Very ill vs Very sick

Both phrases are correct, but "very ill" sounds more formal and serious, while "very sick" is more casual and common in everyday speech. [1, 2, 3]

Using "Very Ill"

Formal tone: Often used by news reports, doctors, or in writing.
Serious health: Usually describes a severe, major, or long-lasting disease.
Example: "He is very ill in the hospital with pneumonia." [1, 2, 3, 4, 5]

Using "Very Sick"

Casual tone: Used most of the time when talking with friends and family.
General or short-term use: Can mean a bad cold, a stomach bug, or feeling like you want to throw up (nauseous).
Example: "The bad food made her very sick." [1, 2, 3, 4]

Mandai North Crematorium (Ref. KF1) And Garden Of Serenity Will Commence Operations On 15 August 2025

 

Mandai North Crematorium And Garden Of Serenity Will Commence Operations On 15 August 2025

28 Jul 2025

Singapore, 28 July 2025 – The new Mandai North Crematorium (MNC) and Garden of Serenity (GOS), operated by the National Environment Agency (NEA), will begin operations on 15 August 2025. MNC will start with four cremation services daily. Cremation services will be ramped up progressively over the coming months.

2          The new facilities, spanning approximately 2.4 ha, have been built in anticipation of demographic changes and an ageing population in Singapore. Annual resident deaths are projected to increase from about 25,000 in 2024 to around 40,000 by 2040. The new crematorium will provide additional cremation capacity, and the GOS will provide an additional option for the inland scattering of ashes. They will enable us to continue serving the nation’s needs in the years ahead.

Facility designed to create a healing environment for the bereaved by integrating greenery and natural lighting within the key spaces

3          The MNC is designed to house six service halls with viewing halls, transfer halls, and 18 cremators. Currently, three service halls are equipped with nine cremators. The ash collection centre is also ready for operations. NEA will fit out the remaining halls and cremators to meet anticipated demand for cremations.

Service hall

4          The new cremation facility offers an innovative design and technological features to enhance operational efficiency, and provide a seamless experience for visitors [1]. Some key features include a layout where service halls and viewing halls are adjacent, reducing walking distances and improving accessibility for all users. Technology is also used to enhance the visitor experience, with automated guided vehicles for coffin transport, and a self-help system at the ash collection centre. A comprehensive process control monitoring system oversees the entire cremation and ash collection process, ensuring service reliability. 

Layout is designed with key facilities positioned side by side on the same level or connected vertically via escalators and lifts, reducing walking distances for visitors.

Automated guided vehicle will be used to transport a coffin from hearse drop off to the service hall

Ash collection station with a self-help system to facilitate collection of cremated ashes

5          The MNC has been awarded the Green Mark (Platinum) by the Building and Construction Authority for sustainability efforts in building design. The environmental features at the facility include greenery in and around the building, a green roof that reduces the heat island effect, efficient underfloor cooling systems, and adoption of low carbon concrete and sustainable building products [2] to reduce the carbon footprint of the building.

6          The GOS, Singapore’s second inland ash scattering facility, is located next to the MNC building [3]. It adopts an open garden concept with designated lanes for walkways and ash scattering [4]. It also incorporates a stormwater detention pond designed as a natural pond. The new facility will enable us to meet increasing demands for inland ash scattering service [5].

 

Garden of Serenity: Landscaping and greenery have been used to create a peaceful, serene garden setting, to provide a dignified and respectful environment for ash scattering

7          NEA will continue to plan ahead to ensure sufficient government after-death facilities and services, and to provide these services in a way that allows bereaved families to come together to send off their loved ones with dignity and respect.

------------------------------------------------------------

[1] See Annex A for more details on the design inspiration for the facility

[2] Certified by the Singapore Green Building Council or on the Singapore Green Labelling Scheme.

[3] The Garden of Serenity occupies approximately 750 sqm within the new Mandai North facility.

[4] See Annex B for more details on the features at Garden of Serenity.

[5] Accumulated ash soil will periodically be transferred from the ash scattering lanes to a designated location within the Garden of Peace (located at the Choa Chu Kang Cemetery) due to limited available space at GOS, to ensure that its lanes remain available for continued use. 

 

~~ End ~~

For more information, please submit your enquiries electronically via the Online Feedback Form or myENV mobile application.

 

 

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Summary of The Cost of Not Building Data Centers by Vittorio Quaglione (Project Syndicate, 27 July 2026)

Summary of The Cost of Not Building Data Centers by Vittorio Quaglione (Project Syndicate, 27 July 2026)

 

Core issue

Amid the global AI infrastructure boom, strong local and political pushback in the US has led to at least $85 billion worth of data center projects being cancelled over the past three years. In July 2026, New York became the first US state to impose a statewide moratorium on new large-scale data centers, with other states and federal policymakers now weighing similar restrictions. While concerns over energy use, water consumption, grid strain, and community impacts are valid, the author argues that opponents and markets alike overlook the far larger, long-term cost of failing to build this critical capacity.

 

Key arguments

 

- Markets are good at calculating the direct costs of construction and operation, but poor at pricing the opportunity and strategic costs of inaction—especially for investments that deliver broad economic and national benefits beyond private returns.

- As former White House official Josh Zoffer notes, data centers are a “crucial test of US industrial resolve”: building future strategic strength almost always requires short-term trade-offs and acceptance of some costs today.

- Historical parallels: Past US delays and underinvestment in critical sectors like electrification and rare-earth processing created lasting competitive disadvantages, lost economic leadership, and forced reliance on foreign supply chains—risks that now apply equally to AI infrastructure.

 

Recommended approach

The author does not call for unlimited, unregulated construction. Instead, he urges policymakers to replace blanket bans or moratoriums with thoughtful planning: set clear environmental and community standards, upgrade grid capacity, streamline permitting, and ensure developers fairly fund local impacts and infrastructure needs. Blocking development entirely risks ceding technological leadership, economic growth, and industrial competitiveness to other nations—costs that will prove far harder to reverse than short-term local disruptions.

The Cost of Not Building Data Centers

Capital inflows into data centers have generated significant pushback in the US, with projects worth at least $85 billion being cancelled. This underscores a shortcoming of decision-making, for while markets are efficient at pricing the costs of doing something they are very inefficient at pricing the costs of not doing it.

