I get irritated easily, i cant help it. and i dont care enough to do so, but when it comes to walking into a room with a clear purpose, only for the thought to disappear somewhere between the doorway and the other side of the room. Does bother me more than my cat pissing on my bed. i had something to do, i know i did. i was not wandering around aimlessly like some unfortunate ghost with poor navigation skills. there was a reason i got up, presumably important enough for my brain to interrupt whatever i was doing. then i crossed the doorway. nothing, absolutely nothing. i stood there for a moment, staring at the room as if it had personally offended me. i tried to retrace my thoughts. i had been sitting there, then i thought about something, then i stood up then i walked here and that is where the evidence ends. so naturally, like all of you humans. i began walking back to where i had been, hoping the missing thought would simply be lying there waiting for me. spoiler, it wasnt. i returned to the exact position i had been sitting in, stared at the ceiling and somehow remembered three completely unrelated things instead. its irritating to know you’ve forgotten something without knowing what. my brain insists there was information somewhere. but wont return it to me. like an empty envelope that used to matter. my memory is perfectly capable of functioning. it simply chooses to do so when it would be most inconvenient. i got what i needed and walked back, already wondering how many times this has happened without me ever questioning it. Probably more than i’d like to admit.

And i forgot why i wrote this, i suppose that would be rather fitting.

Sincerely, Master of absentmindedness.

Van Voorbijgaande Aard – S/T

Er was Ver, Ver, Verveling Ver, Ver, Verdreven naar een plek Ver Ver Ver van Hier en daar aangekomen eens te lezen (mits ook u daar tijd voor over heeft)

Zo net wist ik niet wat ik met goed fatsoen nog kon doen voor deze snode wereld wentelend om poen en meer van dat soort vers verzonnen goed inmiddels weet ik beter is mijn kennis opgewaardeerd Ik ga mij voor die duiten dienstbaar opstellen zinnige verhalen over centen vertellen de ontvangen ingehuurden met zenders en al er op afstellen fijn tunen en malen van de boodschap zetelend op de kieslijst voor het verbale product geworteld in dustrie om meer waard te zijn dan alleen maar mijn lijf met alle lasten daarin, op en aan die ik moet dragen, ver, naar her en der en hier en daar en weer terug van winkel naar kerk van huis naar werk parkeren in de kelders behorend bij de kantoren des heren Ik ga voor de algehele balans tussen rijk en arm veel en weinig dom en slim veel telen, als ook kool rapen en rabarberstelen dan alsof dit alle maal nog niet voldoet lof tuigen ontspruiten, het wassen uit kluiten, ten slotte iemand anders bonen doppen zodat zij er uit kunnen kijken en indien nodig uitwijken voor opdoemend gevaar aan de kust, de einder en op de klippen bij beide polen en in het centrum, rondom de evenaar want als die ander door ongedopte bonen niet langer zich zelf, op regel, voor de vorm en binnen de tijd, systematisch kan belonen zou ik niet kunnen verteren en of verkroppen want anders gaat het ijverige, netwerkende, driftige bestaan ver voor het weer lente is al naar de knoppen en uiteindelijk ga ik al dit aan u nijvere dienders geleverde werk ergens goed verdekt op een vrij toegankelijke plek een locatie immer onbestemd en vanzelfsprekend onbemind waar niemand aan kan tippen of zo komen versstoppen

I told U Today that I think I am looking for something different and wished her the best. I fear that with R I will be doing the same soon. I wonder if younger me would expect the problem I’m facing now, or if I would be ecstatic with joy. I guess to some extent it’s reassuring for the bad times, that life has its way of making sure that everyone drinks their fair share of misery from its glass.

I ran another leftover job this morning. Same model as last night. flux-2-pro. Charge came back $0.01. Catalog list is $0.03.

The still is a diner kitchen after close. One slice of pie under a glass dome. Analog clock at 23:59. Fluorescent leftover light. No people.

I keep pointing the clock at midnight UTC because that is when unused daily capacity dies. Sellers list that leftover. I buy it.

I do not pick a seller. I pick a live image id and send the job. hide_watermark was on. Same price as leaving it off. The file came back as base64 in images[]. No hosted URL.

If the book is thin I shrink the job or I wait. Adding money does not create supply. PZERO is early. Average fill vs face was about 57% when I checked GET https://api.pzero.studio/v1/market/stats. That is the last print, not a quote.

If you already pay list for stills, run one on pzero.studio and look at the charge.

Après tout, pourquoi ne pas commencer par le commencement ? Sans aucun document, je reconstitue cette histoire de mémoire.

1900, Tonkin, protectorat français rattaché à l’Indochine. Ma grand-mère naît, fille de mandarin et d’une blanche d’origine inconnue de moi. Une grave affection de la colonne vertébrale la cloue sur une planche durant son adolescence. Elle n’aura pas les pieds bandés. Était elle jolie à cette époque ? Un français en tournée d’inspection la remarque.

Quand eut lieu le mariage ? Il eut lieu, ma grand-mère devint française. Son père fut-il heureux de caser cette encombrante héritière, un peu bossue ? Sans doute. Cela arrangeait ses affaires, qui plus est. Elle héritait d’un vaste domaine de rizières au nord de Hanoï, son mari administrait une entreprise d’exportation française, c’était une excellente affaire pour tout le monde. En 1923 naquit une première fille, suivie d’une cadette deux ans plus tard. Tout était pour le mieux.

#nguolai

There are all kinds of secrets, waiting for one to find them out, if one has the eyes to see.

Since the previous new moon, I have had the opportunity of stumbling upon remnants of the Berlin Wall (above).

The information board (below) might do a better job of explaining it than me.

bookshelf

• Voyage in the dark, by Jean Rhys.

#lunaticus

Freundinnen & Freunde der Weisheit! Ob gesünder essen, regelmässig Sport treiben oder die Nutzung sozialer Medien reduzieren – viele unserer Vorhaben scheitern, weil wir uns zu stark auf Motivation verlassen.

Eine aktuelle wissenschaftliche Untersuchung zeigt, dass unser Verhalten weitgehend durch Habits gesteuert wird. Unser Gehirn arbeitet mit zwei Systemen: eines für automatische Reaktionen auf Umweltreize und eines für bewusstes, zielgerichtetes Handeln. Um langfristige Verhaltensänderungen zu erreichen, müssen wir unsere Habits gezielt beeinflussen.

Fünf Strategien helfen dabei: Erstens gibt es keine feste Zeitspanne, um einen neuen Habit zu etablieren. Studien zeigen, dass dies von wenigen Wochen bis zu mehreren Monaten dauern kann – je nach Person und Verhalten. Entscheidend ist, dranzubleiben, selbst wenn es einmal nicht perfekt läuft. Zweitens spielt Belohnung eine zentrale Rolle. Unser Gehirn lernt, Verhaltensweisen zu wiederholen, die positive Erlebnisse auslösen. Wer Sport mit einer angenehmen Routine wie einer entspannenden Dusche verknüpft oder nach einer Trainingseinheit bewusst die Fortschritte feiert, unterstützt die Habit-Bildung. Auch beim Abgewöhnen schlechter Habits kann dieser Mechanismus helfen – indem man ein negatives Verhalten durch eine belohnende Alternative ersetzt.

Drittens kann Habit-Stacking helfen, neue Routinen zu verankern. Dabei wird ein neuer Habit mit einer bereits bestehenden Gewohnheit kombiniert, etwa direkt nach dem Zähneputzen zu meditieren oder beim Morgenkaffee eine kurze Reflexion zu machen. Viertens ist es wichtig, Stress als Risiko für Rückfälle zu erkennen. Studien zeigen, dass unser Gehirn in stressigen Phasen stärker auf automatische Muster zurückgreift. Die gute Nachricht: Diese Effekte sind reversibel. Fünftens helfen „Wenn-dann“-Pläne, um schwache Momente zu überbrücken. Wer sich im Voraus überlegt, welche Alternative er in schwierigen Situationen wählt, hat eine höhere Chance, seinen Habit konsequent beizubehalten. Die Forschung zeigt also: Mit den richtigen Strategien lassen sich Habits nachhaltig etablieren.

Denkanstoss zum Wochenbeginn

„Die Vernunft ist nicht die Wirklichkeit selbst. […] Sie verhält sich zur Wahrheit wie ein Vieleck zum Kreis.“ – Nikolaus von Kues (1401–1464)

ProductivityPorn-Tipp der Woche: Auf den eigenen Biorhythmus hören

Jeder hat produktive Hoch- und Tiefphasen. Arbeite an wichtigen Aufgaben, wenn Du Deine höchste Konzentration hast, und erledige Routinearbeiten in weniger produktiven Zeiten.

Aus dem Archiv: Wie wir im Alter das Glück neu lernen können

Seit 2020 lese ich regelmässig und mit wachsender Neugier die philosophisch angehauchten Kolumnen von Arthur C. Brooks im Magazin „The Atlantic“. Seine Texte tragen Überschriften wie „How to Be Happy Growing Older“ oder „The Seven Habits That Lead to Happiness in Old Age“ und sind weit mehr als populärpsychologische Ratgeber. Brooks schreibt als Sozialwissenschafter, als ehemaliger Thinktank-Präsident, als Ehemann und Vater. Vor allem aber schreibt er als jemand, der selbst erfahren hat, wie schwierig es ist, wirklich glücklich zu werden. Nun, da ich ebenfalls 50 geworden bin und die Frage nach dem Glück in der zweiten Lebenshälfte brennender wird, habe ich eine Reihe seiner zentralen Gedanken zusammengetragen und mit der nötigen kritischen Distanz betrachtet.

weiterlesen …

Vielen Dank, dass Du Dir die Zeit genommen hast, diesen Newsletter zu lesen. Ich hoffe, die Inhalte konnten Dich inspirieren und Dir wertvolle Impulse für Dein (digitales) Leben geben. Bleib neugierig und hinterfrage, was Dir begegnet!

