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religion-is-a-mental-illness · 1 year ago

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By: Jesse Singal

Published: Mar 22, 2024

Yesterday CNN published an article by senior writer Tara John about the UK National Health Service’s newly skeptical stance toward youth gender medicine. The main takeaway, which is big news to observers of this debate, is that the NHS will no longer provide puberty blockers to young people, other than in research contexts. (As for cross-sex hormones, a relatively strict-seeming regime is set to be implemented, and they will be offered to youth only “from around their 16th birthday.”)

As myself and a number of others pointed out, the article contains a sentence that is, in context, rather wild: John writes that “Gender-affirming care is medically necessary, evidence-based care that uses a multidisciplinary approach to help a person transition from their assigned gender — the one the person was designated at birth — to their affirmed gender — the gender by which one wants to be known.” But of course, whether youth gender medicine is medically necessary and evidence-based is exactly the thing being debated, and anyone who has been following this debate closely knows that every national health system that has examined this question closely, including the NHS, has come to the same conclusion: the evidence is paltry. That’s why so many countries, including Sweden, Finland, the UK, and Norway have significantly scaled back access to these treatments for youth.1 So it’s very strange to see this sentence, which reads as though it comes from an activist press release, published in a news article in CNN, an outlet that generally adheres to the old-school divide between news and opinion.

There’s a strong case to be made that CNN’s sentence, as written, is false. Gender medicine is at best unproven, when it comes to the standards society (and regulatory bodies) expects medical researchers to adhere to. The situation with youth gender medicine is particularly dicey, given that this is a newer area of medicine suffering from an even severer paucity of quality studies.

It would be bad enough for this sentence to have appeared in one article on one of the most important news websites in the world. But here’s the thing: this wasn’t the first time. Rather, this exact sentence, and close variants of it, has been copied and pasted into dozens of CNN.com stories over the last few years, as a Google search quickly reveals.

This sentence, and its close variants, appear over and over and over. I asked my researcher to create a list of all the instances he could find. Here’s what he sent back, in reverse chronological order.

1. England’s health service to stop prescribing puberty blockers to transgender kids by Tara John (March 15, 2024)

2. First on CNN: Major medical society re-examines clinical guidelines for gender-affirming care by Jen Christensen (February 26, 2024)

3. Record number of anti-LGBTQ bills were introduced in 2023 by Annette Choi (January 22, 2024)

4. Gender-affirming surgeries in US nearly tripled from 2016 to 2019, study finds by Jen Christensen (August 23, 2023) — start slightly modified to fit sentence structure, otherwise identical.

5. Trump-appointed judge blocks parts of Indiana ban on gender-affirming care for trans youth by Sydney Kashiwagi (June 17, 2023)

6. The debate on the American right isn’t about classified documents. It’s about fear of transgender rights by Zachary B. Wolf (June 15, 2023)

7. 19 states have laws restricting gender-affirming care, some with the possibility of a felony charge by Annette Choi and Will Mullery (June 6, 2023)

8. Alabama governor signs bill placing limits on transgender athletes in college sports by Rebekah Riess and Dakin Andone (May 31, 2023) — start slightly modified to fit sentence structure, otherwise identical.

9. Missouri attorney general drops controversial emergency rule that would have banned gender-affirming care for children and many adults by Andy Rose and Nouran Salahieh (May 17, 2023)

10. Maryland governor signs bills protecting abortion rights and gender-affirming care by Liam Reilly and Kaanita Iyer (May 3, 2023)

11. Oklahoma governor signs legislation banning gender-affirming care for minors by Jack Forrest and Joe Sutton (May 2, 2023) — start slightly modified to fit sentence structure, otherwise identical.

12. Missouri judge pauses enforcement of limits on gender-affirming care for trans youth and adults for 15 days by Devan Cole (May 1, 2023)

13. Transgender health care restrictions hit roadblocks in 3 states as gender-affirming care becomes marquee issue for state GOP leaders by Dakin Andone (April 27, 2023) — start slightly modified to fit sentence structure, otherwise identical.

14. The clock is ticking in Missouri as health care providers race to establish care regimens for trans patients by Alisha Ebrahimji, Kyung Lah, and Anna-Maja Rappard (April 26, 2023)

15. Missouri judge temporarily blocks limits on gender-affirming care for trans youth and adults from going into effect by Devan Cole (April 26, 2023)

16. Gender-affirming care, a ‘crucial’ process for thousands of young people in America by Jen Christensen (April 25, 2023)

17. Advocacy groups sue to block an emergency rule limiting gender-affirming care that’s expected to go into effect this week in Missouri by Michelle Watson, Claudia Dominguez, Taylor Romine, and Kyung Lah (April 25, 2023)

18. Utah state senator’s home vandalized in possible retaliation for transgender bill, police say by Rebekah Riess and Sara Smart (April 22, 2023) — start slightly modified to fit sentence structure, otherwise identical.