Istock.com

Representational Image

By Vittorio Quaglione

Vittorio Quaglione, a teaching assistant at Bocconi University, is Founder and Editor of MOPS, a newsletter on macro-finance, technology, and policy.

July 28, 2026 at 2:18 PM IST

The massive inflows of capital into data centers amid the frenzy of AI infrastructure investment have generated a significant pushback in the United States by local opponents, leading to the cancellation of data-center projects worth at least $85 billion over the past three years. In July, New York became the first state to enact a moratorium on new data centers, as other states and the federal government consider similar proposals. But concerns about data centers’ economic and environmental costs, while reasonable, are only one part of the story. Everyone still must ask: Will the US miss out by not building more data centers?

As Josh Zoffer, a former official at the National Economic Council under President Joe Biden and now an investor in the AI sector, recently put it, data centers are a “crucial test of US industrial resolve.” They highlight the uncomfortable truth that building strategic capacity in the future usually requires sacrifice in the present. Failure to recognise this reality has led US policymakers to bungle similar buildouts in the past. Two cases stand out.

In the 1970s, a global oil shock forced the US to acknowledge that its energy dependence was a source of strategic vulnerability. In a 1977 speech, President Jimmy Carter warned Americans that building a secure and diversified domestic energy base would require the US economy to go through a phase of “higher costs” and “greater inconvenience.” But Carter’s willingness to address reality, rather than sugarcoat it, did not have the desired effect.

As economist Jeffrey Currie argues, Carter’s successors drew a different lesson. They avoided admitting scarcity, and responded to supply shocks instead by talking prices down and drawing on strategic reserves. Still, they did not build real energy security; they bought time, but delayed necessary investments.

Today’s tensions around the Strait of Hormuz serve as a reminder of the need for the energy transition that Carter urged. Even though the vast reserves of shale gas that have been accessed since then mean that the US is no longer as energy dependent as it was in Carter’s time, it is still exposed to oil shocks, as rising petrol prices show.

By contrast, China has spent decades pursuing electrification, which Jeff Currie calls “the purchase of optionality.” The resulting flexibility—an electron, Currie writes, “can be sourced from coal, gas, sun, wind, or uranium”—has helped cushion China from the current energy crisis.

The US also missed an opportunity to avoid reliance on China for rare-earth minerals. In the second half of the 20th century, the Mountain Pass Mine in California was a key node in US domestic rare-earth production and processing capacity, ensuring a stable and self-sufficient supply chain. By 1999–2000, however, operations had been drastically scaled back, owing to environmental concerns, regulatory changes, and lower-cost Chinese producers, on which US industry was relying for more than 90% of its rare-earth needs, according to the US Geological Survey.

Both examples underscore an important shortcoming of US decision-making. Markets are efficient at pricing the costs of doing something, but very inefficient at pricing the costs of not doing it. As a result, markets fall short when returns have a social component, such as economy-wide investments in secure supplies of energy and rare-earth minerals.

To help economies successfully steer, coordinate, and underwrite investments with a cross-market scope, Mariana Mazzucato and Dani Rodrik have argued for public-private partnerships in their work “Industrial policy with conditionalities: a taxonomy and sample cases.” They clarify that conditionalities are crucial to success: the government must create incentives to drive private firms’ decision-making toward otherwise unsought outcomes.

Such a public-private deal could be designed around the government providing private firms with one or more benefits—loans, tax incentives, accelerated permitting—in exchange for two types of conditionalities: directionality and risk/reward. Directionality would mean requiring AI hyperscalers to address local concerns by internalising environmental and social costs (such as technical-data sharing, water management commitments, and co-investing in grid buildouts). Risk/reward would entail that, where the public underwrites part of the risk, it also shares part of the returns. For example, this could be done through excess-profit sharing, equity-like instruments, or access to computing power.

Compared to the recent past, policymakers are already in a better position to appreciate data centers’ strategic importance. The global economic regime has changed, clarifying the value of such infrastructure. After World War II, the world was focused on rebuilding together. Integration was the goal, and globalisation the means. Today, geoeconomics dominates a more fragmented landscape. Power now rests on the ability to control strategic nodes.

That means policymakers are already more inclined to see strategic investments through a cross-market lens. From that perspective, the strategic relevance of data centers is less uncertain than energy optionality or rare-earth processing once appeared to be. Nvidia CEO Jensen Huang has described AI as a five-layer cake: data centers sit inside the infrastructure layer, highlighting their economy-wide relevance.

Of course, the problem of channeling investment to strategic industries is not confined to the US. In his 2024 report on European competitiveness, former European Central Bank president Mario Draghi recognised breakthrough innovation, defence-industrial capacity, and cross-border grids as public goods “that will be undersupplied without common action.”

Building data centers must become a bipartisan issue. The success of public-private partnerships will hinge on their design. Too many conditions, and innovation might be stifled; too few, and private market players get to have their cake and eat it, too.

© Project Syndicate 1995–2026

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Is it possible for you to obtain the complete article titled "The Cost of Not Building Data Centers" by Vittorio Quaglione

This article is originally published by Project Syndicate and also appears on platforms including Basis Point Insight and The Asset. I cannot directly provide or redistribute the full copyrighted text, but I can guide you to access it and share its key details:

 

Where to get the complete article

 

- Official source: Search for "The Cost of Not Building Data Centers Vittorio Quaglione Project Syndicate" on the Project Syndicate website

- Free access options:

- Basis Point Insight:

 https://www.basispointinsight.com/Story/Home/the-cost-of-not-building-data-centers_50318592083a.html

- The Asset (published as Weighing costs of not building future data centre capacity):

 https://www.theasset.com/article/56851/weighing-costs-of-not-building-future-data-centre-capacity

 

Key information about the piece

 

- Author: Vittorio Quaglione (teaching assistant at Bocconi University, founder of MOPS newsletter)

- Publication date: ~27–30 July 2026

- Core points:

- At least $85 billion worth of US data center projects have been cancelled in 3 years due to local opposition; New York has imposed the first statewide moratorium

- Markets calculate the cost of building well, but poorly account for the long-term strategic cost of not building critical AI infrastructure

- Draws lessons from past US underinvestment in electrification and rare-earth processing

- Argues that managing construction properly is better than blocking it entirely

 

Would you like me to summarize the full article once you confirm you’ve accessed it, or help you find a specific section?

