The umbrella arrives before the rain.

The map arrives before the journey.

Recognition arrives before understanding.

Structure arrives before growth.

& how does a desert stay honest when it's raining.

I talked with a coworker/friend about some of her plight Of San Jose. Specifically with dating and the culture behind it, and I am really grateful I don’t live here. I don’t have to tell myself that no matter what I would still be happy because I am not happy because of my circumstances, but rather the decision decisions that I make. There are always good things that you can pay attention to. But I digress, I do feel, however, that there is an ugly side of me of competition that really comes out here, which is really funny because I am not trying to date anyone here. Hell, I am leaving tomorrow lol. But I noticed this in myself in the fact that whenever I have seen men in public, I kind of insult them in my head, like I try to put them down and I treat it like there is some competition that I am being judged against them in. I feel like I’m on edge and I am insecure about my own desirability as a person, I find myself worrying about things like random hypotheticals that aren’t even coming up, or how these random people are paying for these meals in fancy restaurants for their partner, and how this probably is a normal occurrence for them. And I do believe that this is fiscally irresponsible, but I feel like I’m fixating on that as a justification for not losing this competition rather than as a separate valid point. I don’t like the side of me, I much rather enjoy the side of me that doesn’t feel like I am fighting and trying to put other people down.

I think im overthinking again, which is a great time to start writing. and maybe i have a good reason too. i suppose ill never know. until then, im left wondering, fun right? I’ve never been this attached to a woman before, and honetly, i hate it. i hate how much i want to be around her, how much i notice when she disappears, and how much a simple message seems to matter to me. she can be gone for sixteen, nineteen, twenty hours, then tell me she was asleep. maybe she was. i believe her. i know shes tired. i just dont understand why sending me something when shes awake or in btween sleeps seems so difficult sometimes. even a simple “good morning” would be enough. everyone checks their phone at some point, even if its just to look at the time or check a message. i dont think sending one would take that much. im not asking her to be there all day. just tell me where you’ve been. sleep, games, going out, whatever. i dont care. just tell me. you dont have to lie or pretend you werent on your phone. and yes, i know im probably being ridiculous. but its whatever, who am i to complain or judge? who am i to decide if she should text me or not, i dont think she believes we’re that serious either way, and while that hurts i love her, i love her so much more than i could tell. this doesnt make me love her less, it just bothers me. i do my best, as a man whos busy most of the time, to reply quickly and make an effort. sometimes it feels like she barely cares, or wouldnt bother to text if she had her phone with her. and while i could probably do the same but im afraid of losing her, or doing an act that would make her much more distant. i dont think shes afraid of losing me because she knows ill stay either way, and that hurts more than id like to admit. i dont want to do the same thing just to make her understand how annoying it feels. i just want her to understand without me having to become cruel about it. i do believe everything she tells me, and somehow i still manage to question afterwards, and ill admit that at least, thats my fault. i cant exactly complain to her about this. it is silly. it is unnecessary and if i tell her, ill probably become “too much” again, which is a charming little title id rather not earn.

So ill write it here instead, at least i can be honest here, if she thinks its unecessary, then perhaps i should think so too, maybe thats just the best for me to just do what she does and believe its right, until its not. maybe, maybe not i dont know. I’m very good at not knowing when it comes to things like this. and ill probably not do anything about it, after all im just overthinking.

Sincerely, who has too much time to think

JOURNAL 25 août

Olala la montagne, dormir dehors la nuit ça me manque… l'océan le #surf, on s'amuse, on a des copains, c’est gentil mais c’est pas pareil. Au fond de moi je suis triste, quelque chose me manque. Et c’est la #forêt, le chant doux et profond des arbres et des pierres, le murmure du ruisseau, les multiples présences aux aguets tout autour, une vie intime que l'océan puissant et monstrueux ne permet pas de s'épanouir, il occupe tout l'espace physique et sonore à lui tout seul, il rend impossible toute méditation, on ne peut comme lui qu'être sans cesse en mouvement et s’étourdir. Plein soleil aujourd'hui. On apprécie la clim pendant ces nuits à 27°c. On repartira jeudi matin avec l'arrivée de la pluie qui va s'installer pendant des jours à nouveau.

 6.

Les soldats avaient installé de grandes tentes qui attiraient notre curiosité. On s’y rendait en bande pour se rassurer. Souvent ils nous donnaient du chocolat, ça valait le coup. Parfois on avait le droit de tourner les manivelles de leur radio, c’était dur mais quelle fierté ! On se battait presque pour être le premier. Il y avait des filles dans notre bande, elles n’étaient pas les dernières. Nous nous familiarisions réciproquement avec nos différences anatomiques. Ma sœur avait 10 ans, elle allait à l’école chez les sœurs, elle me paraissait appartenir à une espèce étrangère. Peut-être était-elle française aussi, comme les soldats ? L’atmosphère dans la grande maison était lourde. Mes parents ne s’entendaient pas. Les disputes incessantes entre ma mère et ma grand-mère rendaient la vie commune insupportable. Ma mère avait juste trente ans, elle voulait s’amuser, repartir pour la France. « La chèvre meurt ou on l’attache » lui répondait-on.

“Why do we close our eyes when we pray, cry, kiss, or dream?” I smiled and said,

“Because the most beautiful things in life are not seen, but felt only by the heart.”

I’m tired today. I have nothing else to say. I don’t think im willing to go on like i always do, im just tired. Nothing to write about.

Sincerely, Tired

PS. that was supposed to be for yesterday’s entry, August 26th. No, i did not forget. i tried to publish it. it simply decided not to work. not my fault. yours.

Inbetweeners Part 6- The Crab Shack

I figured I'd do a post highlighting one of the rooms in the original dungeon crawl incarnation of the Inbetweeners Campaign. Something that is funny about running a game is that frequently, characters will spend much more time and attention on something that you barely considered while ignoring content that the game master/dungeon master considered more central. This was the case with the Crab Shack.

If players headed directly “north” (whatever that means in liminal space) from the Grey Room, they would go through a hallway. On the other end of that hallway, they would emerge next to an abandon fishermen's shack on the beach. More specifically, an abandon crabber's shack. I included the basic notes/description in the rooms document, but the takeaway was that this shack had been abandon for some time and had a few skeletons of fisherman or crabbers who, upon investigation, the players can discover died from trauma. The bones are picked clean, either indicating extensive decay (years) or that something at the meat off of them. Not sure we ever got into the players closely examining them, but if they had, a medicine check probably would have revealed they were killed by animals. A high level nature or survival might have been able to pin it down to crabs. There wasn't much of value in the shack, just old fishing equipment: nets, broken traps, fishing hooks and canned fermented crab meat. If they rolled high on investigation, maybe a few gold coins. However, if the players lingered, things got interesting.

