#algorithm computer services

[onenettvchannel]

BALITANG LOKAL: Algorithm Computer Services failed to repair PC broadcasting equipment for OneNETtv Channel due to extreme financial difficulties [#OnlyOnOneNETnews]

(Written by Mishelle Patangui / Area Manager for OneNETtv Channel and Assistant News Writer of OneNETnews)

DUMAGUETE, NEGROS ORIENTAL -- On Wednesday morning (October 17th, 2024), a local computer repair shop 'Algorithm Computer Services' (ACS) was unable to repair Rhayniel's personal computer (PC) broadcasting equipment at 'National Highway, Brgy. Daro, Dumaguete City, Negros Oriental'.

OneNETnews was first to learn that the broadcasting services of OneNETtv Channel in Dumaguete City refuses to pay the computer repair costs by Rhayniel Calimpong's parents, who is the only independently-funded internet broadcaster and online news reporter, and his parents are facing financial difficulties, making it hard for them to cover the said costs of computer repairs for his small freelance news business.

The repair costs is much more than PHP10,000 (or roughly U$D173). Social media and physical donors have also refused to donate internet TV station, which automatically resulted unfortunately in financial difficulties for indefinite suspension of broadcasting operations beginning this Friday (October 18th, 2024) after the mall opening hours at Power Mac Center for the nationwide launch of Apple's iPhone 16. ONC Foundation will start a massive donation drive effective this week in mid-October 2024, all the way up to early 2025.

Dumaguetenos can support to send donations at 0994-269-6055 for both GCash and Maya users. International donations like Ko-Fi, Throne, Wise or PayPal are all available at "https://allmylinks.com/feling009". Taxes will be shouldered through the Bureau of Internal Revenue (BIR).

Supposedly, due to a series of PC crashes… Rhayniel wants to upgrade the system from his outdated operating system of Windows 7 to Windows 10 in the section of Solid State Drive (SSD), then cloning his own 1-terabyte hard disk drive (HDD) at Western Digital as a back-up, following the expensive production of audio and video editing between Sony Vegas and CapCut, and the only broadcast source is on Open Broadcasting Software (OBS). His online broadcasting media and journalist career for nearly 15 years, may or may not come to an end without your support.

This would allow him to resume full broadcasting operations for OneNETtv Channel and continue his nearly 15-year career in online media and journalism in the City of Gentle People.

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EDITOR's NOTE: We are featured on Dumagueteno's FB Group, but too controversial to discuss from the sucked mods in Dumaguete City. For those interested to donate, you can visit over to our news blog or via AllMyLinks site.

PHOTO COURTESY: Rhayniel Saldasal Calimpong (Freelance Photojournalist and News Presenter of OneNETnews)

-- OneNETnews Online Publication Team

[mjrdm]

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[jcmarchi]

Beyond the Cloud: Exploring the Benefits and Challenges of On-Premises AI Deployment

New Post has been published on https://thedigitalinsider.com/beyond-the-cloud-exploring-the-benefits-and-challenges-of-on-premises-ai-deployment/

Beyond the Cloud: Exploring the Benefits and Challenges of On-Premises AI Deployment

When you mention AI, both to a layman and an AI engineer, the cloud is probably the first thing that comes to mind. But why, exactly? For the most part, it’s because Google, OpenAI and Anthropic lead the charge, but they don’t open-source their models nor do they offer local options. 

Of course, they do have enterprise solutions, but think about it—do you really want to trust third parties with your data? If not, on-premises AI is by far the best solution, and what we’re tackling today. So, let’s tackle the nitty gritty of combining the efficiency of automation with the security of local deployment. 

The Future of AI is On-Premises

The world of AI is obsessed with the cloud. It’s sleek, scalable, and promises endless storage without the need for bulky servers humming away in some back room. Cloud computing has revolutionized the way businesses manage data, providing flexible access to advanced computational power without the high upfront cost of infrastructure. 

But here’s the twist: not every organization wants—or should—jump on the cloud bandwagon. Enter on-premises AI, a solution that’s reclaiming relevance in industries where control, speed, and security outweigh the appeal of convenience.

Imagine running powerful AI algorithms directly within your own infrastructure, with no detours through external servers and no compromises on privacy. That’s the core appeal of on-prem AI—it puts your data, performance, and decision-making firmly in your hands. It’s about building an ecosystem tailor-made for your unique requirements, free from the potential vulnerabilities of remote data centers. 

Yet, just like any tech solution that promises full control, the trade-offs are real and can’t be ignored. There are significant financial, logistical, and technical hurdles, and navigating them requires a clear understanding of both the potential rewards and inherent risks.

Let’s dive deeper. Why are some companies pulling their data back from the cloud’s cozy embrace, and what’s the real cost of keeping AI in-house?

Why Companies Are Reconsidering the Cloud-First Mindset

Control is the name of the game. For industries where regulatory compliance and data sensitivity are non-negotiable, the idea of shipping data off to third-party servers can be a dealbreaker. Financial institutions, government agencies, and healthcare organizations are leading the charge here. Having AI systems in-house means tighter control over who accesses what—and when. Sensitive customer data, intellectual property, and confidential business information remain entirely within your organization’s control.

Regulatory environments like GDPR in Europe, HIPAA in the U.S., or financial sector-specific regulations often require strict controls on how and where data is stored and processed. Compared to outsourcing, an on-premises solution offers a more straightforward path to compliance since data never leaves the organization’s direct purview.

We also can’t forget about the financial aspect—managing and optimizing cloud costs can be a painstaking taking, especially if traffic starts to snowball. There comes a point where this just isn’t feasible and companies have to consider using local LLMs. 

Now, while startups might consider using hosted GPU servers for simple deployments

But there’s another often-overlooked reason: speed. The cloud can’t always deliver the ultra-low latency needed for industries like high-frequency trading, autonomous vehicle systems, or real-time industrial monitoring. When milliseconds count, even the fastest cloud service can feel sluggish. 

The Dark Side of On-Premises AI

Here’s where reality bites. Setting up on-premises AI isn’t just about plugging in a few servers and hitting “go.” The infrastructure demands are brutal. It requires powerful hardware like specialized servers, high-performance GPUs, vast storage arrays, and sophisticated networking equipment. Cooling systems need to be installed to handle the significant heat generated by this hardware, and energy consumption can be substantial. 

All of this translates into high upfront capital expenditure. But it’s not just the financial burden that makes on-premises AI a daunting endeavor. 

The complexity of managing such a system requires highly specialized expertise. Unlike cloud providers, which handle infrastructure maintenance, security updates, and system upgrades, an on-premises solution demands a dedicated IT team with skills spanning hardware maintenance, cybersecurity, and AI model management. Without the right people in place, your shiny new infrastructure could quickly turn into a liability, creating bottlenecks rather than eliminating them.

Moreover, as AI systems evolve, the need for regular upgrades becomes inevitable. Staying ahead of the curve means frequent hardware refreshes, which add to the long-term costs and operational complexity. For many organizations, the technical and financial burden is enough to make the scalability and flexibility of the cloud seem far more appealing.

The Hybrid Model: A Practical Middle Ground?

Not every company wants to go all-in on cloud or on-premises. If all you’re using is an LLM for intelligent data extraction and analysis, then a separate server might be overkill. That’s where hybrid solutions come into play, blending the best aspects of both worlds. Sensitive workloads stay in-house, protected by the company’s own security measures, while scalable, non-critical tasks run in the cloud, leveraging its flexibility and processing power.

