal distance, however that drastically increases the processing time needed to create the mosaic.

The third and last mosaic (below) shows the same issues of gradients and repeating waves in the sky and sea; you can compare with the original. However, it highlights something that isn't obvious in the previous mosaic, and that's the impressive quality of Metapixel's matching algorithm, which makes it possible to make out the bridge's suspension system, and even individual cables. I'm also quite fond of the grain added by the mosaic to the rusty chain and pillars in the foreground.

It's possible to make out the bridge's suspension system and individual cables, thanks to Metapixel's matching algorithm.

To wrap up: I like the additional potential of creativity provided by mosaics as a medium, and I'll probably play with them again in the future. But for now, after this introduction, I'll start posting individual photos, many of which have been hinted at in the mosaics. I've quite enjoyed rediscovering forgotten pictures as I was exploring the super-high resolution versions of the mosaics, and I hope you'll enjoy them as well, as they start popping up in this gallery.

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Guillaume Paumier — Wed, 12 Jun 2013 18:42:50 GMT

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