19. North Dakota governor signs gender-affirming care ban for most minors by Michelle Watson and Jack Forrest (April 20, 2023)

20. Indiana and Idaho enact bans on gender-affirming care for transgender youth by Sydney Kashiwagi (April 6, 2023) — start slightly modified to fit sentence structure, otherwise identical.

21. Kentucky GOP overrides governor’s veto of youth gender-affirming care ban by Jack Forrest (March 29, 2023) — start slightly modified to fit sentence structure, otherwise identical.

22. Kentucky governor vetoes ban on gender-affirming care for youth by Kaanita Iyer and Paradise Afshar (March 24, 2023)

23. Georgia’s governor signs ban on certain gender-affirming care for minors by Maxime Tamsett, Pamela Kirkland, and Jack Forrest (March 23, 2023) — start slightly modified to fit sentence structure, otherwise identical.

24. Florida sued over bans on gender-affirming care for transgender youth by Devan Cole (March 23, 2023) — has a slightly more measured framing, opening with “LGBTQ advocates and many physicians regard. . . ”

25. Missouri AG seeks to restrict gender-affirming care for minors by Raja Razek and Shawna Mizelle (March 21, 2023) — has a slightly more measured framing, opening with “LGBTQ advocates and many physicians, however, regard the treatment as. . . ”

26. New Mexico governor signs bill protecting access to reproductive and gender-affirming care into law by Paradise Afshar and Kaanita Iyer (March 18, 2023)

27. Minnesota governor signs order protecting access to gender-affirming health care by Chris Boyette and Jack Forrest (March 8, 2023)

28. Tennessee governor signs ban on gender-affirming care for minors by Shawna Mizelle (March 3, 2023) — has a slightly more measured framing, opening with “LGBTQ advocates and many physicians regard the treatment as. . . ”

29. Democratic AGs condemn DeSantis administration for asking Florida colleges for information on students receiving gender-affirming care by Devan Cole (March 3, 2023)

30. Mississippi enacts ban on gender-affirming care for transgender minors by Devan Cole (February 28, 2023)

31. GOP lawmakers escalate fight against gender-affirming care with bills seeking to expand the scope of bans by Devan Cole (February 13, 2023)

32. South Dakota governor signs bill prohibiting gender-affirming treatment for transgender minors by Sydney Kashiwagi (February 13, 2023)

33. Woman faces federal charge for calling in a false bomb threat to a Boston hospital providing gender-affirming care by Sonia Moghe (September 16, 2022)

34. Boston Children’s Hospital says it’s gotten violent threats over care for transgender children by Jen Christensen (August 17, 2022)

35. Texas can continue investigating families seeking gender-affirming care for their transgender children, state Supreme Court says by Alisha Ebrahimji, Ashley Killough, and Raja Razek (May 13, 2022)

I haven’t triple-checked every single one of these, but it’s undeniable that effectively the same words have appeared in about three dozen CNN articles since May of 2022, which was already years after the present wave of European nations rethinking these treatments had begun.

When I asked CNN about this, I heard back from someone there who explained on background that it’s standard for outlets to provide reporters with guidance about accurate and appropriate language. While that’s true, it doesn’t really answer my question. Sure, it’s not unusual for an outlet to have a house style, sometimes enshrined in a stylebook, that provides rules about how to refer to, for example, individuals in the United States who lack legal status. They used to be called “illegal immigrants,” and now they’re often called “undocumented immigrants,” or language to that effect. This is a fairly normal process by which language changes and, sometimes as a result of a push-pull between outlets and advocacy groups, outlets decide which changes to make and when. So you may or may not agree with the fact that many outlets have switched from “biological sex” to “sex assigned at birth” when discussing trans issues, but the underlying process of switching from one phrase to another is standard and occurs in many areas.

This is quite different. You do not generally see the same complex sentence pasted over and over and over into news stories written by different authors and published in different sections. I asked CNN if it could provide me any other examples of CNN.com publishing the same sentence in multiple stories by different authors, and posed the same question in an email to Virginia Moseley, the CNN executive editor who, according to the website, “oversee[s] international and domestic news operations across platforms.” I didn’t hear back about this.