Let’s take the manufacturing sector as an example, shall we? Real-time process monitoring and predictive maintenance often rely on on-prem AI for low-latency responses, ensuring that decisions are made instantaneously to prevent costly equipment failures. 

Meanwhile, large-scale data analysis—such as reviewing months of operational data to optimize workflows—might still happen in the cloud, where storage and processing capacity are practically unlimited.

This hybrid strategy allows companies to balance performance with scalability. It also helps mitigate costs by keeping expensive, high-priority operations on-premises while allowing less critical workloads to benefit from the cost-efficiency of cloud computing. 

The bottom line is—if your team wants to use paraphrasing tools, let them and save the resources for the important data crunching. Besides, as AI technologies continue to advance, hybrid models will be able to offer the flexibility to scale in line with evolving business needs.

Real-World Proof: Industries Where On-Premises AI Shines

You don’t have to look far to find examples of on-premises AI success stories. Certain industries have found that the benefits of on-premises AI align perfectly with their operational and regulatory needs:

Finance

When you think about, finance is the most logical target and, at the same time, the best candidate for using on-premises AI. Banks and trading firms demand not only speed but also airtight security. Think about it—real-time fraud detection systems need to process vast amounts of transaction data instantly, flagging suspicious activity within milliseconds. 

Likewise, algorithmic trading and trading rooms in general rely on ultra-fast processing to seize fleeting market opportunities. Compliance monitoring ensures that financial institutions meet legal obligations, and with on-premises AI, these institutions can confidently manage sensitive data without third-party involvement.

Healthcare

Patient data privacy isn’t negotiable. Hospitals and other medical institutions use on-prem AI and predictive analytics on medical images, to streamline diagnostics, and predict patient outcomes. 

The advantage? Data never leaves the organization’s servers, ensuring adherence to stringent privacy laws like HIPAA. In areas like genomics research, on-prem AI can process enormous datasets quickly without exposing sensitive information to external risks.

Ecommerce

We don’t have to think on such a magnanimous scale. Ecommerce companies are much less complex but still need to check a lot of boxes. Even beyond staying in compliance with PCI regulations, they have to be careful about how and why they handle their data. 

Many would agree that no industry is a better candidate for using AI, especially when it comes to data feed management, dynamic pricing and customer support. This data, at the same time, reveals a lot of habits and is a prime target for money-hungry and attention-hungry hackers. 

So, Is On-Prem AI Worth It?

That depends on your priorities. If your organization values data control, security, and ultra-low latency above all else, the investment in on-premises infrastructure could yield significant long-term benefits. Industries with stringent compliance requirements or those that rely on real-time decision-making processes stand to gain the most from this approach.

However, if scalability and cost-efficiency are higher on your list of priorities, sticking with the cloud—or embracing a hybrid solution—might be the smarter move. The cloud’s ability to scale on demand and its comparatively lower upfront costs make it a more attractive option for companies with fluctuating workloads or budget constraints.

In the end, the real takeaway isn’t about choosing sides. It’s about recognizing that AI isn’t a one-size-fits-all solution. The future belongs to businesses that can blend flexibility, performance, and control to meet their specific needs—whether that happens in the cloud, on-premises, or somewhere in between. 

[tutorsindia152]

The Role Of Algorithms In Modern Software Development

In the fast-paced world of software development, algorithms play a pivotal role in crafting efficient, scalable, and robust solutions. From powering artificial intelligence systems to optimizing logistics and enhancing user experience, algorithms form the backbone of modern applications. At Tutors India, we understand the significance of algorithms and offer the best code and algorithm development services tailored to meet diverse needs.

Why Algorithms Matter in Software Development

Algorithms are a set of well-defined instructions that solve problems and perform computations. In software development, they:

Enhance Efficiency: Algorithms optimize resource usage, reduce processing time, and ensure that applications run smoothly.

Drive Innovation: Machine learning, data analytics, and predictive modeling rely heavily on advanced algorithms.

Ensure Scalability: Properly designed algorithms allow systems to scale and adapt to growing user demands.

Solve Complex Problems: From scheduling to encryption, algorithms address challenges that would be otherwise insurmountable.

Applications of Algorithms in Modern Technology

Artificial Intelligence and Machine Learning Algorithms power intelligent systems, enabling them to learn, predict, and adapt. For instance, recommendation systems in e-commerce or fraud detection systems in finance are driven by machine learning algorithms.

Big Data Analytics With the explosion of data, algorithms sift through massive datasets, identifying trends and insights that drive decision-making.

Cybersecurity Encryption algorithms secure data, ensuring confidentiality and integrity in digital communication.

Optimization in Operations Algorithms are used to optimize processes in industries like logistics, supply chain management, and transportation.

Challenges in Algorithm Development

Creating algorithms isn’t just about writing code—it requires expertise to ensure efficiency, accuracy, and adaptability. Common challenges include:

Handling edge cases and ensuring robustness.

Balancing complexity with performance.

Integrating algorithms seamlessly into software architectures.

Why Choose Tutors India for Algorithm Development?

At Tutors India, we specialize in offering cutting-edge coding and algorithm development services. Our team of experts ensures:

Custom Solutions: We develop algorithms tailored to your unique requirements, whether it’s for research, business, or academic purposes.

Scalability: Our solutions are designed to adapt and grow with your needs.

Efficiency: We prioritize performance, ensuring algorithms run optimally even under heavy loads.

Support: From concept to implementation, we provide comprehensive support, ensuring successful integration into your systems.

Partner with Tutors India

Whether you’re looking to develop a new application, optimize an existing system, or solve a complex computational problem, Tutors India is your trusted partner. Our expertise in code and algorithm development ensures that your projects are backed by innovative, efficient, and reliable solutions.

Contact us today to elevate your software development journey with the best algorithms tailored to your needs!

[markcraft3636]

What is ServiceNow |Introduction | User Interface| Application & Filter Navigation | Complete Course

ServiceNow is planned with intelligent systems to speed up the work process by providing solutions to amorphous work patterns. Each employee, customer, and machine in the enterprise is related to ServiceNow, allowing us to make requests on a single cloud platform. Various divisions working with the requests can assign, prioritize, correlate, get down to root cause issues, gain real‑time insights, and drive action. This workflow process helps the employees to work better, and this would eventually improve the service levels. ServiceNow provides cloud services for the entire enterprise. This module consists User Interface and Navigation. The Objective of this module is to make beginners learn how to navigate to applications and modules in ServiceNow, using the Application and Filter Navigators. To Create views and filters for a table list and to update record using online editing.

[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/

[probablyasocialecologist]

Amazon’s recent decision to stop allowing people to download copies of their Kindle e-books to a computer has vindicated some of my longstanding beliefs about digital media. Specifically, that it doesn’t exist and you don’t own it unless you can copy and access it without being connected to the internet. The recent move by the megacorp and its shiny-headed billionaire CEO Jeff Bezos is another large brick in the digital wall that tech companies have been building for years to separate consumers from the things they buy—or from their perspective, obtain “licenses” to. Starting Wednesday, Kindle users will no longer be able to download purchased books to a computer, where they can more easily be freed of DRM restrictions and copied to e-reader devices via USB. You can still send ebooks to other devices over WiFi for now, but the message the company is sending is one tech companies have been telegraphing for years: You don’t “own” anything digital, even if you paid us for it. The Kindle terms of service now say this, explicitly. “Kindle Content is licensed, not sold, to you,” meaning you don’t “buy a book,” you obtain a “digital content license.”

[...]

