I feel so optimistic, joyful, and full of hope that I am starting to feel impatient for the next few weeks to arrive. Don’t get me wrong, I still have a lot of stuff to get through and lots of tasks to complete, but I am so excited for the future. I am so excited at the idea of meeting new people and putting myself out there in a completely new experience. This will be so different from all the things I have done before.

I also can’t help but feel excited about the potential of seeing a specific individual again. I think (unless he is already seeing someone new) I am leaning toward telling him how I feel. It is super scary, but I don’t want to go through life realizing I missed out on something because I was afraid.

Thank you for your message. I am currently out of office — but will return at the end of the month.

I've been on a kick recently—a new mindset I've adopted for myself. I am fully convinced I'm not suited for love. Not unless I am to undergo thousands of dollars in therapy, and even then it would cost the sacrifice of multiple victims before I could get it right. Even so, I still find people attractive. My peers tend to get excited when one of my crushes is mentioned because they know my beliefs. Recently, I went as far as to tell two different friends of mine that there was someone I had thought was pretty cute. In fact, I even told them I thought this person was funny! They scavenge for any juicy material like this just to get their hands on it. All of this was just a joke, of course. I had no motivation to pursue the infatuation. I kept it discreet, although my friends and I shared plenty of smirks and glances whenever the person made conversation with me. Having a crush has always been the most exciting part of a romance. I believe most people can concur, believing that the honeymoon phase is always the liveliest. Most people would concur that the honeymoon phase is always the liveliest. I have always enjoyed the thrill of the cat-and-mouse game—getting to sneak in a casual flirt one day and anticipating a response the next. I think that’s the aspect I miss most. Crushes, however, eventually lead to a crossroad. It’s a choice that calls for you to decide if you will remain faithful to this person, show them your most vulnerable sides, and stick with them through thick and thin. And that is exactly when I back out. Yes, I know—red flag and all that. I open with all of this to say that a mutual friend of mine wants to pursue the very person who holds my infatuation. Naturally, I was ecstatic. We spent the remainder of the evening plotting how they'd end up on their first date. She seemed excited to see everyone agree that she has a fairly good chance at winning the prize. Driving home that night, it all hit me. Like a sickening punch to the throat, I forced my jaw to unclench. I do not care that my crush is soon to be pursued by another person. I simply do not, and that is a fact I know to be true in my heart. What hurt the most was realizing I had gotten so set in my ways that I hadn't even considered saying something to them in the first place. If I really had nothing to lose—seeing as I wasn't striving for a long-term relationship anyway—I may as well have seized the day Even now, typing all of this out, I know it's just nonsense. A small fairytale I entertain myself with sometimes—one in which I am a fully healed person who believes they are worthy of love, and that love will seek them out. It's a shame I have made this decision for the rest of my life. I cannot wait to see them be happy, or not. I will simply choose to live in my quiet corner, choosing not to use my voice or go out on a limb to tell that person I think they're cute. Even if a relationship doesn't follow through, wouldn't it mean something just to say something? Here's an even scarier thought: what if I knew I couldn't say anything because I would care? I would care that, regardless of the outcome, I would have had to say something that exposes me. For all these walls I've put up, and for as much as I've claimed not to care, I have found in all of this that I do, in fact, care. I wish I knew how to stop. Really, I do.

Chapter 1: The Smile That Kept the Peace

At a coffee counter, a man who walks with difficulty waits while his drink is being made. A stranger notices him, smiles warmly, and says, “Good for you for getting out today.” The man smiles back. He says thank you because the stranger is already turning away, and because explaining himself would turn an ordinary morning into a conversation neither of them expected. I think about that quiet second after the smile. It is one reason I wanted to talk about what respectful encouragement sounds like for adults with disabilities in the YouTube conversation that accompanies this article.

I have lived with the effects of a stroke since I was sixteen. I know how much kindness matters, and I know that not every awkward sentence deserves a confrontation. But I also know what it feels like to be measured against expectations that seem to have been lowered before I entered the room. That is why the difference between Christian compassion and pity matters so much to me. Someone may believe they are lifting you up while their words quietly suggest that ordinary life is more than they expected you to manage.

The hardest part can be deciding what to say afterward. If you correct the person, you risk embarrassing someone who was genuinely trying to be good. If you accept the compliment, you may feel as though you have agreed with an idea of yourself that isn't true. So you keep the peace. You carry your coffee to the car. Later, while the engine idles, you think of the answer you wish you had given: “I'm glad to be here too. But this is just my Tuesday.”

I don't want that stranger to feel ashamed. I want them to understand why the words landed differently than intended. A person with a disability can be tired, irritated, funny, stubborn, faithful, skilled, and sometimes wrong, just like anyone else. We do not live every hour as an example of perseverance for the people around us. Sometimes we want coffee without being asked to represent courage. Sometimes we want someone to ask whether we like the weather or whether the line is always this slow.

There is a small spiritual discipline hidden inside this ordinary exchange. Before speaking, we can ask ourselves whether our encouragement recognizes the person or mostly celebrates our own surprise. Jesus did not teach us to love an idea of our neighbor. He called us to love the actual person beside us. For me, that begins with letting another adult have an ordinary day, without asking them to smile through a compliment that makes them feel less than equal.

Chapter 2: The Difference Between Needing Help and Being Helpless

The zipper on a winter coat catches just before someone is ready to leave a friend's house. One hand isn't cooperating, and the little piece of metal refuses to move. A friend notices from across the room but doesn't rush over. Instead, she asks, quietly enough that nobody else turns around, “Want me to get that started?” The answer is yes. It takes her three seconds. They keep talking about the evening as they walk toward the door, and the moment passes without either person having to make something of it.

I wish more people understood how much that ordinary exchange can mean. Having a disability doesn't mean I never need assistance. I do. There are things that take more effort because of the stroke I experienced when I was sixteen. I have no interest in pretending otherwise. What can be difficult is knowing whether asking for help will change how someone sees me. Sometimes a small request seems to become evidence, in another person's mind, that I must need help with everything. So even the decision to ask can carry more weight than it should.

The friend at the door did something important without making a speech about it. She noticed a specific difficulty, offered a specific kind of help, and left the rest of the person's life alone. She didn't need to call anyone brave. She didn't need an explanation of the disability. Once the zipper moved, the conversation went back to what it had been. That is the kind of assistance that lets someone remain an equal participant in the moment.

When I read John 13, where Jesus washes His disciples' feet, I notice that He doesn't use service to place Himself above the people He is serving. The act is humble and personal. He bends down to do a necessary task, then calls His followers to care for one another. I don't think that passage gives us permission to decide what another person needs without asking. I think it invites us to examine why we help. Are we trying to make someone comfortable, or are we hoping to feel important because they needed us?

This also changes how I think about receiving kindness. I don't become less of an adult when I say yes to a hand. Able-bodied people ask for help every day, whether it's lifting a sofa, finding a lost address, or understanding a difficult bill. Nobody considers those requests proof that they cannot manage their lives. We ought to extend that same understanding to a person whose need happens to be visible.

Maybe the next time someone asks for a little help, we can give exactly what they've asked for, with no applause attached. Then we can pick up the conversation where we left off. There is something deeply comforting about being allowed to need another person without having to become someone else's lesson.

