#Render presentation with big data

[blubberquark]

I can't tell you which laptop to buy

Over the years, I have been asked "Which laptop should I buy?" many, many times. We have now arrived at a point where I can no longer meaningfully answer this question.

In the past, I would have said something like "Get a laptop with enough RAM, and make sure the keyboard, trackpad, and display all work for you. You can't swap out the keyboard, mouse and display like you can in a desktop."

In the past, I would have said something like "Are you just using it for e-mail, Facebook, and Netflix? Are you doing a lot of office productivity stuff? Will you be giving slide presentations? Do you also want to do some light gaming? Do you need an SD card slot for your digital camera? Are you doing video editing/CAD modelling/cinematic 3D rendering?"

It really depends on what kind of laptop you want. You could want a desktop replacement, because you don't have the space for PC. You could want a "luggable" machine that you take from your office to the meeting room or the lecture hall, and back and occasionally to a different location in the trunk of your car. You could want a portable laptop that you keep in a laptop bag, backpack, or briefcase, and sometimes use on your lap or in a café if you are one of these people. You could want a "light" laptop that you carry with you at all times.

I still couldn't tell you which laptop to buy. So many people these days use their phone or a tablet for e-mail, facebook, and netflix that it doesn't really make sense to recommend a small and light laptop. You'd want to be a step above that. So many people use the "cloud" for all their documents and data, so it doesn't make sense for them to think about having a large HDD. If you own a gaming a console and a desktop PC, and you write your e-mails on a tablet, it doesn't really make sense to have a cheap laptop, but it also doesn't make sense to have a big laptop, unless you change your workflow.

Some people still need a CD drive, a large hard disk, or VGA-out, but usually there's a USB-C dock for that use case.

If you never use your laptop anyway, you may as well give Ubuntu a chance. It even runs Steam!

[microposting]

Loyal

macro/micro, plot with porn, m/m, m/f

His shit job has given Kim one good thing─his beloved rescue tiny, October, who adores him like a god. But will their relationship survive when the tables are turned?

Chapter 1

The little package dropped onto his desk with a thunk and Kim shot up, bleary-eyed, gulping down saliva. He turned to the presence blocking out his decayed yellow ceiling light. Bob smiled back down at him, laugh lines rendered starkly in the harsh lighting, eyes oddly inexpressive. Kim blinked and grunted out a little animal mbuh . “Jesus,” he slurred. “M’so sorry, there-there was a cop car out my window all night, lights going the whole time, and─”

Bob laughed. “Don’t worry, I get it, you’re on your break. Just passing out some treats for end of semester. Go dogs!” 

He sauntered off. Kim glanced over to that package. Paper-wrapped, crumpled, boxy. He’d dropped one on Whitney’s desk too. Oh, yeah, all four of them got one. Kim unwrapped it and found a gorgeous little brownie, top flaking off, soft middle. Wow. Delicious. That was… really nice of him.

When Whitney returned to her seat, she was about as surprised. “Does he bake? Were these─ is he married, or something?”

“Don’t think so.” Kim smiled. “You think he’d be as weird with Ava if he was?”

She made a face. “Well. It’s a good brownie, and I guess it’s good that he didn’t scream at you for sleeping… but I still don’t have to like him.”

Kim laughed. They smiled at each other, for a moment.

“Well,” Kim said. “I should do my rounds.”

Ava was prepping syringes on the far end of the lab and gave him a toothy smiled and a wave when he entered. Kim returned a nod. She was… nice, he just did not have the energy to match her today. Jesus. 

The cages were stacked on the wall closest to the entrance. A foot across each and a little less shadow, but they still seemed plenty roomy for their occupants, as much as they loved complaining. Kim grabbed the two slates of syringes from the mini-fridge on the counter top. SAFETY TRIAL DITAX ONE on the side of one container, TWO on the other. 1.5μL per. Right. Perfect. He logged into the lab desktop and opened the data spreadsheet in one tab, the cage reference map on the other. He had it memorized, of course, but if Bob saw that he didn’t have it open, he’d have his head.

Kim tapped the PIN into cage A1 and its four inhabitants watched, bored, passive, as his hand reached in and grabbed the closest one. ��Anything new?” he asked.

“No,” the tiny man droned. 41744 on his ankle tag. Kim wasn’t supposed to know this, but the other tinies called him Bingo. 

“Alright.” He typed one-handed. Kim picked up syringe A1-1, flipped Bingo around, tugged down his pants, and stuck him in the ass. 

It just went on. Day after day, jab after jab, depressive “no” after “no”. Maybe the occasional excitement of someone developing a stomachache. It got to him, honestly. The way the new ones squealed and writhed but the old ones barely tensed. Just laid on their bellies in his palm, eyes down, shoulders slumped. 

After a cage was finished, they did get their lunches. He could feel good about that. Even if it usually was some sort of slurry.

Then back to his desk to run data, then errands for Bob, then doing laundry for the tinies, and finally he was shoving his empty tupperware into his eight-year-old backpack and heading for the bus stop.

He’d developed a habit of beelining to his IKEA dresser the moment he got home. Inside the cage, little October sat on his knees in front of the glass, smiling up with those big yes-master, I-love-you-master eyes. He had a decade on his owner, but Kim could still only seem to think of him as cute. Obedient and sweet, easily excited, and overall just so… soft. Soft face, soft body, wavy dirty-blond hair that was maddeningly soft, like mouse fur. Kim unhooked the hatch on the cage’s top and let October hoist himself into the presented hand. He settled onto his bed, and October got comfortable on top of his pillow.

Kim cupped his fingers around his tiny back and stroked a thumb along the side of his face. “Hey, sweetheart,” he cooed. “Did you have a good day today?”

October nodded eagerly. “I finished Pokemon Platinum, sir.”

He grinned. “Really! How’d you like it? Do you want another game?”

“It-it was good, sir. I’d like to do some more reading, though. I─” October fell silent. His jaw went lax, his eyes unfocused, his shoulders slumped. Kim grimaced and dug the notebook out from under his bed.

October came to, saw him scribbling something down, and put his face in his hands. “No, no, no,” he grumbled. “That’s the third this week.”