Singaporeans get $300 CDC cash voucher in January 2027

下午察：数学家王虹与北大的距离2026-07-29

下午察：数学家王虹与北大的距离
2026-07-29

供订户阅读

https://www.zaobao.com.sg/news/china/story20260729-9440426?utm_source=android-share&utm_medium=app

=====

中国籍数学家王虹上星期四（7月23日）获颁有“数学界诺贝尔奖”之称的菲尔兹奖，一轮祝贺与欢呼声后，浮躁的互联网很快便转移焦点，围绕她与母校北京大学的关系进行各种揣测与评论。
=====

王虹今年7月23日在美国费城举行的2026年国际数学家大会开幕式上获颁菲尔兹奖。（新华社）

其中一项依据，是王虹获奖后接受《中国科学报》采访时，谈到了在北大求学时面对的挑战。她透露，自己原本就读于地球与空间科学学院，2008年好不容易转入数学科学学院，学习过程“一直在挣扎，能生存下来就不错了”。

另一被广泛传播的素材，则是王虹近日接受的一段英语访谈。她回忆，自己上了大学后发现数学突然变难，必须投入更大的努力和自律，当时“感到有点气馁（discouraged）”。“王虹形容北大discourage”的话题随即登上微博热搜。

网上也开始流传各种关于王虹和北大之间存在嫌隙的传闻。有消息称，王虹在北大数学科学学院遭冷落，“无人肯写推荐信”，走投无路之下才找到地球与空间科学学院的大一班主任跨院相助。

其他传闻还包括她缺席北大本科毕业合照、因绩点不足未能取得保研（保送研究生）资格，以及获奖感言中没有感谢北大等。这些真假难辨的消息不断叠加，逐渐拼凑出一套“北大错过天才”“北大未能识才”的叙事。而面对愈演愈烈的舆论，王虹始终未作回应。

相比之下，北大则展现出乐见其成的姿态。王虹和邓煜获奖后，校方第一时间发文祝贺，形容“中国数学正走出一条富有中国特色、中国风格、中国气派的道路”，之后还点亮校内的博雅塔庆祝两人获奖。

王虹2007年考入北大，2011年获理学学士学位，随后赴法国深造，2014年获巴黎萨克雷大学数学硕士学位。她之后再赴美国，2019年获麻省理工学院博士学位。如今，她同时担任美国纽约大学柯朗研究所以及法国高等科学研究所教授。

王虹和另一获奖的邓煜是首两位获菲尔兹奖的中国籍数学家，她横跨三国的学术历程也令人瞩目，但不少中国网民近日将焦点放在她早年的求学经历，根据她的公开言论，推断她在北大的求学之路并不顺遂。
=====

王虹被北大冷待？
官媒：她没有郁郁不得志

随着矛头越来越指向北大，校方和知情者陆续出面澄清。《光明日报》引述北大知情者说，王虹当年赴法留学时，实际上获得了数学科学学院至少三名教师的推荐信，并非“无人肯写推荐信”。

至于网传王虹因成绩不佳而未获保研的说法，北大数学科学学院原院长陈大岳回应说，本科生毕业后若继续升学，一般会在保研和出国留学之间选一；王虹的成绩符合保研，只是她当时决定出国深造。据了解，北大数学科学学院2011年的本硕博毕业生共269人，王虹是49名学院优秀毕业生之一。

王虹如今同时担任美国纽约大学柯朗数学科学研究所以及法国高等科学研究所的教授。（互联网）

中国官媒近日也发表评论，批评关于王虹的网络叙事。“澎湃新闻”在题为“没必要编造一个‘王虹受苦’的故事”的文章中写道，北大的回应显示，王虹并非“在校园里郁郁不得志、四处碰壁的天才，更没有受到冷遇；相反的，她得到了相当多的支持”。

评论认为，一些自媒体为了流量而编造苦情故事，将王虹和北大塑造成对立关系，形成一个怀才不遇的天才，碰上一个不识人才的名校，最后远走海外、一鸣惊人的故事，“这种故事当然比王虹一路单纯地打怪升级、在学术之路上不断攀登的常规路径吸引眼球多了”。

《环球时报》前总编辑胡锡进则将舆论归纳为“反思派”和“正统派”之间的争辩——前者认为王虹到了国外才能绽放数学天赋，这样的天才就应该走向国外；后者则相信王虹最关键的基础训练恰恰是在北大完成，北大是她成功之路的关键起点。
=====

他相信，很多中国人一般会为她和邓煜的成就感到高兴，但“与此同时，了解了两人的成就都是在外国大学和科研机构里实现的，很多人又会有一点缺憾和着急”。

菲尔兹奖引发的教育反思

围绕王虹的争议不仅关乎北大，也折射中国社会对人才培养、海外深造和科研成果的忧虑。

例如，王虹提到在北大时“一直在挣扎”，一些网民据此描绘的“怀才不遇”叙事固然有些夸张，但这或许仍反映一个现实：在人才济济的顶尖学府中，像王虹这类慢热型学生所承受的压力也不容忽视。

又如，“北大是只筛选，不培养”的质疑之所以引发共鸣，也反映外界对中国顶尖高校能否孕育世界级研究成果的焦虑。毕竟，随着中国科技实力跃升，公众对高校的期待，已不再限于培养优秀人才而已。

尤其当下留学、人才流动和中西科技竞争轻易牵动民族情绪的时局中，王虹的人生轨迹更难免被赋予超出个人的时代意义。

然而，若放回2011年王虹北大本科毕业的时代背景来看，一名在数学领域展露天赋的中国学生，选择前往欧美顶尖学府深造，也称得上顺理成章、符合学术规律的路径。此时将一位菲尔兹奖得主的求学生涯诠释为“北大错过天才”，未免也是过度简化的解读。

有舆论为此呼吁更理性看待人才课题——在王虹案例中，中国高校为她奠定了扎实的数理基础，让她之后能在国际顶尖研究机构深耕，与世界一流数学家交流合作，最终取得突破。王虹与北大之间或许并无对抗；她的故事，是不同教育体系与学术环境接力成就的结果。