Chapter 3: A Conversation Worth Having

After dinner, an old friend stays at the kitchen table while the others move into the living room. He has been telling me how remarkable he thinks I am for getting through each day after my stroke. I can hear the affection in his voice. I also know he hasn't quite understood what he is praising. So instead of smiling and changing the subject, I tell him something I usually keep to myself: “I appreciate that you care about me. But I would rather you notice what I do with my life than be amazed that I live it.”

He looks surprised, and for a moment I worry that I have hurt him. That is a familiar worry. People with disabilities often face the choice between protecting a relationship from discomfort and saying what needs to be said. But friendship has to be able to hold a little truth. I explain that I want him to celebrate the work I finish, laugh at my bad jokes, and tell me when he disagrees with me. I don't want to be placed somewhere above ordinary human life, where nobody expects much and every small effort earns praise.

My friend listens. He tells me he hadn't thought about it that way. Nothing is fixed in a grand moment of understanding. We still have different experiences of the world, and there will be things each of us gets wrong. But I feel more at ease because I have been honest, and he has cared enough not to defend himself. The conversation has become a place where both of us can learn.

James 1:19 tells us to be quick to listen and slow to speak. I think that is good counsel for these encounters. If someone tells us an offer of help felt intrusive, we can hear them without insisting that our intentions should settle the matter. If someone uses an awkward phrase while trying to be kind, we can respond with patience when possible. And when a person says they don't want assistance, we can respect that answer without withdrawing our friendship.

I cannot speak for everyone who lives with a disability. We differ in what we need, what we enjoy discussing, and how we want others to approach us. That is precisely why listening matters more than memorizing a perfect set of words. A person is never fully explained by a cane, a wheelchair, a changed voice, or a medical history. They deserve the chance to tell you who they are.

When my friend finally gets up from the table, we begin talking about plans for next weekend. He asks what I would like to do. He doesn't assume the answer or search for a safer one before I've spoken. It feels wonderfully ordinary. I believe Jesus calls us toward that kind of love, where kindness leaves enough room for another person to be themselves.

Your friend,

Douglas Vandergraph

Explore the complete Douglas Vandergraph Master Index: https://douglasvandergraph.com/douglas-vandergraph-master-index/

Watch Douglas Vandergraph’s faith-based videos on YouTube: https://www.youtube.com/@douglasvandergraph

Most experts on Stack Exchange started out as somebody asking a basic question.

That is how the system was designed. A newcomer posts something simple about a regression model or a pandas dataframe. A stranger with more reputation points answers it. Someone else edits the question so it makes sense, and a third person links a related thread. The newcomer comes back a week later with a harder question and, months on, answers someone else's basic one. Multiply that loop by a few million people and you get one of the largest bodies of freely shared technical knowledge ever assembled.

Now take the first step away. Ask the basic question to a chatbot instead. You get an answer, often a decent one, in seconds, and nobody laughs at you. But no question goes up, no stranger answers or corrects it, and the loop that might have turned you into a contributor never starts.

Multiply that by a few million people too.

A new study of seven Stack Exchange communities suggests this is not a thought experiment. In the sites where AI answers best, the share of newcomers still active a year after arriving fell by close to half after ChatGPT launched, while the core of committed regulars barely moved. A separate study of roughly 12,000 office workers found generative AI widening people's networks while also nudging them towards colleagues they already relied on. Put those findings alongside half a century of sociology on “weak ties”, and an uncomfortable question comes into focus. When machines quietly absorb the casual, low-stakes questions that once pulled strangers into a community, what happens to the community?

Seven Communities, One Natural Experiment

The Stack Exchange study is titled “Generative AI and interrupted peripheral participation: evidence of structural closure from Stack Exchange”. Its authors are Junlei Du, Shuang Li, Yinpei Yu, Xinyi He and Jingxi Chen of Beijing Normal University, and it was posted to the preprint server Research Square on 4 August 2026. The page lists it as under review. It has not yet passed peer review, and its findings should be read with that in mind.

The authors followed seven Stack Exchange sites from 2016 to 2025, grouped by how easily their knowledge can be turned into something a machine could produce and a user could check. Artificial Intelligence and Data Science are “algorithmic” communities, heavy with code. Statistics, Physics and Chemistry are “disciplinary”, heavy with formulas and derivations. Cooking and Travel are “practice-based”, built on situated experience that resists neat codification.

All seven share the same reputation system, moderation tools and interface, which lets the type of knowledge do the varying. The authors treated ChatGPT's public release in late November 2022 as a shock that should hit the codifiable communities hardest, and compared what happened before and after using a difference-in-differences design. Robustness checks included synthetic controls and placebo tests that pretend the shock happened in an earlier year. They then examined three layers at once: the network of who answers whom, the retention of different kinds of users, and the content of the questions.

Where the Halving Actually Happened

The headline figure is that newcomer one-year retention “falls by nearly half”. That phrase comes straight from the paper's abstract. The detail sits in the results tables, and it matters.

In the two algorithmic communities, the proportion of newcomers still active a year later fell from 11.5 per cent before generative AI to 6.0 per cent after it. That is a drop of 5.5 percentage points, or 47 per cent in relative terms. It is the “nearly half”.

The other groups fell too, but by less. Newcomer retention in the disciplinary communities slipped from 12.7 per cent to 9.7 per cent. In the practice-based communities it went from 6.9 per cent to 5.1 per cent. The authors describe this as a dose-response gradient: the more codifiable the knowledge, the bigger the hit. So the accurate version of the headline is narrower than it first sounds. Retention nearly halved in the communities where code answers dominate, not across all seven.

The same pattern shows up among what the authors call low-activity users, people with just one or two recorded interactions, who serve as a proxy for the periphery. Their retention fell significantly only in the algorithmic group, from 9.5 per cent to 6.2 per cent. Among high-activity users, people with ten or more events, retention did not change significantly anywhere. In the algorithmic communities it moved from 57.7 to 54.1 per cent, a shift the authors do not treat as statistically meaningful. In the practice-based communities it actually rose.

One more detail complicates the story. The share of askers posting for the first time fell fairly evenly across all three groups. The authors read this as a sign that AI “shapes who returns more than who first arrives”. People still turn up. Fewer of them stay.

That is the periphery thinning in real time. The regulars are still there; what is missing is the stream of newcomers who would have replaced them. The paper puts it bluntly: the algorithmic core shrank in absolute numbers “even though per-member core retention stayed stable, because newcomers no longer arrive at the rate needed to replenish it”.

The overall scale of the contraction is stark. By the authors' count, activity in the algorithmic communities fell by around 80 per cent from its 2020 peak. Disciplinary communities fell by 30 to 50 per cent and practice-based ones by about 40 per cent. In one network diagram, the connected core of the Data Science site collapses from 70 users in 2019 to four in 2024.

Bonding Up, Bridging Down

Retention is the human story. The network story is where the paper earns its title.

Sociologists distinguish two kinds of social capital. Bonding capital is the dense web of ties inside a tight group. Bridging capital is the thinner set of ties that cross between groups and carry information from one cluster to another. Healthy knowledge communities need both.

In the algorithmic communities, the balance tipped. Modularity, a measure of how strongly a network splits into separate clusters, rose by 0.085 in the main estimate and by 0.158 in the synthetic control version. Average path length, roughly the number of hops it takes to get from one user to another, also went up. Interaction increasingly stayed inside tight topic clusters instead of crossing between them. A third bridging measure, the E-I index, swung sharply inward in the algorithmic group, though the authors are careful to note that this indicator failed their pre-treatment trend test and so cannot carry causal weight on its own.