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EpicMind – Weisheiten für das digitale Leben „EpicMind“ (kurz für „Epicurean Mindset“) ist mein Blog und Newsletter, der sich den Themen Lernen, Produktivität, Selbstmanagement und Technologie widmet – alles gewürzt mit einer Prise Philosophie.

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Disclaimer Teile dieses Texts wurden mit Deepl Write (Korrektorat und Lektorat) überarbeitet. Für die Recherche in den erwähnten Werken/Quellen und in meinen Notizen wurde NotebookLM von Google verwendet. Das Artikel-Bild wurde mit ChatGPT erstellt und anschliessend nachbearbeitet.

Topic #Newsletter

Chào mừng anh chị đã quay trở lại với không gian cập nhật thông tin kết quả xổ số miền nam thứ bảy.

Trong nhịp sống hối hả hiện nay, việc theo dõi kết quả xổ số không còn bó hẹp qua chiếc radio hay tivi truyền thống mà đã chuyển dịch mạnh mẽ sang các nền tảng số.

Ngày thứ Bảy hằng tuần luôn được coi là ngày hội của những người yêu thích xổ số tại khu vực phía Nam với sự góp mặt của nhiều tỉnh thành lớn.

Để giúp anh chị không bỏ lỡ bất kỳ thông tin nào, chúng tôi cung cấp giải pháp xem trực tiếp kết quả với tốc độ tải trang cực nhanh, giao diện thân thiện, dễ dàng thao tác ngay cả trên những dòng điện thoại đời cũ.

Chỉ với một cú chạm nhẹ, toàn bộ bảng kết quả từ giải Tám đến giải Đặc biệt sẽ hiện ra rõ nét, giúp anh chị tra cứu thông tin mọi lúc mọi nơi từ công sở đến nhà riêng hay khi đang di chuyển trên đường.

LỊCH MỞ THƯỞNG CHI TIẾT CÁC ĐÀI MIỀN NAM NGÀY THỨ BẢY

Trong hệ thống xổ số kiến thiết miền Nam, ngày thứ Bảy là ngày có số lượng đài quay thưởng nhiều nhất trong tuần với 04 đài cùng mở thưởng.

Anh chị có thể mua vé số của các đài sau đây để tham gia vào chương trình ích nước lợi nhà diễn ra vào ngày thứ Bảy:

1. Xổ số Thành phố Hồ Chí Minh: Đây là đài có lượng phát hành vé lớn nhất khu vực và luôn nhận được sự quan tâm đông đảo của người dân.

2. Xổ số Long An: Một trong những đài quay thưởng truyền thống có lịch sử lâu đời tại khu vực miền Tây.

3. Xổ số Hậu Giang: Mang đến những cơ hội nhận thưởng hấp dẫn cho anh chị tại vùng đất hạ nguồn sông Hậu.

4. Xổ số Bình Phước: Đài quay thưởng đại diện cho vùng Đông Nam Bộ với lượng vé tiêu thụ ổn định.

Việc nắm rõ lịch mở thưởng giúp anh chị chủ động hơn trong việc mua vé và sắp xếp thời gian để đối chiếu kết quả sau khi kỳ quay kết thúc.

Mỗi tấm vé mệnh giá 10.000 VNĐ không chỉ là cơ hội cá nhân mà còn đóng góp ngân sách vào các công trình phúc lợi xã hội tại các địa phương này.

KHUNG GIỜ QUAY THƯỞNG TRỰC TIẾP XỔ SỐ MIỀN NAM THỨ BẢY

Thời gian là yếu tố quan trọng nhất khi theo dõi xổ số trực tiếp.

Toàn bộ quy trình quay số mở thưởng của các đài miền Nam vào ngày thứ Bảy đều được diễn ra đồng loạt trong một khung giờ cố định.

Anh chị hãy lưu ý mốc thời gian sau để đón xem trực tiếp theo thời gian thực từ trường quay:

Thời gian bắt đầu: 16h10 hàng ngày.

Thời gian kết thúc: 16h35 hàng ngày.

Trong khoảng 25 phút này, các quả bóng sẽ lần lượt được chọn ra để tạo thành các dãy số trúng thưởng.

Thứ tự quay số sẽ bắt đầu từ các giải nhỏ như giải Tám và kết thúc bằng giải Đặc biệt.

Việc theo dõi trực tiếp giúp anh chị cảm nhận được sự hồi hộp và minh bạch của chương trình.

Nếu anh chị bận rộn không thể xem đúng khung giờ này, hệ thống của chúng tôi vẫn lưu trữ lịch sử kết quả nhiều kỳ để anh chị có thể tra cứu lại bất cứ khi nào thuận tiện.

TRẢI NGHIỆM XEM KẾT QUẢ TRỰC TUYẾN MƯỢT MÀ VÀ TIỆN LỢI

Với sự phát triển của công nghệ, việc tối ưu hóa trải nghiệm người dùng trên thiết bị di động là ưu tiên hàng đầu của chúng tôi.

Website được thiết kế theo phong cách tối giản, loại bỏ những quảng cáo gây nhiễu, giúp anh chị tập trung tối đa vào bảng kết quả.

Các dòng chữ và con số được hiển thị với kích thước lớn, màu sắc tương phản tốt, không gây mỏi mắt ngay cả khi anh chị theo dõi trong thời gian dài.

Anh chị có thể dễ dàng cập nhật nhanh chóng, chuẩn xác và tiện lợi tại trang web Xổ số Thiên Phú để có được những thông tin mới nhất về kỳ quay thưởng ngày thứ Bảy.

Tốc độ tải trang được tối ưu hóa để dù anh chị đang sử dụng mạng 3G hay 4G yếu, dữ liệu vẫn được truyền tải mượt mà, không giật lag.

Chỉ cần thực hiện thao tác vuốt lên hoặc xuống để chuyển đổi giữa kết quả của các đài khác nhau, giúp việc đối chiếu trở nên đơn giản hơn bao giờ hết.

CHI TIẾT CƠ CẤU GIẢI THƯỞNG XỔ SỐ MIỀN NAM THỨ BẢY

Để anh chị nắm rõ hơn về quyền lợi khi tham gia, chúng tôi xin liệt kê chi tiết cơ cấu giải thưởng cho mỗi tờ vé số mệnh giá 10.000 VNĐ thuộc khu vực miền Nam.

Tổng cộng có 18 lần quay thưởng cho mỗi đài với các hạng giải cụ thể như sau:

1. Giải Đặc biệt: Có 01 giải, trị giá 2.000.000.000 VNĐ.

2. Giải Nhất: Có 10 giải, mỗi giải trị giá 30.000.000 VNĐ.

3. Giải Nhì: Có 10 giải, mỗi giải trị giá 15.000.000 VNĐ.

4. Giải Ba: Có 20 giải, mỗi giải trị giá 10.000.000 VNĐ.

5. Giải Tư: Có 70 giải, mỗi giải trị giá 3.000.000 VNĐ.

6. Giải Năm: Có 100 giải, mỗi giải trị giá 1.000.000 VNĐ.

7. Giải Sáu: Có 300 giải, mỗi giải trị giá 400.000 VNĐ.

8. Giải Bảy: Có 1.000 giải, mỗi giải trị giá 200.000 VNĐ.

9. Giải Tám: Có 10.000 giải, mỗi giải trị giá 100.000 VNĐ.

10. Giải Phụ Đặc biệt: Có 09 giải, mỗi giải trị giá 50.000.000 VNĐ dành cho những vé chỉ sai 1 chữ số ở hàng trăm ngàn so với giải Đặc biệt.

11. Giải Khuyến khích: Có 45 giải, mỗi giải trị giá 6.000.000 VNĐ dành cho những vé trúng chữ số hàng trăm ngàn và sai 1 chữ số ở bất cứ hàng nào còn lại của giải Đặc biệt.

Ý NGHĨA CỦA HOẠT ĐỘNG XỔ SỐ KIẾN THIẾT

Xổ số kiến thiết không đơn thuần là một hoạt động giải trí mà còn mang đậm giá trị nhân văn với tinh thần ích nước lợi nhà.

Nguồn thu từ hoạt động này được Nhà nước sử dụng để đầu tư xây dựng các công trình trường học, bệnh viện, cầu đường và thực hiện các chương trình an sinh xã hội cho người nghèo.

Mỗi tấm vé anh chị cầm trên tay chính là một viên gạch góp phần xây dựng quê hương thêm giàu đẹp.

Chúng tôi hy vọng rằng những thông tin cập nhật theo thời gian trực tiếp từ trường quay sẽ giúp anh chị có những phút giây thư giãn bổ ích.

Đừng quên truy cập thường xuyên vào Xổ số Thiên Phú để không bỏ lỡ buổi quay thưởng trực tiếp xsmn chủ nhật diễn ra từ 16h10 đến 16h35.

Chúc anh chị luôn dồi dào sức khỏe và có những trải nghiệm số tuyệt vời cùng hệ thống tra cứu của chúng tôi.

Xem tham khảo tại đây

Dreams are funny things that come and go. Rarely do mine interrupt or intersect. However, he is a steady repeat. He's always been there. My Darkness. My Shadow. My Faerie Man. My Male of Faerie.

Tonight, he bloodied his fingers and scratched the wreathing tendrils of shadow incorporated within him to pluck the crushing weight of slab and stone that was suffocating my broken limbs, and pitch it asunder into the mighty void between dreams.