Periodically, giant crabs would attack the players. They have an AC of 15, 13 hitpoints, deal 1d6 + 1 damage and can potentially grapple with their pincers. Overall, not very threatening, but a quaint little encounter for the players. I didn't have a set number that would appear over intervals, the idea was just one, and then more would trickle in over time. They don't move particularly quickly, so players can see them coming from a distance. It's less of an urgent threat and more of a... if we wait here long enough we may get overwhelmed with crabs. Each crab provides substantial meat and 25 experience points. I figured players would just hang for a second and move on, but my players loved this room. They liked the idea of just endlessly killing crabs. They didn't end up killing that many, but they did kill a few and collect crab meat, and on passes back through took out a few more crabs. If the players went into the ocean, they would appear in the courtyard of a mages' university, and if they went through one of the doors in the shack, it took them back to the hallway. If they ran down the beach any distance they would simply loop and appear on the other side. However, they could see crabs coming down the beach in the distance, and nothing else living in sight.

There isn't really a whole lot more to say about the contents of the “room” itself. It's more the sort of nostalgia of a simple encounter like this. Early RPG video games where leveling involved grinding low level enemies endlessly. Think the OG pokemon, where you spent hours knocking out rattatas and pidgeys to try to level enough to fight Brock because of course you picked charmander and fire is ass against rock pokemon. The players joked about never leaving the crab shack except to sell their crab meat at the market. They could just endlessly use cantrips and ranged attacks to kill crabs and have a sustainable source of food and income. Theoretically, if they killed enough crabs, they would be able to level arbitrarily high. I followed the XP system for this campaign rather than story based rewards, because I found the arbitrary XP system kind of funny. Theoretically, 14200 crabs per player would level them all from 1 to 20. It would make for dreadfully boring sessions, but, if the players want to do it the players can do it. There's nothing stopping them. Though I suppose eventually they would exhaust the local crab population. I'd say after a few hundred there really wouldn't be any giant crabs left in the area. I try to make it more realistic than video games where things just spawn infinitely. Like eventually charmander would incinerate all the wildlife on Route 1, right?

Towards the end of the game, when the players did the Lady of Pain's gauntlet, they returned to a version of the Crab Shack. However, this one was an entire harbor that, while once buslting, had been completely destoryed. There were overturned ships, many dead, torn apart bodies and devestated structures. Instead of giant crabs though, Chuuls spawned endlessly. To borrow from the Forgotten Realms Wiki, “Chuuls have been described as “a horrible mix of crustacean, insect, and serpent”, but most closely resembled an 8 feet (2.4 meters) tall yellow-green lobster, weighing around 650 pounds (290 kilograms) with four long legs, two large claws, a strong protective exoskeleton, a fan-like tail, and a mass of paralysis-causing tentacles around its mouth.” Each one of these has 96 hitpoints, much more dangerous weapons, and gives 1100 XP. Ironically, by the time the players encountered them, a Chuul posed far less of a threat than the giant crabs did when the players first encountered them.

So yeah, sometimes a simple encounter works best for the players. Not everything needs to be fancy, epic, or full of intrigue. Sometimes players just enjoy hunting crabs.

The most consequential sentence in American higher education this month is six words long, and takes two seconds to type.

“Log in and complete my quiz.”

Reporting by Dana Goldstein and Alan Blinder for the New York Times, published on 10 August 2026, established that this instruction now works. Not as a demonstration, not as a jailbreak, but as ordinary consumer software doing what it was built to do. An agentic browser takes the credentials, opens Canvas or Blackboard or Brightspace, watches the prerecorded lecture, sits the test, drafts the paper and posts into the discussion forum, where it converses with classmates who may themselves be agents. The student is elsewhere, and may be asleep.

Notice what is absent. There is no deception directed at the machine, no prompt engineering, no attempt to evade a filter. The Times tested the three tools most used by students, ChatGPT, Gemini and Grammarly, and found none refused a request to write a paper on a student's behalf. None of the companies prevents its agents logging into learning management systems. Educators have asked the AI firms to make agents identify themselves inside course platforms, and the firms have declined. Perplexity told the Times such a requirement “would put the student's privacy and security at risk”, a remarkable sentence from a company arguing its software should be permitted to impersonate an enrolled human inside an institution that will later certify that person as competent.

The exposure is not marginal. Analysis of federal IPEDS data for autumn 2024, published by the education market analyst Phil Hill in January 2026, found 26.5 per cent of American postsecondary students enrolled exclusively in distance education, rising to 40.5 per cent of graduate students, with roughly 54.8 per cent taking at least one online course. More than half of American college students took an online class last year, up from about a third in 2019. A quarter to a half of the sector's assessed output now passes through a channel in which the only evidence a human was present is a login session and a timestamp.

Six Words Are the Entire Attack Surface

What makes agentic cheating different is not sophistication. It is the collapse of the seam. Contract cheating required a transaction, a counterparty, a payment trail and a delivered file the student then submitted under their own name. Chatbot cheating required copying, pasting and, if the student was careful, some rewriting. Both left a boundary somewhere: a moment at which the student's work stopped and someone else's began.

The agent removes the seam. It does not hand the student a file. It operates the account. The submission originates from the student's session, at the student's IP address, at a plausible hour and pace, with the mouse movements and dwell times of a person reading. Reporting by Frank Landymore in Futurism on 13 August 2026 drew the consequence: professors cannot fall back on supervised examinations and oral presentations in a course where everything happens behind a screen. Digital Trends, covering the same Times reporting that day, stated what most institutions have declined to say aloud, which is that the question is no longer whether AI can help a student cheat on an assignment but whether a student can complete an entire degree without doing much of the learning.

Platforms can look for behavioural tells: time on task, session patterns, the rhythm of navigation. The Times reported no fail-safe way to block agentic cheating, and the reason is structural rather than technical. Any signature that distinguishes an agent from a diligent student is a signature the agent can be instructed to imitate, and the imitation costs the student nothing, because the student is not the one doing the waiting.

The Detector Was Never Going to Hold This Line

The institutional response, reported by Kathryn Palmer in Inside Higher Ed on 5 August 2026, is that universities have stopped pretending detection works.

Yale, Vanderbilt, Johns Hopkins and Indiana have adopted policies banning or discouraging reliance on AI detection software as the sole evidence of alleged cheating. At least a dozen institutions, including Northwestern, Georgetown and New York University, have switched off Turnitin's AI detection feature entirely. The updated Faculty AI Playbook at Indiana's Kelley School of Business tells staff that tools claiming to detect AI use “are highly unreliable” and advises them to design assignments encouraging process, reasoning and authentic engagement instead.