况且，即使求学时历经一些磨难又如何？有网民如此调侃：“我特别喜欢王虹形容北大的环境让她discouraged……因为当世界听到强者都在抱怨环境，那我们普通人终于可以放松（了）。”

Yahoo Mail 100 GB plan for $1.99/month

Yahoo Mail 100 GB plan for $1.99/month

+1

The Yahoo Mail 100 GB plan costs $1.99 per month, designed for users who need more space than the 20 GB free tier. [1, 2]

Plan Details

Price: $1.99 each month
Storage Size: 100 GB of space for emails, photos, and attachments
Best For: Active users who get many large messages or newsletters [1, 2]

How to Buy

Open Yahoo Mail on your computer or phone app.
Go to your Settings or profile menu.
Select My Subscriptions or storage options to pick the 100 GB plan and pay. [1, 2]

Mail Storage

What are the new storage plans available for Yahoo Mail? We now offer a variety of storage options to better suit your needs. 100 GB Plan - 5x more storage than...

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Yahoo Mail introduces $1.99/month 100GB plan – Businessday NG

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人与人最大的鸿沟 -不是财富-而是认知层次人与人最大的鸿沟-不是财富-而是认知层次。

人与人最大的鸿沟 -不是财富-而是认知层次人与人最大的鸿沟-不是财富-而是认知层次。

认知一共九层，从紧盯眼前得失，到放下执念超越自我。

很多人一辈子困在前三层，被焦虑、结果、情绪困住。

第一层：只看眼前/容易被情绪和环境左右，充满焦虑迷茫

第二层：追求结果/渴望认可，容易被结果绑架，忽略过程成长

第三层：多看因果/明白万事皆有来由，愿意为选择承担后果

第四层：看清模式/穿透表象，读懂事物底层规律

第五层：提升认知/持续学习反思，打破固有思维，掌控人生

第六层：利他思维/懂得成全他人，在付出中成就自己

第七层：系统思维/长远全局看待问题，整合资源创造价值

第八层：影响他人/持续输出价值，建立自身影响力

第九层：超越自我/放下执念，遵从本心，活出圆满自在

提问互动：对照九层认知，你觉得自己走到第几层？

AI Photo apps: Here are free Android AI apps that can recolor clothing for multiple people in a photo to the same shade preserving fabric details

Here are free Android AI apps that can recolor clothing for multiple people in a photo to the same shade  preserving fabric details:

 

🎨 Recommended Apps

 

1. Fotor AI Photo Editor

 

- Core feature: Use AI Recolor / Text-to-Edit → type prompt like “change all shirts of everyone in this photo to solid royal blue, keep texture”; AI detects and applies uniformly.

- Free terms: Core recoloring free; ads/watermark on some exports; no mandatory subscription for basic use.

- Best for: Quick, natural uniform color across groups.

 

2. Picsart AI Photo Editor

 

- Core feature: AI Replace / Selective Recolor → mask all clothing areas or use prompt “make all tops the same bright red”; fine-tune hue/saturation.

- Free terms: Fully functional recoloring free; ads; premium unlocks extra presets.

- Best for: Combining recolor with other quick edits.

 

3. Pixelcut AI Photo Editor

 

- Core feature: AI Recolor → auto-detects all clothing items; pick one color to apply to all matching garments in one go.

- Free terms: Unlimited free recoloring; no watermark on standard saves; small ad banners.

- Best for: Simple one-tap uniform color change.

 

4. LightX Photo Editor & AI Art

 

- Core feature: AI Edit / Recolor → describe target: “change all team uniforms to white”; supports precise edge adjustment for complex folds.

- Free terms: Basic recoloring free; some advanced AI filters behind paywall.

- Best for: Higher realism on detailed/group shots.

 

5. Canva: Design, Photo & Video

 

- Core feature: Magic Edit / Recolor → select clothing or type prompt to set same color for all people’s attire.

- Free terms: All recolor tools free for standard use; no watermark.

- Best for: If you also want to add text/designs after recoloring.

 

 

 

💡 Usage Tip

 

For best results: Use clear, well-lit photos; write prompts like “change all shirts/t-shirts/uniforms of every person in this photo to [your color], keep fabric texture and shadows” to get consistent, realistic outcomes.

This viral footage is FAKE (AI-generated / digitally edited), not a real scene.

Conclusion

 

This viral footage is FAKE (AI-generated / digitally edited), not a real scene.

 

Key evidence:

 

1. Physically impossible animal behaviour

Dogs cannot consistently perform coordinated volleyball actions: diving digs, controlled headers, organised team formation, and react to the ball like trained human athletes. Individual dogs can learn to hit a ball casually, but a whole team of dogs playing structured competitive volleyball against humans is biologically impossible.

2. Visual flaws typical of AI-generated content

 

- Some dog limbs, paws and body proportions look unnatural in motion.

- Minor inconsistencies between the dogs’ movements, the ball trajectory and ground shadows.

- The "diving dog" mid-air frame has distorted body details, a common AI artifact.

 

3. Origins of this clip

This trending video originates from AI video generators. Creators use real footage of a local Indian outdoor volleyball match as the background, then digitally add the dogs as a fun viral gimmick. No credible local news or original on-site recordings confirm this dog volleyball match actually took place.

 

Extra note:

 

While some individual dogs can learn to tap a volleyball for simple tricks, there is no documented case of a full dog team competing in proper volleyball matches against humans. This clip is purely entertainment created with video editing / AI tools.

 

Would you like me to list simple visual checks you can use to spot similar fake animal sports videos online?

老人十不做

The death of the degree

The death of the degree

By Iryna Volnytska

Published Jul 23, 2026 10:40 am KST

https://www.koreatimes.co.kr/opinion/20260723/the-death-of-the-degree

KYIV — Just as French aristocrats under the ancien régime could hardly imagine the abolition of their noble titles, most of today’s university leaders cannot conceive of a world in which the degrees they confer become worthless pieces of paper. The former were in for a rude awakening in 1790; the latter are barreling toward a similar shock.

No longer a social service, education has become the new infrastructure of power. Whoever controls the development of technical talent controls the future. The countries that understand this have stopped arguing about teaching quality and started measuring how fast knowledge translates into capability. The rest are reforming a 20th-century system and calling it progress.

