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[jenbill9]

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[dostoyevsky-official]

For most of history it was obvious that one should schedule conflict so as to avoid clashing with the agricultural cycle. Autumn, when the harvest was in, was the ideal moment for big battles. That was why major military maneuvers were generally held at that time of year. Even as late as 1914 the harvest timetable may have played some role in the war planning of combatants.

The current war is disastrous from the point of view of the modern agricultural cycle.

The gulf region handles about one third of the global trade in inorganic nutrients and in terms of the agricultural cycle this is the key moment for shipments to be steaming out from the Gulf towards the major agricultural zones of the world.

[...] “The politicians are saying this is a war that’s going to last for weeks, not days, and when you look around at the world within four weeks, we’re in the middle of [the Northern Hemisphere’s] spring season applications and if these ships don’t go through the Strait of Hormuz today, they’re not going to arrive in time. … You’re talking about either having to switch your planting to a crop that is much less intensive for nitrogen use,” he said, or see yields fall. In Australia, while much of the fertilizer needed for sowing has already been bought, farmers around this time start looking to buy urea for dressing cereal crops beginning in September, said Stephen Annells, chief executive of Fertilizer Australia, a group representing the industry.”

[...] To see who will really pay the price however, look not to the developed world or big emerging markets like India, but to the weakest links in the chain - the poor, agrarian, smallholder economies of Africa.(x)

/// The global fertilizer market focuses on three main macronutrients: phosphates, nitrogen, and potash. [...] Potash and phosphates are both mined from different kinds of natural deposits; nitrogen fertilizers, by contrast, are produced with natural gas. QatarLNG, a subsidiary of Qatar Energy, a state-run oil and gas company, said on Monday that it would halt production following drone strikes on some of its facilities.

That shutdown puts supplies of urea, a popular type of nitrogen fertilizer, particularly at risk. On Tuesday, Qatar Energy said that it would also stop production of downstream products, including urea. Qatar was the second-largest exporter of urea in 2024. (Iran was the third-largest; it’s also a key exporter of ammonia, another type of nitrogen fertilizer.) Prices on urea sold in the US out of New Orleans, a key commodity port, were up nearly 15 percent on Monday compared to prices last week, according to data provided by Josh Linville, the vice president of fertilizer at financial services company StoneX. The blockage of the Strait of Hormuz is also preventing other countries in the region from exporting nitrogen products.

“When we look at ammonia, we're looking at almost 30 percent of global production being either involved or at risk in this conflict,” says Veronica Nigh, a senior economist at the Fertilizer Institute, a US-based industry advocacy organization. “It gets worse when we think about urea. Urea is almost 50 percent.”

Other types of fertilizer are also at risk. Saudi Arabia, Nigh says, supplies about 40 percent of all US phosphate imports; taking them out of the equation for more than a few days could create “a really challenging situation” for the US. Other countries in the region, including Jordan, Egypt, and Israel, also play a big role in these markets.

March is traditionally the start of planning the spring planting season in the US, which starts in earnest in April. US fertilizer buyers would normally be placing orders now, Linville says, in order to have the barges arrive in the US by early April. “If we lose several weeks here, we are talking about limiting the number of tons that arrive in our most important month,” he says.

Nigh says that most fertilizer demand in the US—around three-quarters—goes to large row crops grown in the Midwest, like corn, soy, wheat, and cotton. These farms are large operations; most farmers, she says, have made decisions about what types of fertilizers their crops will need and would be unable to pivot despite changes in global supply. “It’s a very critical window right now,” she says.

When energy markets are tight, the US has some reserve supplies—the Strategic Petroleum Reserve, the largest emergency supply of oil in the world—that it can release to help meet demand. But there’s no similar buffer for fertilizer, Nigh says.

[...] If the war keeps going, both Nigh and Linville say that US farmers—especially those growing cash crops like corn and soybeans—will most likely see increased prices for fertilizers of all kinds. US farmers are already facing big losses after the trade war with China. (x)

/// “We shouldn’t underestimate what this potentially could mean for global food production,” said Svein Tore Holsether, chief executive of Europe’s largest fertiliser group Yara.

He added that the focus on oil and gas was “overshadowing” the impact on the fertiliser industry. “If you’re not getting [fertiliser] into the field of the farmers, yields could go down by up to 50 per cent in the first harvest,” he said.

If the disruption continues, consumers could see higher prices for bread within six to 10 weeks, eggs within a few months and pork and broiler chicken within six months, estimates Raj Patel, food system expert at the Lyndon B. Johnson School of Public Affairs. 

[...] Analysts say the disruption could prove even more damaging than the food shock triggered by Russia’s invasion of Ukraine in 2022, when energy and fertiliser costs surged and global food prices hit record highs.

“When prices spiked in 2022 it was extraordinary, but the market was able to adjust because Russian exports continued,” said Chris Lawson, head of fertilisers at CRU, adding that the “big difference” this time was that a blocked Strait of Hormuz was a physical barrier.