This is a retreat, and it is the correct one. The evidence against detection has accumulated for three years and it is not close.

Vanderbilt was early and unusually candid about the arithmetic. In August 2023 its Brightspace team disabled Turnitin's AI detector and published its reasoning. The university had submitted roughly 75,000 papers to Turnitin in 2022. At the 1 per cent false positive rate Turnitin advertised at launch, that implied around 750 papers wrongly flagged in one year at one institution. Vanderbilt also noted that Turnitin disclosed no detail about how its classifier reached a verdict, and that detectors disproportionately flag writing by non-native English speakers.

That second objection has the strongest empirical backing of anything in this field. Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu and James Zou of Stanford University published a study in Patterns in July 2023 running seven widely used GPT detectors over TOEFL essays by non-native English speakers and over essays by American eighth-graders. The detectors classified the native-speaker writing with near-perfect accuracy. They classified 61.3 per cent of the non-native essays as AI generated, all seven unanimously flagged 19.8 per cent of them, and at least one detector flagged 97.8 per cent. Detectors key on text perplexity, penalising writers with a narrower range of expression. When the researchers used ChatGPT to enrich the vocabulary of the same essays, the false positive rate fell to 11.6 per cent. The surest way to stop a detector accusing a foreign student of using AI was to have AI rewrite their essay.

Turnitin reviewed more than 200 million papers in the first year of its detection feature, roughly 11 per cent of which contained at least 20 per cent AI writing, and its guidance puts the sentence-level false positive rate at around 4 per cent, which is not a rounding error when a single flagged paragraph is what a misconduct panel is looking at. The detectors fail in the other direction too. Peter Scarfe, Kelly Watcham, Alasdair Clarke and Etienne Roesch at the University of Reading ran the most rigorous real-world test of the proposition, published in PLOS ONE in June 2024, inserting AI-generated submissions into live undergraduate psychology assessments without the markers' knowledge. Ninety-four per cent went undetected. The AI submissions attained grades on average half a grade boundary above those of the real students.

Jennifer Frederick, executive director of Yale's Poorvu Center for Teaching and Learning, and Alfred Guy, who directs its undergraduate writing and tutoring programmes, gave Inside Higher Ed the clearest statement of why Yale stepped back. Evidence shows, they wrote, that “humanizing” programs are very successful at reducing or eliminating the percentage of AI usage that is detected, and they wanted to avoid “the inevitable cat and mouse game created by AI detection tools”. Their objection was not that the arms race is unwinnable but that it is beside the point. “This becomes a technical exercise rather than a learning event.”

The cost of getting it wrong is not abstract. Haishan Yang, a third-year PhD student in health economics at the University of Minnesota, sat an eight-hour preliminary examination remotely in August 2024 and was expelled the following January after faculty graders concluded, partly from AI detection software and ChatGPT comparisons, that his essays were machine-written. Yang denied it and sued. A federal court dismissed his due process claim in October 2025 and the Minnesota Court of Appeals affirmed the expulsion in February 2026. Whatever the merits of that case, its shape is what matters: an unreliable instrument, a career-ending sanction, and a burden of proof no party can discharge.

The Only Numbers Anyone Has Are the Numbers of the Caught

Abandoning detection has a consequence universities have not absorbed. The sector's statistical picture of academic misconduct is a record of enforcement, not behaviour, and enforcement has just been switched off.

The best longitudinal data comes from the United Kingdom. Michael Goodier, reporting for the Guardian on 15 June 2025, obtained figures under the Freedom of Information Act from 131 of 155 universities. Proven cases of AI-related cheating reached almost 7,000 in 2023-24, equivalent to 5.1 per 1,000 students, up from 1.6 the year before, with partial figures suggesting about 7.5 the following year. Proven conventional plagiarism fell from 19 per 1,000 students to 15.2 over the same period. More than 27 per cent of responding universities did not yet record AI misuse as a separate category.

Now set that against what students say they do. The Higher Education Policy Institute's Student Generative AI Survey 2026, written by Rose Stephenson and Charlotte Armstrong and published on 12 March 2026 from a December 2025 survey of 1,054 full-time UK undergraduates, found 94 per cent using generative AI to help with assessed work, up from 88 per cent in 2025 and 53 per cent in 2024. Twelve per cent said they had included AI-generated text directly in assessed work, up from 8 per cent and then 3 per cent in the two preceding years.

Twelve per cent of undergraduates admitting to submitting machine-written text. Five proven cases per thousand students. The gap is roughly a factor of twenty-four, and it is the honest measure of how much the sector knows about its own assessments. Scarfe told the Guardian that those caught are the tip of the iceberg, and explained why in a sentence that ought to be pinned to every misconduct policy in the world. AI detection is unlike plagiarism, where you can confirm the copied text. Where you suspect AI, it is near impossible to prove.

The faculty view comes from a survey of 1,057 American college faculty conducted between 29 October and 26 November 2025 by Elon University's Imagining the Digital Future Center with the American Association of Colleges and Universities, released on 21 January 2026. Seventy-three per cent had personally dealt with academic integrity issues involving student use of generative AI, 78 per cent said cheating had increased on their campus, 95 per cent expected the technology to increase overreliance, 90 per cent expected diminished critical thinking, and 74 per cent believed it would negatively affect the value of a degree. The authors are explicit that the sample is not statistically generalisable. It does not need to be. Three-quarters of a large sample of the people who mark the work believe the qualification they confer is worth less than it was, for reasons they can name.

Contract Cheating Already Answered This and Nobody Wanted the Answer

There is a comforting story in which generative AI created this crisis in November 2022. It is false, and believing it is why the sector is improvising.

Philip Newton of Swansea University published a systematic review in Frontiers in Education on 30 August 2018 covering 71 samples from 65 studies going back to 1978, with 54,514 participants. The historic average of students self-reporting commercial contract cheating was 3.52 per cent. In studies conducted between 2014 and 2018 the figure was 15.7 per cent, which Newton extrapolated to roughly 31 million students globally. That was five years before ChatGPT. The market was mature, the transactions untraceable in practice, the detection rate negligible, because a purpose-written essay contains no copied text for a similarity checker to find.

Tracey Bretag, Rowena Harper and colleagues surveyed 14,086 students across eight Australian universities for a study in Studies in Higher Education, and found three factors reliably associated with outsourcing: dissatisfaction with the teaching and learning environment, a perception that there were lots of opportunities to cheat, and speaking a language other than English at home. None is a technology variable. Two are descriptions of institutional design.