“Calm down. You’re still trending down.”

He nodded. “I’m sorry, sir. But, I-I think I had more seizures today, while you were at work. When I was playing the game, it felt like I kept missing─”

“October.”

“Sorry, master.” He looked down.

“We can only track what we’re sure of.”

“Yes, master. Thank you for helping.”

In some attempt to salvage his mood, Kim retrieved October’s DS from the cage and talked through his defeat of the Elite Four, his final team, all his inspirations for their nicknames. Playing was a bit of a workout for October, he noted with amusement. He had to move back and forth between the two sides of the console and even the button presses took some effort. It was probably good for him, since he was cooped up in his cage so often. That was a bummer, sure, but it wasn’t like Kim could just give him free range when he was out. 

It was six before he knew it. He was starting to feel groggier─must be getting hungry. “You want anything special for dinner?”

“No…” October mumbled back. He was engrossed in the game again. Disrespectful of him, maybe, but Kim could find it in himself to forgive the little guy just this once. 

“Alright. Gonna pop you in your cage again. I’ll be right back.”

He let him re-situate with his DS and then latched the top shut. 

Instant mashed potatoes were an option. He thought he had some frozen broccoli and ground beef to go with that. Could make a sandwich, but October got pretty disappointed when Kim made something like that while he got scraps, even if he tried not to show it… but he still loved whatever he got. Probably anything was an upgrade from the lab slop, or sharing quarters with three strangers, or getting a daily ass-jab or being forced to swallow a massive pill, or developing absence seizures from one of those pills… Okay, well, they still didn’t know if that was from the BEV trial. But still. What else could it have been?

Kim grabbed the mashed potato box from his cabinet. He turned it around to refresh himself on the directions… goddamn, he just couldn’t focus his eyes. That grogginess─just getting worse, way past peckishness. Way more than… He blinked. The text got blurrier, and he swallowed down vomit, and collapsed onto the kitchen tile.

[puckpocketed]

Hello. You might have seen this floating around on twt:

link 1 // link 2, archive link

If by any chance you or someone you know are thinking about joining in on the challenge… no one can stop you but I implore you as someone who makes art, as someone with friends in an often-exploited creative industry, as someone who lives in late stage capitalism alongside you and has seen this play out before: proceed with caution.

Read the fine print on that form. There is NO guarantee of an internship, much less a job at the end of it. I haven't gone further than this form, but if anyone reading this does, and if there's no written agreement that your work won't be used without credit to you + payment for services rendered - RUN.

This is a common corporate tactic to get free labor out of people. I'm not saying this is necessarily what’s happening; for all we know this was done as a completely innocent move to drum up some fan engagement and as a genuine search for talent for their analytics team. WHO KNOWS. But I can't ignore that I’ve seen this situation play out again and again, at every scale.

Job interviews, when they ask you how YOU think they should improve their systems, how YOU would solve their problems? When they require that you do some problem-solving for them, and it goes beyond a simple task? That’s a free consultation you’re giving them, that's free work you or someone else should be getting paid for.

When big streamers/influencers ask their fans to join in on a fan art contest to choose their new pfp/banner? That’s hundreds, possibly thousands of pieces of free art they never would’ve gotten otherwise. They could've gone to the trouble of paying someone in-house to do it, hiring someone for that position, commissioning a professional for a piece. It's free work from their dedicated fans.

In this case, Utah HC is asking fans to not only choose/provide their own dataset, but to do a complex analysis on it AND do the work of visual and verbal communication to senior management, who likely do not have a deeper grasp of the concepts and will need it simplified. The stipulation that you will present your work could be ANYTHING!! The "five page deliverable" is already bananas to me, having dipped my toe into what analytics is and how complex the fun ones are. Condensing it all is WORK. The presentation portion may include speaking time and answering questions; the groundwork for doing this effectively may include producing data visualisations, making spreadsheets, time consuming write-ups. Maths and science communication is hard. It is WORK. They are asking for free labor.

Many have already called it out, but it's still gaining traction via retweets from big accounts uncritically sharing it. I found out through the official Puckpedia account. Jack Han called it out pretty eloquently on twitter and on his substack:

Many people aspire to work as an analyst in the NHL. Earlier today the Utah Hockey Club gave those people a glimmer of hope. Utah’s Summer Analytics Challenge is unusual in that it doesn’t provide a dataset or detailed instructions. The open-ended contests contrasts with other public (ex: Big Data Cup) or private (ex: NHL team interview) events. In those scenarios, participants are given proprietary data to clean, model and analyze, which influence direction and methodology. Meanwhile, Utah is seemingly happy with anything as long as the writeup is under five pages long. Utah’s contest also stands out in its near-total absence of legal fine print. There are no mention of intellectual property implications, which is perhaps fitting when the team is asking participants to bring their own data and analysis. [...] Open casting calls such as Utah’s analytics challenge start out as a lose-lose-lose proposition: > The employer loses because it will have to invest massive human resources to trawl/filter/evaluate/reverse-engineer the hundreds of write-ups it is sure to receive, with no guarantee that any of them will be of use > Applicants lose because the vast, vast majority of them will have nothing to show for their efforts, while a tiny minority risks having its IP stolen > Good ideas lose because they’ll be born into an environment where their parents (the applicant & the employer) have no defined relationship and won’t be in a position to grow together

link, archive link

I do try to keep things light on this blog, but this is super personal for me <3 thank u for listening

[fenrislorsrai]

Hey, saw your reblog of the post about flyers being a good way to reach not-so-online folks. Do you have any examples to draw on for inspiration? I'm no graphic designer so appreciate some examples. No matter what I promise not to use Comic Sans or Arial. 😀

Ironically, Comic Sans and Arial are some of the best font choices because they're easier for people with dyslexia to read! They're also widely available on most machines, so if you're going to send an editable version to other folks to print on a home computer, they'll generally render correctly. (you can also export a PDF and force it to do the same if you don't want them to edit)

Some sample full page flyers.

This is a two up, where I printed on a single page and split the page in half for hanging

Conveniently, one of the things in the twoup is the same as the full page. We did a smaller, less detailed one for this as it was a mini-event, so we hang in a much more limited area where folks likely already knew a lot of details about the venue as they walked past it.