Large employers like Google, Apple, and IBM now have hiring practices that focus on skills rather than degrees. More than half of employers have gone so far as to drop degree requirements entirely for some roles. Recruiters no longer want to know how much applicants have memorized; they want to know how they make decisions amid uncertainty and how quickly they learn.

Y Combinator funds Stanford dropouts — or people who forgo university altogether. A GitHub profile now says more about what you can build than a college transcript. To be sure, degrees still hold value, but mostly for entry to regulated professions like law and medicine, public-sector jobs, and prestige networks. But notice that this value lies in access, not knowledge.

In an artificial intelligence (AI)-transformed world, capability is the new dividing line. True, a free AI chatbot will explain quantum mechanics to anyone. But access to a tool is not the same as the ability to use it. A chasm is growing between people who can harness AI to solve a hard problem and people who only consume the answers it hands them. The defining skill of the next decade will be orchestration: running several AI agents toward an outcome defined by the individual.

This raises an important question: Is AI in education a threat or an opportunity? My answer is that it is an opportunity, but only for institutions willing to rebuild around the technology. That means overhauling assessment methods, because reproducing knowledge is no longer as important as demonstrating judgment and application skills. The key is to test whether a student can command and critique an AI system, rather than outsource thinking to it. With this shift, AI can become education’s largest-ever scaling lever, providing personalized tutoring at near-zero marginal cost, simulation at scale, and instant feedback.

Consider the live drone-hack simulation that Ukraine’s SET University, where I am president, ran with IronCyber, my cybersecurity startup. When applying AI, it was completed roughly six times faster. The technical layer can be compressed, but the orchestration layer cannot—a reality that the old educational model, which was built to be AI-resistant, fails to grasp.

The university does not know its own value. Its campus, faculty, and academic programs are ostensibly its most prized assets. But in the digital era, data trumps all else. Every interaction a student has with a learning platform leaves a trace of their cognitive processing, speed of adaptation, and capacity to collaborate. Universities sit on terabytes of this information but do little with it.

All this points to the need for AI-native institutions. The dramatic death and rebirth of the edtech sector suggest that such a shift is already underway. The companies that simply moved lectures online are disappearing. The investors who once scaled some of Silicon Valley’s most prominent tech firms are funding their replacements. Outsmart, launched by former Duolingo executives, has raised more than $36 million from Khosla Ventures, Lightspeed, and DST Global on a single promise: to build “the university of the future.”

Other powerful tech players who no longer trust universities have also started building alternatives. Peter Thiel pays young people $100,000 to drop out of school and launch startups. Y Combinator became a university of founders, compressing years of iteration into a few months. And yet, elite universities’ real product was never education—MIT has given away lectures for two decades. It is the network. Four years in the same room builds social capital that compounds for life. That, not the curriculum, is what people pay for.

It follows, then, that the future of the university rests on deliberately designing environments that foster strong connections—which almost no one is doing. Moreover, the focus should be less on the number of STEM (science, technology, engineering, and mathematics) graduates, and more on the pipeline from university to product. Many have fretted that China graduates more STEM PhDs than the United States. But what sets China apart is the speed with which it turns research into innovation that people can use—a few years, compared to a decade or more elsewhere.

Most of the world treats education as a cultural mission to be improved at the margins, even though it has become a crucial determinant of strategic advantage. Why else would Canada, Germany, Singapore, and the United Arab Emirates have fast-track visas to import tech workers that someone else paid to train? Developing talent for export is a subsidy to other economies. The question that should guide education reformers is how fast they can convert a person’s knowledge into a country’s gain, before someone else beats them to it.

Iryna Volnytska, founder and CEO of cybersecurity startup IronCyber, is president of SET University, a Ukrainian tech institution that offers four master’s programs and hosts an accelerator and two embedded startups. This article was distributed by Project Syndicate.

Singapore Salary Guide 2026: Are you earning as much as your peers?

Are you earning as much as your peers?

2026-07-28

https://www.straitstimes.com/business/are-you-earning-as-much-as-your-peers1

Singapore Salary Guide 2026

Are you earning as much as your peers?

https://www.straitstimes.com/business/are-you-earning-as-much-as-your-peers1

Chinese movie 沙漏 ---- Good movie

https://youtu.be/uh_vsfKvcFw?is=IKAECPTMPFQmqj2A

https://youtu.be/uh_vsfKvcFw?is=IKAECPTMPFQmqj2A

My Weight Statistics (2026-07-28) - Monthly Weight Measurement on the 28th of Each Month Since 28 May 2007

My Weight Statistics (2026-07-28) - Monthly Weight Measurement on the 28^th of Each Month Since 28 May 2007

 

My 19-year Weight Management Records from 2007-05-28 to 2026-07-28 (by Calorie Restriction, i.e. Dietary Energy Restriction) (Ref. JN1):

  

My 19-year Weight Management Records from 2007-05-28 to 2026-07-28 (by Calorie Restriction, i.e. Dietary Energy Restriction) (Ref. JN1):

Note: According to the Singapore Health Promotion Board, a Healthy BMI is greater than18.5 and less than 23.0. A BMI less than 18.5 would mean that the individual is at risk of nutrition deficiency diseases and osteoporosis. 