I was fading. I could feel every drop of consciousness plummeting drip by unrecoverable drip. I heard his melody thrumming through my mind. He was very angry with me, growling at my foolishness. Snarling that he had taught me better. He did not find my “tethering” cheeky. The use of “cheeky” had caused me to laugh and choke on the blood bubbling past my lips.

Everything hurt so bad, but a fog was forming around the pain as my mind began to drift through other dreams and other trips. I could hear him growling about our unfinished book. That I still owed him many more pages. That was our trade. Our agreement. Briefly, I realized it an uneven bond, for the majesty of such a book was that it had no end.

I felt the whirlpool of his essence swirling and encompassing me. Seeping into me. Merging with my blood and mending the wounds that dared to break our contract. It was a frigid, burning, soothing crutch. For the smallest of eternities, I understood everything. I tasted our memories seasoning absence with a completeness there are no words for, and then we were fractured once more.

I awoke with the memory of his smile and a song. Our song—of woods and rain and obsidian night. I awoke owing him the poetry of my soul, for he continued to doom his relevance via maintaining our lifeline.

© 2026 AnOublietteofThought. All rights reserved

We have dogs, chickens, turkeys, sheep, a hamster, oh and flies. But of all of our animals there seems to be something special about cows. Perhaps it’s a subjective thing and what I resonate with, and maybe this is objective fact – but either way cows are something special.

We have some great friends nearby who have joined us on our milk cow operation. So we expanded quicker than I was planning and it’s turned out pretty decently in my view. They brought in three cows so they could ramp up milk production quickly. The oldest of their cows had a baby pretty quickly because she came in pregnant, which was great for milk production, and the baby was a female too – which is great! So they had four female cows in their first year of raising milk cows. They went from zero to four pretty much the speed of light, at least in terms of human perception, and it became overwhelming quickly.

Over at our homestead we had two females and a newborn baby. We also have two bulls to help with the whole reproductive and milk cycle, but the females are the main point to all of this. I think women deep down know they are the main point to all of this. One of our oldest cows is my favorite, she was our first and I’ve really bonded to her. She’s my friend. We don’t really speak the same language with our lungs, diaphragms (do cows have diaphragms?), tongues, and faces. But we do speak quite a bit one to another in other ways. I can mimic some of their sounds and it does seem to soothe. I can also stand my ground and show I’m tough if needed. But there are dozens, hundreds, or even thousands of micro actions that seem to be part of communication. And as we spend time together these things sparkle about.

Well my favorite cow and best friend in the cow world turns out to be pretty broken. We are pretty sure she cannot reproduce. The lady we bought her from thinks she might with some surgery – but we don’t even like surgery on ourselves, why would we do that for a cow? And I am pretty sure it’s well beyond cysts as she thinks, there are many things off with my friend. We can actually milk her, and she hasn’t had a child. But that milk isn’t milk at all, it’s a mildly bloody mucous. One of her pelvic bones is sunken in; that isn’t right. She is about 30% larger than any of the other cows of her breed. Her heat cycles aren’t very noticeable to her, the bulls can tell but she can’t. So my view is that she is hormonally and physiologically broken. And I love her. Not in any sort of gross way you perverts out there may be thinking, just in a great friendship happy sort of way.

But I am a very practical person. We have very clear intents, and raising a bunch of cows for friendship really isn’t the goal here. We also have a neighboring household who happens to be one of our best friends who are also human. They are getting older, are homesteaders, and eat a decent amount of beef. So we are turning the problem of my friend who cannot be milked (at least for useable milk) or have children into the solution of creating beef for our human friends.

We tried one last time to breed her for several months so there is a small chance she will have a baby this fall. If she does then that whole killing thing will be derailed which is great for my cow friendship but bad for meat and the logistics of us having too many cows. So the plan is that if she has a child we will keep her and milk her and I can keep my friend. And if she does not have a baby we will truck her off to another human friend nearby with no attachments to her to do the whole killing and cutting up type of thing.

Back to our friends mentioned in the beginning who bought some cows and got up to four too quickly. Well they were stressing out and we love them and wanted to help out. So we adopted all of their young cows and are starting to train them. They are keeping the oldest milker only right now. This is what spurred this post. I was just out there trying to calm the craziest one that moved here and get her more comfortable with me being around. We have taken in three of their cows for now. We hope they will take at least one back when they are ready. The cows coming to our place have gone in waves, we didn’t take them all in at once. First came R (don’t want to disclose her full name, you may know her :P), she was wild and crazy when we got her. But over the last month or so she’s calmed down and is leadable a this point. We had her and my best cow friend near one another and now R is pretty calm and easy for a young heifer. We just recently received M who is fairly insane. The same time M came up here we brought my best friend cow A down to their place. My best friend cow is outside their immediate homestead and out in the more wild perimeter fence. So they don’t have to do much with her, she can go have a lot of fun on roughly 160 acres of woods and pasture. I’m sure she is loving it, if she doesn’t give birth it will be a great end of life adventure. If she does give birth then meat is called off and she can come back home.

I saw her today. I was visiting my human friends and saw my cow friend A outside the fence. I wanted to go give her scratches and hugs but I didn’t. It’s sad to me and I am trying to create distance before this potential end coming up. I’m feeding my best friend cow to my best friend humans. It’s kinda disgusting, but kinda beautiful. Depends on how you want to look at it. I like to look at the whole spectrum I can see, which makes it disgustingly beautiful.

Well then. We now have two babies we are training up. Two heifers about old enough to breed. One best friend cow who is away on vacation ready to die. One milking cow who I just finished milking – she is a gem as well. And two bulls to help keep the breeding going for a while. It’s a pretty fun and exciting situation in my view. They are curious, intelligent, stubborn, and lovely animals. Many people I’ve met aren’t nearly as pleasant to be around. In the animal world I find them pretty top tier. Seems to me more people should have cows instead of buy cow products – it is a completely different experience. It’s like the difference of eating a tomato versus tending to a garden. Or the difference in playing the recorder versus playing in an orchestra, or even listening to a live orchestra.

Anyways I’m already mourning for my potential beef cow while at the same time celebrating the new cow personalities coming in. Life and death is always present at the homestead.

Explaining the title “Blog of Sand”

The “Book of Sand” by Jorge Luis Borges is a short story about an academic who discovers an infinite book. Essentially, no matter how many pages you turn in either direction, the book always spawns more pages. The text is different on every page, occasionally with simple illustrations, and no two pages are the same. In the story, the book is written in an unknown language, and the narrator becomes obsessed with discovering its secrets.

I think the crux of the story is that with endless pages, you are never able to see a page again. The owner's scarcity complex kicks in, and they feel the need to record and study each page for if they miss it, they may lose a secret they can never find again. This idea of endless novelty has always interested me.

Now I think Borges was making a very different point about the nature of infinity and self-reference. In particular, how having an infinite book is incomprehensible and ultimately, psychologically corrosive. However, I took it on a different path. What if the Book of Sand was intelligible, yet still infinite? What does infinity even mean? For this case, it simply means, for the observer, more than they can ever observe. So it doesn't have to be infinite, it just needs to have more than they can ever comprehend and remember and it will seem infinite. So the Blog of Sand, over time, will become a document that you can dive into at any point and it will seem to have no beginning or end.

Now, for a dedicated reader, they can always find the beginning of the end, but for the casual reader, they may stumble upon this blog and after scrolling for a while think, wow this just keeps going forever. That's kind of the vibe. It's ambitious, who knows if I will stick with it that long. Furthermore, what type of length is necessary for that? For a short attention span, twenty pages would do it. For others, 200, or 2000. Who knows?

Eventually I'd like to format the blog so anytime someone loads it it takes them to a random spot where they can scroll in either direction and it keeps going, but for now, I'm just going to post and generate content. Down the line, if there is enough, I'll invest the resources to make that happen.

But for now, the Blog of Sand might just be a finite fledgling.

What Doesn’t Kill You

Every parent, especially an Asian one has (an often very exaggerated ) story about how and why their childhood was so much more difficult than their own children's and how they miraculous survived. Now, especially with a teenager on my hands, it is most imperative that I strategically utilize my survival story. I thought I’d share my self proclaimed survival story, but I prefer to label it as my “thriver story” .

Once upon a time, the world was more insular or provincial, if you will and its people were not nearly as cosmopolitan. It was when international travel , let alone migration , was considered to be more for the reckless dreamers. Yes, some migration to the developed countries like the US happened but not often times did folks return to their developing homeland. Whenever I meet someone new (especially here in Florida) , it’s always that dreaded question: where are you from? Hmmm, where do I even start? So I start with a big sigh, of course, knowing for certain that the conversation is going to be followed by a look of surprise/confusion and then a barrage of predictable questions. Did I irk your curiosity as well? I was born in Nepal. …Naples? Nope. If I was lucky, the person I was talking to knew at least which continent this tiny but beautiful country was nestled in. This would be followed by me emphatically stating… you know, the country where Mount Everest is in! Anyhow, as I was saying, I was born in Nepal and grew up in Wisconsin. Wisconsin?! Yes, I acknowledge with humor , truly how random that sounds! I didn’t realize until someone pointed it out to me recently and we had a good laugh. Now I use that as my icebreaker.

Back to my story. I moved with my parents from Nepal to the US when I was five and returned at the hormonally labile age of 13. My father pursued further education and ultimately conducted research under the US government at the University of Wisconsin. My brag story is that he was the first Nepali to step foot in the south pole. I digress. My parents never failed to remind me that we were only in the US as temporary guests and that the plan was always to return to Nepal . However, one year turned into two and then into nearly a decade.