It's not obvious with these, but generally the smallest text on here is 14 point. These are all in Arial.

The initial header is at least 34 point and some are up to 50, if I had space. The subheading is generally in range of 20-30

Bigger is better! People are generally going to see these while walking so need a really big, clear headline to get attention. You still want to keep it overall large because often walkers are older folks who may not have their reading glasses with them. GO BIG.

Less is more for this type of flyer.

Event Title

date and location

Other info

That's it! That's the basics! Use declarative sentence. One clause per sentence. If you can present things in a list, even better!

If you're doing a BIG event that had sponsors and stuff, you'll want to use fancier posters with their logos and stuff in prime locations and then these little guys that are just "event, date, location" in outdoor locations with low traffic.

If printing at home, buy yourself a light colored pack of card stock in a bright color. I personally use a safety orange. If you're getting them copied somewhere, spring for the card stock. It'll withstand getting rained on a few times and if it's pinned to an indoor bulletin board, you can hang with a single pin without worrying about it curling.

If it's going to be a flyer for recurring event or just info about a local resource like where the food pantry is, put a small "hung on X/y/z" on the very bottom. Some places require a date on posters for hanging and they'll take them down after a certain point to make sure they're still current. If you date them, they may stay up longer, because they know when they were hung instead of going on vibes of "oh that's been here forever" and it was actually only two weeks.

QR codes are something some people like on posters, but I personally am old and suspicious about anything I can't see the destination URL on, so don't use them. If you do use them, look at the place generating them carefully. If you can't figure out how they make their money making free QR codes... you're the product. Be suspicious, don't sell out folks data via QR code data harvesting.

My top spots for hanging flyers:

Bulletin boards at grocery stores

Laundromats

Coffee shop

Library (you will probably have to take it to circulation for approval, so make sure its All Ages appropriate as there are free range children in that library)

trailhead or parking pulloff by a trail.

Transfer station/dump (you may need approval there as it's town property, but you will get ONLY town residents there. It's worth asking at the booth! Same rules as library.)

Good luck with the flyers!

[mariacallous]

What is the future of the like button in the age of artificial intelligence? Max Levchin—the PayPal cofounder and Affirm CEO—sees a new and hugely valuable role for liking data to train AI to arrive at conclusions more in line with those a human decisionmaker would make.

It’s a well-known quandary in machine learning that a computer presented with a clear reward function will engage in relentless reinforcement learning to improve its performance and maximize that reward—but that this optimization path often leads AI systems to very different outcomes than would result from humans exercising human judgment.

To introduce a corrective force, AI developers frequently use what is called reinforcement learning from human feedback (RLHF). Essentially they are putting a human thumb on the scale as the computer arrives at its model by training it on data reflecting real people’s actual preferences. But where does that human preference data come from, and how much of it is needed for the input to be valid? So far, this has been the problem with RLHF: It’s a costly method if it requires hiring human supervisors and annotators to enter feedback.

And this is the problem that Levchin thinks could be solved by the like button. He views the accumulated resource that today sits in Facebook’s hands as a godsend to any developer wanting to train an intelligent agent on human preference data. And how big a deal is that? “I would argue that one of the most valuable things Facebook owns is that mountain of liking data,” Levchin told us. Indeed, at this inflection point in the development of artificial intelligence, having access to “what content is liked by humans, to use for training of AI models, is probably one of the singularly most valuable things on the internet.”

While Levchin envisions AI learning from human preferences through the like button, AI is already changing the way these preferences are shaped in the first place. In fact, social media platforms are actively using AI not just to analyze likes, but to predict them—potentially rendering the button itself obsolete.

This was a striking observation for us because, as we talked to most people, the predictions mostly came from another angle, describing not how the like button would affect the performance of AI but how AI would change the world of the like button. Already, we heard, AI is being applied to improve social media algorithms. Early in 2024, for example, Facebook experimented with using AI to redesign the algorithm that recommends Reels videos to users. Could it come up with a better weighting of variables to predict which video a user would most like to watch next? The result of this early test showed that it could: Applying AI to the task paid off in longer watch times—the performance metric Facebook was hoping to boost.

When we asked YouTube cofounder Steve Chen what the future holds for the like button, he said, “I sometimes wonder whether the like button will be needed when AI is sophisticated enough to tell the algorithm with 100 percent accuracy what you want to watch next based on the viewing and sharing patterns themselves. Up until now, the like button has been the simplest way for content platforms to do that, but the end goal is to make it as easy and accurate as possible with whatever data is available.”

He went on to point out, however, that one reason the like button may always be needed is to handle sharp or temporary changes in viewing needs because of life events or situations. “There are days when I wanna be watching content that’s a little bit more relevant to, say, my kids,” he said. Chen also explained that the like button may have longevity because of its role in attracting advertisers—the other key group alongside the viewers and creators—because the like acts as the simplest possible hinge to connect those three groups. With one tap, a viewer simultaneously conveys appreciation and feedback directly to the content provider and evidence of engagement and preference to the advertiser.

Another major impact of AI will be its increasing use to generate the content itself that is subject to people’s emotional responses. Already, growing amounts of the content—both text and images—being liked by social media users are AI generated. One wonders if the original purpose of the like button—to motivate more users to generate content—will even remain relevant. Would the platforms be just as successful on their own terms if their human users ceased to make the posts at all?

This question, of course, raises the problem of authenticity. During the 2024 Super Bowl halftime show, singer Alicia Keys hit a sour note that was noticed by every attentive listener tuned in to the live event. Yet when the recording of her performance was uploaded to YouTube shortly afterward, that flub had been seamlessly corrected, with no notification that the video had been altered. It’s a minor thing (and good for Keys for doing the performance live in the first place), but the sneaky correction raised eyebrows nonetheless. Ironically, she was singing “If I Ain’t Got You”—and her fans ended up getting something slightly different from her.

If AI can subtly refine entertainment content, it can also be weaponized for more deceptive purposes. The same technology that can fix a musical note can just as easily clone a voice, leading to far more serious consequences.