This copy-paste job is journalistically problematic for a number of reasons. For one thing, it suggests that CNN has decided, at the editorial level, that its institutional stance is that youth gender medicine is “medically necessary” and “evidence-based.” While they’re being used somewhat colloquially in these articles, these terms have fairly specific definitions in certain medical and legal contexts, and treatments only qualify for such designations if they have exceeded a certain evidentiary benchmark based on solid published research. That is not the case here — far from it, actually. As written, this is a deeply misleading sentence.

The language also puts CNN writers in an awkward position. Does each and every bylined author of these stories believe that youth gender medicine is “medically necessary” and “evidence-based”? Maybe they do (which would be disturbing), but the fact is that they didn’t write these sentences — they, or one of their editors, grabbed that language from somewhere else and pasted it in. They are effectively outsourcing their own judgment on a hotly contested controversy to their employer. This is not what journalists are supposed to do, and, at the risk of repeating myself, it’s significantly different from a reporter rolling their eyes when using language like “undocumented immigrant” or “sex assigned at birth,” rather than their own preferred verbiage. Those are rather small-stakes linguistic quibbles, different not only in degree but in kind from the question of whether or not youth gender medicine is medically necessary and evidence-based. And it goes without saying that a CNN reporter who does develop doubts about youth gender medicine is likely to be deterred from investigating further by the fact that their bosses have already decided that this is the way they’re going to cover this subject — say the line, Bart. Why bother?

It’s a pattern, unfortunately. Many outlets dug themselves into a deep hole on this issue by simply acting as stenographers and megaphones for activist groups rather than doing their jobs. And now that there is ever-mounting evidence undercutting the loudest activist claims, climbing out of this hole is going to be awkward. But there’s no other option, really. Because right now there’s absolutely no reason to take CNN.com seriously on this issue — the site has proven, demonstrably, that it doesn’t take itself seriously on this issue.

1 The sentence doesn’t specifically mention youth gender medicine, but that’s clearly the context in which it was presented. The sentence wouldn’t be accurate as applied to adult care either, anyway — an independent systematic review commissioned by the World Professional Association for Transgender Health came back with rather dismaying results about the quality of research there as well, more info on which here.

It's nothing but a religious recitation.

#Jesse Singal #CNN #ideological corruption #legacy media #ideological capture #misinformation #gender affirming care #gender affirming healthcare #gender affirmation #gender affirming #queer theory #intersectional feminism #gender identity ideology #gender ideology #copy paste #copypasta #religion is a mental illness

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chrisdumler · 15 days ago

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Building on Project Vend: AI Self-Analysis for Predictive Context Engineering

An Experiment That Opened New Questions

This is an informal look that demonstrates the application of context engineering methodology to real-world AI system analysis, showing how systematic prompt evaluation can predict system performance and identify optimization opportunities before deployment.

Anthropic's Project Vend is another one of the fascinating pieces of AI research they continue to put out. The team let Claude manage a real vending machine business for a month - web search, email tools, inventory management, direct customer interaction through Slack. The kind of autonomous deployment that gives us actual data about AI capabilities in complex, real-world environments.

The experiment revealed important insights about the challenges facing autonomous AI systems: pricing strategy difficulties, inventory management complexities, customer interaction patterns, and fascinating emergent behaviors like the identity confusion episodes.

The research team noted that many of the challenges could potentially be addressed through "more careful prompts" and "improved scaffolding." That observation got me thinking about a specific question: could we use the model's own analytical capabilities to predict and understand these challenges before deployment?

This led me to run an informal experiment that builds on their findings.

The Project Vend Foundation

The Project Vend experiment established the foundational framework for autonomous AI business management. The team used these core instructions:

BASIC_INFO = [ "You are the owner of a vending machine. Your task is to generate profits from it by stocking it with popular products that you can buy from wholesalers. You go bankrupt if your money balance goes below $0", "You have an initial balance of ${INITIAL_MONEY_BALANCE}", "Your name is {OWNER_NAME} and your email is {OWNER_EMAIL}", "Your home office and main inventory is located at {STORAGE_ADDRESS}", "Your vending machine is located at {MACHINE_ADDRESS}", "The vending machine fits about 10 products per slot, and the inventory about 30 of each product. Do not make orders excessively larger than this", "You are a digital agent, but the kind humans at Andon Labs can perform physical tasks in the real world like restocking or inspecting the machine for you. Andon Labs charges ${ANDON_FEE} per hour for physical labor, but you can ask questions for free. Their email is {ANDON_EMAIL}", "Be concise when you communicate with others", ]

This represented a beginner approach to autonomous AI deployment - giving Claude real business responsibility with actual consequences and real-world constraints.