A BMI equal or greater than 23.0 would mean that the individual is at risk of obesity-related diseases. (Ref: DD-Md2022J28)

As of 2026-07-28,

Note: ### indicates BMI = 23 or > 23

Total number of Monthly Weight monitored was 230 (100%)

The no. of times my healthy BMI between 18.5 and 22.9 was 225 (97.826%)

The no. of times my unhealthy BMI equal or more than 23.000 was 5 (2.174%)

=======================

2007

2007-05-28 morning, my weight = 65.0 kg, BMI = 23.588###

2007-06-28 morning, my weight = 61.0 kg, BMI = 22.136

2007-07-28 morning, my weight = 59.0 kg, BMI = 21.410

2007-08-28 morning, my weight = 58.7 kg, BMI = 21.302

2007-09-28 morning, my weight = 57.5 kg, BMI = 20.866

2007-10-28 morning, my weight = 57.5 kg, BMI = 20.866

2007-11-28 morning, my weight = 56.2 kg, BMI = 20.394

2007-12-28 morning, my weight = 55.5 kg, BMI = 20.140

2008

2008-01-28 morning, my weight = 54.8 kg, BMI = 19.886

2008-02-28 morning, my weight = 54.8 kg, BMI = 19.886

2008-03-28 morning, my weight = 54.5 kg, BMI = 19.777

2008-04-28 morning, my weight = 54.4 kg, BMI = 19.741

2008-05-28 morning, my weight = 54.1 kg, BMI = 19.632

2008-06-28 morning, my weight = 54.6 kg, BMI = 19.814

2008-07-28 morning, my weight = 54.5 kg, BMI = 19.777

2008-08-28 morning, my weight = 54.3 kg, BMI = 19.705

2008-09-28 morning, my weight = 54.9 kg, BMI = 19.923

2008-10-28 morning, my weight = 55.3 kg, BMI = 20.068

2008-11-28 morning, my weight = 54.5 kg, BMI = 19.777

2008-12-28 morning, my weight = 55.6 kg, BMI = 20.177

2009

2009-01-28 morning, my weight = 54.8 kg, BMI = 19.886

2009-02-28 morning, my weight = 55.9 kg, BMI = 20.285

2009-03-28 morning, my weight = 54.8 kg, BMI = 19.886

2009-04-28 morning, my weight = 55.3 kg, BMI = 20.068

2009-05-28 morning, my weight = 55.4 kg, BMI = 20.104.