The disciplinary and practice-based communities barely moved on these structural measures, even though they also lost users. That contrast is the paper's strongest argument against a simple “everything is shrinking” explanation. Cooking and Travel lost about 40 per cent of their activity yet kept their shape. The algorithmic sites lost more and changed shape.

The authors call the outcome “structural closure”. The abstract summarises it like this: “bridging weakens as bonding among incumbents remains comparatively intact.” The community does not die; it closes in on itself. Regulars keep talking to regulars, and the threads that once ran outwards to occasional visitors and curious strangers fray.

They also rule out one tempting explanation. If a handful of super-users were simply hoovering up all the remaining activity, inequality in how connected people are should have risen. In the algorithmic communities it fell. This is not a power grab by an elite. It is the edge falling away.

The Slower Question

Does any of this matter if the questions still get answered? The paper suggests it does, though the authors call these links associational rather than proven.

Median time to a first answer rose by two to five hours across all three groups after generative AI arrived. Across the community-year data, higher modularity went with longer waits. A 0.1 increase in modularity was associated with roughly 3.6 extra hours before a question got its first answer. The intuition is straightforward: if the network has split into tight clusters, a question that falls between them has to travel further to reach someone who can help.

The content of the questions shifted too. The authors measured how often different topic tags appear together on the same question, a rough proxy for how much the community's knowledge is connected across subjects. That density fell by 37 per cent in the algorithmic communities. The questions that remained were also more scattered across niche topics and more focused on debugging specific code, the kind of problem a chatbot cannot solve without seeing the actual artefact.

The authors describe this as two fabrics fraying together. The social fabric of ties between subgroups and the semantic fabric of links between topics erode at the same time. In their discussion, they tie this directly to sociology's most famous argument about networks, describing the pattern as Granovetter's “weak-ties argument in reverse”.

Why Routine Questions Were Never Just Routine

To see why the authors frame this as more than a traffic problem, you need a concept from 1991.

That year, the anthropologist Jean Lave and the learning theorist Etienne Wenger published “Situated Learning: Legitimate Peripheral Participation” with Cambridge University Press. They studied apprenticeship among midwives, tailors, quartermasters, butchers and recovering alcoholics, and argued that people learn a practice by participating in it, starting at the edges. A newcomer is given small, legitimate, low-stakes tasks. Doing them in the presence of old-timers lets the newcomer absorb how the community thinks and talks, and gradually move towards the centre.

Stack Exchange, the Beijing Normal team argues, worked in a similar way. Asking a routine question in public “exposes a newcomer to public reasoning, establishes an initial interaction, and creates the possibility of return”. It is the entry ticket.

A large language model, they write, “separates the informational outcome from this social process”. You get the answer without the exchange. The edge-of-the-room apprenticeship never begins, because there is no room.

Separate evidence supports the idea that answers recruit contributors. In a paper posted to arXiv in April 2026, Lenard Strahringer, Sven Eric Prüß and Kai Riemer analysed more than 21 million Stack Overflow questions and found that “receiving an answer significantly increases a user's propensity to help others”. The effect, they reported, “is concentrated among newcomers and declines with platform experience”, and peaks when answers arrive within roughly 30 to 60 minutes. Being helped is how newcomers become helpers. Veterans do not need the nudge. Remove the moment of being helped, and the recruitment pipeline loses its first stage.

There is an honest caveat here, and the authors state it up front: “individual decisions to use an LLM are not directly observed.” What the study shows is a community-level pattern, timed to ChatGPT's release and concentrated where AI answers are easiest to generate and check. It is strong circumstantial evidence, not proof.

The Weak Ties Granovetter Counted

The sociological backbone of this argument is Mark Granovetter's 1973 paper “The Strength of Weak Ties”, published in the American Journal of Sociology. Its core logic explains why losing a periphery is more than losing numbers.

Granovetter's argument is structural. Your close friends tend to know each other, so they mostly hear the same news you do. Your acquaintances move in different circles. In networks of any size, he showed, a strong tie is almost never the only route between two parts of a network, because close friends share other friends. In his words, “all bridges are weak ties.” The loose connections are the ones that carry new information between groups.

He tested this on job seekers in a Boston suburb. Among the respondents he interviewed in person who had found work through a personal contact, 16.7 per cent said they saw that contact often at the time, 55.6 per cent occasionally and 27.8 per cent rarely. The subsample was small: 54 people from a total sample of 282. But the skew was clear. The useful contact was usually someone on the edge of a person's life, such as an old college friend or a former colleague.

The paper's closing paragraph contains a line that could almost caption the Stack Exchange network diagrams: “strong ties, breeding local cohesion, lead to overall fragmentation.”

Half a century later, the theory got its large-scale causal test. In a paper published in Science in September 2022, Karthik Rajkumar, Guillaume Saint-Jacques, Iavor Bojinov, Erik Brynjolfsson and Sinan Aral analysed randomised experiments on LinkedIn's “People You May Know” feature. Over five years these experiments varied the share of weak ties in the networks of more than 20 million people, a period in which about two billion new ties and 600,000 new jobs were created. Weak ties did increase job mobility, with caveats. When tie strength was measured by mutual connections, the relationship was an inverted U: moderately weak ties helped most. Weak ties also mattered more in digital industries, and strong ties more in less digital ones.

So the familiar claim that weak ties are the lifeblood of mobility has solid support. For innovation and social inclusion it is a reasonable reading of Granovetter's wider argument, not something these studies measured directly. He called weak ties “indispensable to individuals' opportunities and to their integration into communities”. The careful version is that weak ties are the main way information and opportunity cross social boundaries. On Stack Exchange, a stranger answering a newcomer's question was a weak tie in its purest form.

Inside a Company Rolling Out Copilot

The second strand of evidence moves from public forums into a corporate office, and the picture there is more complicated than it is often made to sound.

“The Effects of Generative AI on Collaboration and Social Networks Among Knowledge Workers” was posted to arXiv on 23 October 2026 by Giuseppe Soda and Alessandro Iorio of Bocconi University and Leonardo Rizzo of SDA Bocconi School of Management. Like the Stack Exchange paper, it is a preprint.

The authors studied a single business unit of a large multinational consumer products company, which they do not name. The unit started with 12,040 employees. Between September 2024 and September 2025, the company rolled out Microsoft Copilot in stages, which gave the researchers a natural experiment. Their final sample was 11,868 workers: 1,992 who adopted Copilot and 9,876 who never did. Using passively collected data on who collaborated with whom, and for how long, they tracked what happened to each adopter's network.

They tested two competing predictions: that AI replaces human interaction, or that it generates more, because its outputs need interpreting, checking and approving by other people. The second won. Adopters collaborated with about 2.18 more colleagues on average than comparable non-adopters. Their networks became less clustered, meaning their contacts were less likely to know each other, which the authors read as broader collaboration. Total collaboration time went up too, by 0.87 hours in the post-adoption average.

The third result is the one that matters most here. Adopters, the authors write, “also concentrated more of their collaboration on ties with stronger prior histories.” Put simply, Copilot users leaned harder on colleagues they already had a track record with, which the authors describe as deepening reliance on already trusted colleagues. The effect is clearer in some models than others. One summary estimate falls short of conventional statistical significance, while the post-adoption average and the instrumental-variable model both reach it.