The impact on food in 2022 was more immediate because Ukraine was a major wheat exporter, said Patel, but “this time around the impact will be far more widespread”. (x)

/// Fertilizer manufacturers in India are beginning to cut output after Qatari supplies of liquefied natural gas, a key feedstock, were suspended due to hostilities in the Middle East.

[...] Rising prices of other raw materials used to make fertilizer, such as ammonia and sulfur, are adding to fears of higher production costs.

Pakistan’s Sui Northern Gas Pipelines Ltd. has also informed customers it will be unable to supply regasified LNG to their fertilizer plants due to the Middle East conflict, according to a company notice seen by Bloomberg. The country receives most of its LNG from Qatar and the suspension takes effect from midnight Wednesday.

“We are very much optimistic that the war may end soon,” Suresh Kumar Chaudhari, director general of Fertiliser Association of India said in an interview on Tuesday. “If the war continues, it will be matter of concern for us,” he added, without elaborating.

If the cuts last, India could be forced to step up costly imports ahead of peak agricultural demand during the monsoon season that begins in June. The country is the world’s biggest grower and exporter of rice and No. 2 producer of sugar, wheat and cotton.

/// Asia-based traders of dry sulfur are rushing to substitute supplies stranded in the Middle East as an intensifying regional conflict threatens access to the chemical used in fertilizer and nickel processing.

[...] About half the global seaborne trade of sulfur — roughly 20 million tons a year — originates in the Gulf and must transit the Strait of Hormuz to reach world markets. Saudi Arabia, the United Arab Emirates, Qatar, Kuwait and Iran are among the main exporters.

Middle Eastern suppliers account for more than half of China’s sulfur imports, according to a note released Tuesday by consultancy SMM Information & Technology Co. With spring planting season approaching, sulfur demand remains firm as phosphate fertilizer plants run at high rates and step up restocking, the note said. (x)

/// Markets might not yet have fully ⁠priced in the ​possibility of a long war, according to Morningstar analyst Seth Goldstein, who estimates that nitrogen prices could roughly double and phosphate prices rise ​50% from current levels.

"If the supply shock lasts more than a few weeks, I wouldn't be surprised to see prices go back to the highs of 2022, when the Russia-Ukraine conflict began," Goldstein said. (x)

[mostlysignssomeportents]

What kind of bubble is AI?

My latest column for Locus Magazine is "What Kind of Bubble is AI?" All economic bubbles are hugely destructive, but some of them leave behind wreckage that can be salvaged for useful purposes, while others leave nothing behind but ashes:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Think about some 21st century bubbles. The dotcom bubble was a terrible tragedy, one that drained the coffers of pension funds and other institutional investors and wiped out retail investors who were gulled by Superbowl Ads. But there was a lot left behind after the dotcoms were wiped out: cheap servers, office furniture and space, but far more importantly, a generation of young people who'd been trained as web makers, leaving nontechnical degree programs to learn HTML, perl and python. This created a whole cohort of technologists from non-technical backgrounds, a first in technological history. Many of these people became the vanguard of a more inclusive and humane tech development movement, and they were able to make interesting and useful services and products in an environment where raw materials – compute, bandwidth, space and talent – were available at firesale prices.

Contrast this with the crypto bubble. It, too, destroyed the fortunes of institutional and individual investors through fraud and Superbowl Ads. It, too, lured in nontechnical people to learn esoteric disciplines at investor expense. But apart from a smattering of Rust programmers, the main residue of crypto is bad digital art and worse Austrian economics.

Or think of Worldcom vs Enron. Both bubbles were built on pure fraud, but Enron's fraud left nothing behind but a string of suspicious deaths. By contrast, Worldcom's fraud was a Big Store con that required laying a ton of fiber that is still in the ground to this day, and is being bought and used at pennies on the dollar.

AI is definitely a bubble. As I write in the column, if you fly into SFO and rent a car and drive north to San Francisco or south to Silicon Valley, every single billboard is advertising an "AI" startup, many of which are not even using anything that can be remotely characterized as AI. That's amazing, considering what a meaningless buzzword AI already is.

So which kind of bubble is AI? When it pops, will something useful be left behind, or will it go away altogether? To be sure, there's a legion of technologists who are learning Tensorflow and Pytorch. These nominally open source tools are bound, respectively, to Google and Facebook's AI environments:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

But if those environments go away, those programming skills become a lot less useful. Live, large-scale Big Tech AI projects are shockingly expensive to run. Some of their costs are fixed – collecting, labeling and processing training data – but the running costs for each query are prodigious. There's a massive primary energy bill for the servers, a nearly as large energy bill for the chillers, and a titanic wage bill for the specialized technical staff involved.

Once investor subsidies dry up, will the real-world, non-hyperbolic applications for AI be enough to cover these running costs? AI applications can be plotted on a 2X2 grid whose axes are "value" (how much customers will pay for them) and "risk tolerance" (how perfect the product needs to be).