Amazon is far from alone in this long-running trend towards eliminating digital ownership. For many people, digital distribution and streaming services have already practically ended the concept of owning and controlling your own media files. Spotify is now almost synonymous with music for some younger generations, having strip-mined the music industry from both ends by demonetizing more than 60% of the artists on its platform and pushing algorithmic slop while­ simultaneously raising subscription fees.  Of course, surrendering this control means being at the complete mercy of Amazon and other platforms to determine what we can watch, read, and listen to—and we’ve already seen that these services frequently remove content for all sorts of reasons. Last October, one year after the Israeli military began its campaign of genocide in Gaza, Netflix removed “Palestinian Stories,” a collection of 19 films featuring Palestinian filmmakers and characters, saying it declined to renew its distribution license. Amazon also once famously deleted copies of 1984 off of people’s Kindles. Fearing piracy, many software companies have moved from the days of “Don’t Copy That Floppy” to the cloud-based software-as-a-service model, which requires an internet connection and charges users monthly subscription fees to use apps like Photoshop. No matter how you look at it, digital platforms have put us on a path to losing control of any media that we can’t physically touch. How did we get here? 

28 February 2025

[mariacallous]

A lawsuit filed Wednesday against Meta argues that US law requires the company to let people use unofficial add-ons to gain more control over their social feeds.

It’s the latest in a series of disputes in which the company has tussled with researchers and developers over tools that give users extra privacy options or that collect research data. It could clear the way for researchers to release add-ons that aid research into how the algorithms on social platforms affect their users, and it could give people more control over the algorithms that shape their lives.

The suit was filed by the Knight First Amendment Institute at Columbia University on behalf of researcher Ethan Zuckerman, an associate professor at the University of Massachusetts—Amherst. It attempts to take a federal law that has generally shielded social networks and use it as a tool forcing transparency.

Section 230 of the Communications Decency Act is best known for allowing social media companies to evade legal liability for content on their platforms. Zuckerman’s suit argues that one of its subsections gives users the right to control how they access the internet, and the tools they use to do so.

“Section 230 (c) (2) (b) is quite explicit about libraries, parents, and others having the ability to control obscene or other unwanted content on the internet,” says Zuckerman. “I actually think that anticipates having control over a social network like Facebook, having this ability to sort of say, ‘We want to be able to opt out of the algorithm.’”

Zuckerman’s suit is aimed at preventing Facebook from blocking a new browser extension for Facebook that he is working on called Unfollow Everything 2.0. It would allow users to easily “unfollow” friends, groups, and pages on the service, meaning that updates from them no longer appear in the user’s newsfeed.

Zuckerman says that this would provide users the power to tune or effectively disable Facebook’s engagement-driven feed. Users can technically do this without the tool, but only by unfollowing each friend, group, and page individually.

There’s good reason to think Meta might make changes to Facebook to block Zuckerman’s tool after it is released. He says he won’t launch it without a ruling on his suit. In 2020, the company argued that the browser Friendly, which had let users search and reorder their Facebook news feeds as well as block ads and trackers, violated its terms of service and the Computer Fraud and Abuse Act. In 2021, Meta permanently banned Louis Barclay, a British developer who had created a tool called Unfollow Everything, which Zuckerman’s add-on is named after.

“I still remember the feeling of unfollowing everything for the first time. It was near-miraculous. I had lost nothing, since I could still see my favorite friends and groups by going to them directly,” Barclay wrote for Slate at the time. “But I had gained a staggering amount of control. I was no longer tempted to scroll down an infinite feed of content. The time I spent on Facebook decreased dramatically.”

The same year, Meta kicked off from its platform some New York University researchers who had created a tool that monitored the political ads people saw on Facebook. Zuckerman is adding a feature to Unfollow Everything 2.0 that allows people to donate data from their use of the tool to his research project. He hopes to use the data to investigate whether users of his add-on who cleanse their feeds end up, like Barclay, using Facebook less.

Sophia Cope, staff attorney at the Electronic Frontier Foundation, a digital rights group, says that the core parts of Section 230 related to platforms’ liability for content posted by users have been clarified through potentially thousands of cases. But few have specifically dealt with the part of the law Zuckerman’s suit seeks to leverage.

“There isn’t that much case law on that section of the law, so it will be interesting to see how a judge breaks it down,” says Cope. Zuckerman is a member of the EFF’s board of advisers.

John Morris, a principal at the Internet Society, a nonprofit that promotes open development of the internet, says that, to his knowledge, Zuckerman’s strategy “hasn’t been used before, in terms of using Section 230 to grant affirmative rights to users,” noting that a judge would likely take that claim seriously.

Meta has previously suggested that allowing add-ons that modify how people use its services raises security and privacy concerns. But Daphne Keller, director of the Program on Platform Regulation at Stanford's Cyber Policy Center, says that Zuckerman’s tool may be able to fairly push back on such an accusation.“The main problem with tools that give users more control over content moderation on existing platforms often has to do with privacy,” she says. “But if all this does is unfollow specified accounts, I would not expect that problem to arise here."

Even if a tool like Unfollow Everything 2.0 didn’t compromise users’ privacy, Meta might still be able to argue that it violates the company’s terms of service, as it did in Barclay’s case.

“Given Meta’s history, I could see why he would want a preemptive judgment,” says Cope. “He’d be immunized against any civil claim brought against him by Meta.”

And though Zuckerman says he would not be surprised if it takes years for his case to wind its way through the courts, he believes it’s important. “This feels like a particularly compelling case to do at a moment where people are really concerned about the power of algorithms,” he says.

[elbiotipo]

I've said this before but the interesting thing about AI in science fiction is that it was often a theme that humanity would invent "androids", as in human-like robots, but for them to get intelligent and be able to carry conversations with us about deep topics they would need amazing advances that might be impossible. Asimov is the example here though he played a lot with this concept.

We kind of forgot that just ten years ago, inventing an AI that could talk fluently with a human was considered one of those intractable problems that we would take centuries to solve. In a few years not only we got that, but we got AI able to generate code, write human-like speech, and imitate fictional characters. I'm surprised at how banal some people arguing about AI are about this, this is, by all means, an amazing achievement.

Of course these aren't really intelligent, they are just complex algorithms that provide the most likely results to their request based on their training. There also isn't a centralized intelligence thinking this, it's all distributed. There is no real thinking here, of course.

Does this make it less of a powerful tool, though? We have computers that can interpret human language and output things on demand to it. This is, objectively, amazing. The problem is that they are made by a capitalist system and culture that is trying to use them for a pointless economic bubble. The reason why ChatGPT acts like the world's most eager costumer service is because they coded it for that purpose, the reason why most image generators create crap is because they made them for advertising. But those are not the only possibilities for AI, even this model of non-thinking AIs.

The AI bubble will come and pop, it can't sustain itself. The shitty corporate models will never amount to much because they're basically toys. I'm excited for what comes after, when researchers, artists, and others finally get models that aren't corporate shit tailored to be costumer service, but built for other purposes. I'm excited to see what happens when this research starts to create algorithms that might actually be alive in any sense, and maybe the lines might not exist. I'm also worried too.