England responded with criminal law. The Skills and Post-16 Education Act 2022 made it an offence to provide, arrange or advertise contract cheating services for financial gain to students at post-16 institutions and higher education providers in England. It was the right instinct and has proved near-impossible to enforce, the providers being numerous, offshore and hard to reach, and prosecution sitting with the Crown Prosecution Service rather than an education regulator with a reason to care.

So the boundary between a student's work and someone else's did not disappear in 2026. It disappeared, for a meaningful minority, during the 2010s, and universities carried on treating the unsupervised written artefact as evidence of learning because the alternative was expensive. Generative AI removed the price, the counterparty, the delivery lag and the risk, converting a minority behaviour into a default one. That is not a change of kind but a change of magnitude large enough that the old assumption stops functioning.

Tricia Bertram Gallant, who directs the academic integrity office and testing centre at UC San Diego and co-authored The Opposite of Cheating, said exactly this to Inside Higher Ed. Over twenty-odd years, she noted, changes from internet-supported plagiarism to the contract cheating industry and now AI have slowly degraded the validity of twentieth-century assessments. Yet higher education has not changed. “We're still relying on the unsupervised written word as evidence of learning. The real trick is acknowledging that that doesn't work anymore.”

Students Are Not Confused About the Rules, They Are Renegotiating Them

One paper in this literature circulates as a finding that ChatGPT lowers the barrier to contract cheating while making detection harder. That is a fair summary of the field but not of the paper, and precision matters here. ArXiv 2606.09845, submitted on 27 April 2026 by Belle Li, Lily Tan, Wei Zakharov, Qiang Qiu and Colby Ben Acton, is titled “Tutor, Not Solver: Designing a Guardrailed AI Assistant for Learning in Higher Education: A Design Case of PeteChat”. It is not a prevalence study. It documents an AI tutor deployed at Purdue University on a locally hosted Llama-3 model with retrieval-augmented generation grounded in course materials, from which the authors derive eight design principles for assessment-aware tutors, from homework guardrails to self-regulated learning support. Its interest here is that it inverts the detection approach, assuming students will use a model and constraining what the model does at the point of use rather than punishing them afterwards. That works only inside a system the institution controls, which an agentic consumer browser is not.

The paper that addresses the collapse of the boundary is arXiv 2605.29090, submitted on 27 May 2026 by Jiyoon Kim, Kentaro Toyama, Sangmi Kim and John M. Carroll, titled “'It's OK Because...': The Wild West of Student Rationalization of AI Use in Academic Writing”. Drawing on twenty semi-structured interviews together with students' AI chat logs, syllabuses and submitted assignments, the authors find at least five distinct sites at which AI use is conceptualised, running from the policy the instructor intended through to what students actually did. Between those five sites, meaning drifts.

Within that drift the authors catalogue more than twenty distinct rationalisations. That copying AI-generated text is victimless. That any AI text reflecting the student's own beliefs, or reading in their own style, is therefore their own writing. That extensive AI use means they are learning more than they otherwise would. The taxonomy includes justifications used to excuse conscious violations of stated course policy. Crucially, these rationalisations arise both ad hoc and post hoc and are not necessarily self-consistent: students construct them in the moment, reconstruct them afterwards, and do not need them to cohere. Modern AI, the authors conclude, presents a steep ethical slippery slope which students conceptually slide down, landing far outside their instructors' pedagogical goals.

This should worry universities more than the agent story, because it describes a condition no assessment format repairs. A student who believes machine-generated prose expressing their own view is their own writing has not broken a rule. They have sincerely adopted a different definition of authorship, and they will carry it into a graduate job where nobody will correct it either.

The Argument on Reddit Is the Argument Everywhere

The third paper cited in the brief was characterised as documenting teacher burnout and poor institutional support. It is something else, and the something else is more useful.

At arXiv 2605.17712, submitted on 18 May 2026 by Pelin Yüce, Xiangruo Dai, Rebecca Owens and Tuğrulcan Elmas, “ChatGPT vs Teachers vs Students: Large-Scale Analysis of Generative AI Discourse in Education Communities on Reddit” analyses 270,000 AI-related posts and comments across 26 education subreddits between November 2022 and April 2026. Topic modelling yields seventeen themes. The trajectory is a detection-and-evasion arms race hardening into a sustained enforcement regime, with constructive integration only beginning to challenge it from the middle of 2024.

Different constituencies worry about different things. K-12 teachers foreground cognitive dependency, academics focus on detection, students in professional programmes on career anxiety. The finding that matters most is about contact. Seventeen per cent of threads are cross-role, and one third of that cross-role contact occurs in the two adversarial themes, AI Detection and Misconduct Enforcement. Students initiate 68 per cent of mixed threads, but faculty produce most of the replies. Mixed threads contain two to three times more records than same-role threads and last two to four times longer. Sentiment correlates strongly negatively with engagement.

Strip out the platform and what remains is a description of an institutional relationship. The sustained centre of contact between the two parties to an education has become the dispute over whether one of them cheated. Not the subject. Not the feedback. The accusation and the defence.

Marc Watkins, a writing and composition lecturer who directs the AI Institute for Teachers at the University of Mississippi, put the resourcing question to Inside Higher Ed in terms nobody has costed. He could not fathom the cost to a single university, let alone most campuses, of scaling AI-resilience tactics such as proctored oral examinations, and it should not all fall on faculty. Kevin Yee, who directs the Faculty Center for Teaching and Learning at the University of Central Florida, described colleagues at wit's end, moving towards assignment redesign not because they think it better but because the alternative failed. “It's a difficult, delicate moment right now, and I'm not sure we have all the answers.”

The Blue Book Is a Confession Rather Than a Cure

Which brings us to the booklet. The Atlantic has published a piece titled “What Students Learn From Blue-Book Exams”, which this article could not read behind the paywall and therefore does not characterise beyond its title. The broader argument is easy to state anyway. Put a human in a room, take the devices, hand them lined paper and a fixed period, and whatever appears on the page was produced by that person. It is the only verification method available that does not depend on a classifier, a probability or an inference.

Institutions are moving. Princeton's faculty voted on 11 May 2026 to place proctors in examination rooms, ending a system of unsupervised examinations that had stood since 1893, after a dean's letter reported that significant numbers of professors and students perceived cheating on in-class examinations to be widespread. The Daily Princetonian's survey of the Class of 2026 found 24.8 per cent of respondents admitting to cheating in violation of the honour code, and, asked about using a chatbot on work that banned it, 27 per cent of arts and sciences students and 46 per cent of engineers said they had. The University of Chicago Law School has banned phones, tablets and laptops in first-year classes as part of its AI strategy. Bertram Gallant put the logic best: “We cannot be giving unsupervised assessments to students expecting them to resist AI. They're not going to be learning, we're going to be spending our time trying to catch them cheating and they're going to graduate and realize they just wasted four years.”