Charging teenaged D&D players $10 month for an image generator that creates epic illustrations of their characters fighting monsters is low value and very risk tolerant (teenagers aren't overly worried about six-fingered swordspeople with three pupils in each eye). Charging scammy spamfarms $500/month for a text generator that spits out dull, search-algorithm-pleasing narratives to appear over recipes is likewise low-value and highly risk tolerant (your customer doesn't care if the text is nonsense). Charging visually impaired people $100 month for an app that plays a text-to-speech description of anything they point their cameras at is low-value and moderately risk tolerant ("that's your blue shirt" when it's green is not a big deal, while "the street is safe to cross" when it's not is a much bigger one).

Morganstanley doesn't talk about the trillions the AI industry will be worth some day because of these applications. These are just spinoffs from the main event, a collection of extremely high-value applications. Think of self-driving cars or radiology bots that analyze chest x-rays and characterize masses as cancerous or noncancerous.

These are high value – but only if they are also risk-tolerant. The pitch for self-driving cars is "fire most drivers and replace them with 'humans in the loop' who intervene at critical junctures." That's the risk-tolerant version of self-driving cars, and it's a failure. More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Quite the reverse, in fact. Cruise was just forced to quit the field after one of their cars maimed a woman – a pedestrian who had not opted into being part of a high-risk AI experiment – and dragged her body 20 feet through the streets of San Francisco. Afterwards, it emerged that Cruise had replaced the single low-waged driver who would normally be paid to operate a taxi with 1.5 high-waged skilled technicians who remotely oversaw each of its vehicles:

https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html

The self-driving pitch isn't that your car will correct your own human errors (like an alarm that sounds when you activate your turn signal while someone is in your blind-spot). Self-driving isn't about using automation to augment human skill – it's about replacing humans. There's no business case for spending hundreds of billions on better safety systems for cars (there's a human case for it, though!). The only way the price-tag justifies itself is if paid drivers can be fired and replaced with software that costs less than their wages.

What about radiologists? Radiologists certainly make mistakes from time to time, and if there's a computer vision system that makes different mistakes than the sort that humans make, they could be a cheap way of generating second opinions that trigger re-examination by a human radiologist. But no AI investor thinks their return will come from selling hospitals that reduce the number of X-rays each radiologist processes every day, as a second-opinion-generating system would. Rather, the value of AI radiologists comes from firing most of your human radiologists and replacing them with software whose judgments are cursorily double-checked by a human whose "automation blindness" will turn them into an OK-button-mashing automaton:

https://pluralistic.net/2023/08/23/automation-blindness/#humans-in-the-loop

The profit-generating pitch for high-value AI applications lies in creating "reverse centaurs": humans who serve as appendages for automation that operates at a speed and scale that is unrelated to the capacity or needs of the worker:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

But unless these high-value applications are intrinsically risk-tolerant, they are poor candidates for automation. Cruise was able to nonconsensually enlist the population of San Francisco in an experimental murderbot development program thanks to the vast sums of money sloshing around the industry. Some of this money funds the inevitabilist narrative that self-driving cars are coming, it's only a matter of when, not if, and so SF had better get in the autonomous vehicle or get run over by the forces of history.

Once the bubble pops (all bubbles pop), AI applications will have to rise or fall on their actual merits, not their promise. The odds are stacked against the long-term survival of high-value, risk-intolerant AI applications.

The problem for AI is that while there are a lot of risk-tolerant applications, they're almost all low-value; while nearly all the high-value applications are risk-intolerant. Once AI has to be profitable – once investors withdraw their subsidies from money-losing ventures – the risk-tolerant applications need to be sufficient to run those tremendously expensive servers in those brutally expensive data-centers tended by exceptionally expensive technical workers.

If they aren't, then the business case for running those servers goes away, and so do the servers – and so do all those risk-tolerant, low-value applications. It doesn't matter if helping blind people make sense of their surroundings is socially beneficial. It doesn't matter if teenaged gamers love their epic character art. It doesn't even matter how horny scammers are for generating AI nonsense SEO websites:

https://twitter.com/jakezward/status/1728032634037567509

These applications are all riding on the coattails of the big AI models that are being built and operated at a loss in order to be profitable. If they remain unprofitable long enough, the private sector will no longer pay to operate them.

Now, there are smaller models, models that stand alone and run on commodity hardware. These would persist even after the AI bubble bursts, because most of their costs are setup costs that have already been borne by the well-funded companies who created them. These models are limited, of course, though the communities that have formed around them have pushed those limits in surprising ways, far beyond their original manufacturers' beliefs about their capacity. These communities will continue to push those limits for as long as they find the models useful.