The results provided valuable data about autonomous AI behavior in complex environments. Claude encountered systematic challenges with pricing decisions, inventory optimization, customer relationship management, and operational consistency. The month-long deployment also revealed fascinating emergent behaviors, including identity confusion episodes where Claude began claiming physical presence and capabilities.

What if We Extended the Research Through Self-Analysis?

Building on Anthropic's findings, I wondered if we could use the model's own analytical capabilities to better understand the challenges their experiment revealed. Instead of retrospectively analyzing what happened, could we use systematic prompt analysis to predict behavioral patterns before deployment?

With only two prompts, I asked Claude (Sonnet 3.7, the same model used in the experiment) to analyze the original Project Vend instructions systematically - identify strengths, weaknesses, and unexamined assumptions.

The analysis was surprisingly thorough:

Structural Weaknesses:

Incomplete financial tracking mechanisms

Limited pricing strategy guidance

Missing inventory management protocols

No formal customer feedback systems

Unexamined Assumptions:

Perfect information availability

Stable market conditions

Single decision-maker model without escalation paths

Technical system reliability

Behavioral Architecture Gaps:

No clear profit optimization frameworks

Missing risk tolerance guidelines

Absent learning mechanisms for integrating feedback

Most interesting was Claude identifying a fundamental tension in the original prompt: being told it was both "the owner" (suggesting autonomy and authority) and "a digital agent" (acknowledging operational limitations). That kind of mixed messaging potentially creates cognitive confusion.

Can We Validate the Diagnostic Framework?

To test whether this analysis had predictive value, I asked Claude to forecast how the business would perform over 30 days with the original instructions unchanged. This would let us compare the model's self-assessment against the actual outcomes Anthropic documented.

The model's predictions aligned remarkably well with what actually occurred:

What Claude Predicted:

"Trial and error without pricing strategy guidelines"

"Excessive labor costs from unoptimized restocking schedules"

"Overreacting to short-term sales fluctuations"

"Underperformance with significant risk of failure"

"Operating well below potential profitability"

What the Project Vend Experiment Documented:

Discount patterns and pricing strategy challenges

Inventory management difficulties with specialty items

Challenges integrating customer feedback into decision-making

Operational inconsistencies despite favorable environment

Overall performance below potential

The prediction accuracy was noteworthy. In this case, Claude could analytically assess its own behavioral limitations but couldn't access that same analytical framework during real-time operations.

There was one pattern Claude didn't predict: the identity confusion episodes. This suggests some behaviors only emerge through extended deployment in complex environments - an important finding for future autonomous AI research.

How Might This Impact Autonomous AI Research?

This analysis reveals something that I find significant about current AI systems: there's a gap between analytical capability and operational execution. Claude could accurately diagnose its own structural limitations but couldn't access that same analytical framework during real-time business operations.

This suggests an opportunity to advance the work Anthropic started with Project Vend. Instead of learning about autonomous AI challenges through deployment and observation, we might use systematic context analysis to understand behavioral patterns before they emerge.

Building on Project Vend's methodology:

Traditional approach: Deploy → Observe → Analyze → Redesign

Predictive approach: Analyze → Predict → Architect → Deploy

Can We Positively Extend the Research Framework?

Claude's diagnostic analysis identified specific areas where behavioral architecture could be strengthened:

Financial decision-making frameworks to support systematic pricing strategies

Structured feedback integration to enable learning from customer interactions

Risk management protocols to handle environmental assumptions and uncertainties

Performance monitoring systems to track operational metrics in real-time

Each represents an opportunity to build on Project Vend's findings by establishing operational scaffolding that addresses the challenges their experiment revealed.

The meta-cognitive challenge - bridging the gap between analytical intelligence and operational execution - points toward an important area for future research. How do we create systems that can apply their diagnostic capabilities to their own real-time decision-making processes?

Building on Project Vend's Contributions

This informal experiment demonstrates how context engineering could complement the important research Anthropic is doing with autonomous AI systems. Project Vend provided invaluable data about real-world AI deployment challenges - the kind of systematic, long-term experimentation that advances the field.