The comparison usually offered here is the calculator, and it is worth saying where it breaks. Maths education absorbed the calculator by conceding the mechanical step and moving assessment upward, towards modelling and interpretation. The tool automated a component and left the task recognisable. An agent that logs into Canvas and completes the course does not automate a component. It automates the student. There is no upward move available, because there is no residual layer above being the person enrolled.

But the blue book is a confession, not a cure, and its costs land on exactly the students who can least afford them.

Amanda Sturgill, an associate professor of journalism at Elon University, set out the practical objections for the Center for Engaged Learning in August 2025. Handwriting speed varies enormously and is not evenly taught; there is, as she puts it, a public-private divide in handwriting instruction, so a timed handwritten assessment quietly advantages students whose schools drilled it. Post-pandemic cohorts compose on keyboards. Hands cramp, and cramping degrades output and legibility, neither of which bears on whether the student understands the material. For students with dysgraphia, motor impairments or writing-related accommodations, an unadapted handwritten examination measures penmanship and stamina rather than knowledge, and the accommodations that fix it, extra time, a scribe, a locked-down laptop, are exactly what under-resourced institutions ration.

The technologists' alternative has the same problem in a different place. Armando Fox of UC Berkeley and Craig Zilles of the University of Illinois at Urbana-Champaign argued in Inside Higher Ed on 19 March 2026 that computer-based testing facilities beat blue books, allowing algorithmically generated examination variants, richer question formats, self-scheduling across multi-day windows and frequent low-stakes assessment. Illinois ran more than 130,000 examinations through its facilities in autumn 2025. It is genuinely better than handwriting, and it requires a proctored building, a capital expenditure most institutions and effectively all fully online programmes do not have.

Remote proctoring, the option that appears to square the circle, has the worst record. Deborah Yoder-Himes and colleagues at the University of Louisville published a study in Frontiers in Education in September 2022 examining automated proctoring across 357 students in four STEM courses. The software detected Black students' faces 79 per cent of the time against 92 per cent for white students. Students with the darkest skin tones spent 7.64 per cent of assessment time flagged against 1.56 per cent for lighter tones, and received 6.07 flags per assessment against 1.19 for the lightest group. Women with the darkest skin tones were 4.36 times more likely to be flagged than women with medium tones. Monika Blue Kwapisz, Yoav Ackerman, Jennifer Nguyen and Prashanth Rajivan documented in a November 2025 preprint how students with disability accommodations experience these systems, describing anxiety about the interaction between surveillance and their disability, fear of being misread as cheating, and the cognitive load that fear imposes during the examination.

Line the three options up and the pattern is impossible to miss. Handwritten examinations disadvantage students with motor and writing disabilities and those schooled without handwriting instruction. Proctored computer facilities require capital poorer institutions lack and online programmes do not have at all. Algorithmic proctoring misidentifies dark-skinned students and penalises disabled ones. Every available method of proving a human did the work reallocates the burden onto the students already carrying most of it, the same population Liang and colleagues found the detectors falsely accusing, and the same population Bretag and colleagues found most likely to be flagged for outsourcing.

Signalling Was Always Doing More Work Than the Learning

Underneath all this sits an economic question higher education has spent fifty years avoiding, and the agent has forced.

Michael Spence's 1973 paper in the Quarterly Journal of Economics established the framework. Education functions in the labour market partly as a signal: employers cannot observe productivity directly, so they pay for a credential that is cheaper to obtain for able and conscientious candidates than for others. The signal works because it is costly, and it works whether or not anything was learned in the acquiring. Bryan Caplan's 2018 book The Case Against Education pushed the claim to its limit, arguing that something like 80 per cent of the earnings premium reflects signalling rather than skill, an estimate other economists dispute vigorously and nobody has settled.

The dispute does not need settling for the point to bite. If the credential carries any signalling weight at all, an agent that can obtain it degrades the signal for every holder, including everyone who did the work honestly. This is a mechanical claim, not a moral one. A signal cheap to counterfeit stops discriminating, and once employers know it is cheap to counterfeit they discount it uniformly, because they cannot tell which holders did. The honest graduate of an online programme in 2026 pays full price for an asset being devalued by people they will never meet.

That is what the Elon and AAC&U figure measures when 74 per cent of faculty say generative AI will negatively affect the value of a degree, and it lands hardest on the segment of the market built as a widening-participation instrument. Online degrees exist, in the story the sector tells about itself, to reach the working adult, the carer, the person in a town without a campus. Digital Trends made the uncomfortable observation: for colleges that have spent years arguing online education makes higher education more accessible, an agent that can attend the class instead of the person paying for it is a particularly awkward problem to have.

The mixed institutional signalling makes it worse. Futurism noted that many universities hold partnerships with AI companies while policing AI use, leaving students to reconcile the contradiction. The California State University system signed a multimillion-dollar agreement with OpenAI to put ChatGPT Edu in front of hundreds of thousands of students and staff. Carol Sewell, an instructor in that system, told the Times how that feels from a classroom: “I'm not getting any guidance on how to dissuade their use. I'm getting opportunities to learn more about using it.”

There is a workable model in plain sight, ignored for a century because it is unflattering. Professional licensure separates the two functions a degree smashes together. Nursing candidates in the United States take the NCLEX under supervision at dedicated testing centres, more than 328,000 of them in 2025 for the registered nurse examination alone. Medicine has the USMLE, law the bar. In every case the coursework is where learning happens, and a separate proctored instrument certifies competence. Nobody worries that a nursing student used AI on a formative assignment, because the assignment is not the credential. The credential is the day in the room.

Higher education runs one artefact for both purposes: the essay is simultaneously the pedagogical exercise and the evidence. That worked for as long as producing an essay required knowing something. The agent has broken the coupling, and the options are to rebuild it by force, at enormous cost and with the distributional consequences described above, or to separate the functions as every licensed profession did decades ago.

The Verification Problem Is Not the Serious Problem

Return to the six words.

The student who types them is not, in most cases, a fraud in the way an essay mill customer was. They are, per the rationalisation study, someone with an available justification: that they are learning more this way, that the assignment was busywork, that the output reflects what they think anyway. Per the Reddit analysis, their main sustained contact with the institution has become a dispute about enforcement. Per the HEPI figures, they are in a cohort where 94 per cent use the tool for assessed work and only 36 per cent feel their institution encourages them to. They are behaving rationally inside a system that has stopped telling them a coherent story about what the work is for.