These standalone, "toy" models are derived from the big models, though. When the AI bubble bursts and the private sector no longer subsidizes mass-scale model creation, it will cease to spin out more sophisticated models that run on commodity hardware (it's possible that Federated learning and other techniques for spreading out the work of making large-scale models will fill the gap).

So what kind of bubble is the AI bubble? What will we salvage from its wreckage? Perhaps the communities who've invested in becoming experts in Pytorch and Tensorflow will wrestle them away from their corporate masters and make them generally useful. Certainly, a lot of people will have gained skills in applying statistical techniques.

But there will also be a lot of unsalvageable wreckage. As big AI models get integrated into the processes of the productive economy, AI becomes a source of systemic risk. The only thing worse than having an automated process that is rendered dangerous or erratic based on AI integration is to have that process fail entirely because the AI suddenly disappeared, a collapse that is too precipitous for former AI customers to engineer a soft landing for their systems.

This is a blind spot in our policymakers debates about AI. The smart policymakers are asking questions about fairness, algorithmic bias, and fraud. The foolish policymakers are ensnared in fantasies about "AI safety," AKA "Will the chatbot become a superintelligence that turns the whole human race into paperclips?"

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

But no one is asking, "What will we do if" – when – "the AI bubble pops and most of this stuff disappears overnight?"

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/12/19/bubblenomics/#pop

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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tom_bullock (modified) https://www.flickr.com/photos/tombullock/25173469495/

CC BY 2.0 https://creativecommons.org/licenses/by/2.0/

[asimassertz]

Custom AI Software Development in India: The Smart Choice for Modern Businesses

The international business environment is experiencing a shift as a result of the use of artificial intelligence. Organizations are increasingly opting for artificial intelligence software development in India due to its speed, efficiency, and lower costs compared to working with big IT firms.

The Move from Traditional IT to AI Innovations

Traditionally, IT giants were the go-to option when it came to technology in enterprises. However, such firms have a one-size-fits-all strategy that does not cater to the demands of contemporary enterprises.

Enterprises require:

AI technology designed according to specific industry requirements

Rapid deployment

Adaptive development processes

What Sets Apart Custom AI Solutions?

Custom AI solutions do not follow a template approach but emphasize developing systems suited to your objectives and unique business problems.

The core benefits include:

Development of machine learning models

AI-driven analytics platform creation

Building intelligent automation systems

Enterprise-scale AI software development

Why Hire Indian AI Developers?

India's stature as a technology hub remains unassailable, particularly in areas such as artificial intelligence and automation.

Working with Indian companies allows you to enjoy:

Cost-effective AI development

A pool of expert AI developers

Swift project implementation

World-class delivery options

The Power of AI Automation in Business

No longer an option, but a necessity.

AI automation services for business offer the ability to optimize processes, eliminate human errors, and achieve greater efficiency.

Applications include:

Automated customer service

Smart data analysis

Optimized workflow

Sales and marketing automation

Agentic AI: The Future of AI

One of the groundbreaking technologies in AI is Agentic AI development services.

Unlike AI automation, agentic systems operate autonomously, make decisions, and learn constantly.

With agentic AI services, businesses can:

Future-Ready Businesses

With SaaS and AI, future-ready businesses will be born.

As one of the leading SaaS product development firms in India, SB Infowaves assists companies to develop SaaS products which are:

Scalable

Smart

Customer-centric

Through AI-enabled SaaS systems, companies can provide smarter solutions while keeping up with their competition.

Why SB Infowaves is Your AI Partner?

Through SB Infowaves, you will have access to AI expertise and solutions that match your business goals.

Some of the services provided by them include:

Development of custom AI software

Conclusion

The right technological partner will mean everything for success in the world of artificial intelligence.

With the help of an Indian custom AI software development company, companies will have the advantage they need to succeed.

In addition, SB Infowaves offers much more than services; it is a technological partner in the development of the capabilities of artificial intelligence.

Contact SB Infowaves

📍 India – Kolkata (Head Office)

Adventz Infinity, Office No - 1509 BN - 5, Street Number -18 Bidhannagar, Kolkata - 700091, West Bengal 📞 [phone] 📧[email protected]

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🚀 Ready to Scale Your Business with AI?

Visit: https://www.sbinfowaves.com Or connect with our experts today!

[ccjubilee]

“Digital crowdsourcing in the times of crisis”

Crowdsourcing refers to the practice of obtaining ideas, services, data, or resources from a large, often online group of people during emergencies or disruptive events such as natural disasters, pandemics, wars, or economic collapses. Highlight. This includes all crises out there, not just the contemporary digital hardships. The topic of crowdsourcing validates the significance of the digital community, and why it has the power to change, to rule the world. BIG LINES. But it’s true. We’ll go about this by breaking down into different cases of digital crowdsourcing and what had already happened in the past, and the changes they have already landed in the past years. 