The picture, then, is a network that is both wider and more anchored on trusted people. The authors' own reading is that AI output creates new demand for human judgement, and people send that demand towards colleagues whose judgement they already trust.

What the Office Study Did Not Measure

It is tempting to go further and say this research shows the casual, cross-topic exchanges that connect different social worlds eroding. It is worth saying plainly that the paper does not show that.

Soda, Iorio and Rizzo did not analyse weak ties, casual ties or cross-topic ties. In fact their method screened out many of them. To reduce what they call “noise from incidental interactions”, they kept only ties above the monthly median weight, about one hour of collaboration. A quick corridor chat or a two-line message to someone in another department is exactly the kind of contact a filter like that is likely to remove. The study cannot tell us whether those contacts shrank, grew or stayed the same.

The authors are also candid about other limits. “We know who interacts with whom and for how long, but not what they talk about,” they write. Their data cover one business unit in one company, and they say it is “not clear” whether the findings apply to other industries or workplaces. The results cover the first three months after each person adopted the tool.

What can fairly be said is narrower: in this organisation, Copilot users reached more people while concentrating more of their sustained collaboration on familiar partners. There is a related precedent. When Longqi Yang, David Holtz, Sonia Jaffe, Siddharth Suri and colleagues studied 61,182 Microsoft employees during the shift to remote work in the first half of 2020, they found collaboration became more static and siloed. Time spent on cross-group collaboration fell by about 25 per cent of its pre-pandemic level, and employees added fewer new collaborators. That paper, published in Nature Human Behaviour, was about remote work, not AI, and found narrower networks rather than wider ones. But it shows how quickly a change in working conditions can pull people towards their inner circles. Whether Copilot does the same to the casual edges of a network is a question the Bocconi study raises without answering.

The Volume Collapse Around It

The Beijing Normal paper builds on two earlier peer-reviewed studies that measured the drop in Stack Overflow activity directly.

In a paper published in PNAS Nexus in September 2024, R. Maria del Rio-Chanona, Nadzeya Laurentsyeva and Johannes Wachs compared Stack Overflow with sites where ChatGPT was less available or less capable, such as its Russian and Chinese-language equivalents. They estimated that posting on Stack Overflow fell by about 25 per cent within six months of ChatGPT's release, relative to those comparisons, and called that a lower bound. Their title states the stakes plainly: “Large language models reduce public knowledge sharing on online Q&A platforms.”

One finding complicates the newcomer story: posts from first-time users fell only slightly, while inexperienced, experienced and expert users all posted significantly less. That does not contradict the Beijing Normal results, which concern whether newcomers stay rather than whether they post once, but it shows how much depends on what is measured.

In Scientific Reports in May 2024, Gordon Burtch, Dokyun Lee and Zhichen Chen of Boston University's Questrom School of Business estimated that Stack Overflow lost about a million daily visitors, roughly 12 per cent of its traffic, after ChatGPT arrived. The decline was concentrated among newer users. Their most striking finding was that Reddit's developer communities showed “no evidence” of a similar drop. The authors attributed the difference to “social fabric”. Reddit's communities, they argued, have stronger social bonds, which act as “a buffer against the community-degrading effects of LLMs”. That buffer is bonding capital, the kind the Beijing Normal team saw surviving; bonding without bridging is what Granovetter warned against.

The raw numbers since then have been brutal. In May 2025, the engineering newsletter The Pragmatic Engineer, citing a Stack Exchange Data Explorer query shared by the long-time contributor Marc Gravell, reported that Stack Overflow was on course for about 14,000 questions that month. That is fewer than the 17,668 it received in September 2009, in the site's infancy. The newsletter's author, Gergely Orosz, noted that the decline began around 2014 and sped up from mid-2020, well before ChatGPT. The fall has not slowed since. In January 2026, Tim Anderson of DevClass, drawing on Stack Overflow's public query tool, reported that only 3,862 questions were posted in December 2025, a drop of 78 per cent on the previous year. In August 2026, Luís Rijo of PPC Land reported a comparison posted on X by the developer Daniel Lockyer, using the Stack Exchange Data Explorer: 1,442 new questions in July 2026, about 0.7 per cent of the 207,204 posted at the site's peak in March 2014. The whole of 2025 produced 108,981 questions, little more than half the total for March 2014 alone. AI did not start the slide. It turned a slow decline into a fast one.

Nor is the pattern limited to programmers. In October 2025, Marshall Miller of the Wikimedia Foundation reported that human pageviews on Wikipedia were down about 8 per cent on the same months of 2024. He linked this to search engines answering questions directly, often using Wikipedia's own content, and to younger people searching on social video. His warning about the consequences was explicit: with fewer visits, “fewer volunteers may grow and enrich the content”. Editors are recruited from readers; fewer readers means a smaller pool of future editors.

A Periphery That Was Already Fraying

It would be too easy to cast generative AI as the villain that wrecked a welcoming commons. Stack Overflow's periphery had problems long before ChatGPT.

In April 2018, Jay Hanlon wrote a post on Stack Overflow's official blog under the headline “Stack Overflow Isn't Very Welcoming. It's Time for That to Change.” He admitted that “too many people experience Stack Overflow as a hostile or elitist place”, especially “newer coders, women, people of color, and others in marginalized groups”. He wrote that “too often, someone comes here to ask a question, only to be told that they did it wrong”, and that “condescension and sarcasm have been reluctantly tolerated in comments for too long.”

Wikipedia went through something similar earlier. In a 2013 paper in American Behavioral Scientist, Aaron Halfaker, R. Stuart Geiger, Jonathan Morgan and John Riedl found that the encyclopaedia's decline in active editors was driven by falling retention of good-faith newcomers, not by fewer newcomers arriving. They pointed to Wikipedia's quality-control tools, including algorithmic tools used to reject contributions, as part of the cause.

Both cases show communities damaging their own on-ramps with gatekeeping that feels reasonable to insiders. Part of what AI is doing is exploiting a weakness that already existed. A newcomer choosing between a chatbot that never sneers and a forum that might close their question as a duplicate is not making an irrational choice.

That cuts both ways. It weakens any nostalgic case for the pre-AI periphery, and it suggests the communities now losing their edge had been running down their own renewal for years.

The Case for the Machine Answer

Does the convenience of an instant answer outweigh the long-term cost to community renewal? Any honest answer has to take the convenience seriously.

For the individual asker, the chatbot is often better. It is available at three in the morning, never demands a minimal reproducible example, and will explain the same concept five ways. For people who were made to feel unwelcome, which by Stack Overflow's own admission included many women, people of colour and beginners, that is not trivial. A learner who used to give up after a hostile reception can now keep going.

The communities that remain may also be doing more interesting work. Denis Helic and Tiago Santos, in a paper first posted in September 2025 and later published in the Journal of Systems and Software, found that ChatGPT had “effectively raised the bar for questions on Stack Overflow”, with question difficulty, length and code length all increasing. The routine traffic left. The hard problems stayed. The Beijing Normal team's finding that remaining questions skewed towards debugging specific code fits the same pattern.