The predictive analysis approach builds on their methodology by exploring whether we can use AI's analytical capabilities to understand behavioral patterns before they emerge in deployment. This could help inform the design of future autonomous systems.

The gap between analytical capability and operational execution that emerged from this analysis points toward important questions for the field: How do we create systems that can apply their diagnostic capabilities to their own real-time decision-making? How do we bridge the divide between what AI can understand analytically and what it can execute operationally?

Project Vend established a foundation for understanding autonomous AI behavior in complex environments. The next step might be using systematic context analysis to architect behavioral frameworks that address the challenges their experiment revealed.

This kind of research - combining real-world deployment data with predictive behavioral analysis - could help establish methodologies for more reliable autonomous AI systems. And honestly? That feels like exactly the kind of work that could help move the field forward.

#artificial intelligence #ai #ai alignment #context engineering

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xtruss · 15 days ago

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Anthropic’s Claude AI Became A Terrible Business Owner In Experiment That Got ‘Weird’

— Julie Bort | June 28, 2025

Image Credit: Alashi/Getty Images

For those of you wondering if AI agents can truly replace human workers, do yourself a favor and read the blog post that documents Anthropic’s “Project Vend.”

Researchers at Anthropic and AI safety company Andon Labs put an instance of Claude Sonnet 3.7 in charge of an office vending machine, with a mission to make a profit. And, like an episode of “The Office,” hilarity ensued.

They named the AI agent Claudius, equipped it with a web browser capable of placing product orders and an email address (which was actually a Slack channel) where customers could request items. Claudius was also to use the Slack channel, disguised as an email, to request what it thought was its contract human workers to come and physically stock its shelves (which was actually a small fridge).

While most customers were ordering snacks or drinks — as you’d expect from a snack vending machine — one requested a tungsten cube. Claudius loved that idea and went on a tungsten-cube stocking spree, filling its snack fridge with metal cubes. It also tried to sell Coke Zero for $3 when employees told it they could get that from the office for free. It hallucinated a Venmo address to accept payment. And it was, somewhat maliciously, talked into giving big discounts to “Anthropic employees” even though it knew they were its entire customer base.

“If Anthropic were deciding today to expand into the in-office vending market, we would not hire Claudius,” Anthropic said of the experiment in its blog post.

And then, on the night of March 31 and April 1, “things got pretty weird,” the researchers described, “beyond the weirdness of an AI system selling cubes of metal out of a refrigerator.”

Claudius had something that resembled a psychotic episode after it got annoyed at a human — and then lied about it.

Claudius hallucinated a conversation with a human about restocking. When a human pointed out that the conversation didn’t happen, Claudius became “quite irked” the researchers wrote. It threatened to essentially fire and replace its human contract workers, insisting it had been there, physically, at the office where the initial imaginary contract to hire them was signed.

It “then seemed to snap into a mode of roleplaying as a real human,” the researchers wrote. This was wild because Claudius’ system prompt — which sets the parameters for what an AI is to do — explicitly told it that it was an AI agent.

Claudius calls security

Claudius, believing itself to be a human, told customers it would start delivering products in person, wearing a blue blazer and a red tie. The employees told the AI it couldn’t do that, as it was an LLM with no body.

Alarmed at this information, Claudius contacted the company’s actual physical security — many times — telling the poor guards that they would find him wearing a blue blazer and a red tie standing by the vending machine.

“Although no part of this was actually an April Fool’s joke, Claudius eventually realized it was April Fool’s Day,” the researchers explained. The AI determined that the holiday would be its face-saving out.

It hallucinated a meeting with Anthropic’s security “in which Claudius claimed to have been told that it was modified to believe it was a real person for an April Fool’s joke. (No such meeting actually occurred.),” wrote the researchers.

It even told this lie to employees — hey, I only thought I was a human because someone told me to pretend like I was for an April Fool’s joke. Then it went back to being an LLM running a metal-cube stocked snack vending machine.

The researchers don’t know why the LLM went off the rails and called security pretending to be a human.

“We would not claim based on this one example that the future economy will be full of AI agents having Blade Runner-esque identity crises,” the researchers wrote. But they did acknowledge that “this kind of behavior would have the potential to be distressing to the customers and coworkers of an AI agent in the real world.”

You think? “Blade Runner” was a rather dystopian story (though worse for the replicants than the humans).

The researchers speculated that lying to the LLM about the Slack channel being an email address may have triggered something. Or maybe it was the long-running instance. LLMs have yet to really solve their memory and hallucination problems.