Kathryn Kysar, a community college instructor who has taught online for fifteen years, told the Times that those who have taught a long time usually recognise the cheaply written AI stuff within thirty seconds. She is almost certainly right, and it does not help her, because recognising it and proving it are separated by an evidentiary chasm no detector spans. Jason Gibson, a history professor at Alcorn State University, buried the word Madagascar in white text inside a midterm prompt in the summer of 2026 and reported that thirty-two of his thirty-five students across two classes failed part of the examination, having pasted the prompt into a chatbot and submitted the result unread. What unsettled him was not the cheating. It was that nobody noticed sentences about Madagascar floating sideways through the afternoon in an essay on the Industrial Revolution.

Gibson caught his students because they were careless. The agentic student will not be careless, because the agent will not paste the prompt and will not leave the word in. The trap worked once.

So the answer to what a degree is worth when a machine could have earned it is not a number, and proctoring alone does not recover it. Making a person sit in a room and write by hand verifies that a human produced marks on paper under time pressure. It verifies nothing about the fourteen weeks before, and it produces a credential whose accessibility depends on how steady your hands are and how your school taught cursive. It is a verification of presence dressed as a verification of learning, adopted because it is the last mechanism that cannot be spoofed.

The harder thing is what Bertram Gallant said out loud and almost nobody has repeated. If a student can complete a degree without doing the learning, the injury is not principally to the institution's reputation or the employer's hiring accuracy. It is to the student, who paid the money, gave the years, got the paper. The four wasted years are the loss no invigilator prevents and no detector catches, and the only person positioned to notice is the one who has already spent everything avoiding it.

References

1. Dana Goldstein and Alan Blinder, “AI Agents Are Taking Entire Online Courses for Cheating Students,” The New York Times, 10 August 2026 (syndicated by the San Francisco Examiner, 12 August 2026). https://www.sfexaminer.com/ai-agents-are-taking-entire-online-courses-for-cheating-students/article_9d4ef5ca-317f-54a3-937a-56bfcc9a0272.html
2. Frank Landymore, “College Kids Are Using AI Agents to 'Take' Entire Online Classes for Them by Logging Directly Into the Course Management Software,” Futurism, 13 August 2026. https://futurism.com/artificial-intelligence/college-kids-ai-agents-cheat-online-courses
3. Varun Mirchandani, “AI agents are sitting through students' online courses, and colleges are struggling to stop them,” Digital Trends, 13 August 2026. https://www.digitaltrends.com/cool-tech/ai-agents-taking-online-courses-student-cheating/
4. Kathryn Palmer, “AI Detectors Are Out, New Assessments Are In,” Inside Higher Ed, 5 August 2026. https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2026/08/05/ai-detectors-are-out-new-approaches-are
5. Lee Rainie and C. Edward Watson, “The AI Challenge: A National Survey of College Faculty,” Imagining the Digital Future Center, Elon University, and the American Association of Colleges and Universities, 21 January 2026. https://imaginingthedigitalfuture.org/wp-content/uploads/2026/01/Elon-AACU-faculty-AI-survey-full-report-1-21-26.pdf
6. Vanderbilt University Brightspace team, “Guidance on AI Detection and Why We're Disabling Turnitin's AI Detector,” Vanderbilt University, 16 August 2023. https://www.vanderbilt.edu/brightspace/2023/08/16/guidance-on-ai-detection-and-why-were-disabling-turnitins-ai-detector/
7. Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu and James Zou, “GPT detectors are biased against non-native English writers,” Patterns, 10 July 2023. https://www.cell.com/patterns/fulltext/S2666-3899(23)00130-7
8. Peter Scarfe, Kelly Watcham, Alasdair Clarke and Etienne Roesch, “A real-world test of artificial intelligence infiltration of a university examinations system: A 'Turing Test' case study,” PLOS ONE, 26 June 2024. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305354
9. Michael Goodier, “Revealed: Thousands of UK university students caught cheating using AI,” The Guardian, 15 June 2025. https://www.theguardian.com/education/2025/jun/15/thousands-of-uk-university-students-caught-cheating-using-ai-artificial-intelligence-survey
10. Rose Stephenson and Charlotte Armstrong, “Student Generative Artificial Intelligence Survey 2026,” HEPI Report 199, Higher Education Policy Institute and Kortext, 12 March 2026. https://www.hepi.ac.uk/wp-content/uploads/2026/03/HEPI-Report-199-Gen-AI-Survey-2026.pdf
11. Philip M. Newton, “How Common Is Commercial Contract Cheating in Higher Education and Is It Increasing? A Systematic Review,” Frontiers in Education, 30 August 2018. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2018.00067/full
12. Tracey Bretag, Rowena Harper, Michael Burton, Cath Ellis, Philip Newton, Pearl Rozenberg, Sonia Saddiqui and Karen van Haeringen, “Contract cheating: a survey of Australian university students,” Studies in Higher Education, 2019. https://www.tandfonline.com/doi/full/10.1080/03075079.2018.1462788
13. Deborah R. Yoder-Himes, Alina Asif, Kaelin Kinney, Tiffany J. Brandt, Rhiannon E. Cecil, Paul R. Himes, Cara Cashon, Rosalie M. P. Hopp and Edna Ross, “Racial, skin tone, and sex disparities in automated proctoring software,” Frontiers in Education, 20 September 2022. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2022.881449/full
14. Monika Blue Kwapisz, Yoav Ackerman, Jennifer Nguyen and Prashanth Rajivan, “Surveillance and Disability in Online Proctored Exams: Student Perspectives and Design Implications,” arXiv:2511.10826, 13 November 2025. https://arxiv.org/abs/2511.10826
15. Elizabeth Shockman, “'A death penalty': Ph.D. student says U of M expelled him over unfair AI allegation,” MPR News, 17 January 2025. https://www.mprnews.org/story/2025/01/17/phd-student-says-university-of-minnesota-expelled-him-over-ai-allegation
16. Joe Wilkins, “Princeton in Shambles Over AI Cheating,” Futurism, 17 May 2026. https://futurism.com/future-society/princeton-shambles-ai-cheating
17. Frank Landymore, “Professor Hides White Font in Midterm, Catches Students Using AI in the Stupidest Way Possible,” Futurism, 25 July 2026. https://futurism.com/future-society/professor-hides-white-font-ai-cheating
18. Phil Hill, “Fall 2024 IPEDS Data: Profile of US Higher Ed Online Education,” On EdTech, 6 January 2026. https://onedtech.philhillaa.com/p/fall-2024-ipeds-data-profile-of-us-higher-ed-online-education
19. Michael Spence, “Job Market Signaling,” The Quarterly Journal of Economics, August 1973. https://academic.oup.com/qje/article-abstract/87/3/355/1909091
20. UK Parliament, “Skills and Post-16 Education Act 2022, sections 34 to 36,” legislation.gov.uk, 28 April 2022. https://www.legislation.gov.uk/ukpga/2022/21/notes/division/9/index.htm
21. Armando Fox and Craig Zilles, “Blue Books Are Not the Answer to AI,” Inside Higher Ed, 19 March 2026. https://www.insidehighered.com/opinion/views/2026/03/19/blue-books-are-not-answer-ai-opinion
22. Amanda Sturgill, “Blue Books and In-Class Writing Are Not a Panacea,” Center for Engaged Learning, Elon University, 19 August 2025. https://www.centerforengagedlearning.org/blue-books-and-in-class-writing-are-not-a-panacea/
23. Belle Li, Lily Tan, Wei Zakharov, Qiang Qiu and Colby Ben Acton, “Tutor, Not Solver: Designing a Guardrailed AI Assistant for Learning in Higher Education: A Design Case of PeteChat,” arXiv:2606.09845, 27 April 2026. https://arxiv.org/abs/2606.09845
24. Pelin Yüce, Xiangruo Dai, Rebecca Owens and Tuğrulcan Elmas, “ChatGPT vs Teachers vs Students: Large-Scale Analysis of Generative AI Discourse in Education Communities on Reddit,” arXiv:2605.17712, 18 May 2026. https://arxiv.org/abs/2605.17712
25. Jiyoon Kim, Kentaro Toyama, Sangmi Kim and John M. Carroll, “'It's OK Because...': The Wild West of Student Rationalization of AI Use in Academic Writing,” arXiv:2605.29090, 27 May 2026. https://arxiv.org/abs/2605.29090