Developers have not stopped wanting other people either. Stack Overflow's 2026 Developer Survey, which drew 30,903 responses from 169 countries, found that 73 per cent of respondents now use coding assistants or agents every day. Their trust is conditional. Some 48 per cent said they trust AI when they can easily validate its answers, and 79 per cent rated source attribution important or very important. A year earlier, more developers had distrusted the accuracy of AI output, 46 per cent, than trusted it, 33 per cent. Asked which actions they turn to first when trying to find answers to a problem, 70 per cent chose an AI agent, but 29 per cent chose to send a message directly to a human they trust, and the people they work with remained their most trusted source of information, at 72 per cent. A mixed-methods study posted to arXiv in March 2026 by Ching Christie Pang, Xuetong Wang, Yuk Hang Tsui and Pan Hui, based on a survey of 217 people and 11 interviews, reached a similar conclusion. Users rely on AI for convenience but still go to communities for questions that are complex, ambiguous or depend on trust.

So the human community is not obsolete. It is being pushed upmarket, towards hard cases and trust-sensitive judgement. The problem is that its future experts have to come from somewhere. The easy questions were the training ground. That is where convenience runs into renewal. The individual gain is immediate and visible; the collective cost is delayed and shows up only in network statistics years later.

Training Data Eats Its Own Source

The loss of the periphery has a second-order cost the AI industry has its own reasons to care about. Public question-and-answer sites are a major source of training data for large language models. The Beijing Normal authors note that “sustained bridging erosion thins the very base future AI depends on.” The PNAS Nexus team made a parallel argument: less open posting means less public data to train future models, and some of that knowledge now sits inside privately owned AI systems instead.

The commercial relationship makes the loop explicit. On 6 May 2024, Stack Overflow and OpenAI announced a partnership giving OpenAI access to Stack Overflow's data through its OverflowAPI product, with Stack Overflow using OpenAI models to build its own AI features. Google had earlier signed up as a customer of the same API. The questions and answers that newcomers and regulars wrote for each other for free became a licensed asset sold to the companies whose products are now absorbing the newcomers.

The irony needs no exaggeration. The models answer routine questions well partly because millions of people once asked them in public and strangers answered. If the public asking stops, the archive stops growing while the software keeps changing. Next year's routine question about a framework that does not yet exist will need someone to have answered it first.

Designing an On-Ramp Back In

If the periphery is worth saving, the evidence points to a few practical moves rather than nostalgia.

The first comes from the Beijing Normal paper. Its central principle is that “AI assistance should protect rather than absorb the periphery's entry path”. In practice the authors propose an in-community assistant that answers routine questions from the community's own verified knowledge, and then points the asker towards the people who wrote it. The aim is to turn a one-off answer into a first contact.

There is early evidence this can work. In a paper accepted at the CSCW 2026 conference, Junling Wang, Lahari Goswami, Gustavo Kreia Umbelino, Kiara Chau, Mrinmaya Sachan and April Yi Wang built a chatbot that brings Kaggle community content and social features into its answers. Across two studies with 28 and 12 data science learners, it increased user trust and encouraged engagement with the community. The samples are small and the setting is not a live platform, but a chatbot can evidently be built as a bridge rather than a replacement.

The second move is to measure the right thing. The Beijing Normal authors found the structural shift appeared before activity hit bottom, and suggest modularity and path length could serve as early-warning signals alongside volume dashboards. Platforms that only watch question counts will notice the decline after the periphery has already gone.

The third is targeted intervention. Because the damage concentrates where knowledge can be expressed as checkable code, the authors would put the strongest safeguards in algorithmic communities. They also propose recommending collaborators across topics based on the content of their work rather than existing ties, an explicit attempt to manufacture bridges.

Inside organisations, the Copilot study suggests a parallel lesson. If AI pushes people towards trusted colleagues for judgement, managers who value cross-boundary knowledge may need to create reasons for different groups to meet.

And communities have to make the edge worth staying at. Burtch and colleagues recommended “enabling socialization, as a complement to pure information exchange”. Belonging only among insiders is closure, so the welcome has to reach newcomers too.

Communities Renew From the Edge

Can knowledge communities survive the loss of their periphery? The best current evidence suggests they can survive in the short term, in the narrow sense that the core keeps going. The regulars stay, the hard questions still get asked, and the bonds among incumbents hold. What looks threatened is renewal: the slow, unglamorous process by which strangers become members and members become experts.

The evidence has limits. The Stack Exchange study is a preprint covering seven communities, only two of which carry the dramatic result, and it infers rather than observes individual behaviour. The Copilot study covers one business unit in one company for a few months, and it did not measure casual cross-topic ties at all. Granovetter's insight is half a century old and was built on a small interview sample, though the LinkedIn experiments have since confirmed it at enormous scale. None of this proves that AI is hollowing out every knowledge community.

But the pieces line up uncomfortably well. Routine questions recruited newcomers. Newcomers formed the weak ties that bridged clusters, and bridges kept knowledge moving and the core replenished. Generative AI answers routine questions instantly and privately. In the places where it does that best, newcomers are staying at roughly half the old rate, clusters are closing in on themselves and questions are taking longer to reach the right person.

Cohesion without bridges, Granovetter warned, ends in fragmentation. Knowledge communities have always been renewed by people who arrived knowing very little and asked basic questions in public. If those people now ask a machine instead, the communities will keep their experts for a while. What they risk losing is the way new experts were made.