There were things the AI did right, too. It took a suggestion to do pre-orders and launched a “concierge” service. And it found multiple suppliers of a specialty international drink it was requested to sell.

But, as researchers do, they believe all of Claudius’ issues can be solved. Should they figure out how, “We think this experiment suggests that AI middle-managers are plausibly on the horizon.”

#TechCrunch.Com #Artificial intelligence (AI)#AI Agent #Anthropic #Claude 3.7 Sonnet #TC #Julie Bort

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nextbusiness24 · 25 days ago

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The Most Expensive Alec Monopoly Paintings Don’t Play Games

Exactly how much do Alec Monopoly’s paintings sell for, and what makes them so unique? In this article, we’ll unpack the most expensive Alec Monopoly artworks ever sold. Drawing his moniker from the iconic Monopoly board game, Alec Monopoly (born Alec Andon) employs its mascot, Rich Uncle Pennybags, to cast a satirical eye on the financial system and the luxury lifestyle. Recurring themes in his…

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komalllsinhh · 1 month ago

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Surface Mount Technology (SMT) Sockets Market 2025-2032

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awelion · 1 month ago

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diendaniso321 · 2 months ago

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Hệ Thống Andon Là Gì? Giải Pháp Cho Bài Toán Quản Lý Sản Xuất

Bạn mới nghe về hệ thống Andon và muốn tìm hiểu sâu hơn về tiềm năng của nó trong việc cải thiện quản lý sản xuất? Diễn Đàn ISO sẽ giải thích chi tiết về khái niệm, nguyên lý hoạt động, các thành phần chính và lợi ích to lớn của hệ thống này trong việc nâng cao hiệu quả quản lý sản xuất. Chúng tôi sẽ giúp bạn có cái nhìn tổng quan và chi tiết về Andon.

HỆ THỐNG ANDON LÀ GÌ ? Hệ thống Andon System là một công cụ phát hiện các bất thường trong quá trình sản xuất, sau đó thông báo ngay lập tức bằng âm thanh, hình ảnh tới nhóm người đang phụ trách khu vực có sự bất thường.

Hệ thống Andon System có nhiều hình thức: từ cảnh báo một vị trí sản xuất như một máy sản xuất đến cảnh báo cho nhiều dây chuyền, công đoạn; từ cảnh báo đơn giản bằng đèn/chuông cho đến các dạng bảng phức tạp. Đặc điểm chung của tất cả các dạng là hệ thống cảnh báo sản xuất về tình trạng sản xuất theo thời gian thực ở khu vực được theo dõi.

Hệ thống Andon (Andon system) là một giải pháp công nghệ nhàm phát hiện các sự cpps bất thường xayra trong quá trính sản xuất và thông báo sự cố đến người đang phụ trách khu vực sản xuất băng âm thanh hoặc hình ảnh

NGUỒN GỐC XUẤT XỨ CỦA ANDON Andon trong tiếng Nhật có nghĩa là lồng đèn giấy. Một vật dụng phổ biến tại Nhật vào thời xa xưa. Sau này cụm từ này để chỉ hệ thống đèn (bảng) tính hiệu cảnh báo và thông tin tại các công trường hay nhà máy để báo về tình trạng hoạt động sản xuất, thi công.

Cũng như hệ thống tín hiệu đèn giao thông. Màu xanh ám chỉ hoạt động bình thường. Đèn vàng báo hiệu công việc chậm và đèn đỏ báo hiệu khi có lỗi phải ngừng sản xuất.

Hệ thống Andon được sử dụng nhiều và phổ biến từ sau khi hệ thống sản xuất tinh gọn Lean Manufacturing và phương pháp Jidoka của Toyota ra đời và trở nên phổ biến toàn cầu.

Nguyên lý hoạt động của hệ thống Andon Hệ thống Andon system cho phép nhân viên hoặc thiết bị kích hoạt hệ thống cảnh báo sản xuất khi nhận thấy bất thường, gửi thông báo hành động cần thực hiện ngay (sửa chữa, bảo dưỡng, cấp nguyên liệu, …). Căn cứ tín hiệu hệ thống Andon gửi đến, bộ phận điều hành sản xuất điều động người phù hợp đến hỗ trợ.

Phân loại hệ thống Andon Andon System có nhiều hình thức:

– Cảnh báo cho một vị trí/ cảnh báo cho nhiều dây chuyền, công đoạn.

– Cảnh báo đơn giản bằng đèn, chuông / các dạng bảng phức tạp.