[collapsedsquid]

I have a personal Gmail account which I use for correspondence about a book project I am working on. I woke up one morning in November to discover that I could no longer access it. A message from Google said my access had been “restricted globally” because “it looks as though Gmail has been used to send unwanted content. Spamming is a violation of Google’s policies.” The note said the decision had been made by “automatic processing” and that if I thought it was a mistake, I could submit an appeal. I had not sent any spam and couldn’t imagine why Google’s algorithm thought that I had. That made it hard to know what to write in the “appeal” text box, other than a panicked version of something like, “I didn’t do it (whatever it is)!” and, “Please help, I really need access to my email and my files”. (To my relief, I realised later that I hadn’t lost access to my drive.) Two days later, I heard back: “After reviewing your appeal, your account’s access remains restricted for this service.” I wasn’t given any more information on what I had supposedly done or why the appeal had been rejected, but was told that “if you disagree with this decision, you can submit another appeal.” I tried again and was rejected again. I did this a few more times — curious, at this point, about how long this doom loop could continue. A glance at Reddit suggested other people had been through similar things. Eventually, I gave up. (Google declined to comment on the record.) Among regulators, one popular answer to the question of how to make automated decisions more “fair” is to insist that people can request a human to review them. But how effective is this remedy? For one thing, humans are prone to “automation complacency” — a tendency to trust the machine too much. In the case of the UK’s Post Office scandal, for example, where sub-postmasters were wrongly accused of theft because of a faulty computer system called Horizon, a judge in 2019 concluded that people at the Post Office displayed “​​a simple institutional obstinacy or refusal to consider any possible alternatives to their view of Horizon”. [...] As for my email account, when I decided to write about my experience for this column, I emailed Google’s press office with the details to see if I could discuss the issue. By the end of the day, my access to my email account had been restored. I was pleased, of course, but I don’t think many people would see that as particularly fair either.

Unfriendly AI

[tangentiallly]

One way to spot patterns is to show AI models millions of labelled examples. This method requires humans to painstakingly label all this data so they can be analysed by computers. Without them, the algorithms that underpin self-driving cars or facial recognition remain blind. They cannot learn patterns.

The algorithms built in this way now augment or stand in for human judgement in areas as varied as medicine, criminal justice, social welfare and mortgage and loan decisions. Generative AI, the latest iteration of AI software, can create words, code and images. This has transformed them into creative assistants, helping teachers, financial advisers, lawyers, artists and programmers to co-create original works.

To build AI, Silicon Valley’s most illustrious companies are fighting over the limited talent of computer scientists in their backyard, paying hundreds of thousands of dollars to a newly minted Ph.D. But to train and deploy them using real-world data, these same companies have turned to the likes of Sama, and their veritable armies of low-wage workers with basic digital literacy, but no stable employment.

Sama isn’t the only service of its kind globally. Start-ups such as Scale AI, Appen, Hive Micro, iMerit and Mighty AI (now owned by Uber), and more traditional IT companies such as Accenture and Wipro are all part of this growing industry estimated to be worth $17bn by 2030.

Because of the sheer volume of data that AI companies need to be labelled, most start-ups outsource their services to lower-income countries where hundreds of workers like Ian and Benja are paid to sift and interpret data that trains AI systems.

Displaced Syrian doctors train medical software that helps diagnose prostate cancer in Britain. Out-of-work college graduates in recession-hit Venezuela categorize fashion products for e-commerce sites. Impoverished women in Kolkata’s Metiabruz, a poor Muslim neighbourhood, have labelled voice clips for Amazon’s Echo speaker. Their work couches a badly kept secret about so-called artificial intelligence systems – that the technology does not ‘learn’ independently, and it needs humans, millions of them, to power it. Data workers are the invaluable human links in the global AI supply chain.

This workforce is largely fragmented, and made up of the most precarious workers in society: disadvantaged youth, women with dependents, minorities, migrants and refugees. The stated goal of AI companies and the outsourcers they work with is to include these communities in the digital revolution, giving them stable and ethical employment despite their precarity. Yet, as I came to discover, data workers are as precarious as factory workers, their labour is largely ghost work and they remain an undervalued bedrock of the AI industry.

As this community emerges from the shadows, journalists and academics are beginning to understand how these globally dispersed workers impact our daily lives: the wildly popular content generated by AI chatbots like ChatGPT, the content we scroll through on TikTok, Instagram and YouTube, the items we browse when shopping online, the vehicles we drive, even the food we eat, it’s all sorted, labelled and categorized with the help of data workers.

Milagros Miceli, an Argentinian researcher based in Berlin, studies the ethnography of data work in the developing world. When she started out, she couldn’t find anything about the lived experience of AI labourers, nothing about who these people actually were and what their work was like. ‘As a sociologist, I felt it was a big gap,’ she says. ‘There are few who are putting a face to those people: who are they and how do they do their jobs, what do their work practices involve? And what are the labour conditions that they are subject to?’

Miceli was right – it was hard to find a company that would allow me access to its data labourers with minimal interference. Secrecy is often written into their contracts in the form of non-disclosure agreements that forbid direct contact with clients and public disclosure of clients’ names. This is usually imposed by clients rather than the outsourcing companies. For instance, Facebook-owner Meta, who is a client of Sama, asks workers to sign a non-disclosure agreement. Often, workers may not even know who their client is, what type of algorithmic system they are working on, or what their counterparts in other parts of the world are paid for the same job.

The arrangements of a company like Sama – low wages, secrecy, extraction of labour from vulnerable communities – is veered towards inequality. After all, this is ultimately affordable labour. Providing employment to minorities and slum youth may be empowering and uplifting to a point, but these workers are also comparatively inexpensive, with almost no relative bargaining power, leverage or resources to rebel.

Even the objective of data-labelling work felt extractive: it trains AI systems, which will eventually replace the very humans doing the training. But of the dozens of workers I spoke to over the course of two years, not one was aware of the implications of training their replacements, that they were being paid to hasten their own obsolescence.

— Madhumita Murgia, Code Dependent: Living in the Shadow of AI

[sirfrogsworth]

Frogman's Camera Buying Guide

A few weeks ago someone asked if I could recommend an interchangeable lens camera (ILC) to supplement their smartphone photos and hopefully get better pictures of important things like vacations and pets.

I decided to go very extra with my response and due to that... I'm still not finished with it.

I'm worried I am letting this person down because they did not ask for a giant post explaining every detail about cameras in the history of forever.

So I am going to do a camera recommendation post without as much explanation and hopefully I can finish the giant post at some point in the near future.

If you want to take better pictures you are probably going to need a camera with a decent sized sensor, a fast lens, a tripod, and a flash.

The bigger sensor gives you more dynamic range so you can capture brighter and darker things in the photo.

A fast lens has a giant hole in the front that lets in a ton of light. That hole is called the aperture and the bigger it is, the better your photos in dark environments will be. So you will want something that does f/1.8 or f/1.4 (lower f-stop number = bigger hole = more light). This can also help you get a lot of cool background blur.

A tripod will help get you longer exposures without any blur from camera shake. Especially good for landscape photos.

And a flash is for taking photos of pets and other moving subjects when you are indoors and don't have a lot of light. A flash is an absolute game changer for indoor photos.

HOWEVER, never point it directly at your subject.

Point it at a large white ceiling or wall. The flash happens so fast that it freezes motion. It is how I got all of my indoor photos of Otis.

Here he was playing and being rambunctious and he is not blurry.

I used no special settings. I just stuck on a flash and pointed it at the ceiling and suddenly sheep are sticking to things.

Oh, and one other huge benefit of using a flash... you can take much better photos of pets with dark fur. So if you have a cute little void in your home, a flash can help you capture detail in their fur.

Just lift the shadow slider in your image editor and that beautiful fur will reveal itself.

If you get an ETTL or TTL flash, it will output the correct amount of light automatically. You can literally just put your camera in automatic mode, aim the flash at the ceiling, and press the shutter button.

Before I talk about recommendations I want to make one thing very clear.

GETTING A GIANT CAMERA WILL NOT AUTOMATICALLY GIVE YOU BETTER PHOTOS.