---

References and Sources

1. Du, Junlei; Li, Shuang; Yu, Yinpei; He, Xinyi; Chen, Jingxi (Beijing Normal University). “Generative AI and interrupted peripheral participation: evidence of structural closure from Stack Exchange.” Research Square preprint (under review, not peer reviewed), posted 4 August 2026. DOI: 10.21203/rs.3.rs-10221893/v1. https://www.researchsquare.com/article/rs-10221893/v1 (PDF: https://www.researchsquare.com/article/rs-10221893/latest.pdf). Accessed 30 October 2026.
2. Soda, Giuseppe; Iorio, Alessandro (Bocconi University); Rizzo, Leonardo (SDA Bocconi School of Management). “The Effects of Generative AI on Collaboration and Social Networks Among Knowledge Workers.” arXiv preprint 2610.32461v1, 23 October 2026. https://arxiv.org/abs/2610.32461 (HTML full text: https://arxiv.org/html/2610.32461). Accessed 30 October 2026.
3. Granovetter, Mark S. “The Strength of Weak Ties.” American Journal of Sociology, 78(6), 1360–1380, May 1973. University of Chicago Press. JSTOR stable URL: https://www.jstor.org/stable/2776392 (full text copy: https://www.cs.umd.edu/~golbeck/INST633o/granovetterTies.pdf). Accessed 30 October 2026.
4. Rajkumar, Karthik; Saint-Jacques, Guillaume; Bojinov, Iavor; Brynjolfsson, Erik; Aral, Sinan. “A causal test of the strength of weak ties.” Science, 377(6612), 1304–1310, 16 September 2022 (published online 15 September 2022). DOI: 10.1126/science.abl4476. Summary: Stanford Digital Economy Lab, https://digitaleconomy.stanford.edu/publication/a-causal-test-of-the-strength-of-weak-ties Accessed 30 October 2026.
5. MIT Initiative on the Digital Economy. “New Publication: MIT, Harvard and Stanford Scientists Show Weaker Ties Are More Beneficial for Job Seekers on LinkedIn.” 15 September 2022. https://ide.mit.edu/insights/new-publication-mit-harvard-and-stanford-scientists-show-weaker-ties-are-more-beneficial-for-job-seekers-on-linkedin/ Accessed 30 October 2026.
6. Lave, Jean; Wenger, Etienne. Situated Learning: Legitimate Peripheral Participation. Learning in Doing series. Cambridge University Press, 1991. DOI: 10.1017/CBO9780511815355. https://doi.org/10.1017/CBO9780511815355 Accessed 30 October 2026.
7. del Rio-Chanona, R. Maria; Laurentsyeva, Nadzeya; Wachs, Johannes. “Large language models reduce public knowledge sharing on online Q&A platforms.” PNAS Nexus, 3(9), pgae400, published 11 September 2024. DOI: 10.1093/pnasnexus/pgae400. https://academic.oup.com/pnasnexus/article/3/9/pgae400/7754871 Accessed 30 October 2026.
8. Burtch, Gordon; Lee, Dokyun; Chen, Zhichen (Questrom School of Business, Boston University). “The consequences of generative AI for online knowledge communities.” Scientific Reports, 14, 10413, published 6 May 2024. DOI: 10.1038/s41598-024-61221-0. https://pmc.ncbi.nlm.nih.gov/articles/PMC11074245/ Accessed 30 October 2026.
9. Strahringer, Lenard; Prüß, Sven Eric; Riemer, Kai. “Help Converts Newcomers, Not Veterans: Generalized Reciprocity and Platform Engagement on Stack Overflow.” arXiv preprint 2604.03209, 3 April 2026. https://arxiv.org/abs/2604.03209 Accessed 30 October 2026.
10. Yang, Longqi; Holtz, David; Jaffe, Sonia; Suri, Siddharth; Sinha, Shilpi; Weston, Jeffrey; Joyce, Connor; Shah, Neha Parikh; Sherman, Kevin; Hecht, Brent; Teevan, Jaime. “The effects of remote work on collaboration among information workers.” Nature Human Behaviour, 6, 43–54, published online September 2021. DOI: 10.1038/s41562-021-01196-4. Microsoft Research summary: https://www.microsoft.com/en-us/research/publication/the-effects-of-remote-work-on-collaboration-among-information-workers/ Accessed 30 October 2026.
11. Berkeley Haas Newsroom / Greater Good Science Center. “How Remote Work Affects Our Communication and Collaboration.” 2021. https://greatergood.berkeley.edu/article/item/how_remote_work_affects_our_communication_and_collaboration Accessed 30 October 2026.
12. Orosz, Gergely. “The Pulse #134: Stack Overflow is almost dead.” The Pragmatic Engineer, 15 May 2025 (citing Stack Exchange Data Explorer data shared by Marc Gravell). https://newsletter.pragmaticengineer.com/p/the-pulse-134 Accessed 30 October 2026.
13. Anderson, Tim. “Dramatic drop in Stack Overflow questions as devs look elsewhere for help.” DevClass, 5 January 2026 (citing Stack Overflow's public query tool). https://devclass.com/2026/01/05/dramatic-drop-in-stack-overflow-questions-as-devs-look-elsewhere-for-help Accessed 2 November 2026.
14. Rijo, Luís. “Stack Overflow drops to 1,442 questions in July, down 99% from 2014 peak.” PPC Land, 8 August 2026 (citing Stack Exchange Data Explorer figures posted on X by Daniel Lockyer, 7 August 2026). https://ppc.land/stack-overflow-drops-to-1-442-questions-in-july-down-99-from-2014-peak/ Accessed 2 November 2026.
15. Miller, Marshall. “New User Trends on Wikipedia.” Diff, Wikimedia Foundation, 17 October 2025 (updated 13 February 2026). https://diff.wikimedia.org/2025/10/17/new-user-trends-on-wikipedia/ Accessed 30 October 2026.
16. Computing. “Wikipedia reports traffic drop, blames AI search and social media.” October 2025. https://www.computing.co.uk/news/2025/ai/wikipedia-reports-traffic-drop Accessed 30 October 2026.
17. Hanlon, Jay. “Stack Overflow Isn't Very Welcoming. It's Time for That to Change.” Stack Overflow Blog, 26 April 2018. https://stackoverflow.blog/2018/04/26/stack-overflow-isnt-very-welcoming-its-time-for-that-to-change/ Accessed 30 October 2026.
18. Halfaker, Aaron; Geiger, R. Stuart; Morgan, Jonathan T.; Riedl, John. “The Rise and Decline of an Open Collaboration System: How Wikipedia's Reaction to Popularity Is Causing Its Decline.” American Behavioral Scientist, 57(5), 664–688, 2013. Research summary: https://meta.wikimedia.org/wiki/Research:The_Rise_and_Decline (University of Minnesota release: https://cse.umn.edu/college/news/wikipedia-inadvertently-causing-its-own-decline-participation). Accessed 30 October 2026.
19. Helic, Denis; Santos, Tiago. “Stack Overflow Is Not Dead Yet: Crowd Answers Still Matter.” arXiv preprint 2509.05879 (v1 7 September 2025; v2 16 June 2026); journal version in Journal of Systems and Software, 241, 112988, 2026. https://arxiv.org/abs/2509.05879 Accessed 30 October 2026.
20. Stack Overflow. “2026 Developer Survey” (sections: AI, https://survey.stackoverflow.co/2026/ai ; Knowledge, https://survey.stackoverflow.co/2026/knowledge ; Community, https://survey.stackoverflow.co/2026/community). 2026. https://survey.stackoverflow.co/2026/ Accessed 2 November 2026.
21. Stack Overflow. “2025 Developer Survey: AI.” 2025. https://survey.stackoverflow.co/2025/ai Accessed 30 October 2026.
22. Pang, Ching Christie; Wang, Xuetong; Tsui, Yuk Hang; Hui, Pan. “The Decline of Online Knowledge Communities: Obstacles, Workarounds, and Sustainability.” arXiv preprint 2603.27399, 28 March 2026. https://arxiv.org/abs/2603.27399 Accessed 30 October 2026.
23. SD Times. “Stack Overflow and OpenAI announce partnership to enable ChatGPT to use knowledge from Stack Overflow answers.” 6 May 2024. https://sdtimes.com/ai/stack-overflow-openai-partnership-chatgpt-use-knowledge-from-stack-overflow-answers/ Accessed 30 October 2026.
24. TechRadar. “Stack Overflow signs deal with OpenAI to strengthen models on its data.” May 2024. https://www.techradar.com/pro/stack-overflow-signs-deal-with-openai-to-strengthen-models-on-its-data Accessed 30 October 2026.
25. Wang, Junling; Goswami, Lahari; Umbelino, Gustavo Kreia; Chau, Kiara; Sachan, Mrinmaya; Wang, April Yi. “Bridging Instead of Replacing Online Coding Communities with AI through Community-Enriched Chatbot Designs.” arXiv preprint 2601.18697 (accepted at CSCW 2026), 26 January 2026. https://arxiv.org/abs/2601.18697 Accessed 30 October 2026.

---

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

Support What We Do

Tell the tides

To wash away my writings.

To the wind,

My unlove, it shall breathe.

My heart has

Buried too much grief.

It can no longer —

Love without being loved,

My dear

---

Dites aux vagues

De brouiller mes poèmes.

Puis au vent,

De souffler que l'on ne m'aime.

Mon coeur a

Enterré bien trop de peines.

Il ne peut plus

Aimer sans qu'on l'aime.