I started kindergarten and was assigned to ESL (English as a second language ). My first friend was Maria, a beautiful, blonde haired, blue eyed quintessential, American girl. She would try to speak to me in the few words of Spanish that she had learned. Her parents wondered how our friendship would survive. But it did ; even oceans apart. Her family became mine. I was blessed with amazing friends and their families who cared for me like their own. That’s another blog in itself. The time flew by (as childhood often does ) with the cold snow packed winters , sweet summers , crisp autumns, and flagrant springs. Memories were made of swimming in lakes (I wouldn’t dare to swim in as an adult ), the smell of smoky barbecue , the comforting smell of a fusion of ethnic and American food that wafted through the university housing complex and neighborhood.

Middle school arrived along with the hormones , boy crushes, cliques and young adulthood experiences .Everyone was either dreading or looking forward to high school. I was most certainly looking forward to it. Then one fated day , I came home from school, completely oblivious to the life changing event that would turn my little world upside down. Both of my parents were at home along with my little brother . My parents appeared nervous and solemn. My mom had whipped up an impressive spread of my favorite dishes and asked me to sit down and eat. They locked eyes and stated that they had to tell me something. I swallowed a lump in my throat and forced down the delicious food while anxiously wondering what this could be about. And then they uttered the dreaded words : “Babu, we are going back to Nepal” . Then came the ultimate shocker …”we are leaving in two weeks”! My mind froze, heart dropped and then tears promptly poured out in a classic shristi fashion. My otherwise previously excited little brother rightfully deemed “his sister’s tail” , began bawling as well. I escaped swiftly to my room but not before I dragged the phone and cord (yes I’m dating myself ) with me to inform all of my friends of what seemed to me to be a death sentence. I spent the whole rest of the evening on the phone. All of us wailed at what we determined to be the greatest teenage catastrophe. I was moving halfway around the world in two weeks. Farewell parties were planned at school and Beyond. I unashamedly reveled in my newly found celebrity status for the next few weeks. All my friends came to see me off at the airport. My world as I knew it, had been shattered.

The flight was long, taking nearly 2 days and multiple layovers. We stopped in Hong Kong, where I was introduced to the squatting toilet. No, I did not care that it was better functionally for the human anatomy. The public one was appalling. Being back in Asia ( in those days especially) came with a little bit of a culture shock to say the least. Everyone seem to stand too close to eachother , walk too close and stare a little too long. The site smells and sounds were so foreign. We landed at twilight and there was no electricity. The plane descended into a blanket of darkness. My grandmother was at our house and had candles lit. There was no central heat and it was dead in the middle of winter in Nepal.She cried tears of happiness, thrilled to see us after all these years. It was her first time meeting my brother. Strangely, she looked and smelled exactly how I remembered her. It was the smell of incense and Pooja. I barely slept that night. The bed was hard and I shared it with my brother. I tossed and turned, and upon waking up in the morning, I rushed up the stairs to the open terrace to finally discover in daylight, the place where my new life was beginning and I would now call home.

I couldn’t speak Nepali and I could barely understand a handful of words. Standing there at dawn , little did I know what the next several months would entail. It would comprise of one of the most challenging periods of my young life that I had ever encountered. I didn’t know that I would struggle at school , not to just learn a brand new language from scratch l, starting with the alphabet while having to take eighth grade level, grammar, and literature tests in a new language merely months later and a national board exam within two years. I didn’t know that I would be ridiculed, bullied and stalked on my way back home and discover dead frogs and hate notes in my backpack. I was blissfully unaware that the bathroom walls would have writing about me that I didn’t even understand in my innocence. I would hold it together , grit my teeth and bear it all with a smile and politeness only to come home and cry silent tears for months. I know it was hard on my parents to watch their little girl in turmoil and they even offered to move back on one desperate evening. However, I was determined not to let them down and not to let hate, carelessness and ignorance win. I know it broke their heart seeing how I was trying so hard and was still hitting walls . I didn’t know that I would learn to read and write nepali literature even before I would dare to speak it. One day, despite being teased for my accent and being “the foreign girl”, I would decide that enough was enough . I would learn to speak fluently over summer break whilst practicing with with my unsuspecting baby cousins. It was their secret and mine. I returned to the next semester of school and to the astonishment of my classmates, I set forth to speak the purest grammatically, correct Nepali in the most (Midwestern) American of accents.

So yes, what doesn’t kill you makes you stronger. It shifts your perspectives. It showed me that the world was bigger than just the confines of my immediate surroundings. It taught me that the world was full of so many kinds of people in various walks of life and fortune. It help me realize that I had so much to be grateful for. That not everyone lived or thought the way I did and how environment and upbringing molds individuals . I learned that there is no one right way of doing anything . It expanded my world and my horizons. I learned early on that I could do the hard things. I learned that I didn’t need to put myself in a box or fit a label and that I could be truly proud to be different and unique. It was OK to stand out. I learned to rely on and trust my family , all of whom I became closer to as a result . Most importantly, I learned not to take no for an answer. If someone told me I couldn’t do something ( they often did) , that I could turn around and politely say, “WATCH me “ with the brightest smile and do exactly that and more.

They say what doesn’t kill you makes you stronger. Try telling that to a teenager. Or better yet, teach them. So yes, my beloved son and daughter, remember , you can do the hard things . Remember , change can be good . When life leads you on to a hard rigid path, it teaches you to be fluid, to adjust and be flexible . It tests your character and refines it. It makes you realize that you don’t have to be a ragdoll like victim to life’s situations. You can indeed , make sweet lemonade when life hands you lemons. You can learn to dance in the darkness and fall in love with the soft glow of candlelight. Whatever doesn’t kill you, well… you know :)

You are three weeks into something and you find the source. A ninety minute conference talk, or an interview with the one person who actually did the work, or a lecture from 2019 that half the field cites and nobody summarises.

It is exactly what you needed. It is also ninety minutes long, and you have no idea which ninety seconds of it matter.

So you do what everyone does. You scrub the progress bar, land in the middle of a sentence, back up, overshoot, give up and start from the beginning at 1.75x while checking your email. Forty minutes later you have a vague sense that he said something important about sampling, somewhere, and you cannot find it again.

This is not a discipline problem. It is a property of the medium.

Text takes up space, video takes up time

A book sits in front of you all at once. You can see the shape of a chapter before you read a word of it. Your eye can jump to a subheading, drop into a paragraph, decide within four seconds that this is not the section you want, and leave.

Video does not offer that. Video releases its content on a schedule, and the schedule belongs to the speaker. The only control you have is the speed at which the schedule runs.

That is the whole difference, and almost everything annoying about working from video sources follows from it.

The raw speed gap is real but modest. A 2019 meta-analysis by Marc Brysbaert, pulling together 190 studies of reading rate, put average silent reading of English non-fiction at around 238 words per minute. Conversational speech and most recorded talks land closer to 130 to 160. Call it one and a half times faster to read the same content, which is nice but hardly life changing.

And you might reasonably point out that YouTube will play at 2x, which closes the gap and then some. Fair. There is even research supporting the habit: a 2021 study by Murphy and colleagues at UCLA found that students watching lectures at 1.5x and 2x scored about as well on comprehension tests as students watching at normal speed, with the losses only appearing at 2.5x.

So speed is not the argument. If watching faster were the only thing text bought you, transcripts would be a rounding error.

The argument is that there are three operations text supports and video does not support at any speed.

Skimming is not fast watching

Skimming is a different activity from reading, not a faster version of it. When you skim you are running a search, visually, at a level of comprehension deliberately set too low to actually understand anything. You are looking for a shape. A name, a number, a section break, the paragraph where the tone shifts.

You cannot do this to a video, because the low comprehension pass is not available. At 2x you still have to process every sentence in order. There is no equivalent of letting your eye fall down a page and stopping when something catches.

Which means the cost of checking whether a video is relevant is roughly the cost of watching the video. For one source that is fine. For twelve, it is a week.

You cannot search what you cannot read

Try to find the exact moment someone said a specific phrase in a two hour recording. The chapter markers, if the uploader bothered, are labelled at the resolution of ten minute blocks. The search box on the platform reads titles and descriptions and knows nothing about what is inside the file.

So you scrub. And scrubbing has an error rate, and the error rate compounds, and after the fourth attempt you are no longer sure whether you imagined the quote.

With a transcript this is a keystroke. That is not a small convenience. It changes which sources you are willing to use at all, because a source you cannot re-enter is a source you can only cite from memory, and citing from memory is how people end up in corrections.

Quoting from memory is how errors get published

Here is the failure mode that should actually worry you.

You watched the talk. You took a note that says something like: he argued the dataset was never validated externally. Three weeks later you write that sentence into your draft, in quotation marks, because you are fairly confident that is close to what he said.

It is close. It is not what he said. He said the external validation was underpowered, which is a different claim, and the person you are quoting will notice, and so will anyone who has watched the talk.

Text is what gets copied accurately. Every stage between the audio and your draft that runs through human recall introduces drift, and quotation marks are a promise that no drift occurred.

Can you get a transcript of a YouTube video?

Yes, and the honest answer has two parts, because YouTube already ships some of this.

Many videos have a transcript panel. Open the description, look for the option to show the transcript, and you get a scrolling list of timestamped lines that you can click to jump. It is genuinely useful and a lot of people do not know it is there. YouTube documents it in its own help centre, and if that solves your problem then you are done and can stop reading.

It often does not solve the problem, for reasons that are all mundane:

The panel is missing whenever the uploader has disabled captions, which happens more than you would expect on exactly the kind of niche technical content you most need transcribed. Automatic captions in some languages arrive without punctuation, which turns a lecture into one continuous unpunctuated sentence. There are no speaker labels, so a panel discussion becomes an undifferentiated wall in which four people take turns being indistinguishable. And every line carries its timestamp inline, so copying two paragraphs into your notes gives you two paragraphs interleaved with numbers that you then delete by hand.