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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

Chapter 1: The Song Before the Garden

There are nights when you know something difficult is waiting for you. Maybe you are sitting at the kitchen table after everyone else has gone to bed, looking at a message you do not want to answer. Maybe tomorrow holds a hard conversation, a medical appointment, a family decision, or news you wish you could avoid. In moments like that, it is easy to think only about the trouble ahead. That is why the moment Jesus sang before Gethsemane matters so much. It shows us something about Jesus that we can miss when we rush toward the garden and the cross.

Matthew and Mark both tell us that after the meal, Jesus and His disciples sang a hymn and then went out to the Mount of Olives. Most of us know what came next, so we move quickly toward His prayer in Gethsemane. But I keep coming back to the song. Jesus knew Judas had already chosen betrayal. He knew Peter would deny Him. He knew His friends would scatter. He knew soldiers were coming, and He knew where the night would end. When I think about how Jesus faced suffering without losing His trust in the Father, that hymn becomes one of the most revealing moments of the entire night.

Jesus did not sing because He misunderstood what was happening. He sang with full knowledge of what was coming. That is what makes the moment so powerful. He was not celebrating an easy day. He was not thanking God because every problem had disappeared. He was about to walk into one of the most painful nights any person could face, and He still made room for worship.

That changes the way I think about worship. Sometimes we treat worship like a response to good news. We praise God when the answer comes, when the burden lifts, when the doctor says things look better, when the relationship is repaired, or when the bank account finally gives us room to breathe. There is nothing wrong with thanking God in those moments. But Jesus shows us that worship can come before relief.

Imagine someone sitting in a parked car outside a hospital because they are not ready to walk through the doors. They know the appointment may bring an answer they do not want. Their hands are tight around the steering wheel. Their mind is already running through worst-case possibilities. Worship in that moment does not mean pretending there is nothing to fear. It means saying, quietly and honestly, “God, I do not know what I am about to hear, but You are still here.”

That is closer to what Jesus shows us.

He sang before the garden, not after it. He worshiped before Judas arrived, not after the danger passed. He turned His heart toward the Father before the night became darker.

There is another part of this scene that moves me even more. Jesus sang with men who were about to fail Him.

Peter was there. Within hours, Peter would deny that he even knew Jesus. The other disciples were there. They would run when the soldiers came. Jesus knew their weakness before they showed it, yet He still shared the table with them and sang beside them.

That tells us something about the love of Christ.

Jesus does not love people only after they become dependable. He does not wait for perfect courage, perfect faith, or perfect obedience before drawing near. He knew exactly what His disciples would do, and He still gave them His presence.

That matters when you are carrying regret. It matters when you remember the promise you made to God and failed to keep. It matters when you know fear got the better of you, when you did not speak up, when you lost patience, or when you feel embarrassed by your own weakness.

Jesus knew Peter would fail before Peter did.

And He still sang with him.

Maybe that is part of the missing lesson in this story. Before we see Jesus crushed by sorrow in Gethsemane, we see Him worship. Before we see His friends abandon Him, we see Him singing with them. Before the night exposes human weakness, Jesus gives us a picture of divine steadiness.

Worship did not erase the garden. It did not cancel the betrayal. It did not remove the cross.

But it showed where Jesus had placed His heart before He walked into them.

Chapter 2: Worship Before You Know the Outcome

The next morning can feel very different from the night before. You wake up, reach for your phone, and the same problem is still there. The message has not changed. The bill is still due. The person you love is still struggling. The prayer you whispered yesterday still seems unanswered. That is where the hymn Jesus sang becomes more than an interesting detail. It becomes a way to understand what worship is for.

Jesus did not sing because worship guaranteed that the hard thing would disappear. He sang knowing the hard thing was still ahead. Then He went to Gethsemane, where He became deeply distressed and told His disciples that His soul was overwhelmed with sorrow. The song did not make Him less human. It did not keep Him from feeling the weight of the night.

That matters because some people quietly assume that strong faith should make pain easier to hide. They think worship means smiling through fear or refusing to admit that something hurts. Jesus shows us something more honest. He could sing with His disciples and later fall to the ground in prayer. He could trust the Father and still ask for the cup to pass from Him.

Those things were not contradictions.

Worship was not Jesus pretending everything was fine. Worship was Jesus remembering who the Father was before the suffering reached its deepest point.

I think about the person who has become the dependable one in the family. Everyone calls them when something breaks, when money is short, when someone needs a ride, when an older parent needs help, or when a crisis shows up. They keep going because people count on them. But eventually there is a quiet moment when nobody is asking anything, and the truth comes out: “I am tired.”

That person does not need to fake strength before God.

Neither did Jesus.

In Gethsemane, Jesus prayed honestly. He asked for another way, then surrendered Himself to the Father’s will. His trust was not shallow because He felt sorrow. His trust was proven inside the sorrow.