More chilling is the trend that the US Federal Communications Commission (FCC) and its equivalents elsewhere have recently cracked down on: uses of AI to “clone” an individual’s voice and effectively put words in their mouth. It sounds like them speaking, but it may not be them—it could be an impostor trying to trick that person’s grandfather into paying a ransom or trying to conduct a financial transaction in their name. In January 2024, after an incident of robocalls spoofing President Joe Biden’s voice, the FCC issued clear guidance that such impersonation is illegal under the provisions of the Telephone Consumer Protection Act, and warned consumers to be careful.

“AI-generated voice cloning and images are already sowing confusion by tricking consumers into thinking scams and frauds are legitimate,” said FCC chair Jessica Rosenworcel. “No matter what celebrity or politician you favor, or what your relationship is with your kin when they call for help, it is possible we could all be a target of these faked calls.”

Short of fraudulent pretense like this, an AI-filled future of social media might well be populated by seemingly real people who are purely computer-generated. Such virtual concoctions are infiltrating the community of online influencers and gaining legions of fans on social media platforms. “Aitana Lopez,” for example, regularly posts glimpses of her enviable life as a beautiful Spanish musician and fashionista. When we last checked, her Instagram account was up to 310,000 followers, and she was shilling for hair-care and clothing brands, including Victoria’s Secret, at a cost of some $1,000 per post. But someone else must be spending her hard-earned money, because Aitana doesn’t really need clothes or food or a place to live. She is the programmed creation of an ad agency—one that started out connecting brands with real human influencers but found that the humans were not always so easy to manage.

With AI-driven influencers and bots engaging with each other at unprecedented speed, the very fabric of online engagement may be shifting. If likes are no longer coming from real people, and content is no longer created by them, what does that mean for the future of the like economy?

In a scenario that not only echoes but goes beyond the premise of the 2013 film Her, you can also now buy a subscription that enables you to chat to your heart’s content with an on-screen “girlfriend.” CarynAI is an AI clone of a real-life online influencer, Caryn Marjorie, who had already gained over a million followers on Snapchat when she decided to team up with an AI company and develop a chatbot. Those who would like to engage in one-to-one conversation with the virtual Caryn pay a dollar per minute, and the chatbot’s conversation is generated by OpenAI’s GPT-4 software, as trained on an archive of content Marjorie had previously published on YouTube.

We can imagine a scenario in which a large proportion of likes are not awarded to human-created content—and not granted by actual people, either. We could have a digital world overrun by synthesized creators and consumers interacting at lightning speed with each other. Surely if this comes to pass, even in part, there will be new problems to be solved, relating to our needs to know who really is who (or what), and when a seemingly popular post is really worth checking out.

Do we want a future in which our true likes (and everyone else’s) are more transparent and unconcealable? Or do we want to retain (for ourselves but also for others) the ability to dissemble? It seems plausible that we will see new tools developed to provide more transparency and assurance as to whether a like is attached to a real person or just a realistic bot. Different platforms might apply such tools to different degrees.

[justforbooks]

Hum by Helen Phillips review

This thoughtful novel about a family trying to breathe clean air and not spend too long on their devices is mesmerising and scary

May loses her office job when the “hums” – humanoid robots – render her role obsolete. It’s hard to find work again. She hears of an opportunity to earn several months’ salary by receiving an experimental facial injection, and takes it. The injection will render May’s face unrecognisable to the ubiquitous hums. She’s a guinea pig for a form of adversarial AI, a technology designed to confound the processing of other tech. She returns home in pain, and looking very subtly different. “It’s really OK,” says her husband, Jem. “I just have a slightly new wife.”

Life is not easy for May and Jem and their two young children, Lu and Sy. Jem, formerly a photographer, takes gig work via an app, doing the odd jobs that rich people don’t want to do: removing corpses from pest traps, or clearing rotting food from a fridge. The air in their city is poisonous and the tap water is tainted. Rubbish blows around; birds, plants and animals are traumatised, shrivelled or extinct. May, Jem, Lu and Sy are all addicted to their devices, spending long periods alone in their individual “wooms”: networked isolation chambers, like a smartphone you can crawl inside.

Hum is Helen Phillips’s sixth book and third speculative novel. Her first, The Beautiful Bureaucrat, was praised by Ursula Le Guin, and her second, The Need, was shortlisted for a US National Book award. The novels are conceptual, dealing with big data and paleobotany, but they have a thriller-like intensity. Hum induces a nervous wariness that comes in part from the disturbing events May reads about in the news. “According to a new survey, more humans had experienced intense negative emotions in the past calendar year than at any other time in recorded history.” A woman is arrested for hiding needles inside strawberries on shop shelves. “Five hundred million plastic bottles are discarded in your city each year!” An orca carries the corpse of her calf, pushing it before her or holding its tail in her mouth, for 10 days.

These news headlines, scattered through the novel, contribute to Hum’s world building, enveloping May in an unsettling atmosphere. At the back of the book, references disclose that they are all true stories or events that Phillips has lightly rewritten. Her depiction of a conventional family struggling to pay the bills and breathe clean air, and trying not to pass too much of their lives on their devices, is also painful and familiar.

The real world is present in May’s gruelling life, its exploitative systems and the distressed environment that she longs to escape. When she receives payment for her injection, she immediately overspends on a family holiday to the Botanical Garden, a forested area walled off behind the city. Soon after they arrive in the garden, something disastrous happens. The crisis that unfolds fuses several different contemporary predicaments: online shaming, the stranglehold of tech corporations, and the bewildering proliferation of surveillance, which can both cause and resolve an emergency.

Hum has a convincing quality of understatement: it is gripping, but its plot doesn’t abide by the spikes and crescendos that the dystopian setup led me to anticipate. There is no dramatic or bloody confrontation. The stakes rise and then somewhat recede. It’s a thoughtful and graceful novel, not very long, told in short chapters, with an offhand turn of phrase that immerses us in May’s threatening environment, and just occasionally permits a glimpse of the richer world she craves: clean air and water, the smell of cedar, or the “quartet of cardinals” her mother sees one day, “such a red”.