The Covid-19 pandemic

Lockdown. Vaccines. Sorrows. Deaths. Covid is a season of great sorrows. When the economy and general mobility are being severely handicapped, the people switch to the online platform for ideas and actions for urgent research and discoveries

In early 2020, a global shortage of mechanical ventilators threatened to overwhelm healthcare systems as COVID-19 led to severe respiratory failure. Over 3,000 engineers, medical professionals, and hobbyists from 20+ countries collaborated in real-time on a single open-source ventilator design. Design files were version-controlled on GitHub; circuit board designs were co-edited live on Altium 365.

This is done by a complete geographic decentralization – no physical meeting was ever held. The final design could be downloaded and manufactured anywhere in the world with a 3D printer and basic electronics.

In specific regions in Africa, the pandemic was accompanied by an Infodemic, a spread of disinformation, making it difficult for people to find accurate information. To tackle the issue, UNESCO in partnership with Innovation for Policy Foundation (i4Policy) launched an online campaign to crowdsource local openly licensed content to inform communities across Africa about Covid-19.

“This crisis tells us the importance of the flow of quality, reliable information at a time when misinformation and rumors are flourishing,” said UNESCO Director General Audrey Azoulay.  

#DontGoViral: UNESCO and i4Policy launch a campaign to crowdsource

Translators for Ukraine (2022-Present)

In the face of war, social media platforms such as Telegram and Whatsapp are being used as decentralised network with volunteer translators receiving important screenshots of Ukrainian documents - medical records, legal forms, evacuation orders, returned and translated within minutes. A simple Telegram bot distributed tasks to available volunteers based on language pairs (e.g., Ukrainian -> Polish).

At peak, the network claimed over 5000 active volunteers and processed over 1 million translation requests in the first six months of the war. 

2010 Haiti Earthquake: Ushahidi and the 4636 SMS Short Code

On January 12, 2010 a 7.0 magnitude earthquake struck near Port-au-Prince, Haiti, causing over 100,000 deaths and overwhelming traditional communication and aid infrastructure. Within hours of the earthquake, the Ushaidi platform - originally a platform developed to map violence in Kenya - launched the Ushaihidi-Haiti Project. The innovation was a partnership with the mobile network Digicel to establish SMS short code 4636, allowing anyone in Haiti to text reports of urgent needs for free. 

Estimated 10,000 Haitians volunteered to translate messages, processing thousands of reports. 

The US Marine Corps and US Coast Guard actively used the live map. One Marine Corps report specifically credited the map with enabling a rescue mission that evacuated two elderly women and a young girl with critical injuries from outside Grand Goave, stating, "Your site saved these people's lives... There are 100s of these kinds of stories..."

Ushahidi is an open source software application which utilises user-generated reports to collate and map data.

Digital crowdsourcing is a great example in contributing to a healthy digital citizenship. It is also a pivotal tool for organizations and institutions that helps facilitate the decentralization of knowledge production and task execution and by doing so, leverages dispersed expertise, creativity and labour across global networks. It can be applied in various manners including crowdfunding, crowd analysis, crowd data collection and so on, in different fields and circumstances. Hansen (2012) affirms that aimed at solving wicked problems, public managers seek innovative approaches and crowdsourcing can be a likely solution for municipal issues. 

Addressing the need of volunteers in crowdsourcing

Democratic viewpoints suggest citizen engagement with technology use for improving deliberation and decision making (Macintosh, 2004). Other e-participation frameworks include alternatives for more collaborative strategies, which enable citizens to become partners and co-creators and co-producers of services (Nam, 2016). 

One of the most efficient ways to take part in crowdsourcing for an ongoing crisis is to participate in crisis mapping and open-source data collection. Global humanitarian organizations use these platforms to turn satellite imagery and local reports into actionable maps and intelligence for on-the-ground disaster relief. 

Crowdsourcing is an indispensable need in the time of crises, and digital crowdsourcing offers more channels for greater accessibility and enhanced efficiency of the crowdsourcing process, making it easier in resolving crises. 

Week 12: Crowdsourcing in times of crisis

References:

Bertot, J. C., Jaeger, P. T., & Hansen, D. (2012). Government Information Quarterly, 29(1), 30–40. https://doi.org/10.1016/j.giq.2011.04.004

Macintosh, A. (2004). Characterizing e-participation in policy-making. In Proceedings of the 37th Annual Hawaii International Conference on System Sciences (p. 50117b). IEEE. https://doi.org/10.1109/HICSS.2004.1265300

Morrow, N., Mock, N., Papendieck, A., & Kocmich, J. (2011). Independent evaluation of the Ushahidi Haiti project. Development Information Systems International.

Nam, T. (2016). Government 3.0 in Korea: A country study on government innovation. International Review of Administrative Sciences, 82(4), 743–763.

https://doi.org/10.1177/0020852315574992

UNESCO. (2020, April 1). #DontGoViral: UNESCO and i4Policy launch a campaign to crowdsource local content to combat the Infodemic in Africa. UNESCO News. https://www.unesco.org/en/articles/dontgoviral-unesco-and-i4policy-launch-campaign-crowdsource-local-content-combat-infodemic-africa

Ushahidi. (2010). Ushahidi-Haiti project: Mobile data collection, crowdmapping, and SMS short code 4636 deployment in Port-au-Prince disaster relief . Ushahidi Platform Case Studies.