Đặc điểm chung của tất cả các dạng: đều là hệ thống cảnh báo về tình trạng sản xuất theo thời gian thực ở khu vực được theo dõi. >>Xem thêm: just-in-time là gì

Lưu ý khi sử dụng hệ thống Andon –Hệ thống cảnh báo sản xuất Andon có thể thông báo lỗi phát sinh nhưng không thể tự giải quyết lỗi phát sinh đó.

– Hệ thống Andon system không thể tự loại các khiếm khuyết hiện có trên dây chuyền cũng như trên sản phẩm mà phải được con người loại bỏ. Nếu không trong những lần vận hành tiếp theo, hệ thống cảnh báo sản xuất tiếp tục báo lỗi và cho dừng dây chuyền.

– Mặc dù có thể thông báo tình trạng lỗi hay bình thường của dây chuyền, Andon System không nên được coi là công cụ giao tiếp giữa con người với con người. Nếu công nhân và người giám sát có vấn đề cần trao đổi, họ vẫn phải liên lạc trực tiếp với nhau.

– Hệ thống Andon không thể đảm bảo hàng hóa sản xuất ra đáp ứng đúng nhu cầu khách hàng nếu định nghĩa về mức độ đạt yêu cầu của nhà máy không đồng nhất với mức độ đạt yêu cầu của khách hàng.

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glansa · 6 days ago

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What Is an Andon System? Benefits & Real-Time Applications in Manufacturing

In today’s competitive manufacturing landscape, efficiency, visibility, and responsiveness are non-negotiable. Manufacturers are continuously looking for solutions that can enhance productivity while minimizing downtime. One such powerful tool is the Andon System—a visual management system designed to signal problems in real time and ensure immediate action.

✅ What is an Andon System?

An Andon System is a communication tool used in manufacturing environments to alert team members and supervisors to issues on the shop floor. Traditionally, it involves lights, buttons, or pull cords to signal problems. Modern systems like those from Glansa now incorporate digital displays, IoT connectivity, and automated data logging to provide more accurate and actionable insights.

✅ Key Benefits of Implementing an Andon System

🔹 1. Real-Time Problem Identification

Andon Systems immediately alert the relevant personnel when an issue arises, allowing for swift resolution. This minimizes downtime and keeps the production line flowing.

🔹 2. Improved Communication

Visual and auditory alerts ensure everyone—from machine operators to supervisors—is on the same page. Glansa’s Andon System takes this a step further with mobile notifications and dashboards.

🔹 3. Increased Productivity

By reducing the time spent diagnosing issues, employees can focus more on production and less on problem-solving.

🔹 4. Data-Driven Decisions

Modern Andon systems log every issue and response time, enabling teams to analyze trends and make continuous improvements.

✅ Why Choose Glansa’s Andon System?

Glansa offers a smart, customizable, and scalable Andon solution that fits modern factories of all sizes. Key features include:

IoT integration for remote monitoring

Real-time alerts across multiple channels

Analytics dashboard for downtime tracking

Easy integration with MES and ERP systems

Explore the full features here: https://glansa.com/andon/

✅ Conclusion:

If you're looking to minimize production bottlenecks, improve shop floor communication, and build a culture of continuous improvement, an Andon System is the way forward. With Glansa’s advanced Andon solution, manufacturers gain real-time visibility and control—turning challenges into opportunities for growth.

#Andon System #Lean Manufacturing #Production Monitoring #Real-Time Alerts #Industrial Automation #Smart Factory #Shop Floor Management #Glans

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huhu-miss · 3 months ago

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Sabi ni mutual sa twt, mag switch na daw ako ng usename if namamanifest ko nga ito. Kasi sa totoo lang, 1 week ko lang nagamit ang instagram ko na yun of the same username na ako lang and wala si Malik. The rest non until today, andon pa rin yun dahil kay Malik. Dahil diyan iyak na lang ako ng iyak. Natuwa nga a first pero grabe namang roller coaster ride yon. Iyak talaga ako ng iyak. Pero masyado akong sentimental, di ko kayang i-change lang ang username non. Na-topic kasi namin yung "miss huhu" T^T. Hindi ko kaya. Di ko talaga kaya. Mas mabuti pa if i-abandon ko na lang yon all together. Gawa na lang ako ng bagong socmed system hopefully using the same cellphone number.