There is a crucial scene in which a hum uses a screen on its abdomen to show May and her family images of their potential futures, created using their extant data. It’s a turning point and also a moment of provocation, reminding the reader how the many recent films and novels set in disastrous near-futures can collectively be seen as a kind of forecast: frightening but not implausible. Many of these stories propose extreme and terrifying scenarios of apocalypse or cruelty. Hum, instead, is poised between speculation and reportage. It’s mesmerising and scary.

Daily inspiration. Discover more photos at Just for Books…?

[japanese-cryptic-beauty]

Phonetic composition for Wanikani

Remember how I mentioned the book "The Kanji Code" in my lengthy article about on'yomi ("Chinese") readings of kanji?

Turns out the benefits of the book can be had for Wanikani users through the magic of "userscript."

What's the benefit

So, phonetic composition is how a great many of the kanji you're (supposedly) trying to learn are made. These particular kanji are called 形声 or "keisei" - "form-voice" kanji.

For the "form," they contain a part (often referred to as "radical") that gives you the meaning/semantic category of the kanji, like what the concept is mentally filed under.

For the "voice," they contain another part that hints you at how they are pronounced. It is commonly believed that kanji are only pictorials linked to concepts, but for keisei kanji this is not the case. A big part may not pertain to the meaning at all. Spotting the "voice" part can help you recall the on'yomi sounds of kanji much easier and systematically.

Yes, this is specifically for the "Chinese" readings. The Japanese readings are words from a language that had nothing to do with the formation of the original hanzi script, after all.

(Examples are in the original article.)

How it works

Websites are rendered by your browser, but how your browser does this can be extended and changed by scripts that rely on the Javascript language. Nowadays browsers like Chrome or Firefox allow you to install "extensions" that do various jobs for you - blocking ads, returning the ability to copy/paste to pages that block it, etc.

Wanikani, the kanji-learning website, incorporated early on support for so-called userscripts, both a blessing and a curse. Curse because whenever they want to change something on the website, a very vocal userscript culture will complain about breaking their stuff. Blessing because it allows you to extend what WK can do.

How it's done

You can basically follow the guide here, but I must admit I failed to do so because I think something's missing.

What you do is this:

Install Tampermonkey (guide) - this is the basic engine.

Install WK Open Framework (guide) - this is Wanikani's own script support.

Install Keisei support (guide)

Now, if this worked, you should see something like this on various pages - for kanji readings and radicals:

This will tell you if a phonetic component is present in your kanji or if it is itself present in another kanji as a phonetic component. The readings printed in bold are readings still used for that kanji, others listed are merely present in phonetic components but not in the original kanji. Because history.

It also shows you cases where the link is weakened because the pronunciation changed - in the example you see this marked in red. Basically there is a grading system (so far I've seen "heaven", "above", "middle", and "below").

A Caveat

There's only one caveat. Tampermonkey is very powerful. You need to trust the scripts you install, because it could potentially alter what you see on websites or enter/submit stuff for you.

On Chrome there is a good solution for this. When you click on the "Extensions" icon in the top right, you can select "This can read and change site data" from the three dots behind Tampermonkey.

I recommend doing it like this:

Select "On all Sites" during the installing of the WK Open Framework and the "Keisei" support script. (Else the install links have a habit of not working on Chrome.)

Then change it back to "When you click the extension".

Now go back to a tab with Wanikani open. Go back to "Extensions -> This can read and change site data" and select "On wanikani.com".

Congrats! Now Tampermonkey is only enabled on Wanikani by default.

Alternatively, use it in a separate browser. I haven't found an easy way to restrict permissions on Firefox, for example. But if you use it in a separate browser that you only use for Wanikani, you should be good.

Kanpai to the people who made this possible!!

[opinions-about-tiaras]

I work at the coal face here, so I feel like I have some things to add.

By "at the coal face" I mean "I am employed in a technology role at a company that is essentially the platonic ideal of the business use-case for LLMs." (I try and avoid calling them "AI" because they're not, but I suspect I've lost this philological battle.)

I work at a major (Fortune 1000) real-estate services, property tax services, and credit analytics company. Literally 100% of our work is gathering and analyzing data and providing conclusions, business plans, recommendations, professional and legally actionable tax advice, and other related services based on that analysis... but we are not, ourselves, a tech company, despite an intense branding push on the part of our corporate masters. We have specialized in-house algorithms that have proven to be effective at things like "analyzing and pricing flood risk better than those of competing companies" but that's about it.

You'll not the highlighted part of my last paragraph. I'll come back to that.

Like everyone else in the world, our leadership is going pretty hard in on LLMs. Because we're an ideal use-case for it, right? We do nothing but mess with data.

We struck a major deal with Microsoft for a huge implementation of Copilot, there are trainings and seminars like I've never seen to teach people what it is and how to leverage it, etc. We're spending a lot of money on this.

The results so far?

I'm going to give Copilot credit; it's fantastic at automating away a lot of basic clerical work. It can write lengthy emails that are meant to be nothing more than straightforward conveyances of information like nobodies business if you feed it the information. It can take notes on meetings in a quick and effective manner, its algorithms having a decent understanding of what bullet points to distill out of a ten-minute conversation. We've even been having it work the camera at major company presentations and its better at it than most people are, certainly better than prior "tries to focus on the speaker and their presentation automatically" pieces of software are. A bunch of other things that have to be done but are basically busywork.

This is all very useful. But that's about where its usefulness ends.

The actual BUSINESS business side of things is struggling mightily to find uses for the thing despite massive corporate pressure to do so. And the issues there are twofold.

The first is that the analytical tools we use primarily spit out data... but the core of our business is interpreting that data accurately, and continuing to insure that the data, itself, is accurate. I mentioned flood risk previously? Part of what we do is that every couple of years or so, the guys over in Business Intelligence need to actually start going through the real-world records for what floods happened and where, running them against our algorithms and models, and tweaking them to make sure they continue to be accurate. This task has been automated to the greatest extent it can be already. They are deeply hesitant to let Copilot automate it even more, because Copilot cannot think and render judgments, and thinking and rendering judgments is what we sell.