In Summary: * Have just taken the 1st round of my night meds so I'm moving through the night meds and night prayers phase of this evening. Listening now to the IU Pregame Show ahead of the Hoosier Men's Basketball / Syracuse Orange game. Opening tip is just minutes away. I'm planning to listen to the entire game and will finish the night prayers after the game ends. GO HOOSIERS!

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 220.90 lbs. * bp= 154/70 (61)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, toe raises, wall push-ups, BP breathing exercises, pilates

Diet: * 04:40 – 1 banana, 4 little cookies * 08:15 – 1 ham & cheese sandwich * 10:30 – 3 more little cookies * 12:30 – the “innards” of 4 chicken & potatoes tacos * 16:30 – 1 fresh apple

Activities, Chores, etc.: * 03:30 – listen to local news talk radio * 04:15 – bank accounts activity monitored * 04:50 – read, write, pray, follow news reports from various sources, surf the socials, take daily meds, unpack, organize, and nap * 16:00 – have tuned into Hoosier Country 105, the flagship station for IU Athletics, ahead of tonight's game: the (2-0) IU Men's Basketball team hosting the (2-0) Syracuse Orange.

Chess: * 10:40 – moved in all pending CC games

He who eats the most.

is

King

□

Our latest patch version contains several critical security fixes, including one for an authentication bypass that let unauthenticated attackers use the API as another user. It also fixes several other issues around federation, private blogs, and access control. We recommend all admins update to this version as soon as possible.

This release also updates our Web Monetization support to the latest version of the standard, and includes a handful of bug fixes and improvements for users and admins.

Security

• Critical: Fix authentication bypass via API access tokens (reported and fixed by @diaoqi129, GHSA-7jv7-gf23-wcqq)
• Fix private and password-protected blog content exposed via the ActivityPub outbox (by @thebaer, reported by @awanawana, GHSA-cx5r-gg25-76ph)
• Fix server-side request forgery (SSRF) via WebFinger lookups (by @thebaer, reported by @tikket1, GHSA-wvrr-gx9p-xvxm)
• Fix unauthenticated SSRF via ActivityPub inbox actor / object resolution (by @thebaer, reported by @iaohkut-from-NightWolf-Team, GHSA-7635-vx4w-qww7)
• Fix IPv6 transition address bypass of SSRF protections in federation (by @thebaer, reported by @tonghuaroot, GHSA-22qx-x758-m72p)
• Add CSRF protection to account settings endpoints (/me and /api/me) (by @thebaer, reported by @hacktivat0r and coordinated by @u9000-Nine, GHSA-mp2f-3fq8-r9vj)
• Enforce the maximum blogs per user limit when creating blogs via the API (by @thebaer, reported by @tonghuaroot, GHSA-g4m9-5p4q-ch24)
• Prevent users from changing settings on another user's blog via the API (by @thebaer, reported by @N0el4kLs, GHSA-7mmq-gh5f-97p3)
• Prevent users from changing metadata on blogs they don't own (by @thebaer, GHSA-wqc7-9ph9-m5fm)
• Fix stored XSS via unsanitized post slugs in RSS, email, and ActivityPub output (by @thebaer, reported by @geo-chen, GHSA-cwxm-8ch5-j2vh)
• Prevent users from pinning or unpinning other users' posts (by @thebaer, reported by @5ud0er, GHSA-hwfg-mg9c-cvgf)
• Enforce signup checks consistently across all registration paths, extending the invite code validation from v0.17.2 to API and OAuth signups, by @thebaer in https://github.com/writefreely/writefreely/pull/1748

User-Facing Changes

• Update Web Monetization support by @thebaer in https://github.com/writefreely/writefreely/pull/1784
• Include image alt text in fediverse attachment data by @thebaer in https://github.com/writefreely/writefreely/pull/1729
• Fix password-protected blogs not unlocking after cookie expires by @thebaer in https://github.com/writefreely/writefreely/pull/1727
• Normalize usernames on signups via OAuth by @thebaer in https://github.com/writefreely/writefreely/pull/1730
• Fix bad navigation on collection customize page by @snullp in https://github.com/writefreely/writefreely/pull/1738

Admin-Facing Changes

• Output more helpful error messages at startup by @thebaer in https://github.com/writefreely/writefreely/pull/1731
• Support serving robots.txt on single-user instances by @thebaer in https://github.com/writefreely/writefreely/pull/1736

Developer-Facing Changes

• Hide first property in followers/following collections when 0 items by @thebaer in https://github.com/writefreely/writefreely/pull/1733
• Update Docker and release builds to Go 1.26 by @thebaer in https://github.com/writefreely/writefreely/commit/95e1918e10414e51a38afbd9bf9a1948d39f686c

Dependencies and minor fixes

• Bump alpine from 3.18.4 to 3.24.3 in the Docker image by @dependabot[bot] in https://github.com/writefreely/writefreely/pull/1786
• Remove deprecated -d flag from go get in Makefile by @thebaer in https://github.com/writefreely/writefreely/commit/50dedcd05a31a5679cd34b8aea3bbc82501595b1
• Prefer local lessc install over global when building stylesheets by @thebaer in https://github.com/writefreely/writefreely/commit/37d5e01013f9ee00c078a7eed7d3c7ce78809f9a
• Fix Dependabot Docker update directory by @thebaer in https://github.com/writefreely/writefreely/commit/5906aa9f93f4a7b20f15ffc5b0ef7cdfc0ff64a5
• Bump golang.org/x/crypto from 0.54.0 to 0.58.0 by @dependabot[bot] in https://github.com/writefreely/writefreely/pull/1780
• Bump github.com/writeas/web-core from 1.7.0 to 1.8.0 by @dependabot[bot] in https://github.com/writefreely/writefreely/pull/1714
• Bump docker/login-action from 4.3.0 to 4.6.0 by @dependabot[bot] in https://github.com/writefreely/writefreely/pull/1712

Upgrading from v0.17.x or earlier

1. Download the latest release for your operating system and architecture
2. Stop running your writefreely server
3. Replace all files in your installation (except for the keys directory) with the ones in the archive
4. (Only when upgrading from < v0.17.0) Update your database by running: writefreely db migrate
5. Start your writefreely server again
6. (Recommended) Revoke existing API access tokens, as described below

If you're upgrading from a much earlier version, follow the instructions in each previous release.

Recommended: revoke existing API access tokens

Upgrading closes the authentication bypass (GHSA-7jv7-gf23-wcqq), but doesn't invalidate API access tokens that already exist. As an extra precaution, in case any were exposed before you upgraded, we recommend revoking all existing tokens once your upgraded instance is running.

Doing this will sign all users out of apps and tools that use WriteFreely's API, such as mobile apps, command-line clients, and other integrations. They'll need to log in to those again. People signed in to WriteFreely on the web stay signed in.

There's no writefreely command for this, so you'll need to clear the accesstokens table directly. Use the database details from your config.ini:

SQLite

sqlite3 /path/to/writefreely.db "DELETE FROM accesstokens;"

MySQL / MariaDB

mysql -u <username> -p <database> -e "DELETE FROM accesstokens;"

Optionally, to invalidate web sessions, you can also regenerate your session keys using the steps below. This will require all users to log in again, and for readers to re-enter passwords for any password-protected blogs.