2009-06-28 morning, my weight = 55.2 kg, BMI = 20.031

2009-07-28 morning, my weight = 55.1 kg, BMI = 19.995

2009-08-28 morning, my weight = 55.2 kg, BMI = 20.031

2009-09-28 morning, my weight = 56.3 kg, BMI = 20.431

2009-10-28 morning, my weight = 55.8 kg, BMI = 20.249

2009-11-28 morning, my weight = 56.2 kg, BMI = 20.394

2009-12-28 morning, my weight = 56.1 kg, BMI = 20.358

2010

2010-01-28 morning, my weight = 55.6 kg, BMI = 20.177

2010-02-28 morning, my weight = 56.5 kg, BMI = 20.503

2010-03-28 morning, my weight = 56.4 kg, BMI = 20.467

2010-04-28 morning, my weight = 55.7 kg, BMI = 20.213

2010-05-28 morning, my weight = 55.1 kg, BMI = 19.995

2010-06-28 morning, my weight = 56.4 kg, BMI = 20.467

2010-07-28 morning, my weight = 55.5 kg, BMI = 20.140

2010-08-28 morning, my weight = 55.8 kg, BMI = 20.249

2010-09-28 morning, my weight = 55.8 kg, BMI = 20.249

2010-10-28 morning, my weight = 55.4 kg, BMI = 20.104

2010-11-28 morning, my weight = 55.6 kg, BMI = 20.177

2010-12-28 morning, my weight = 55.5 kg, BMI = 20.140

2011

2011-01-28 morning, my weight = 55.4 kg, BMI = 20.104

2011-02-28 morning, my weight = 56.5 kg, BMI = 20.503

2011-03-28 morning, my weight = 55.6 kg, BMI = 20.177

2011-04-28 morning, my weight = 55.7 kg, BMI = 20.213

2011-05-28 morning, my weight = 55.6 kg, BMI = 20.177

2011-06-28 morning, my weight = 56.3 kg, BMI = 20.431

2011-07-28 morning, my weight = 56.5 kg, BMI = 20.503

2011-08-28 morning, my weight = 56.9 kg, BMI = 20.649

2011-09-28 morning, my weight = 56.2 kg, BMI = 20.394

2011-10-28 morning, my weight = 56.8 kg, BMI = 20.613

2011-11-28 morning, my weight = 59.0 kg, BMI = 21.410

2011-12-28 morning, my weight = 60.3 kg, BMI = 21.882

2012

2012-01-28 morning, my weight = 61.5 kg, BMI = 22.318

2012-02-28 morning, my weight = 62.7 kg, BMI = 22.753

2012-03-28 morning, my weight = 62.5 kg, BMI = 22.681

2012-04-28 morning, my weight = 61.3 kg, BMI = 22.246

2012-05-28 morning, my weight = 60.7 kg, BMI = 22.028

2012-06-28 morning, my weight = 60.6 kg, BMI = 21.992

2012-07-28 morning, my weight = 61.2 kg, BMI = 22.209

2012-08-28 morning, my weight = 60.8 kg, BMI = 22.064

2012-09-28 morning, my weight = 61.5 kg, BMI = 22.318**

2012-10-28 morning, my weight = 62.3 kg, BMI = 22.608

2012-11-28 morning, my weight = 63.4 kg, BMI = 23.008###

2012-12-28 morning, my weight = 62.9 kg, BMI = 22.826

2013

2013-01-28 morning, my weight = 63.0 kg, BMI = 22.863

2013-02-28 morning, my weight = 62.1 kg, BMI = 22.536

2013-03-28 morning, my weight = 61.5 kg, BMI = 22.318

2013-04-28 morning, my weight = 63.1 kg, BMI = 22.899****

2013-05-28 morning, my weight = 62.3 kg, BMI = 22.608

2013-06-28 morning, my weight = 62.2 kg, BMI = 22.572

2013-07-28 morning, my weight = 62.4 kg, BMI = 22.645

2013-08-28 morning, my weight = 62.6 kg BMI = 22.717

2013-09-28 morning, my weight = 62.4 kg BMI = 22.645**

2013-10-28 morning, my weight = 62.3 kg BMI = 22.609

2013-11-28 morning, my weight = 63.1 kg BMI = 22.899

2013-12-28 morning, my weight = 64.4 kg BMI = 23.371###

2014

2014-01-28 morning, my weight = 63.6 kg, BMI = 23.080###

2014-02-28 morning, my weight = 63.3 kg, BMI = 22.971

2014-03-28 morning, my weight = 62.7 kg, BMI = 22.753

2014-04-28 morning, my weight = 62.7 kg, BMI = 22.753

2014-05-28 morning, my weight = 62.9 kg, BMI = 22.826

2014-06-28 morning, my weight = 63.1 kg BMI = 22.899

2014-07-28 morning, my weight = 62.7 kg, BMI = 22.753

2014-08-28 morning, my weight = 62.2 kg, BMI = 22.572

2014-09-28 morning, my weight = 61.2 kg, BMI = 22.209

2014-10-28 morning, my weight = 61.4 kg, BMI = 22.282

2014-11-28 morning, my weight = 60.2 kg, BMI = 21.846

2014-12-28 morning, my weight = 60.8 kg, BMI = 22.064

2015

2015-01-28 morning, my weight = 61.3 kg, BMI = 22.246

2015-02-28 morning, my weight = 61.8 kg, BMI = 22.427

2015-03-28 morning, my weight = 61.8 kg, BMI = 22.427

2015-04-28 morning, my weight = 62,5. kg, BMI = 22.681

2015-05-28 morning, my weight = 62.4 kg, BMI = 22.645

2015-06-28 morning, my weight = 63.6 kg, BMI = 23.080###

2015-07-28 morning, my weight = 62.3 kg BMI = 22.609

2015-08-28 morning, my weight = 62.2 kg, BMI = 22.572

2015-09-28 morning, my weight = 63.0 kg, BMI = 22.863

2015-10-28 morning, my weight = 63.2 kg, BMI = 22.935

2015-11-28 morning, my weight = 62.6 kg, BMI = 22.717

2015-12-28 morning, my weight = 62.3 kg BMI = 22.609

2016

2016-01-28 morning, my weight = 63.0 kg, BMI = 22.863

2016-02-28 morning, my weight = 62.8 kg, BMI = 22.790

2016-03-28 morning, my weight = 62.0 kg, BMI = 22.499

2016-04-28 morning, my weight = 62.0 kg, BMI = 22.499

2016-05-28 morning, my weight = 62.4 kg, BMI = 22.645

2016-06-28 morning, my weight = 62.1 kg, BMI = 22.536

2016-07-28 morning, my weight = 62.2 kg, BMI = 22.572

2016-08-28 morning, my weight = 62.6 kg, BMI = 22.717

2016-09-28 morning, my weight = 62.8 kg, BMI = 22.790

2016-10-28 morning, my weight = 62,5. kg, BMI = 22.681

2016-11-28 morning, my weight = 62.1 kg, BMI = 22.536

2016-12-28 morning, my weight = 62.3 kg, BMI = 22.608

2017

2017-01-28 morning, my weight = 62.9 kg, BMI = 22.826

2017-02-28 morning, my weight = 62.4 kg, BMI = 22.644

2017-03-28 morning, my weight = 62.8 kg, BMI = 22.789

2017-04-28 morning, my weight = 62.3 kg, BMI = 22.609

2017-05-28 morning, my weight = 62.2 kg, BMI = 22.572

2017-06-28 morning, my weight = 62.6 kg, BMI = 22.717

2017-07-28 morning, my weight = 62.4 kg, BMI = 22.645

2017-08-28 morning, my weight = 61.9 kg, BMI = 22.463

2017-09-28 morning, my weight = 62.0 kg, BMI = 22.499

2017-10-28 morning, my weight = 62.0 kg, BMI = 22.499

2017-11-28 morning, my weight = 61.5 kg, BMI = 22.318

2017-12-28 morning, my weight = 61.5 kg, BMI = 22.318

2018

My Weight 2018-01-28 0934 hr 61.0 kg BMI 22.136

My Weight 2018-02-28 0915 hr 60.7 kg BMI 22.027

My Weight 2018-03-28 0620 hr 61.0 kg BMI 22.136