Aside from my flash aimed at the ceiling trick, a big boy camera is not a magic solution for better photos. In some cases, you might actually get *worse* photos than your smartphone. You need to learn the basic fundamentals of photography and you also need to learn some basic photo editing skills.

Smartphones employ powerful algorithms and computational processes to make every photo you take look as good as possible.

ILCs say, "Here is your RAW data, you figure out the rest."

You don't have to become an expert, but if you watch this free 6 hour photography course, that will ensure you have the knowledge needed to improve your photos.

Okay, let's get into the nitty gritty of buying a nice new old ILC.

If you are on a tight budget and cannot afford a fancy mirrorless camera, I would highly suggest a used DSLR. You can get them for very reasonable prices. And unlike just about every other modern technological gadget, cameras and lenses are built to last for decades. So I have no qualms about recommending used photography gear.

However, I do highly recommend using either KEH or MPB, as they have a long trial period and decent customer service. If something goes awry with your used gear, KEH has a 180 day warranty and MPB has a 6 month warranty. So there is much less of a risk than eBay or Facebook Marketplace. You pay a bit of overhead, but the piece of mind is worth it.

Before I start my recommendations I want to quickly explain the difference between APS-C and Full Frame camera bodies. (For brevity's sake I am going to omit Micro Four Thirds bodies as they are not typically geared toward beginner photography.)

APS-C has a "crop" sensor. It is a bit smaller than full frame and does not perform as well in low light (more noise). However these bodies are cheaper and can still produce great photos. You can see above the sensor is still significantly larger than a smartphone. APS-C adds a 1.5x zoom to all lenses. This can be annoying in small spaces but advantageous for outdoor photography like wildlife and sports. You can use full frame lenses on a crop sensor body (within the same brand). APS-C lenses are usually cheaper but of lower quality.

Full frame has a larger sensor that will give you less noise in low light. It is also much easier to get background blur. Full frame also allows you to work in more cramped spaces. You *cannot* use APS-C lenses on a full frame body. However, the lenses meant for full frame cameras tend to be better quality in general.

If you can save up a little more and get a full frame body, I would recommend it. These bodies used to be geared more toward professional use, but since mirrorless cameras became popular, used full frame DSLRs have become much more accessible to those on a budget. Full frame cameras make it easier to get better results in challenging circumstances. And challenging conditions are really the main area where ILCs still kick a smartphone's ass.

For tight budgets I would recommend the following...

Canon or Nikon APS-C DSLR camera body

50mm f/1.8 lens (Nifty Fifty)

18-55mm APS-C lens (good for landscapes and portraits)

Yongnuo ETTL Flash

There are lenses called "superzooms" which can go from (as an example) 18-200mm or 70-300mm and other crazy focal lengths. That sounds fantastic and very versatile... but these are usually utter shite. You may be tempted to get one of these lenses hoping it can do everything you need, but there are no free lunches in lens land. Unless you are spending many thousands of dollars, the wider the focal range, the worse the lens will be.

When you stick to the 18-55mm range, you can be assured the images will be decent. And if you find yourself really needing a telephoto lens, you can save up and add it to your collection later on. The 18-55 will give you wide angle for landscapes all the way to slightly telephoto for portraits and moderately close wildlife. This lens cannot be used indoors or at night without a flash. Which is why I recommend the Nifty Fifty for that purpose. $100 for a moderately sharp low light lens is a no brainer.

Also, stick to Canon, Nikon, Sigma, or Tamron lenses. You can try exotic 3rd party lens brands when you know more what you are doing. And always make sure the lens has autofocus before buying.

It's hard to give you exact recommendations as used items are not reliably in stock. So I'm going to show you an example of the above, but I am not necessarily saying you should buy this *exact* combination. You might be able to get something similar with Nikon as well.

Canon 60D APS-C DSLR

50mm f/1.8 lens

Canon 18-55mm APS-C lens (EF-S mount)

Yongnuo TTL Flash

(I wouldn't recommend getting a used flash, as the Yongnuo is already a great price and you can't know if someone used the flash 100,000 times or 20 times.)

Altogether that is about $500. You can start with the 60D and the 50mm Nifty Fifty for $330 and add on the other two items later on.

My recommended full frame setup...

Full frame Canon or Nikon DSLR body

50mm f/1.8 lens (same as before)

24-70mm full frame zoom lens (full frame equivalent to 18-55mm)

ETTL Yongnuo flash (same as before)

And an example from KEH might be...

Canon 6D Full Frame DSLR

Canon 50mm f/1.8 Lens

Sigma 24-70mm Full Frame Zoom lens (EF mount)

Yonguo ETTL Flash

And that would be about $800 total.

Again, you can start with just the camera and 50mm lens and add the other items later. So invest $500 initially and go from there.

And just to give a Nikon example as well...

Nikon D600 Full Frame DSLR

Nikon 50mm f/1.8 Lens

Tamron 24-70mm

Yonguo ETTL Flash (Nikon version)

I highly recommend researching any camera body and lens before purchase. I can vouch for the items above, but you should definitely check out some YouTube videos before buying.

All of the stuff on KEH and MBP is marked down in price for aesthetic reasons. They do test everything to make sure it is functional. If you care if the camera or lens looks pristine, it will cost a little extra. But if you don't mind if it is beat to hell, you can save some money. Ugly or not, you will get the same photos out of the gear. As I said, photography stuff is built to last for a long time. Almost all repairs are due to user damage and not defects. And usually defects manifest when the product is brand new.

Oh, I forgot about the tripod!

Amazon's $35 tripod is surprisingly decent. It even got a good review on a very picky tripod review site. I recommend starting with this and then upgrading when you know more what you need out of a tripod.

Amazon 60 inch Tripod

I worry I'm leaving out a lot of important information, but hopefully I can expand in the other post I am working on.

That said, if anyone is thinking of buying a camera and you are not sure about the items you selected, please feel free to message me and I will help you assess your choices. Please make sure you include a budget range when asking for buying advice.

I hope that helps. I will try to finish the more in depth post soon. And it will include tips for how to get better photos from your smartphone if you cannot afford an ILC at the moment.

Further resources...

Recipe for Landscape Photos Froggie's Encyclopedia of Lens Terms

[shamrockqueen]

Omega retreat : Chapter 2

Pairing: Alpha Bucky × Omega Reader

Warnings: R18, Eventual Smut, Not what it seems, talk of medical issues/illness, dating site, ABO dynamics

Word count: 2477

Chapter 1

Bucky masterlist

Summary: As an unmarked and lonely omega you find a flyer for a service called The Omega Retreat.

You are paired with a compatible alpha to spend your heat or just a week at a luxurious cabin at a forest resort. Amenities and Utilities included. Enjoy the beautiful scenery, fresh air, as well as the company of an alpha of your choosing. What could possibly go wrong?

The blue screen of your laptop lit up the dark and gloomy room as you booted it up and set your sights on the internet explorer icon.

Your eyes shift back and forth from the slightly crunched flier to the keyboard as you type up the website in the search bar.

Upon clicking enter, there is a cascade of red and pink hearts across the screen before the main page comes into view.

From the photos, it seems kind of like a glamping thing, with each couple or pairing having their own semi-remote cabin.

Singles retreats weren’t a new concept—not that you’ve ever been to one, but this would be a whole week alone with a stranger, a man, an alpha.

That familiar twang of anxiety twisted in your chest at the thought, only for it to be snuffed out by another.

‘We all have to grow up at some point’.