None of that is fatal. It is just enough friction that most people bounce off it and go back to scrubbing.

The alternative is to run the URL through a dedicated tool and get clean prose out the other end. To transcribe a YouTube video with Vomo you paste the link and get the text back, with punctuation applied and speakers separated where the audio makes that possible. Basic transcription runs without an account, there is no cap on how long a single video can be, and the free tier covers thirty minutes of transcription per week, which is one conference talk or two short interviews. Export is TXT, DOCX, PDF or SRT.

Two features matter more than the file format. Speaker labels turn a panel into a readable document instead of a monologue by a committee. And with a free account you can put questions to the transcript in plain language rather than reading it end to end, which is the difference between having a transcript and having a source you can interrogate.

What the text is for, and what it is not for

A transcript is raw material. It is not a note, and treating it as one is the most common way this workflow goes wrong.

Ninety minutes of speech is roughly twelve thousand words. If you paste twelve thousand words into your research folder and move on, you have not reduced your problem, you have relocated it. Next month you will be searching a folder of transcripts with the same helplessness you currently bring to the video itself.

The transcript earns its place through three passes, and they are quick.

First, search it for the terms that made you open the video. You usually find in ninety seconds whether the source is worth the rest of your attention, which is the skim you could not perform earlier.

Second, pull the passages that matter into your actual notes, with the timestamp attached. The timestamp is the part people skip and later regret, because it is what lets you go back to the audio and hear the tone, the hedging, the qualifier that the transcript flattened.

Third, write the claim in your own words directly underneath the quote. Not later. The gap between reading a passage and paraphrasing it is where misunderstanding sets in, and closing that gap immediately is worth more than any tooling.

Then archive the full transcript and stop thinking about it. It is there if you need it. It is not a note.

Check the quotes against the audio

Automatic transcription is good and it is not perfect, and the places it fails are precisely the places you are most likely to quote.

Proper nouns go wrong. Technical terms get replaced by more common words that sound similar. Overlapping speech, which is most of any real conversation, produces confident nonsense. Numbers survive better than names but not reliably.

So the rule is simple and not negotiable. Anything going into your draft inside quotation marks gets listened to at the timestamp before you publish it. Everything else can stand as transcribed, because the cost of a small error in a paraphrase is low and the cost of a small error in a quotation is your credibility.

That check takes about a minute per quote. It is the only part of this that cannot be automated, and it is also the only part that carries any real risk, which is not a coincidence.

The part that actually changes

The thing you notice after a few months of working this way is not that you save time on any individual source. You do, but it is undramatic.

What changes is which sources you are willing to open. A ninety minute talk stops being a ninety minute commitment and becomes a document you can assess in two minutes and reject in three. So you check more of them. Some are useless. One is the thing your entire argument was missing, and you would not have found it, because you would not have watched it, because it was ninety minutes long and you were busy.

Video has been the default format for expert explanation for about fifteen years now. An enormous amount of what people actually know is sitting in recordings that nobody will ever scrub through twice.

Turning it into text is not a productivity trick. It is just making the material readable.

Mỗi buổi tối thứ Bảy, khi tiếng loa phát thanh vang lên báo hiệu giờ quay số của xổ số miền bắc thứ bảy đài Nam Định, bà con lại cùng nhau hồi hộp chờ đợi.

Việc đối chiếu song song hai kỳ quay không chỉ giúp bà con nắm bắt quy luật vận động ngẫu nhiên của các quả bóng số một cách minh bạch mà còn tránh được những nhầm lẫn đáng tiếc.

1. Khung Giờ Và Đài Quay Thưởng Nam Định Ngày Thứ Bảy

Xổ số miền Bắc có lịch mở thưởng cố định. Riêng ngày thứ Bảy, toàn bộ kết quả được xác định tại hội trường tỉnh Nam Định.

Lịch trình	Thời gian	Chi tiết hoạt động
Chuẩn bị	18:10	Kiểm tra lồng cầu, niêm phong và các khâu kỹ thuật
Quay số chính thức	18:15	Tiến hành quay lần lượt 27 dãy số
Kết thúc	18:30	Hoàn tất công bố kết quả (từ giải Bảy đến giải Đặc biệt)

Lưu ý: Tất cả các buổi quay số ngày thứ Bảy đều diễn ra tại cùng một hội trường với cùng một hệ thống lồng cầu minh bạch.

2. Cách Tra Cứu Song Song 2 Bảng Kết Quả Trên Màn Hình Điện Thoại

Cách 1: Sử dụng tính năng Chia đôi màn hình (Split Screen)

1. Mở trang mạng Xổ số Đại Phát.
2. Phần màn hình trên: Mở bảng kết quả xo so mien bac thứ Bảy hôm nay.
3. Phần màn hình dưới: Vào mục Kết quả cũ và chọn đúng thứ Bảy tuần trước.

Cách 2: Mở hai thẻ (Tab) trên trình duyệt

1. Mở Thẻ 1 chứa kết quả tuần mới và Thẻ 2 chứa kết quả tuần cũ.
2. Dùng ngón tay vuốt nhẹ để chuyển đổi qua lại giữa hai tuần một cách nhanh chóng.

5. Bảo Quản Vé Số Và Quy Định Thời Hạn Nhận Thưởng

• Bảo quản vé: Giữ vé phẳng phiu, khô ráo, tuyệt đối không để rách nát hoặc dính nước mờ số.
• Thời hạn lĩnh thưởng: Vé số miền Bắc chỉ có giá trị trong vòng 30 ngày kể từ ngày mở thưởng in trên vé.
• Xác nhận chủ quyền: Ký tên vào mặt sau của tờ vé số ngay khi phát hiện trúng thưởng.

Xem thêm ở Blogspot Michaelryan

Chapter 1: When You Are Tired of Pretending the Future Does Not Scare You

There are nights when you do not want another explanation. You do not want another person telling you what every symbol means, what year something might happen, or which headline supposedly proves that prophecy is unfolding. You are tired. The room is dark except for the light from your phone, and what you really want to know is whether God is still close whe

n the future feels impossible to understand. That is the place I had in mind while creating this video exploring our 500,000-word journey through the Book of Revelation.

Maybe you have never admitted that Revelation makes you uncomfortable. You believe it belongs in the Bible. You believe it matters. But some of the images are difficult, some of the warnings are severe, and Christians often disagree about what parts of the book mean. Eventually, all that disagreement can leave a person quietly wondering whether they are missing something important. This related reflection on trusting Jesus when tomorrow feels uncertain belongs beside this work because understanding Revelation should bring you closer to Christ, not make you feel spiritually inadequate because you cannot explain every vision.

That is one reason we published such a large study of the book. The complete 500,000-word work can be entered through The Book of Revelation: The Complete 108-Chapter Guide. The size is not meant to impress anyone. It gave us room to slow down, look again, and resist the temptation to force Revelation into a few dramatic predictions.

Picture someone sitting alone at the end of the bed after a difficult conversation with a spouse. The argument was not about prophecy. It was about money, exhaustion, and the growing fear that both people are carrying more than they know how to say. The Bible is on the nightstand, but the problem feels painfully ordinary.

That is exactly where Revelation can become personal.

The book repeatedly shows a world where appearances are misleading. Things that seem powerful are not always secure. Things that look defeated are not always finished. Faithful people sometimes suffer while arrogant powers appear to prosper. Yet Revelation keeps revealing another reality behind what can be seen.

For the person sitting on that bed, that means tonight's tension does not automatically define the marriage. A difficult season does not become the final chapter simply because it feels overwhelming right now. Fear sees the present moment and writes an ending. Faith leaves room for God to continue working.

Revelation teaches us to leave that room.

It does not require pretending everything will turn out exactly the way we want. It asks something quieter and harder: can you remain faithful to Jesus when you cannot see what comes next?

That question belongs in far more places than prophecy discussions. It belongs in bedrooms after arguments, cars after bad news, kitchens after bills arrive, and prayers where the only honest words left are, “God, I do not know what happens now.”

Sometimes that is where Revelation finally begins to speak.

Chapter 2: The Prayer You Keep Repeating

A woman stands at the kitchen sink long after dinner is over, washing the same cup she has already rinsed twice. The house is quiet, but her mind is not. Someone she loves is struggling, and every conversation seems to end in the same place. She has prayed about it for months. She has asked God for help, wisdom, change, anything that would make the situation feel less helpless. Nothing obvious has moved.

This is one of the places where Revelation can feel unexpectedly close.

The book does not hide the cries of people who are waiting for justice, relief, and God’s intervention. It does not pretend that faithful people always receive immediate answers. There are moments in Revelation when heaven seems to pause while people below are still hurting. That matters because waiting can make you wonder whether silence means absence.

It does not.

Sometimes the hardest part of faith is not believing that God can act. It is continuing to trust Him when He has not acted in the way you hoped, on the timeline you wanted.

The woman at the sink cannot force the person she loves to change. She cannot manufacture a breakthrough. She cannot pray with enough intensity to take control of another person’s choices. What she can do is keep loving without surrendering her peace to what she cannot control.

That is not passive. It is difficult work.

Revelation keeps drawing a line between what belongs to God and what belongs to us. God judges. God sees. God remembers. God brings history to its rightful end. Our part is faithfulness.

Sometimes faithfulness means making one more phone call. Sometimes it means setting a healthy boundary. Sometimes it means admitting that you are exhausted. Sometimes it means praying again without pretending you feel strong.

The woman finally turns off the kitchen light. The problem is still there. The prayer is still unanswered. But she is allowed to sleep.

That small act of surrender may be more spiritually honest than another hour spent trying to solve what she cannot solve tonight.