They would love if it it could automate shit like "contact various municipal authorities to get their publicly-available data on disasters in their area" but it actually can't; or rather, they don't trust it to do so. Early attempts to try and train it on this have produced results that are sufficiently variable and require so much human cross-checking as to not be worth it. And even if it COULD do that, analyzing that information is a whole other deal.

So that's one barrier. But the main barrier, the BIG one?

We are to a great extent legally responsible for the information we convey to our customers. Our recommendations for customers that make use of our more in-depth services to them aren't protected in this way; we've been wrong before, often to the tune of hundreds of millions of dollars. And we'll be wrong again! Our customers have no recourse on "we thought this was a good idea but it wasn't."

But we are absolutely liable that the data we base those wrong conclusions on has been crunched and analyzed and sourced in the ways we are contractually obligated to do so. Our work is warranted.

For our tax services, that goes one step further. Tax services are serious fucking business. The tax services we provide expose us not just to angry customers walking away or potentially suing us, they expose us to actual-factual criminal liability in the case of certain screwups. That information has to be gathered, stored, and processed properly. We can and have automated a lot of that. But the actual work work there is done by humans. Those humans use analytical tools, some quite powerful, but the work needs to attach to a human whose ass is on the line.

So far nobody whose ass is on the line has been willing to entrust much of this to Copilot.

That's what it comes down to for just about everything. The upper management folks are big-picture guys who look at LLMs and are dazzled by the possibilities. The line workers, like myself (I'm in a technology support role) basically don't get a say and largely don't care, they do what they're told with what they have.

But the specialists and middle-managers? Those are the guys whose name is on the work and who get in trouble if it isn't done properly. Those specialists are professionals in their fields often with many years of experience, and the tax guys in particular are sharp. Those middle-managers have the job of telling the UPPER management folks when they're off the rails and they cannot, if acting as directed, guarantee that our work will be warranted and not expose us to legal liability, and that's something upper management actually does pay attention to.

Does this mean the business side can't get use out of LLMs? No, of course not. But it does mean that they can't utterly transform the business based on what LLMs can do. What they provide is the easing of a lot of basic clerical work and that's it.

This is probably not worth the immense sums of money dumped into LLMs, or what we're paying for Copilot.

It's liability. These LLMs are just tools. They can't be held accountable, not for anything. When a tool is used, and things go badly wrong, you hold accountable the person using the tool. You can't indict a shovel; you CAN indict a guy for using a shovel to beat a man to death.

And again, we're an ideal use-case scenario and this is the barrier we're running up against.

Now, I'm sure there are companies that are going "fuck all this" and just charging ahead with LLMs anyway. That's absolutely happening.

A bunch of those guys are gonna go to jail, and when they're hauled away they're gonna bleat "It wasn't me, I just did what the machine said!"

If anyone wants to know why every tech company in the world right now is clamoring for AI like drowned rats scrabbling to board a ship, I decided to make a post to explain what's happening.

(Disclaimer to start: I'm a software engineer who's been employed full time since 2018. I am not a historian nor an overconfident Youtube essayist, so this post is my working knowledge of what I see around me and the logical bridges between pieces.)

Okay anyway. The explanation starts further back than what's going on now. I'm gonna start with the year 2000. The Dot Com Bubble just spectacularly burst. The model of "we get the users first, we learn how to profit off them later" went out in a no-money-having bang (remember this, it will be relevant later). A lot of money was lost. A lot of people ended up out of a job. A lot of startup companies went under. Investors left with a sour taste in their mouth and, in general, investment in the internet stayed pretty cooled for that decade. This was, in my opinion, very good for the internet as it was an era not suffocating under the grip of mega-corporation oligarchs and was, instead, filled with Club Penguin and I Can Haz Cheezburger websites.

Then around the 2010-2012 years, a few things happened. Interest rates got low, and then lower. Facebook got huge. The iPhone took off. And suddenly there was a huge new potential market of internet users and phone-havers, and the cheap money was available to start backing new tech startup companies trying to hop on this opportunity. Companies like Uber, Netflix, and Amazon either started in this time, or hit their ramp-up in these years by shifting focus to the internet and apps.

Now, every start-up tech company dreaming of being the next big thing has one thing in common: they need to start off by getting themselves massively in debt. Because before you can turn a profit you need to first spend money on employees and spend money on equipment and spend money on data centers and spend money on advertising and spend money on scale and and and

But also, everyone wants to be on the ship for The Next Big Thing that takes off to the moon.

So there is a mutual interest between new tech companies, and venture capitalists who are willing to invest $$$ into said new tech companies. Because if the venture capitalists can identify a prize pig and get in early, that money could come back to them 100-fold or 1,000-fold. In fact it hardly matters if they invest in 10 or 20 total bust projects along the way to find that unicorn.

But also, becoming profitable takes time. And that might mean being in debt for a long long time before that rocket ship takes off to make everyone onboard a gazzilionaire.

But luckily, for tech startup bros and venture capitalists, being in debt in the 2010's was cheap, and it only got cheaper between 2010 and 2020. If people could secure loans for ~3% or 4% annual interest, well then a $100,000 loan only really costs $3,000 of interest a year to keep afloat. And if inflation is higher than that or at least similar, you're still beating the system.

So from 2010 through early 2022, times were good for tech companies. Startups could take off with massive growth, showing massive potential for something, and venture capitalists would throw infinite money at them in the hopes of pegging just one winner who will take off. And supporting the struggling investments or the long-haulers remained pretty cheap to keep funding.

You hear constantly about "Such and such app has 10-bazillion users gained over the last 10 years and has never once been profitable", yet the thing keeps chugging along because the investors backing it aren't stressed about the immediate future, and are still banking on that "eventually" when it learns how to really monetize its users and turn that profit.

The pandemic in 2020 took a magnifying-glass-in-the-sun effect to this, as EVERYTHING was forcibly turned online which pumped a ton of money and workers into tech investment. Simultaneously, money got really REALLY cheap, bottoming out with historic lows for interest rates.