You eyed the two packages listed on the screen, one labeled as Silver and the other Gold. The silver package went by, Forget me knot.” and you felt yourself chuckle a little at how cheesy it sounded. It was a 4-day stay at one of the cabins with an alpha provided by the website's dating algorithm.

The Gold package had another cheesy line listed as “Heat of the Night." It listed a full-week stay for the duration of the omega’s heat with your new Alpha.

The prospect was, of course, very tantalizing, but it still didn’t fail to make you nervous. You had never spent a heat with someone before, and it seemed a little scary. Was a week with a stranger worth seeing what you were missing out on?

You clicked the icon for the Gold package without thinking further, blinking at the screen as it shifted to the sign-up page. You’d only wanted further info but it looked like only members could access it. It was, however, free to sign up, a claim made by many websites and apps before it. Yet, even at the free level, it seemed you could at least get to look at the Alpha bachelors they had in their database. Just another step to pull you in closer to spending the big bucks.

It asked for a photo at first, making you hesitate before finally deciding on one simple photo of yourself. It had been your birthday, and your mother was by your side, hugging your shoulder. You had to crop out most of your mom, but your big smile still beamed just as brightly across the screen. You typed in a shortened version of your name for your little profile, along with your age, before clicking the next button.

The page flipped to a quick questionnaire, asking about your likes and dislikes—everything from your bedtime routine to your bedroom habits. It barely toed the line of TMI, but you supposed it had to be thorough to find you a match. You clicked through each question, making sure every answer felt right. Before you could tell, it had been half an hour and you were only almost finished. You snuggled yourself into your plush couch as you finally clicked the submit button.

A little spinning heart pops up on the scream alongside ‘finding your perfect match’ underneath it. The heart spun around on the screen until the loading bar hit 100 and the page shifted over to show your results.

Your eyes widen at the selection of handsome men flooding the screen. There are more Alphas flashing over your computer than you’ve ever seen in one small space, and already there are too many to choose from.

Part of you figured that to a seasoned romantic, it would seem like small potatoes, but to you, it was more men than you knew what to do with. The only distraction that could tear your eyes away was a heart-shaped character at the corner of the screen babbling away in a little text box. His happy little demeanor reminds you of a certain talking paperclip from old office software. Only you found this little guy less irritating.

‘We have selected 20 of your most suitable partners. Please choose from the profiles below to chat and find your match.’

You clicked the speech bubble away, only for another to pop up.

‘Don’t forget to check out our selection of getaways for your official meetup’ popped up across the page.

You clicked again, and another bubble came after.

‘If for any reason you are unsatisfied with your matches, please take the quiz again.’

You take the little heart man’s words into consideration before clicking back towards the alpha profiles.

The first was a rough-looking man named Brock. Too macho for your type, and you shied away from his profile immediately.

The next one was a sweet, gentle-looking man named Steve. He seemed really interested in a lifetime mate, but as romantic as it seemed, you just weren’t too sure that was what you wanted just yet.

It was a little overwhelming. All these men were stunning, and yet the scared little omega inside of you kept turning tail at the gleam of each of their smiles, leading you to click at the next button again and again.

You’d gone through 12 profiles until you stopped on his picture. His brown hair sat at the base of his neck, looking soft and supple enough to tangle your fingers through, and his smile was immediately infectious.

The name James ‘Bucky’ Barnes sat below the photo in bold, but you barely noticed as your gaze locked on his light, smiling blue eyes.

You feel both your heart and your core flutter, leading to a wave of warmth and a bit of unearned embarrassment. You didn’t think any further before clicking his profile, showing you more about this ‘Bucky’.

It gave a broad list of hobbies, his likes and dislikes, as well as so many more dreamy photos.

His profiles stated he was interested in a mate but “wanted to test the waters first." Not interested in being too serious, but not scared of a commitment.

Even though this man seemed like an absolute dream, you couldn’t help but second-guess yourself. Yet, the butterflies in your stomach overpowered the worries in the back of your mind. You let your cursor hover over the match button on his profile before slowly clicking down on the mouse and watching with bated breath as the screen changed again.

That little heart man, now less animated, was the last sight you saw after you clicked. He was accompanied by a few speech bubbles saying, “The alpha you have chosen will be notified; please feel free to browse our events as you wait.”

The word ‘events’ was lit up in another color separate from the text and clearly a link to the rest of the website. At the end of the day, they WERE trying to sell you something, but curiosity got the better of you, and you clicked the link without another thought.

You looked over the two packages they offered and let your cursor hover over the gold package. You stared at its short description, comparing it with the smaller vacation bundle that sat beside it on the screen. You think it over and cautiously click on the icon.

The prices were the first thing that struck you, as none of them were very expensive for what they were advertising. Saving a few bucks always seemed to sweeten the deal, but it really made it all seem too good to be true.

The resort has a full staff available in case of an emergency and are simply a call away. All meals would come in the form of meal kits or ready-made gourmet dinners, as well as a selection of wine and spirits for those 21 and over.

There was a little policy note at the bottom, in smaller letters.

“All reservations are refundable upon cancellation 7 days before the date of the reservation. If you cancel your stay after 7 days, you will be charged a cancellation fee. In the event that your desired partner declines your match, you will be prompted to choose another alpha from the list given to you.”

The idea of being rejected by a stranger online made some of the appeal wear thin. You x-ed out of the pop-up, only to notice a notification lighting up your screen.

He had matched with you immediately, causing another flutter of hearts to pulse over the computer for one moment. On the little message icon sat the number one to indicate somebody had reached out to you, and you clicked on it right away.

The chat room opens up on your screen to show a little chat box bubble saying, “Hi beautiful ;)". The old-style winky face gave his age away and made some of the insecurities in your belly melt.

This 'James' had matched you so soon, and to have him reach out to you on your screen still made you nervous.

The bouncing dots popped up below the first message to indicate he was still typing. You're frozen on the spot as the messages just keep popping up.

“Hello?”

It seemed a bit impatient, but you didn’t think to care; you were too thrilled by this new encounter.

“Hi, sorry, I was..” Oh god, what could you say? “…away from the phone.” Not true, but telling your possible new beau that you were frozen with fear upon seeing his message seemed, well, lame.

“That’s ok.”

“You new here? I haven’t seen your profile before.”

“Yeah. I just signed up.”

“Does that mean I was your first choice? ;)”

You felt you should be honest after your previous fib, and answered immediately.

“ I just saw your profile and clicked it right away. I didn’t expect you to get back to me so soon.”

“Leave a beautiful Omega like you waiting? Not a chance, doll.”

Every word made the air grow thinner, making your head just swim in the rising heat that started to subtly overtake your body. It was such a new feeling to have warmth in your body feel so good.

Those three dots danced across his next speech bubble, and you waited every second for his next word.

“Have you ever been with an alpha before? I’d hate to come on too strong and scare you away.”

Your breath felt shallow before you answered truthfully. “No, I haven’t.”

There have only ever been two people you’ve given yourself to like that. Two particularly nice betas who just couldn’t help you as you needed, but tried anyway. Being with an Alpha seemed like so much more of a big deal, but the idea of a big, horny monster sinking their teeth into your flesh makes you start to hyperventilate. It was permanent, and you didn’t want to just throw away your forever to someone who could be cruel to you.

But something about this felt different. He looked so soft and kind, you could nearly feel his finger gently caressing your cheek as each word popped up on your screen. Something about this encounter felt safe.

You typed without thinking, letting the question fill the screen as anxiety ate away at the warmth that once sat in your belly. “Does that bother you?”