1. Stop WriteFreely
2. Back up your keys folder temporarily
3. Delete keys/cookies_auth.aes256 and keys/cookies_enc.aes256 (do not delete keys/email.aes256)
4. Run writefreely keys generate as the same user as your server
5. Start WriteFreely again

Full Changelog: https://github.com/writefreely/writefreely/compare/v0.17.2...v0.17.3

#WriteFreely #release

Originally published at Balancy Counselling & Psychotherapy: https://therapy.balancy.ca/blog/how-to-choose-therapist-burlington

The research is consistent on one point: the relationship you build with your therapist matters as much as the method they use. These seven questions will help you find someone qualified, and someone who feels like the right fit.

The short version

• Check the licence first: a Registered Psychotherapist or Registered Social Worker whose name you can verify on the college register.
• Fit matters as much as method. Use the free consultation to ask questions and notice how the conversation feels.
• Ask about fees, format and availability up front. At Balancy, sessions run from $50 to $190 and the first consultation is free.

In this article

1. Are you licensed to practise psychotherapy in Ontario?
2. What experience do you have with what I'm dealing with?
3. What approaches do you use, and what do they actually involve?
4. What does a typical session look like?
5. What are your fees, and do you offer a free consultation?
6. Do you offer in-person, online, or both?
7. How will we know if it's working?
8. What to do next

Searching for a therapist in Burlington can feel overwhelming. Dozens of profiles, unfamiliar credentials, approaches with acronyms, and unclear fees.

Here is a practical way through it: shortlist two or three licensed therapists, then book a free 15-minute consultation with each. Most Ontario therapists offer one. Use that call to ask the questions below and notice how the conversation feels. Do you feel heard? Do they explain things clearly? Do you feel safe enough to be honest? Those answers matter as much as any credential.

1. Are you licensed to practise psychotherapy in Ontario?

This is the non-negotiable. In Ontario, Registered Psychotherapists (RPs) are regulated by the College of Registered Psychotherapists of Ontario (CRPO), and Registered Social Workers (RSWs) by the Ontario College of Social Workers and Social Service Workers. You can verify anyone on the college's public register. If a therapist cannot or will not tell you their registration number, treat that as a clear no.

2. What experience do you have with what I'm dealing with?

A therapist doesn't need to have lived your exact experience, but they should have real, specific experience with it. Ask about their training and how long they've worked with your specific issue. It's fine if they're relatively new, as long as they are genuinely experienced with what you are bringing in. Their answer can also tell you something else: you should feel invited to share, not assessed.

3. What approaches do you use, and what do they actually involve?

You'll hear names like CBT (cognitive behavioural therapy, working with thought and behaviour patterns), DBT (dialectical behaviour therapy, skills for emotions and relationships), and Gestalt (awareness-focused, present-moment work). A good therapist can explain their approach in plain language and say why they think it fits your situation, not just list acronyms.

4. What does a typical session look like?

Some therapists are conversational, some more structured. Some give homework, some don't. Ask how sessions are structured and how they tackle a client's progress. The ideal session is one that gives you a sense of protection, safety, and boundaries that allow for different kinds of learning and healing.

5. What are your fees, and do you offer a free consultation?

Fees in the Burlington area typically range from about $140 to $190 per session for registered psychotherapists, with some offering sliding-scale spots. (At Balancy Counselling & Psychotherapy, fees across our therapists run from $50 to $190.) Ask directly: Is there a free consultation? What is your cancellation policy? Do you offer sliding scale? Money awkwardness now prevents resentment later.

6. Do you offer in-person, online, or both?

Many Burlington-area therapists now work hybrid: in-person locally and online across Ontario. Research suggests online therapy can be as effective as in-person for common concerns like anxiety and depression. Choose based on your life, a dedicated room away from home versus no commute, and confirm the therapist is set up for whichever you pick.

7. How will we know if it's working?

Therapy isn't meant to be endless, and a good therapist will say so. Ask how they track progress and how you'll decide together whether to continue, adjust, or wrap up. Therapists who welcome this question tend to be the ones worth keeping.

A note on red flags. Be cautious of anyone who guarantees results, discourages you from asking questions, pushes long prepaid packages, or suggests they can replace medical care. Ethical therapists welcome scrutiny, it's part of the job.

What to do next

If you're looking for a therapist in Burlington, Oakville, Mississauga, or Toronto, in person or online, our therapists offer a free 15-minute consultation with no pressure and no commitment. Bring these seven questions. See how the conversation feels. Then decide.

Common questions

How much does therapy cost in Burlington, Ontario?

Fees vary by therapist and credential. At Balancy Counselling & Psychotherapy, session fees range from $50 to $190, and several therapists offer sliding-scale options. Many therapists also offer a free 15-minute consultation so you can ask questions before committing.

Should I choose in-person or online therapy?

Research shows online therapy can be as effective as in-person for many concerns, including anxiety and depression. In-person suits people who value a dedicated space away from home; online suits busy schedules and anyone outside the therapist's city. Many Ontario therapists offer both.

What is the difference between a psychotherapist, psychologist, and social worker in Ontario?

Registered Psychotherapists (RPs) are regulated by the CRPO and provide talk therapy. Psychologists are regulated by the CPO and can also diagnose. Registered Social Workers (RSWs) are regulated by the OCSWSSW and often provide counselling alongside other supports. All three can be excellent therapists; what matters most is fit, experience with your concerns, and an approach that makes sense to you.

In my earlier post, I talked about how authenticity is an effective tool against LLMs and it got me thinking about how my own writing changed since ChatGPT got mainstreamed three years ago. First, I tend to write less as automation has made it extremely easy to generate every day texts. Second, the act of writing itself became pointless because it felt that every writing exercise became the foundation of for another datapoint for LLM's (to illegally) train on. Third, my overall vocabulary for things has shrunk vastly than the previous years, which intersects with lesser time spent reading and more time spent watching short form videos and interacting with internet speak online, especially on social media.

I want to address the last point specifically, where the way we use language on social media is not the way we'd use words otherwise, optimized for maximum engagement which means a shrinking vocabulary and speaking the zeitgeist which effects the way we think. After we are the words we use and if this part shrinks so is our ability to express complexity and revert to black/white thinking. See any kind of political nuance on a social media platform of your choice and it eventually transforms to right wing skullduggery devolving into name-throwing and simplistic narratives and if anyone tries to counteract them with nuance then they're presented with the sleight of being intellectual.

In my own experience the ability to write is a way to think, and the removal of this skill from the everyday means that I think less and my brain gets used to not putting effort in seeking information. I no longer do much mental math, go hunt sources, read a whole book when all the information I need is served to me as a digestible glop, like a mass market newspaper going for the dopamine jugular. So I have ever more information but lesser ways to truly figure out what I think and where I stand, which also removes me from the democratic decision making loop where I see any information as being absolute (there is no way to edit LLM based information atleast on the user end or report it being false) and go with the flow rather than finding my voice and expressing it.

So I am exposed to an ever more fatty version of information, one that hooks and titillates but that doesn't want me to engage or question. I ask myself, whose purpose does that serve? Not mine definitely, if dear reader you're reading this, it doesn't serve yours either.

#AI #thinking #information #writing

Into your arms.

The girl I'd never need touch.

Your sand bleached hair,

Your beach bronzed skin.

Your naked shoulders.

The light in your eyes drunk on mine.

Your smile

Enough.

□

We laugh

at the men that start wars.

Then set their table.

□

What wings are these

we've never known?

What wind is this?

On which

we've never flown.

□

Nothing

makes

you

happy.

□