Then the tide changed with the massive inflation that struck late 2021. Because this all-gas no-brakes state of things was also contributing to off-the-rails inflation (along with your standard-fare greedflation and price gouging, given the extremely convenient excuses of pandemic hardships and supply chain issues). The federal reserve whipped out interest rate hikes to try to curb this huge inflation, which is like a fire extinguisher dousing and suffocating your really-cool, actively-on-fire party where everyone else is burning but you're in the pool. And then they did this more, and then more. And the financial climate followed suit. And suddenly money was not cheap anymore, and new loans became expensive, because loans that used to compound at 2% a year are now compounding at 7 or 8% which, in the language of compounding, is a HUGE difference. A $100,000 loan at a 2% interest rate, if not repaid a single cent in 10 years, accrues to $121,899. A $100,000 loan at an 8% interest rate, if not repaid a single cent in 10 years, more than doubles to $215,892.

Now it is scary and risky to throw money at "could eventually be profitable" tech companies. Now investors are watching companies burn through their current funding and, when the companies come back asking for more, investors are tightening their coin purses instead. The bill is coming due. The free money is drying up and companies are under compounding pressure to produce a profit for their waiting investors who are now done waiting.

You get enshittification. You get quality going down and price going up. You get "now that you're a captive audience here, we're forcing ads or we're forcing subscriptions on you." Don't get me wrong, the plan was ALWAYS to monetize the users. It's just that it's come earlier than expected, with way more feet-to-the-fire than these companies were expecting. ESPECIALLY with Wall Street as the other factor in funding (public) companies, where Wall Street exhibits roughly the same temperament as a baby screaming crying upset that it's soiled its own diaper (maybe that's too mean a comparison to babies), and now companies are being put through the wringer for anything LESS than infinite growth that Wall Street demands of them.

Internal to the tech industry, you get MASSIVE wide-spread layoffs. You get an industry that used to be easy to land multiple job offers shriveling up and leaving recent graduates in a desperately awful situation where no company is hiring and the market is flooded with laid-off workers trying to get back on their feet.

Because those coin-purse-clutching investors DO love virtue-signaling efforts from companies that say "See! We're not being frivolous with your money! We only spend on the essentials." And this is true even for MASSIVE, PROFITABLE companies, because those companies' value is based on the Rich Person Feeling Graph (their stock) rather than the literal profit money. A company making a genuine gazillion dollars a year still tears through layoffs and freezes hiring and removes the free batteries from the printer room (totally not speaking from experience, surely) because the investors LOVE when you cut costs and take away employee perks. The "beer on tap, ping pong table in the common area" era of tech is drying up. And we're still unionless.

Never mind that last part.

And then in early 2023, AI (more specifically, Chat-GPT which is OpenAI's Large Language Model creation) tears its way into the tech scene with a meteor's amount of momentum. Here's Microsoft's prize pig, which it invested heavily in and is galivanting around the pig-show with, to the desperate jealousy and rapture of every other tech company and investor wishing it had that pig. And for the first time since the interest rate hikes, investors have dollar signs in their eyes, both venture capital and Wall Street alike. They're willing to restart the hose of money (even with the new risk) because this feels big enough for them to take the risk.

Now all these companies, who were in varying stages of sweating as their bill came due, or wringing their hands as their stock prices tanked, see a single glorious gold-plated rocket up out of here, the likes of which haven't been seen since the free money days. It's their ticket to buy time, and buy investors, and say "see THIS is what will wring money forth, finally, we promise, just let us show you."

To be clear, AI is NOT profitable yet. It's a money-sink. Perhaps a money-black-hole. But everyone in the space is so wowed by it that there is a wide-spread and powerful conviction that it will become profitable and earn its keep. (Let's be real, half of that profit "potential" is the promise of automating away jobs of pesky employees who peskily cost money.) It's a tech-space industrial revolution that will automate away skilled jobs, and getting in on the ground floor is the absolute best thing you can do to get your pie slice's worth.

It's the thing that will win investors back. It's the thing that will get the investment money coming in again (or, get it second-hand if the company can be the PROVIDER of something needed for AI, which other companies with venture-back will pay handsomely for). It's the thing companies are terrified of missing out on, lest it leave them utterly irrelevant in a future where not having AI-integration is like not having a mobile phone app for your company or not having a website.

So I guess to reiterate on my earlier point:

Drowned rats. Swimming to the one ship in sight.

[infyq]

7 beginner SEO tools that can help simplify SEO

Search engine optimization or SEO can be a long and complex process for many SEO professionals — especially for beginners. Having an SEO strategy is not always enough. You also need the right set of tools to execute that strategy.

Here is a list of 7 SEO tools that every newbie SEO must use. These SEO tools can help simplify the process of search engine optimization and make everything a tad bit easier.

1. Google Search Console

Google Search Console is the Swiss army knife for SEO professionals. Every SEO must use the Google Search console because:

First, it provides valuable search performance data.

Second, it provides various tools to SEO professionals to conduct different tests (e.g., URL inspection, core web vitals) and find many potential SEO issues (e.g., crawling, indexing, structured data syntax errors, manual penalties by Google).

2. Google Analytics

Search engine optimization is a long-term process in which you may have to tweak things regularly based on the data you receive. You must know what’s working and what’s not working.

Google Analytics is how you get all that data that enables you to make informed, data-driven decisions about your SEO strategy and the overall direction of your business.

With Google Analytics, you can see:

Which pages drive the most traffic to your website

Who your audience is

Which channels do they use to reach your website

How visitors interact and engage with your website

How many visitors are you converting into leads

At which stage of the sales funnel do most of your potential customers exit And more.

3. Screaming Frog

Search engine optimization often starts with crawling your website and your competitor’s website, and for that, you need an SEO crawling tool.

Screaming Frog is an extremely popular SEO crawler that can crawl any website for you and present all the information in an easy-to-understand way.

With Screaming Frog, you can find a lot of valuable information about any website: crawl mistakes, missing meta titles and descriptions, redirect chains and loops, JavaScript rendering issues, response code errors, and more.