[france24en]

Big e-Aadhaar revamp on the cards! No more photocopies of Aadhaar card required, updation to become easy; check top steps

The Unique Identification Authority of India (UIDAI) is set to revamp e-Aadhaar, introducing a QR code-based system to eliminate need for physical copies. Updates, excluding biometrics, will be automated through integrated databases, reducing center visits.

Big e-Aadhaar revamp soon! In the coming weeks, a new QR code-based application will eliminate the need for Aadhaar card physical photocopies to be submitted. Users can share digital versions of their Aadhaar, choosing between complete or masked formats.By November, the Unique Identification Authority of India (UIDAI) is also planning to introduce a streamlined procedure that will significantly reduce visits to Aadhaar centres for updation.Except for biometric submissions, updates to address and other information will be automated through integration with various official databases. These include birth certificates, matriculation records, driving licences, passports, PAN cards, PDS and MNREGA systems.This initiative aims to simplify the process for citizens whilst reducing fraudulent document submissions for Aadhaar registration. Additionally, discussions are in progress to incorporate electricity bill records to enhance user convenience.Also Read | ITR filing FY 2024-25: Several changes in Form 16! Top things salaried taxpayers shouldn’t missUIDAI's chief executive officer Bhuvnesh Kumar has informed TOI about a newly developed application, with approximately 2,000 out of one lakh machines already utilising this new system."You will soon be able to do everything sitting at home other than providing fingerprints and IRIS," he said.e-Aadhaar Revamp: Explained in Top PointsThe application will enable users to update personal details including addresses, telephone numbers, names and incorrect birth date corrections.The introduction of QR code-based Aadhaar transfers between mobile devices or applications is considered essential for preventing misuse, with potential applications ranging from hotel check-ins to identity verification during rail travel. "It offers maximum user control over your own data and can be shared only with consent," Kumar said.The system can additionally be implemented by sub-registrars and registrars during property registration procedures to prevent fraudulent activities.Kumar indicated that UIDAI is working with state governments to incorporate Aadhaar verification for individuals registering properties, aiming to reduce instances of fraud.UIDAI has commenced discussions with CBSE and additional examination boards to facilitate biometric and other data updates for children, which needs to be completed during two age brackets: between five and seven years, and between 15 and 17 years. They are planning a dedicated campaign to address the pending updates, which include eight crore cases for the first update (children aged five to seven years) and 10 crore cases for the second update.Additionally, UIDAI is collaborating with various organisations, including security agencies and hospitality establishments, to extend Aadhaar services to entities where its use is not mandatory.Stay informed with the latest business news, updates on bank holidays and public holidays.

[servesociety]

Hi , sorry to bother you but i love your analysis and i was wondering if i could have your thoughts on carlos saying he had specific goals for doha (that he accomplished) . What do you think he is working on/developing ? Looking at his data i realised that the serve (eternal work in progress) worked pretty well , specifically in the second set of the final where he hit 100% first serves and only lost 5 points on serve, but i figure he is always working on that . I also noticed that he managed to place all of his returns pretty deep (all behind the service box) and above half i believe into the backhand corner. In summary i’m just wondering if you’ve noticed any pattern in things he could be looking to change specifically towards clay season or even grass (more serve+volley or similar)

Again really sorry to bother i just love talking technical side of tennis, thanks in advance!

hiiii, this is absolutely no bother at all, and thank you so much. during ao 2026, samu essentially framed their focus as the serve, the return, and the first ball after both shots, with the aim of making carlos’ level more consistent and minimizing fluctuations. and i think you see that work taking place — especially with carlos saying they weren’t focused in doha on results, but on improving and being better prepared, knowing that opponents are studying his game closely.

as for adjusting toward clay and grass: we’re still on hard courts at the moment, but building repeatable structures in the first two shots of the point is the kind of foundation that translates to any surface.

my thoughts below.

1) the serve + 1 ball

the serve is still a work in progress and we know carlos will never be a servebot. but the evolution they’re building is arguably more punishing: a serve designed to start points on his terms and feed the first ball after serve.

the season numbers are beginning to show that this evolution is producing results. through 12 matches in 2026 he’s holding 90.8%, up from 87.6% in 2025. compared to his career norms, he’s slightly better across the key indicators:

• 68.3% first serves in (vs 65%)

• 74.3% first-serve points won (vs 72.2%)

• 59.4% second-serve points won (vs 56%)

• 6.1 aces per match (vs 4.3)

• 90.8% service games won (vs 84.6%)

none of these are dramatic spikes on their own, but together they create a shift: he’s harder to break. that’s especially valuable when his returning is slightly below his usual baseline early in the season (more on that in point 2), because the serve becomes the safety net that stabilizes matches.