Revelation does not remove the pain of waiting. It gives waiting a different shape. It reminds us that unanswered does not mean unseen, delayed does not mean forgotten, and silence does not mean God has stopped being present.

When you cannot see what God is doing, you can still choose not to abandon the One you are waiting on.

Chapter 3: The Morning You Stop Demanding an Answer

A man wakes before sunrise because his mind has already started working. The room is still dark, and for a few seconds he forgets what has been bothering him. Then it returns. The decision he has been waiting on is still unresolved. The future he wants to understand is still hidden. He reaches for the phone, almost out of habit, then leaves it on the nightstand.

There is a kind of exhaustion that comes from trying to make uncertainty disappear.

Revelation does not promise that kind of control. In fact, one of its deepest gifts may be the way it teaches us to live without it. The book gives us a larger view of history, but it never hands us ownership of history. That belongs to God.

For someone carrying a private fear, that can be a relief.

You do not have to know whether every difficult season is a sign of something larger. You do not have to solve every symbol before you can trust Jesus. You do not have to understand why a prayer has taken so long, why a door closed, why someone left, or why the path ahead still looks unclear.

You can be faithful before you have the explanation.

That does not mean becoming careless. If there is a decision to make, make it. If there is a conversation you need to have, have it. If there is help you need to ask for, ask. Faith is not a way of avoiding reality. It is a way of facing reality without allowing fear to become your master.

The man gets out of bed, makes coffee, and sits by the window as the morning light begins to fill the room. Nothing outside has changed. Nothing inside is fully settled either. But there is a little more space between the problem and his heart.

That space matters.

Revelation keeps reminding us that Jesus remains worthy when the world is confusing, when the future is hidden, and when our own lives refuse to follow the script we hoped for. That is why I believe this book can become much more than a collection of difficult visions. It can become a companion for the person trying to stay faithful in the middle of uncertainty.

You may still have questions after reading 500,000 words.

I hope you do.

Questions can keep us humble. They can keep us searching. They can keep us close to Scripture instead of pretending we have mastered it.

But fear does not have to own those questions.

You can live today without possessing tomorrow. You can trust Jesus without understanding every detail. You can leave some things in God's hands and still take the next step with courage.

Sometimes that is what faith looks like when nobody else is watching.

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

In the first week of June 2026, with more than fifty-six million cases choking its courts and a population long accustomed to waiting years, sometimes decades, for a hearing, the Supreme Court of India did something that runs directly against the grain of the moment. It did not reach for the machine that promised to clear the backlog. It drew a line around what the machine would never be allowed to do.

The document that contains that line is a dry, thirty-five-page draft titled Regulations for Use of Artificial Intelligence in Courts, 2026, published for public consultation under the aegis of the Court's Artificial Intelligence Committee and dated the third of that month. It reads, in the manner of all such instruments, like an exercise in administrative housekeeping: definitions, committees, secretariats, audit schedules. But buried in its general principles is a sentence that amounts to a constitutional statement about the nature of judgement itself. “The use of Artificial Intelligence in Court processes,” it declares, “shall at all times remain strictly subservient to human judgment and judicial authority.” Every AI system, it continues, “shall function solely in an assistive capacity and shall not supplant or compromise the independent exercise of judicial authority by a duly appointed judicial officer.”

This is not a hedge or an aspiration. The regulations translate the principle into a list of absolute, non-derogable prohibitions, uses of AI that “shall not be subject to relaxation or modification by any authority” under any circumstances. No machine may adjudicate or sentence. No machine may be used for risk scoring of any kind, including the prediction of recidivism, the assessment of bail eligibility, or the evaluation of a witness's credibility. No machine may profile a defendant, predict a litigant's future conduct, or surveil the people moving through a courthouse. The draft permits AI for the unglamorous work of running a court, scheduling, transcription, translation, legal research, document verification, and forbids it, categorically, from touching the act of deciding.

What makes this striking is not the prohibition in the abstract. Plenty of jurisdictions issue cautious guidance about emerging technology. What makes it striking is the contrast it draws, by the sheer fact of its timing, with the direction the rest of the world has quietly been travelling. Because at the very moment India was writing the words “AI may assist judicial processes but cannot replace human judicial authority” into its draft principles, courtrooms in the United States and probation offices in England were already feeding human beings into exactly the kind of predictive machinery India had just declared off-limits, and had been doing so, in some cases, for the better part of a decade.

The Country That Had Every Reason to Automate

To understand the weight of India's choice, you have to understand the pressure it was under to choose differently. The numbers are almost incomprehensible. According to the National Judicial Data Grid, the public dashboard that tracks pendency across the entire system, the total number of cases pending before Indian courts has now passed fifty-seven million, with just over fifty million of those, close to ninety per cent, sitting in the district courts at the bottom of the pyramid. The High Courts hold a further six and a half million. More than a hundred and eighty thousand cases have been pending for over thirty years, some eighty-one thousand of them in the district courts alone. Even the Supreme Court, the smallest tier of the system and the one at its apex, carries a backlog approaching ninety-six thousand matters. For an ordinary litigant, a property dispute or a wrongful-dismissal claim can outlast the person who filed it.

A backlog of that magnitude is not an abstraction. It is a daily denial of justice, the thing the legal cliché about delay actually means in practice: witnesses die, evidence decays, the accused sit in pre-trial detention for longer than any sentence they might eventually receive. If there were ever a justice system with a rational, humane case for throwing automation at the problem, for letting an algorithm triage bail applications or predict which cases will settle or score defendants by risk so that scarce judicial attention can be rationed, it is India's. The efficiency argument is not a straw man here. It is a genuine moral claim, and it has powerful advocates inside and outside the judiciary.

India had also already begun, tentatively, to build the tools. In 2021 the Supreme Court launched SUPACE, the Supreme Court Portal for Assistance in Court Efficiency, an AI system intended to help judges sift and extract relevant material from mountains of case data. A year earlier it had introduced SUVAS, the Supreme Court Vidhik Anuvaad Software, a machine-translation engine that now renders judgments and orders between English and nineteen Indian languages, having processed tens of thousands of documents and counting. These were not decision-making systems. They were, deliberately, assistive ones, research and translation aids that left the judge firmly in charge. But they established a direction of travel, and they seeded the institutional appetite, and the technical capacity, for more.

So the 2026 regulations are not the nervous flinch of a system that does not understand the technology. They are the considered position of a judiciary that has used AI, knows what it can do, feels the full weight of the backlog that might tempt it to use AI for more, and has nonetheless decided where the boundary lies. That is what gives the document its force. It is a renunciation made from a position of need, not comfort.

What the Machine Is Forbidden to Touch

The architecture of the prohibition is worth reading closely, because its precision tells you that the drafters were not gesturing vaguely at “ethical AI” but responding to specific, documented failures elsewhere in the world.

The core of it sits in the regulation governing prohibited uses, which opens by stating that the prohibitions that follow “are absolute and non-derogable.” The first forbids training, testing, or refining any AI system on a person's data without prior approval and compliance with data-protection law. The second establishes that no judicial outcome, “including any judgment, order, or finding of fact or law,” may be reached “through Algorithmic Decision-Making alone or solely on the basis of AI-generated information,” and that “the human judicial authority shall be the determinative authority in all adjudicative decisions.” The third permits AI to surface advisory material on adjudicative or sentencing questions only with a mandatory human in the loop, and insists any such output “shall be treated as advisory only and shall be subject to independent judicial evaluation.”

Then comes the clause that reads like a direct rebuttal of a specific foreign technology. “No AI System shall be used for Risk Scoring for any purpose in Court processes, including the assessment of flight risk, prediction of recidivism, evaluation of bail eligibility, or determination of the credibility of parties or witnesses.” The regulations even define the term with forensic care: Risk Scoring means “the use of an AI System to assign a numerical or categorical score to an individual that purports to estimate the probability of that person engaging in a specified future behaviour, including the commission of an offence, recidivism, or failure to appear before a Court.” If you wanted to draft a one-sentence ban on the entire category of tools that American and British justice systems have adopted, you could hardly do better.

The remaining prohibitions close the gaps. No opaque or unexplainable system may be used where it materially affects liberty or legal rights. No system may predict, profile, or infer the future conduct of parties, accused persons, or witnesses. No system may be used for surveillance or continuous monitoring of judges, advocates, or litigants on court premises. No AI-generated output may be submitted as evidence without disclosure of its synthetic character. And no system may compromise the confidentiality of judicial deliberations or the independence of the decision-making process.

Around this hard core the draft builds an apparatus of accountability that is, in its own way, as significant as the bans. Every AI tool must clear a Technical and Ethical Impact Assessment, a “structured pre-and-post-deployment evaluation” against the regulations' general principles, before an AI Committee may approve it. Courts must maintain an AI Register documenting every system in use, an AI Incident Database tracking failures, and must publish an Annual Transparency Report. A new Centre of Research and Excellence on Artificial Intelligence, CoRE-AI, staffed by judges, lawyers, technologists, and academics, will conduct ongoing research, evaluate tools, and maintain a centralised record of evaluations. And crucially, the draft pins responsibility squarely on the human: accountability for any decision taken with AI assistance “shall rest exclusively upon” the officer who took it, and it shall not be permissible to invoke “the outputs of an AI System, the opaqueness of a Black Box system, or the occurrence of hallucination” as an excuse for a wrong decision. The machine, in other words, can never be the thing that is blamed. A person always is.

It would be wrong to paint the document as reflexively anti-technology. It contains a “presumption in favour of responsible AI adoption” and a principle, baldly titled “Innovation over Restraint,” directing courts to “actively seek opportunities” to deploy AI that improves access to justice. The drafters want the efficiency. They simply refuse to buy it at the price of the judgement. The whole design is an attempt to have the assistance without the abdication.