You waited for a response, watching those little dots until they disappeared without a new message. A solid minute felt like an eternity, and your heart sank further as each one ticked by.

You typed out a quick “I’m sorry," hoping you weren’t the one scaring him off instead with your lack of experience.

You breathed a sigh of relief as his response popped up. “Do not be sorry. There is no problem with wanting to wait.” Followed by another “I feel like a lucky guy.”

“I guess I’m just a little embarrassed; I’m glad it doesn’t bother you.” You typed away, fully engrossed in his attention.

“Don’t be; that kind of thing means more than you’d think in this day in age.”

It popped across your screen, giving you much-needed relief, only for the next message to set your nerves ablaze all over again.

“What made you decide to join the site?”

It popped over your screen faster than you could shoo it away. The reason for you was obvious after dragging yourself through that doctor's office. You needed help, and somehow that simple red flier had shown out to you like a beacon on a stormy shore.

You wanted to be honest, but some things felt better kept close to yourself than within the reach of others. You answered with the shallow truth.

“Dating can be difficult. I found the advertisement today and decided to check things out.” You tapped the enter button and sent the message, but your fingers continued to type. Maybe it was an attempt to keep his questions from probing into your answer even further, as you sent him an inquiry of your own.

“What about you? What made you decide to join the website?”

The laptop sat silently, aside from the whirring of its little fan. No bouncing dots, no indication of his response. Maybe his reasons were somehow more personal than your own.

You began to lose a little faith as the chat room continued to sit empty until his chat bubble finally popped up. Each second it took for the words to show was a second too long.

“I’d say it’s about the same. I guess I just wanted to try something different.”

“And how’s it working out so far?”

“I’d say, far better since you popped up.”

It was such a cliche line, but you loved it. You even laughed a little as you typed back.

“That fast, huh? It’s been less than a day "

“But you’ve already made my whole week.”

It brought an immediate smile to your rosy face. It was so fun—almost a fantasy. No danger, no recourse, no fear. You looked back at his little picture on the screen, his smiling face; it was a far cry from any other alpha already, and you hadn’t even seen him in the very flesh.

But it had been less than a day, and it was an obvious blow to this little oasis that had built around you in the matter of minutes. You didn’t want this moment to end, not when reality was waiting for you afterward.

The hours passed as you did each playful word with this ‘James’.

“I can’t wait to meet you, Omega.”

Your heart fluttered to an unnatural rhythm the moment it popped onto your screen.

"Omego,” you repeated his use of your denomination.

For a whole week, you could be the omega to his Alpha. You thought about the glamorous getaways your matchmaker had advertised. So you thought that, just maybe, that could be you.

Chapter 3

Tag List : @bethyruth-deactivated20231124 @scott-loki-barnes @wintrsoldrluvr

[jcmarchi]

Storage Predictions for 2025: Larry O’Connor Explores the Future of Data Management

New Post has been published on https://thedigitalinsider.com/storage-predictions-for-2025-larry-oconnor-explores-the-future-of-data-management/

Storage Predictions for 2025: Larry O’Connor Explores the Future of Data Management

In this article by Larry O’Connor for LarryJordan.com, the founder and CEO of OWC shares his insightful predictions for the future of data storage in 2025. Highlighting the return to on-premises solutions, the democratization of AI through local implementation, and the growing performance gap between local and cloud-based systems, O’Connor provides a roadmap for businesses to optimize their storage strategies. These predictions focus on enhancing security, improving performance, and reducing costs—key considerations for data management in the digital era.

The Return of On-Premises Data Storage and Computing

O’Connor predicts a major resurgence in on-premises data storage and computing as organizations prioritize security, performance, and cost control. With cloud storage costs on the rise and vulnerabilities becoming more apparent, many businesses are turning to local solutions for critical data management. On-prem data storage offers faster access speeds, greater reliability, and enhanced protection against cyberattacks.

Smaller businesses, in particular, benefit from local storage since it is less attractive to cybercriminals compared to public cloud platforms. O’Connor emphasizes that the cloud should play a supporting role—serving as a tertiary backup or for external data sharing—while local solutions remain the primary strategy for data security. By reducing reliance on cloud storage, businesses can avoid bandwidth costs, accelerate data recovery, and maintain control over sensitive information.

On-Premises AI: Democratizing Technology and Protecting Intellectual Property

One of the most exciting predictions from O’Connor is the rise of on-premises AI. This shift enables smaller businesses to access powerful data processing tools without the high costs associated with cloud-based AI platforms. Running AI systems locally not only democratizes advanced technology but also protects proprietary data, algorithms, and customer insights from being exposed in the cloud.

O’Connor warns of the risks of “data bleed,” where proprietary information inadvertently enhances third-party systems or becomes accessible to competitors. On-prem AI ensures full control over sensitive data while eliminating recurring costs associated with cloud AI services. This makes on-premises AI a cost-effective and secure option for businesses looking to leverage AI capabilities.

The Expanding Performance Gap Between Local and Cloud-Based Systems

O’Connor’s third prediction underscores the growing performance gap between locally operated systems and cloud-dependent solutions. With advancements like Thunderbolt 5 technology, local systems now achieve data transfer speeds of up to 7000MB/s, far surpassing the 100MB/s typical of cloud-based systems.

This dramatic performance boost is essential for tasks such as video editing, high-resolution imagery processing, and managing complex data sets. By leveraging local systems, businesses can streamline workflows and significantly reduce the time and cost associated with cloud dependency. O’Connor suggests that cloud storage is best reserved for distribution and collaboration, while local solutions should handle performance-intensive tasks.

Conclusion: Embracing a Hybrid Storage Strategy

Larry O’Connor’s predictions for 2025 highlight a pivotal shift in data management strategies. Businesses are advised to embrace on-premises solutions for their speed, security, and cost advantages while using cloud services selectively for supplementary needs. Whether it’s safeguarding intellectual property, optimizing performance, or reducing expenses, on-premises storage and computing are poised to take center stage in the future of data management.

For organizations looking to stay ahead of the curve, O’Connor’s insights provide a clear path to success. By adopting a hybrid approach to data storage, businesses can harness the best of both local and cloud technologies, ensuring they remain competitive in an evolving digital landscape.

Read the full article by Larry O’Connor HERE

Learn more about OWC below:

[emergency-plan]

DPxDC Idea

I had a little idea an have no time to actually write a fic, so I just wrote a sorta-summary and am posting it like this.

This is inspired by the game Home Safety Hotline and may contain hints to spoilers for that game. It's really clever, I really like it. I recommend you play it if slightly spooky without any "real" horror appeals to you.

Alright, Danny's been Ghost King for a few years and has realized more than just his usual rogues make their way to the living world, and a lot of those ghosts don't stay in Amity. By himself, it'd take forever to track down all those spirits and specters that are out causing mischief. Luckily, not many that escaped his notice are all that powerful and could only cause minor disturbances, just enough to get noticed by the living.

Many people outside Amity don't even recognize the activity as ghosts, so they blame other sources. Scratching in the walls is mistaken as mice, whispers and apparitions are mistaken as hallucinations and carbon monoxide hallucinations, attempted overshadowings mistaken as stokes or migraines. In this day and age, where does everyone turn to when looking for advice or how to solve problems? The internet.

Team Phantom devise a method to try and track down ghosts that are stuck or tormenting the living by building a website meant to look like a help hotline, and with some algorithm trickery make it one of the top options when searching for signs similar to ghost presences. Add some bits and bobs to make it appear as a more normal-looking website on any computer affiliated with government organizations, and you’ve got some protection from the GIW.