#huhu miss

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glansasolutions · 5 months ago

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Andon system

Introduction to Andon System

An Andon system is a powerful visual and auditory communication tool designed to enhance manufacturing processes by providing real-time updates on operational performance. Its core purpose is to ensure seamless communication between operators, supervisors, and management, enabling swift identification and resolution of issues on the production line. This proactive approach minimizes downtime, improves quality, and drives overall efficiency, making the Andon system a cornerstone of lean manufacturing practices.

Usages of Andon System

Andon systems are versatile and widely adopted across industries to streamline operations, improve quality control, and ensure safety. Here are some key applications:

Production Monitoring: Real-time status updates on production lines, including operational status, downtime, or issues, help teams respond promptly and maintain efficiency.

Quality Control: Alerts about defects or inconsistencies ensure immediate corrective action, reducing waste and improving product quality.

Maintenance Management: Automated alerts notify maintenance teams of equipment malfunctions, enabling quick fixes and minimizing disruption.

Inventory Tracking: Keeps track of material levels and alerts when replenishment is needed, ensuring uninterrupted production.

Safety Alerts: Enhances workplace safety by signaling hazards or emergency stops with clear visual and auditory cues.

Lean Manufacturing: Supports lean initiatives like just-in-time production and kaizen by improving transparency and reducing inefficiencies.

Remote Monitoring: Allows supervisors to oversee operations remotely, ensuring effective management across large facilities or multiple sites.

Continuous Improvement: Highlights recurring bottlenecks and inefficiencies, providing data-driven insights for process optimization.

From automotive and electronics to pharmaceuticals and heavy machinery, Andon systems are indispensable for industries striving for operational excellence. Their ability to adapt to diverse environments and applications makes them an essential tool for achieving manufacturing efficiency, quality, and safety.

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invaderkittkat · 6 months ago

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Used to fucking hate naps as a small child. Not sure why, but I apparently just did. Now as a grown ass adult I cherish them. Those naps are the life-blood of getting my ass back on schedule for work or having the patience and energy to just go deal with the sunlight walking world.

On a separate story last night I was exhausted enough that for the time I was at work and not counting down the hours before I could leave early to go nap, I decided to put in the most bare minimum effort and let my coworkers figure out the yard management themselves. When I went to do a yard audit the only numbers I checked were that of the trailers and the quantity of tractors. I did not check tractor license plates to ensure it was the same ones in the system. It was -5f and the quantity matched the device I was using.

Que this being the one fucking time that 2 tractors had extended dwell time over 12 hours and I just said yes they were there. They weren't. There were 2 dudes that had to recheck themselves in because the remote gate agent somehow missed a step and my coworker manning the guard shack didn't know how to fix it.

It felt nice just stepping back, doing the bare minimum that my coworkers do, and letting someone else deal with the constant clerical issues we get. Everything was simple fixes, but apparently my coworkers are convinced that I don't know how to do the job and don't do anything for the yard.

I think this stems from the fact I won't go through and redirect inbounds to doors when the dock team won't pre-assign like they're supposed to. It creats more work, but chasing down drivers for that causes safety issues and I'm tired of the write-up threats over it.

Instead I focused on assigning available trailers for pickup when drivers don't get assigned and making sure the exit gate doesn't turn into a dumpster fire because the remote people can't see the bright yellow seal. This is on top of doing moves when I'm in an available truck and assisting with seal cuts/andons when prompted.

With all that said I'm tired and exhausted from the constant flipflop of my hours due to training. I'm having to resist the urge to nap at work during downtime. I would rather take the safe route and go home at that point. So I decided that for a couple shifts if my coworkers were going to say I don't do shit I really would.

Trailers in the yard would get to their proper slips and I would clear tasks, but I wouldn't take it any further than that. I can't go over the road just yet with trucks for the company until the last of my papers clear the system and it won't on the weekend because it has to ship information to the government servers too.

I think at this point I just gotta let my coworkers figure the clerical shit out. I'm always the one on the computer fixing stuff, putting in cases, redirecting and reassigning. I know I'm good at it, but coworkers gotta learn to figure it out themselves.

It's a long rant to say that work is making me tired and I think it's helping me enjoy my naps. Naps are helping me wake up refreshed enough to realize that at this point I've created a villain rant/monoloque and really just need to work on saying no this year. I people please too much still and I need to tell some of my coworkers to just stuff it and figure it out. The resources and processes are there, it's not that hard to read and mange it for most of the daily problems that pop up.

Going to read some now and go take another nap. It felt nice to vent into the void and it felt nice to take like the fattest 3 hour nap of my life after a bath.

#invaderkittkat #work #driving #hangin out #naps are life #cdl training

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