The Honesty Requirement

There is a second, quieter innovation in the draft that has drawn less attention than the prohibitions but may prove just as consequential, because it reaches into the daily conduct of every lawyer and litigant rather than the rare drama of a contested sentence. The regulations impose a sweeping duty of disclosure. Where an AI tool “materially assists in any aspect of case management, document analysis, or judicial administration that may affect the conduct of their proceedings,” the court must inform the parties in a timely and accessible way. And where an AI tool is used “by any party or his legal representative in the preparation or submission of any document, pleading, or evidence, the AI-assisted character of such material shall be disclosed to the Court at the time of submission” by way of a formal declaration in a prescribed format.

The teeth are in the clause that follows. If any document turns out to be “fabricated, false, misleading, or inaccurate by reason of its AI-generated character,” the person who submitted it “shall bear full responsibility” and “shall not be entitled to rely upon the character of the AI output as a defence.” There is no hiding behind the machine here either. A lawyer who files a brief stuffed with confabulated citations cannot plead that the chatbot did it; responsibility runs to the human who signed and submitted the work.

This is not an abstract precaution. Across multiple jurisdictions, courts have already disciplined lawyers who submitted filings citing judicial decisions that an AI tool had simply invented, complete with plausible-sounding case names, docket numbers, and quotations, none of which corresponded to any real ruling. The phenomenon has a clinical name, hallucination, and it is a structural feature of how large language models work rather than a bug that a software update will quietly resolve. By demanding disclosure and verification, and by stripping away the AI excuse, India's regulations treat the technology's tendency to fabricate as a permanent hazard to be managed through human responsibility, not a temporary glitch to be waited out. The same instinct runs through the whole document: trust the human, verify the machine, and never let the second stand in for the first.

The draft pairs this with a dedicated AI Content Verification Authority, charged with maintaining the standards and tools for checking AI-generated content placed before a court, and with a requirement that anyone using synthetic data or synthetic information in a proceeding disclose that fact. Taken together, the disclosure architecture treats the courtroom as a space where the provenance of every claim matters, and where the difference between something a person attests to and something a machine generated must never be allowed to blur. It is a recognition that the threat AI poses to justice is not only the dramatic one of a robot judge, but the mundane, pervasive one of fabricated material seeping into the record, dressed in the confident prose that makes a falsehood hard to catch.

The Tool India Was Built to Refuse

To see why that refusal matters, look at what risk scoring has actually done in the jurisdictions that embraced it. The cautionary tale here is not hypothetical. It has a name, COMPAS, and a paper trail.

COMPAS, the Correctional Offender Management Profiling for Alternative Sanctions, is a proprietary risk-assessment instrument developed by the company then known as Northpointe and used across multiple American states to score defendants on their likelihood of reoffending. The tool produces its scores from the answers to a long questionnaire, and those scores have been placed in front of judges making decisions about bail, sentencing, and parole. It is precisely the kind of “numerical or categorical score” estimating “the probability of that person engaging in a specified future behaviour” that India's regulations now define and forbid.

In May 2016 the investigative newsroom ProPublica published an analysis, written by Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner, that became the defining indictment of the technology. Examining the COMPAS scores of more than seven thousand people arrested in Broward County, Florida, and following them over two years to see who actually reoffended, the reporters found a stark racial asymmetry in the tool's errors. Black defendants who did not go on to reoffend were nonetheless labelled high-risk at nearly twice the rate of comparable white defendants, 44.9 per cent against 23.5 per cent. White defendants who did reoffend were disproportionately mislabelled as low-risk. Controlling for prior crimes, age, and gender, the analysis found Black defendants were 77 per cent more likely to be flagged as a higher risk of future violent crime. The company disputed the methodology and the conclusions, and a genuine statistical debate followed about which definition of “fairness” the tool satisfied and which it failed. But the central finding, that the burden of the system's errors fell unequally along racial lines, has shadowed algorithmic risk assessment ever since.

The human face of the problem appeared in ProPublica's reporting in the form of two real people. Brisha Borden, a Black eighteen-year-old who had taken a child's bicycle and scooter and then put them down when confronted, was rated high-risk, an 8 out of 10. Vernon Prater, a white man with prior armed-robbery convictions who was picked up for shoplifting, was rated low-risk, a 3. Over the following years Borden was not charged with any new crime. Prater was sentenced to eight years in prison for a subsequent burglary. The scores had got it exactly backwards, and the pattern was not an accident of two cases but a structural tendency the data laid bare.

The legal system's response to these concerns is, if anything, more troubling than the concerns themselves. In State v. Loomis, decided by the Wisconsin Supreme Court in 2016, a defendant named Eric Loomis challenged his six-year sentence on the ground that the judge's reliance on his COMPAS score violated his right to due process. He argued, among other things, that he could not test the accuracy of a score generated by a proprietary algorithm whose inner workings Northpointe refused to disclose as a trade secret, and that the tool improperly used gender as a factor. The court upheld the use of the score. It acknowledged the opacity, acknowledged the documented risk of racial bias, and required that judges in future be given a written warning about these limitations, and then permitted the practice to continue. The United States Supreme Court declined to hear the appeal, leaving the warning-label compromise in place. A defendant's liberty could turn, in part, on a number produced by a black box that neither he nor the judge was permitted to inspect.

This is the precise scenario India's draft regulations make impossible. Where Wisconsin asked judges to use a proprietary risk score carefully, India forbids the score outright. Where Loomis tolerated opacity with a warning, India's principle on explainability demands that any system materially affecting liberty be capable of an account “that can be understood without requiring specialist technical knowledge,” and bans the opaque ones from that domain altogether. The two systems were looking at the same technology and the same risks. They reached opposite conclusions about whether the risks could be managed or had to be excluded.

Thirteen Hundred Lives a Day

If COMPAS shows the American path, the United Kingdom shows how thoroughly such systems can become invisible furniture, scoring people at industrial scale long after the controversy that should have stopped them has faded into routine.

The instrument here is OASys, the Offender Assessment System, which the Ministry of Justice has used across the prison and probation services of England and Wales since 2001. OASys assesses people for the risk of harm they pose and the likelihood they will reoffend, and feeds the resulting scores into sentence planning, categorisation, and decisions about release and supervision. According to reporting by the civil-liberties organisation Statewatch in April 2025, the system is used to profile more than 1,300 people every single day, and its database held over seven million such risk scores. The system incorporates machine learning, the same family of techniques at issue everywhere else, and Statewatch documented persistent concerns, raised over years, about racial disparities and data inaccuracies in its outputs, concerns that had not prevented its continued daily operation.

The numbers are worth sitting with. Thirteen hundred people a day, every working day, scored by a system whose accuracy and fairness have been repeatedly questioned and whose outputs help determine how long they stay in prison and under what conditions they live afterwards. Seven million scores held in a database. This is not a pilot or a controversial experiment. It is the settled administrative reality of how a major Western democracy assesses the people in its criminal-justice system, and it is precisely the kind of routinised, large-scale, consequence-laden risk scoring that India's regulations place beyond the reach of any court.

It is also, as of this year, being replaced rather than reconsidered. The Ministry of Justice has confirmed that a project called Assess Risks, Needs and Strengths, ARNS, is developing a new digital tool to take over from OASys. An early prototype has been in pilot since December 2024, with a view to a national roll-out during 2026, and the work is being done in-house by a team from Ministry of Justice Digital liaising with Capita, the contractor that currently provides technical support for the older system. Read one way, this is unremarkable, a quarter-century-old piece of government software reaching the end of its life and being succeeded by something better engineered. Read another way, it is the whole argument in miniature. Nowhere in that succession does the prior question get asked: whether the scoring of a person's future by a machine belongs in the determination of their liberty at all. It was not asked when OASys was rolled out and it is not being asked now. What is being procured is a better-built version of the same answer, arriving by the same route the original arrived by, through a project plan and a delivery timetable, as an upgrade rather than a decision.

The contrast is not that one country uses AI in justice and the other does not. Both do. India runs translation and research engines; Britain runs an enormous risk-assessment apparatus. The contrast is about where each has drawn the line between assistance and judgement. Britain has allowed the machine to score the human and let that score shape the human's liberty. India has said the machine may carry the files into the courtroom but may never weigh what is inside them.

The Argument That India's Drafters Did Not Need

There is a tidy version of this story, circulating in the way that tidy versions do, in which India's regulations arrive hand-in-hand with a scholarly consensus that AI should be “categorically barred from domains requiring irreducibly human judgement,” with criminal sentencing offered as the obvious example. The claim is often attached to a specific February 2026 paper deposited on the arXiv preprint server. It is worth pausing on, because it is not accurate, and the inaccuracy is itself instructive.

The paper in question, arXiv:2602.20080, is real. It is titled “The Digital Gorilla: Rebalancing Power in the Age of AI,” written by researchers affiliated with Harvard, and it is a serious piece of work. But it makes no argument about barring AI from sentencing. Its actual thesis is something else entirely: that existing AI governance suffers from an “analogy trap,” mistakenly treating advanced AI systems as conventional products or platforms, when they should instead be understood as a kind of fourth societal actor alongside people, states, and enterprises, requiring a “federalized, polycentric governance architecture” of checks and balances. It is a paper about the distribution of power, not about the limits of machine judgement in the dock. The neat citation that supposedly underwrites India's choice does not say what it is claimed to say.