Calls start slowly, so the three of them can handle it by themselves. Once more people are calling, they decide to start a call center. They hired some trusted people around Amity and even a few ghosts who want to help. To get around worrying about the ghosts messing with the tech while personally taking a call, they decide to automate the system to record caller’s reports for the employees to listen to, and then send a report back, offering their services to bring the spirit back to the Realms.

It’s been surprisingly lucrative, and Danny hasn’t had to dip into his kingly funds much other than at the start. He still keeps prices low, just enough to not garner suspicions at offering a free service while paying his workers fairly (he doesn’t want to know why some of the ghosts want mortal money). What he’s started having more trouble with is not enough employees to take the calls. Sometimes ghosts lose track of time and don’t show up for their shifts (he doesn’t blame them, time gets weird in the Ghost Zone), and he’s run out of people he trusts who want the job.

Eventually he decides to put out an ad, deciding he’ll slowly trust whoever takes the job with a little more information over time, see how they react, and measure to see if they’re trustworthy.

What he doesn’t think about is how posting it on the website will let more people than just those that live in Amity apply.

Meanwhile, in Gotham, one Cassandra Cain is looking for a job. She doesn’t need the money, B gives her access to way too much, but she wants the experience. She’s at the age she’s heard most kids get a job, and she wants to see what it’s like.

And she quickly found out retail and fast food are NOT for her. She doesn’t think those conditions are fit for anyone, honestly. She’d have to see if she could get Bruce to work on that. But that still leaves her out of a job. She got overwhelmed with a lot of people, so virtual options would probably be best, and something that let her interact with people without having to speak. There weren’t a lot of options out there, and she wasn’t skilled enough with a computer yet to take programming ones.

That’s when she found the listing for the hotline call center. Based in a small Illinois town, but had virtual options, listen to recorded customer calls, diagnose their issue, and send an information packet on potential next steps. It was indirect, could also help her practice her reading, and flexible. It was perfect.

It didn’t take long to hear back after she applied (Danny was freaking out, he didn’t think anyone outside Amity would apply. He’d turn this kid down, but she’d mentioned her difficulties with speaking in her application and SWEETY YOU DONT MENTION STUFF LIKE THAT ON AN APPLICATION. But she said the job would be perfect for her and he just couldn’t…) and she got the job!

Her first day rolls around and she’s given access to the database. A lot has been redacted, but she has descriptions for common problems like mice, carbon monoxide, black mold, etc. she gets her first call recording and carefully reads through the entries before selecting the one that sounds right. She sends it off and waits for the next. The calls come a little too regularly, with too similar intervals between them, so she figures her new employer is testing how well she’s doing (Danny’s giving her previous resolved calls that weren’t anything supernatural. She even got the ants right! He had even gotten that wrong!)

Eventually, her shift ends and she tells her family how well her first day went at dinner. They congratulate her and go on patrol as usual. The next day, things ramp up a little.

She logs into the database at the beginning of her shift and noticed some new entries. She now had access to descriptions of shades, blob ghosts, will o’ wisps, and more minor spirits. She gets a recording reminding her all this info is confidential and that she’s not allowed to share it with anyone. She’s a little confused, but she reads through each just as carefully. The calls come less regularly, so she figures she’s actually connected to the system now (Danny gave her access to the most common ghosts they get calls about and is listening in while he’s handling ghosts to make sure she doesn’t get anything she’s not prepared for).

Her shift ends and over dinner, she mentions that she’s had to diagnose some odd things. They assure her there’s more pests and hazards out there than you’d expect. She doesn’t tell her family about the distraught woman haunted by the Ecto-Echo of her husband’s habit of making her coffee every morning after he passed a few weeks ago. Or the person who had a Shade masquerading as their shadow. Just about one of her caller's cockroach problem.

The next day follows a similar pattern; more entries, slightly more powerful ghosts, reminder that the info she's been given access to is confidential and could get people hurt if it got in the wrong hands, congratulated for her good work, read through carefully and learn signs of each, diagnose calls, before calling it a day (Danny was so proud of her, she'd only confused a blob ghost with a ghost animal once, and it hadn't caused him any trouble when he went to collect them).

She'd used the bat-computer to check up on some of the callers she'd diagnosed, and they seemed to be doing fine. Some had posted about their weird experiences on their social media and how her employer had somehow helped them, but often didn't quite know how (Danny liked to hide his powers, so most of what customers saw was him using ghost tech. When it couldn't be solved with just a quick souping, he had to pull a little ghostly trickery while the customer wasn't watching). She didn't know how her boss was somehow across the world multiple times a day to help clients in different countries, but he seemed to at least be helping people. She started not having any stories she could tell her family at dinner.

At some point, she heard reports that one of the speedsters probably messed with time travel again before clocking into her shift. She had almost all the available entries and had gotten very good at recognizing tricky cases. She answered a recorded call, just like at the beginning of each of her shifts, but this one was a little different. Danny had sent out an announcement to be on the lookout for a specific phenomena that often occurred after shifts in reality, as they were highly dangerous and needed to be dealt with swiftly.

She studied each entry and paused on what she was supposed to keep a careful eye out for. Revenants, corpses that came back to life, often seen shambling around the graveyards they were buried in. Something about that sounded familiar. A section in their entry said the person brought back often had a ghost in the Realms (which she still didn't know what that was) that was in terrible pain from shifts in reality trying to pull them back to their body, but the separation of dimensions preventing them.

Expectedly, she did get a call from someone convinced there was a zombie wandering somewhere along the east coast. She double checked it couldn't be anything else before submitting it and notifying her boss.

Curious, and she knew no one would be in the batcave around this time of day, she brought her laptop with her down to the bat-computer. She found cameras in the area the caller reported, and froze at what she saw. Shambling across an abandoned street was a rotting corpse. It really did look like a zombie. It was covered in dirt, wearing an old-fashioned suit, and had skin sloughing off its bones.

But what Cass could only focus on was how much its movements read that it was in pain. It was suffering in such a horrible way its mindless being didn't even deserve. It was horrible.

Then, there was a flash of green and an area of the cameras were covered in static. The glitched portion somehow read with kindness and pity. It slowly approached the corpse, simple reaching out gently (what was presumably a hand), ignoring the way it lashed out. It suddenly fell, caught and slowly lower to the ground by the strange being she couldn't see. It closed the thing's eyes before carrying it off in the direction the map said a graveyard could be found.

After that, she finished her shift and went to dinner. Her family asked if she was alright, and she only replied it'd been a long day.

She clocked in early the next day and messaged her boss for more information on Revenants. Dinner that night was one of the few times Jason agreed to come by, and if he noticed how she kept glancing at him, he didn't say anything.

A week later, she asked her boss what might happen if a Revenant was exposed to, as it was called in its entry, a "Corrupted Ecto-Spring" ("...an ugly hole in the fabric of reality that connects the world of the living to the Realms. The ectoplasm that leaks through the tear stagnates and festers into toxic pools that kills humans and makes ghosts sick."). Danny requested a video call.

[markcraft3636]

ServiceNow | What is Update Sets | Compare , Revert and Merge Update Sets | Complete Course

ServiceNow is planned with intelligent systems to speed up the work process by providing solutions to amorphous work patterns. Each employee, customer, and machine in the enterprise is related to ServiceNow, allowing us to make requests on a single cloud platform. Various divisions working with the requests can assign, prioritize, correlate, get down to root cause issues, gain real‑time insights, and drive action. This workflow process helps the employees to work better, and this would eventually improve the service levels. ServiceNow provides cloud services for the entire enterprise.