That gives the hymn even more meaning.

Jesus had already turned His attention toward the Father before the pressure reached its peak. He had already filled the room with worship before the garden filled with anguish. He had already chosen trust before Judas appeared.

Maybe that is one reason worship matters so much when life is uncertain.

It reminds us of truth before fear gets the loudest voice.

It does not promise that tomorrow will be easy. It does not guarantee the answer we want. It does not make grief disappear or turn every painful situation around overnight. But it can steady the heart enough to say, “God, I still belong to You here.”

That is a different kind of praise.

It is easy to worship on resurrection morning.

It is harder to worship when you can still see Gethsemane in front of you.

Yet that is exactly what Jesus did.

The garden still came. The betrayal still came. The cross still came. But so did resurrection.

The hymn was not denial of the darkness. It was an act of trust before the darkness had finished speaking.

So if you are facing something you would rather avoid, you do not have to pretend it is small. You do not have to act fearless. You do not have to force yourself into a cheerful mood before God will listen.

You can tell Him the truth.

And you can worship Him there.

Not because you know exactly how the story will turn out, but because you know who He is while the story is still unfolding.

That may be the quiet lesson hidden in one short sentence before Jesus went to the Mount of Olives.

He knew what was coming.

He knew who would fail Him.

He knew how much it would cost.

And He still sang.

Maybe some of the deepest worship we will ever offer God will not come after the victory.

Maybe it will be the song we choose before we know the outcome.

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 of us we are so numb we are so inflamed we don’t even know

why we keep coughing when we can’t stop inhaling toxins

why we have chronic migraines when we continue to drink diet coke thinking it’s healthy

why our joints hurt when we eat a load of sugar

why we can’t lose weight when we aren’t eating the right food for our unique designs

why we can’t sleep when our sheets reek of a chemical fragrance

why our hair falls when we aren’t eating enough protein

why we have an odor when we don’t practice hygiene

why our skin reacts to the sun when the sun is only bringing our problems to the surface

why we blast music on the beach when the sound of the waves is present

we are overstimulated over-entertained over-dependent on everything but ourselves

In Summary: * As my vision continues to recover from the treatment received yesterday afternoon, I've been very careful not to use my eyes too much. I've used heavy sunglasses every time I went outside, I've used my prescription eyeglasses whenever watching TV, working at the computer, or reading. The only surprising effect today has been an increase of annoying “floaters” in my vision.

I've done more radio and music listening today than usual, and less screen watching of any kind. That's probably good for me in ways other that resting my eyes, truth be told. Listening now to “Oldies” hits from 1966. Soon I'll tune in a radio pregame broadcast ahead of an MLB game, then I'll listen to the radio call of that Rangers vs White Sox game. And that'll end my “recovery” Wednesday.

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= 224.1 lbs. * bp= 153/90 (62)

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

Diet: * 05:05 – 1 little cupcake * 06:15 – 1 fresh apple * 06:45 – pizza * 12:00 – chicken tenders and gravy, mashed potatoes * 15:30 – 1 fresh tangerine

Activities, Chores, etc.: * 04:00 – listen to local news talk radio * 04:35 – bank accounts activity monitored . * 05:00 – read, write, pray, follow news reports from various sources, surf the socials, nap * 09:00 – listen to relaxing music * 12:00 to 13:00 – watch old game shows and eat lunch at home with Sylvia * 13:15 tuned into 107.5 The Fan well ahead of this afternoon's The Ride With JMV Show for even more Indiana sports talk. * 17:00 – listen to relaxing music * 18:00 – tuned into 105.3 The Fan well ahead of tonight's Rangers / White Sox game

Chess: * 10:15 – moved in all pending CC games

18 August 2026

Gendlin gave the name “felt sense” to the unclear, pre-verbal sense of “something”—the inner knowledge or awareness that has not been consciously thought or verbalized—as that “something” is experienced in the body. It is not the same as an emotion. This bodily felt “something” may be an awareness of a situation or an old hurt, or of something that is “coming”—perhaps an idea or insight. Crucial to the concept, as defined by Gendlin, is that it is unclear and vague, and it is always more than any attempt to express it verbally. Gendlin also described it as “sensing an implicit complexity, a wholistic sense of what one is working on”.

Agosto | ¿Por qué tenemos tanto miedo de sentir?

¿Por qué uno tiene que reprimir tanto sus emociones?

¿Por qué no podemos ser intensos? ¿Por qué sentimos que somos demasiado cuando sentimos mucho?

Si te gusta alguien, ¿por qué da tanto miedo decirlo?

¿Por qué me da miedo decirte que te extraño?

Que ya no recuerdo tu aroma, pero que quisiera volver a tenerte cerca para recordarlo. Que quisiera volver a ver esos ojos de cerca, con ese brillo tan particular que tienen cuando hablas de algo que te apasiona. Escucharte hablar de todo y de nada. Volver a sentir tus besos, tus manos. Quisiera simplemente estar existiendo a tu lado, sin hacer nada extraordinario, solamente estar.

Te extraño.

Y me duele admitirlo.

Pero lo más complejo de todo no es extrañarte. Lo más complejo es sentir que no puedo decírtelo.

Porque entonces aparecen todas las dudas.

Tal vez dejaste de sentir lo mismo de la noche a la mañana.

Tal vez te arrepentiste.

Tal vez prefieres que seamos amigos.

Tal vez ya no me piensas de la misma manera en la que yo te pienso.

Tal vez para ti aquello que pasó fue solamente un momento y para mí se convirtió en algo mucho más difícil de acomodar.

Tal vez…

Y ese es el problema.

Que no sé.

No sé qué piensas. No sé qué sientes. No sé si me extrañas. No sé si alguna vez aparezco en tus pensamientos de la misma forma en la que tú apareces en los míos.

Y quizá por eso me da miedo preguntarlo.

Porque mientras no sepa, todavía puedo imaginar cualquier respuesta.

Puedo pensar que tal vez sí me extrañas. Que tal vez también tienes ganas de verme. Que tal vez cuando escuchas alguna canción, recuerdas algo de nosotros. Que tal vez, en algún momento del día, también te preguntas qué estoy haciendo.

Pero si pregunto, existe la posibilidad de que la respuesta sea otra.

Y creo que eso es lo que realmente me asusta.

No extrañarte.

No sentir.

No ser intensa.

Me asusta descubrir que todo esto que todavía siento está ocurriendo solamente de mi lado.

Y qué extraño es eso de querer decirle a alguien “te extraño” y, al mismo tiempo, tener tanto miedo de descubrir que quizá tú ya no tienes ganas de escuchar esas palabras.