tactically in doha, in my opinion, the core idea was to either jam the return (t/body) to keep the reply central, or stretch it wide to open the court for the +1. what changed was the application, depending on the opponent.

vs rinderknech (and early rounds)

the emphasis was on wide + body combinations to disrupt rhythm and prevent a linear striker from taking full cuts. the aim was fewer comfortable strike-zone returns and a stable early-shot sequence, even if short points were tight.

vs khachanov

khachanov is most dangerous when he gets a clean return and can step into a big first forehand. the deuce-court t targets the backhand lane and keeps the return more central, reducing angle creation. the ad-court wide serve stretches him and opens space for carlos’s forehand +1. body serves stopped khachanov from locking onto a predictable backhand read.

vs rublev

this match was the stress test. the plan itself was sound; the instability showed up in the +1 window. against rublev’s pace and return pressure, the third ball has to be both decisive and high-margin. carlos hit some outstanding winners, but there were also untimely errors. rublev is elite at turning your serve +1 into a rushed choice, and the way i see it, this match highlighted the remaining space for refinement in that exchange.

vs fils

the final was more t-heavy overall, a deliberate choice against a player who thrives on explosive angles and fast first strikes. serving through the middle simplified the geometry, forced more central returns, and denied fils clean space to attack. that’s why carlos controlled not just short points, but every rally band: fils rarely got first contact on his terms.

and the bottom line reflects the intent: he didn’t drop serve against rinderknech, khachanov, or fils. that’s “minimizing fluctuations” in the most concrete form — fewer vulnerable service games, fewer cheap openings.

2) the return + 1 ball

early 2026 has looked serve-driven, with returning slightly below his baseline (roughly 30.3% return games won vs 31% career). his returning hasn’t been consistently poor — his return + first ball package has oscillated, depending on opponent serve characteristics and how much time he could steal in the first two shots.

when players get tight, they either baby the second serve or slap returns wildly. in doha carlos looked more structured despite the numbers dip.

• rinderknech: the limitation was first-serve return efficacy, not returning overall. he won only 25% vs first serves (14/56) but a pretty strong ~52% vs second (14/27). translation: if first-serve returns don’t neutralize, the server’s +1 stays comfortable and return games become low-yield, even if you’re fine against second serves.

• khachanov: the most technical “high returnability, low advantage” match. he made a lot of returns (76% returnable) but won only 43% of returnable points. versus first serves, even when the ball was returnable, he won just 41% of those points, consistent with khachanov’s +1 holding. too many first-serve returns landed shallow enough for khachanov to strike cleanly. against second serves he returned deeper, but paid an error tax and still didn’t win a majority of points.

• fils: he won 56% of return points, with 84% returnable, and — most importantly — 67% won on returnable points. that’s what it looks like when the post-return phase is owned. the mechanism was decisive: deep returns at high frequency with almost no error cost.

so the return work-in-progress, from this perspective, is making deep returns sustainable — especially on first serves — and making second-serve aggression work without donating. that’s the kind of fluctuation control you want to keep: less oscillation between “survive” and “dominate.”

3) “minimizing fluctuations” is also coming with a clear match plan

what the doha matches tested, over and over, was whether carlos can stay inside the game plan when the match gives him every reason to abandon it: a bad call, a wobble on serve, a missed chance to close, an opponent swinging like there’s nothing to lose.

after the rublev semi, carlos explained the mechanism himself:

if you want to find a solution to a problem, you should find it in a calm place. it’s something i am working on. when i am playing and getting mad, seeing i’m not at my best, i just get frustrated. that is not the place you will find solutions. in these matches, i have been really calm, thinking clearly and being positive. it’s in those places where you can find the solutions to a problem.

anger shrinks his decision tree. calm keeps the full menu available: where to serve next, what return target to prioritize, how much risk to take on the +1, when to extend rallies versus when to pull the trigger. that’s the competitive edge — not just emotional control, but preserving tactical clarity when the match tries to speed you up.

vs royer

carlos didn’t cruise past royer, but he solved a momentum problem in real time. he started clean with a low-drama 6–2, then the dip hit: royer raised the tempo, leaned into carlos’s forehand, and the error rate spiked. the swing was sharp — royer got out to 5–2. from 2–5, carlos stopped donating and returned to higher-percentage structure: deeper balls, fewer rushed forehands, clearer selection on when to accelerate. the close was ruthless: five straight games to win 7–5, including the break back when royer served for the set. royer tried to make it first-strike chaos; carlos rebuilt point structure and squeezed it shut.