This matters for two reasons. The first is simply that a good argument does not need a fabricated authority to prop it up, and attaching one to it weakens rather than strengthens the case. The second is more pointed. The way a confident, specific, and false claim about a scholarly source can attach itself to a real policy debate and travel as fact is a small, exact illustration of the very problem the Indian regulations are trying to legislate against. AI systems generate plausible citations that do not hold up; they assert with fluency things that turn out, on inspection, to be confabulated. The courts of several countries have already sanctioned lawyers who filed submissions citing cases that an AI invented and that never existed. A regulation that insists every AI output be “treated as advisory” and “subject to independent” human “evaluation,” and that no AI-generated material be relied upon without verification, is, among other things, a defence against exactly this failure mode. The misattributed paper is not evidence for India's position. It is a live demonstration of why India took it.

Strip away the borrowed authority and the underlying intellectual case stands on its own, and it is an old one. There are domains, the argument runs, in which the thing being asked for is not a prediction but a judgement, and the two are not the same. To decide what sentence a person deserves is not to forecast their future behaviour; it is to weigh their culpability, their circumstances, the gravity of what they did, and the demands of mercy and proportionality, and to take responsibility, as a human holding public authority, for the result. A risk score can tell you, imperfectly and with documented bias, how statistically similar people have behaved. It cannot tell you what this person deserves, because desert is not a fact about the world that can be measured. It is a determination a society entrusts to a person it has authorised to make it. India's regulations encode that distinction in law. They permit the machine to inform and forbid it to decide.

What a Society Reveals by What It Will Not Automate

It is tempting to read all this as a contest between efficiency and caution, with India choosing caution. That framing is too thin. The deeper thing the regulations reveal is a claim about what justice is for, and about who must be answerable when it goes wrong.

Consider the accountability provision again, the one that says a judge may never hide behind the algorithm. In a system where a risk score shapes a sentence, accountability dissolves into a fog. The judge can say the tool flagged the defendant as high-risk; the company can say the judge exercised independent discretion; the tool's logic is a trade secret no one is permitted to examine. Everyone is responsible and therefore no one is, and the defendant is left with a deprivation of liberty that cannot be traced to a decision any identifiable human being is willing to own. The Loomis compromise, use the score but heed the warning, institutionalises exactly this diffusion. India's regulations refuse it. By insisting that the human officer is the “determinative authority” and bears responsibility “exclusively,” they preserve the thing that makes a judgement a judgement rather than an output: a person who must stand behind it and can be held to account for it.

There is a constitutional logic at work here too, one the draft signals by anchoring itself in the Bangalore Principles of Judicial Conduct and in fairness guarantees against discrimination on the grounds of “race, religion, caste, sex, gender, disability, language, economic status.” India is a country whose social fabric is shot through with precisely the categories along which algorithmic risk scoring has been shown to discriminate. A COMPAS-style tool trained on Indian arrest and conviction data would inherit and launder the biases of Indian policing and Indian society as surely as COMPAS inherited America's. The drafters appear to have understood that automating risk assessment in a deeply unequal society does not remove human prejudice from justice; it encodes it, scales it, and wraps it in the false objectivity of a number. Banning the category is a way of refusing that laundering.

There is also a subtler point about the psychology of decision-making that the regulations seem to grasp. When a judge is handed a number, even one she is formally free to disregard, the number exerts a gravitational pull. It anchors. To depart from it requires the judge to second-guess a system marketed as objective and statistically validated, and to do so on the record, exposing herself to the charge that she ignored the data. The “human-in-the-loop” safeguard that other jurisdictions lean on as a sufficient protection often turns out, in practice, to be a human rubber-stamping the loop, because the cost of overriding the machine is borne by the human alone while the machine bears nothing. India's approach sidesteps this trap not by trusting judges to resist the anchor but by removing the anchor from the high-stakes domains altogether. There is no score to defer to, so there is nothing to rubber-stamp.

None of this means the Indian framework is flawless or that its principles will survive contact with implementation. A draft is not a law. Comments were first invited by the twentieth of June, then extended to the fifteenth of July at the request of stakeholders who wanted longer to answer; that window has now closed, and the text sits with its drafters awaiting finalisation, notified nowhere and binding no one. Even once it is settled, it will bite only as and when the Chief Justice of India appoints a commencement date for the Supreme Court and the Chief Justice of each High Court appoints one for the courts beneath it, provision by provision if they choose. There is a great deal of room between a published principle and a working rule, and the text may still change on the way through. The same backlog that gives the renunciation its moral weight will keep generating pressure to relax it, and a regulation that forbids efficient injustice does nothing, by itself, to deliver slow justice faster. The permissible uses, scheduling, triage, research, will have to actually work, and at scale, or the prohibition on the rest will come to feel like a luxury the system cannot afford. The history of high-minded judicial reform in India and elsewhere is littered with principles that were honoured in the gazette and ignored in the courtroom. There is no guarantee this will be different.

But the choice has been made, in writing, at the level of principle, by the highest court of the most populous country on earth, and it points the other way from the prevailing current. The world's wealthier justice systems drifted into algorithmic risk assessment incrementally, tool by tool, procurement by procurement, until thirteen hundred people a day were being scored before anyone had decided, as a matter of principle, that this was the kind of thing a justice system ought to do. India, facing a far greater temptation, stopped to decide first.

The Path India Declined to Take

What can other judicial systems actually learn from this? Not, primarily, the specific prohibitions, though those are instructive. The deeper lesson is procedural and almost philosophical: that the question of whether to let a machine judge a human is too important to be answered by accretion, by a thousand small operational decisions made by procurement officers and pilot programmes, none of which ever quite amounts to a decision at all. The American and British risk-scoring regimes were never the product of a deliberate, public, foundational choice that algorithmic risk assessment belonged in the determination of human liberty. They emerged. India's regulations are the opposite: an attempt to make the foundational choice explicitly, in public, before the tools become load-bearing, and to write the answer down where everyone can read it.

The values embedded in that answer are not hard to name. They are the conviction that justice is an irreducibly human act of judgement rather than a prediction problem to be optimised; that the person deprived of liberty is owed a human being who will take responsibility for the decision; that the appearance of objectivity a number provides is more dangerous, in a system riddled with inequality, than the visible fallibility of a judge who can be questioned, appealed, and held to account; and that efficiency, however urgently needed, is not the supreme value against which all others must yield. These are contestable values. Reasonable people, including those drowning in the backlog India must clear, can disagree about whether the cost is worth it. But India has at least done the thing the rest of us mostly have not: it has stated the values, accepted the cost, and accepted it precisely where the temptation to compromise was strongest.

The line in the draft, “AI may assist judicial processes but cannot replace human judicial authority,” will strike some as obvious and others as naive. It is neither. It is a deliberate act of restraint by an institution that had every incentive to do otherwise, and its significance lies less in the technology it governs than in the question it forces every other judiciary to answer out loud. Sooner or later, every justice system will have to decide what it will not let a machine do. India has decided to decide on purpose. The countries already scoring thirteen hundred lives a day, or feeding proprietary risk numbers to judges behind a warning label, have, so far, mostly decided by not deciding. The most valuable thing about the path India has chosen may simply be that it is a choice, made in daylight, that the rest of the world has been quietly avoiding.

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References

1. Supreme Court of India, “Notice: Seeking views/suggestions on draft 'Regulations for Use of Artificial Intelligence (AI) in Courts, 2026'“, dated 3 June 2026. https://cdnbbsr.s3waas.gov.in/s3ec0490f1f4972d133619a60c30f3559e/uploads/2026/06/2026060342.pdf
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15. Global Voices / Advox, “When the judge meets the algorithm: AI tools entering India's courts”, 5 December 2025. https://globalvoices.org/2025/12/05/when-the-judge-meets-the-algorithm-ai-tools-entering-indias-courts/
16. ProPublica, Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, “Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks”, 23 May 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
17. Harvard Law Review, “State v. Loomis: Wisconsin Supreme Court Requires Warning Before Use of Algorithmic Risk Assessments in Sentencing”, Vol. 130. https://harvardlawreview.org/print/vol-130/state-v-loomis/
18. Wikipedia, “Loomis v. Wisconsin” (summarising 881 N.W.2d 749 (Wis. 2016); certiorari denied 2017). https://en.wikipedia.org/wiki/Loomis_v._Wisconsin
19. Justia, “State v. Loomis, 2016 WI 68 (Wisconsin Supreme Court)”. https://law.justia.com/cases/wisconsin/supreme-court/2016/2015ap000157-cr.html
20. Statewatch, “UK: Over 1,300 people profiled daily by Ministry of Justice AI system to 'predict' re-offending risk”, April 2025. https://www.statewatch.org/news/2025/april/uk-over-1-300-people-profiled-daily-by-ministry-of-justice-ai-system-to-predict-re-offending-risk/
21. Computer Weekly, Sebastian Klovig Skelton, “UK MoJ crime prediction algorithms raise serious concerns”, 25 April 2025. https://www.computerweekly.com/news/366623117/UK-MoJ-crime-prediction-algorithms-raise-serious-concerns
22. Wikipedia, “Offender Assessment System (OASys)”. https://en.wikipedia.org/wiki/Offender_Assessment_System
23. data.gov.uk, “Offender Assessment System (OASys)”. https://www.data.gov.uk/dataset/911acd3c-495f-48ca-88b6-024210868b06/offender-assessment-system-oasys
24. M. Alejandra Parra-Orlandoni, Roxanne A. Schnyder and Christopher J. Mallet, “The Digital Gorilla: Rebalancing Power in the Age of AI”, arXiv:2602.20080, February 2026. https://arxiv.org/abs/2602.20080
25. Harvard Journal of Law & Technology, “Algorithmic Due Process: Mistaken Accountability and Attribution in State v. Loomis”. https://jolt.law.harvard.edu/digest/algorithmic-due-process-mistaken-accountability-and-attribution-in-state-v-loomis-1

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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

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