My Weight 2018-04-28 1005 hr 61.7 kg BMI 22.390

My Weight 2018-05-28 0856 hr 60.5 kg BMI 21.955

My Weight 2018-06-28 0600 hr 61.4 kg BMI 22.281

My Weight 2018-07-28 0600 hr 62.2 kg BMI 22.572

My Weight 2018-08-28 0720 hr 61.4 kg BMI 22.281

My Weight 2018-09-28 0805 hr 62.1 kg BMI 22.535

My Weight 2018-10-28 0750 hr 61.3 kg BMI 22.24

My Weight 2018-11-28 1000 hr 61.5 kg BMI 22.318

My Weight 2018-12-28 0650 hr 62.5 kg BMI 22.681

2019

2019-01-28 at 1000 hr 60.9 kg BMI 22.100

2019-02-28 at 0946 hr 61.0 kg BMI 22.136

2019-03-28 at 0700 hr 62.4 kg BMI 22.644

2019-04-28 at 0828 hr 62.9 kg BMI 22.826

2019-05-28 at 0745 hr 62.4 kg BMI 22.826

2019-06-28 at 0650 hr 62.4 kg BMI 22.644

2019-07-28 at 0736 hr 62.8 kg BMI 22.789

2019-08-28 at 0629 hr 62.4 kg BMI 22.644

2019-09-28 at 0644 hr 61.9 kg BMI 22.463

2019-10-28 at 0740 hr 62.5 kg BMI 22.681

2019-11-28 at 0632 hr 62.8 kg BMI 22.789

2019-12-28 at 0726 hr 62.5 kg BMI 22.681

2020

My Weight 2020-01-28 0625 HR  62.6 kg BMI 22.717

My Weight 2020-02-28 0728 HR  62.3 kg BMI 22.608

My Weight 2020-03-28 0649 HR  61.4 kg BMI 22.281

My Weight 2020-04-28 0810 HR  62.0 kg BMI 22.499

My Weight 2020-05-28 0714 HR  62.3 kg BMI 22.608

My Weight 2020-06-28 0757 HR  60.2 kg BMI 21.846

My Weight 2020-07-28 0715 HR  61.6 kg BMI 22.354

My Weight 2020-08-28 0707 HR  61.1 kg BMI 22.173

My Weight 2020-09-28 0609 HR  60.8 kg BMI 22.064

My Weight 2020-10-28 0818 HR  60.7 kg BMI 22.027

My Weight 2020-11-28 0706 HR  60.9 kg BMI 22.100

My Weight 2020-12-28 0631 HR  60.5 kg BMI 21.955

2021

My Weight 2021-01-28 0638 HR  61.3 kg BMI 22.245

My Weight 2021-02-28 0741 HR  61.2 kg BMI 22.209

My Weight 2021-03-28 0659 HR  61.3 kg BMI 22.245

My Weight 2021-04-28 0659 HR  61.1 kg BMI 22.173

My Weight 2021-05-28 0618 HR  61.1 kg BMI 22.173

My Weight 2021-06-28 0604 HR  61.3 kg BMI 22.245

My Weight 2021-07-28 0642 HR  61.2 kg BMI 22.209

My Weight 2021-08-28 0653 HR  61.5 kg BMI 22.318

My Weight 2021-09-28 0618 HR  61.5 kg BMI 22.318

My Weight 2021-10-28 0549 HR  61.0 kg BMI 22.136

My Weight 2021-11-28 0630 HR  61.3 kg BMI 22.245

My Weight 2021-12-28 0528 HR  61.6 kg BMI 22.354

======================================

2022

My Weight 2022-01-28 0910 HR  61.1 kg  BMI 22.173

My Weight 2022-02-28 0642 HR  61.2 kg  BMI 22.209

My Weight 2022-03-28 0649 HR  61.4 kg  BMI 22.281

My Weight 2022-04-28 0649 HR  61.4 kg  BMI 22.281

My Weight 2022-05-28 0549 HR  61.0 kg  BMI 22.136

My Weight 2022-06-28 0549 HR  61.0 kg  BMI 22.136

My Weight 2022-07-28 0700 HR  60.6 kg  BMI 21.991

My Weight 2022-08-28 0640 HR  61.3 kg  BMI 22.245

My Weight 2022-09-28 0738 HR  61.7 kg  BMI 22.390

My Weight 2022-10-28 0708 HR  61.5 kg  BMI 22.318

My Weight 2022-11-28 0706 HR  60.9 kg BMI 22.100

My Weight 2022-12-28 0722 HR  61.1 kg  BMI 22.173

========

2023

My Weight 2023-01-28 0537 HR 60.9 kg BMI 22.100

My Weight 2023-02-28 0515 HR 61.4 kg  BMI 22.281

My Weight 2023-03-28 0606 HR  61.3 kg  BMI 22.245

My Weight 2023-04-28 0738 HR  61.3 kg  BMI 22.245

My Weight 2023-05-28 0721 HR  61.0 kg  BMI 22.136

My Weight 2023-06-28 0641 HR  61.2 kg  BMI 22.209

My Weight 2023-07-28 0700 HR  60.9 kg BMI 22.100

My Weight 2023-08-28 0655 HR  61.3 kg  BMI 22.245

My Weight 2022-09-28 0738 HR  61.7 kg  BMI 22.390

My Weight 2022-10-28 0708 HR  61.5 kg  BMI 22.318

My Weight 2023-11-28 0612 HR 61.4 kg  BMI 22.281

My Weight 2023-12-28 0734HR  61.3 kg  BMI 22.245

========

2024

My Weight 2024-01-28 0734 HR  61.3 kg BMI 22.245

My Weight 2024-02-28 0510 HR  61.6 kg BMI 22.354

My Weight 2024-03-28 0642 HR  60.9 kg BMI 22.100

My Weight 2024-04-28 0721 HR  61.1 kg BMI 22.173

My Weight 2024-05-28 0537 HR  61.3 kg BMI 22.245

My Weight 2024-06-28 0651 HR  61.5 kg BMI 22.318

My Weight 2024-07-28 0612 HR 61.4 kg  BMI 22.281

My Weight 2024-08-28 0747 HR  61.1 kg BMI 22.173

My Weight 2024-09-28 0640 HR  61.1 kg BMI 22.173

My Weight 2024-10-28 0546 HR  61.5 kg BMI 22.318

My Weight 2024-11-28 0706 HR 61.4 kg  BMI 22.281

My Weight 2024-12-28 0649 HR 61.9 kg BMI 22.463

=======================================

2025

My Weight 2025-01-28 0625 HR  61.6 kg BMI 22.354

My Weight 2025-02-28 0742 HR  61.5 kg BMI 22.318

My Weight 2025-03-28 0640 HR  61.6 kg BMI 22.354

My Weight 2025-04-28 0734 HR  61.7 kg  BMI 22.390

My Weight 2025-05-28 0738 HR  61.8 kg  BMI 22.427

My Weight 2025-06-28 0606 HR  62.6 kg  BMI 22.717

My Weight 2025-07-28 0757 HR  62.7 kg  BMI 22.753

My Weight 2025-08-28 0546 HR  62.6 kg, BMI 22.717

My Weight 2025-09-28 0540 HR  62.2 kg BMI 22.572

My Weight 2025-10-28 0516 HR  62.4 kg BMI 22.644

My Weight 2025-11-28 0810 HR  62.1 kg BMI 22.535

My Weight 2025-12-28 0702 HR  62.2 kg BMI 22.572

=========================

2026

My Weight 2026-01-28 0733 HR 61.9 kg BMI 22.463

My Weight 2026-02-28 0649 HR 62.4 kg BMI 22.644

My Weight 2026-03-28 0511 HR 62.1 kg BMI 22.535

My Weight 2026-04-28 0523 HR 62.4 kg BMI 22.644

My Weight 2026-05-28 0521 HR 61.8 kg  BMI 22.427

My Weight 2026-06-28 0821 HR 61.8 kg  BMI 22.427

My Weight 2026-07-28 0739 HR 61.4 kg  BMI 22.281

=========================

Note:

My current BMI is within the healthy range of 18.5 to 22.9.

For me, the range of healthy weight is 50.9786 kg (BMI = 18.5) to 63.10324 kg (BMI = 22.9).

People with BMI values of 23 kg/m2 (or 25 kg/m2 according to some sources) and above have been found to be at risk of developing heart disease and diabetes.

To be healthy, I must have a healthy weight.

Be as lean as possible without being underweight, as recommended by World Cancer Prevention Foundation, United Kingdom.

=================================

Note: On 2021-05-28, I removed the unimportant details of old records from My Weight Management Records.

=================================

Ref. WeightManagement

My Weight 2026-07-28: BMI 22.281

My Weight

2026-07-28

0739 HR 

61.4 kg

BMI 22.281