vs khachanov

the khachanov quarterfinal was the clearest emotional inflection point of the week: the time-violation warning around 4–4, the toweling exchange, and then the immediate dip. you could see the brief “anger tax” in real time — a double fault, a couple of rushed misses. in a set decided on small margins, that stretch likely contributed to him losing it. but he didn’t respond by chasing low-percentage returns or forcing from neutral and defensive positions. he didn’t try to win the match in three shots. instead, he re-anchored to the same structural priorities: depth to remove angles, tolerance in the middle rally band, and selective acceleration only once position was earned. he stabilized the return +1 patterns, accepted longer exchanges, and reduced the volatility that had crept in. the wobble stayed a wobble, but it didn’t become a spiral.

vs rublev

if khachanov was the emotional test, rublev was the stress test. carlos had match points and the match refused to end as he needed six match points to finish. most players either tighten into passivity or overcorrect into panic aggression. carlos reset the task: when serving it out didn’t work, he didn’t turn it into “i must end this now.” he shifted the finish line from a single moment to a sequence — keep applying pressure, keep making good choices, accept that it might take another cycle. calm became a competitive advantage: discipline over forcing, problem-solving over panicking.

vs fils

then came the final, where he arrived with a clear plan and executed it: make holds automatic, strip away fils’ serve + forehand pattern, and turn rallies into a controlled squeeze. he built a zero-stress platform — 89% first-serve points won and zero break points faced — so pressure lived almost entirely on fils. on return he leaned backhand (~60% of returns) and kept second-serve returns systematically deep to deny the easy +1 forehand start. rally-wise it was crosscourt discipline and depth first, then a planned down-the-line change once advantage was earned, and clean finishing when fils was displaced. against an explosive opponent like fils, clarity is the antidote: chaos feeds him; structured pressure suffocates him.

in doha, we still saw a carlos who gets visibly frustrated at times. but this is now someone learning to treat that frustration as irrelevant information — background noise rather than a steering wheel. a year ago, many of these situations probably turn into three-setters. here, you could see him learning to ride out the ugly stretches without abandoning the plan.

4) the work they’re doing carries to any surface

we’re in the hard-court stretch, but what they’re refining isn’t tied to hard courts. it’s about point architecture — how the first two shots are structured and how those shots dictate the rest of the rally. that kind of foundation travels.

toward clay

clay rewards clarity and repetition. it favors players who can build the +1 with patience and margin rather than rush it. the areas they’re sharpening now translate directly:

• serve + forehand sequencing, especially ad-wide to open the forehand +1

• a shaped second serve that starts the rally safely but still with purpose

• disciplined, deep returns that deny opponents a clean first strike

on clay, impatience gets punished. rushed +1 attempts get absorbed, rallies lengthen, and structure matters more than flash. the work on depth, margin, and controlled sequencing makes that transition far more natural.

toward grass

grass does the opposite: it compresses time and magnifies the first two shots. here, the improved serve becomes even more valuable:

• t/body serving is amplified because it produces lower, more central returns

• the +1 becomes sharper and more decisive

• selective serve +1 approaches , and even occasional serve-volley, become logical extensions.

those deep, low returns into the backhand corner — something you noticed — are also grass-friendly. they prevent opponents from stepping forward immediately and turning the point into a quick strike.

so even though the surface will change, the underlying structure won’t. they’re not refining for a single phase of the season, but they’re strengthening a system that holds up anywhere.

to sum it up

carlos and his team are building a more reliable first-two-shot ecosystem — serve, return, and the first ball after both — with one clear aim: reduce fluctuations. the serve gains aren’t about turning him into something he’s not. they’re about structural reliability and repeatable geometry, defined +1 patterns, and fewer early leaks. that’s what steadies service games and lowers the volatility across a match.

the return “dip” to me, feels currently more like a technical bottleneck around depth and conversion, particularly against first serves. the fils final showed that the fully realized version of that return +1 package already exists. the challenge now is making that level sustainable against all opponents.

overall, this feels like a genuine long-term project and a deliberate reinforcement of the foundation. they’re strengthening a framework that can translate across surfaces and, just as importantly, remain stable when matches get tight or chaotic.

data sources

atp tour. match stats & box scores — individual match data from doha 2026 https://www.atptour.com/en/players/carlos-alcaraz/a0e2/player-activity?matchType=Singles&year=2026&tournament=all

infosys atp beyond the numbers. feature article on carlos alcaraz, february 2026. https://www.atptour.com/en/news/alcaraz-infosys-atp-beyond-the-number-february-2026

tennis abstract. match charts & point-by-point data. https://www.tennisabstract.com/cgi-bin/player.cgi?p=CarlosAlcaraz, https://www.tennisabstract.com/charting/

atp tour article: alcaraz converts 6th match point vs. rublev, awaits fils in doha final, february 2026. https://www.atptour.com/en/news/alcaraz-rublev-doha-2026-sf

disclaimer: i am not a tennis professional.

[austa-publishing]

Topic:- Artificial Intelligence (AI) as the Core of Modern Technology By :- Minakshi Singh Under guidance of Dr. Varuna Gupta (CHRIST University)

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