4. SEMRush

SEMRush is a multi-feature tool that can cover pretty much every base in SEO. it allows you to:

Conduct in-depth keyword research to help you identify which keywords you should target

Conduct detailed competitor analysis to see how your competitors are performing

Conduct a backlink analysis to see where you get all your backlinks from and where you can look for more opportunities

Conduct a site audit to identify various types of technical and SEO issues and learn how to fix them

Track search engine rankings and positions for specific keywords

And more.

5. Ahrefs

Ahrefs is a very similar suite of tools as SEMrush. It mostly comes down to your preferences for whether you want to use Ahrefs or SEMrush.

Having said that, it does focus a bit more on backlink analysis, and their backlink analysis tools are some of the SEO industry right now. 

6. Redirect Path

Redirects are common — especially for established websites that have been around for some time. But how do you check if a page has a proper, functioning redirect?

Redirect Path is a Google Chrome extension that makes it super easy to view that. It charts a path that search engine crawlers take to reach a website, showing all the pages in the path where redirects are in place.

7. Panguin Tool

Have you ever lost a big percentage of search traffic and wondered whether it was just a Google Search algorithm update or something that you did wrong?

Thankfully, there is a tool that can help you answer that question.

Panguin Tool lines up your search traffic with known Google Search algorithm updates, so you can see if the dip in traffic aligns with a Google algorithm update.

You must know how important a part link building is in SEO strategy. If done correctly, it can greatly increase the visibility, trust, and traffic of your website. Our company InfyQ SEO Experts Is the top seo agency in India. we have prepared a method that helps businesses achieve long-term success in the digital world. So, contact us today and know how we can help your business grow.

[c2zens-time-capsule]

Ant Farm was a group of radical architects, designers, and media artists active from 1968-1978. Perhaps most well known for creating the iconic Cadillac Ranch and pulling off spectacular media events like Media Burn (1975), the group also created a variety of video works, popularized inflatable architecture, and produced participatory performances and a futuristic house in Texas (House of the Century, 1972). The core members — Chip Lord, Doug Michels, Hudson Marquez, and Curtis Schreier — sustained an interest in American cultural iconography, nomadic living, technology, and big cars over a ten year collaborative career. The group officially disbanded in 1978, following a devastating studio fire. Doug Michels, one of the original members of Ant Farm, died in 2003.

LST is a contemporary group including Ant Farm members Chip Lord and Curtis Schreier joined by the artist and architect Bruce Tomb. The contemporary group formed around the creation of the Ant Farm Media Van v.08 [Time Capsule], which contains the Media HUQQUH. The HUQQUH is a digital media time capsule creation device which uploads files from participants. The first iteration of the Ant Farm Media Van v.08 [Time Capsule] was created for The Art of Participation: 1950 to Now, a 2008 exhibition at SFMOMA. (which also holds a number of Ant Farm works in its collection). [...]

Ant Farm’s time capsules deviated from typical examples of the form: they did not preserve a stable cultural self-portrait that could persist through time. Rather, encased within everyday objects such as a refrigerator and a 1968 Oldsmobile Vista Cruiser (Citizens Time Capsule, 1975), they were defiantly homemade with little promise of endurance. Whether destroyed, stolen, lost, or deemed “environmentally hazardous,” each work was a categorical failure, underscoring the humor and countercultural critique at the core of the Ant Farm and LST ethos. [...]

Filling the entirePioneer Works main exhibition space is a monumental inflatable structure custom-designed by LST that recalls Ant Farm’s radical architectural environments from the sixties and seventies. The transitory structure cocoons the exhibition’s main feature: Ant Farm Media Van v.08 [Time Capsule] (2008-2016), a contemporary interactive media sculpture and time capsule by LST that re-envisions Ant Farm’s historic 1971 Media Van. Within the contemporary version, a device called the HUQQUH, randomly downloads media files from participants’ smartphones throughout the duration of the exhibition. The compiled data creates a digital time capsule at Pioneer Works.

Digital archives and the nature of time perception is further explored through Time Capsule Triptych (2009). This 18-foot-long work by LST consists of three parts: an installation of over 4,000 digital audio and visual files collected through the HUQQUH in 2008; a video documentation of the (fictional) reemergence of the original Media Van from a missile silo in California; and an architectural rendering of the future resting place of the contemporary media van / time capsule. Together, the media within the triptych manifests a larger artistic fiction that attempts to document the past (discovery of the 1971 Media Van), frame the present (digital archive from the contemporary media van), and posit the future (the eventual site of the contemporary media van).

Over the course of five decades, and specifically through their time capsule works, Ant Farm and LST have routinely transcended disciplinary boundaries and, through experimentation, pioneered new artistic mediums. Through this exhibition, Pioneer Works celebrates and learns from those who established the practices and approaches that the organization strives to foster and emulate.

[brocoffeeengineer]

Will AI Take Over Finance? Why CFA Professionals Are More Relevant Than Ever

In an alarming yet fascinating way, finance professionals are asking themselves: Does AI stand to replace them? With the finance world primarily focused on data and precision, it surely seems fit for automation. Robots are at work like never before, doing everything from acting as robo-advisors to generative AI creating the earnings report. But is it the end of the road for human finance experts-or the dawn of a new age?

In fact, rather than replace, AI is helping in the evolution of finance; and those compe- tent in its use-some of whom are perhaps CFA charterholders-would rather lead than follow.

AI's Expanding Role in Finance

Let us begin with what is happening now. AI is no longer just a name-it is deeply integrated into present-day finance. In 2024, JPMorgan pioneered the launch of IndexGPT, the first AI-powered stock index recommendation tool based on GPT technology. Goldman Sachs is executing trade executions using AI, and BlackRock's risk management platform is powered by machine learning within Aladdin. These invention are not going to happen in some distant future; they are already being utilized and continuously developed.

AI can now screen thousands of earnings reports while scanning social sentiment and portfolio risks in real time. ChatGPT-style generative AI is even lending a hand to junior analysts in generating financial summaries or pitch decks. This type of rapid automation makes one think that it will drive people out of work. But that isn't the whole story.

Why Finance Still Needs Human Insight

Nothing is too predictable for artificial intelligence. But finance, on the other hand, is an altogether complex, emotionally driven, and unpredictable world. Markets react not to data but also perception, emotion, and escalating geopolitical tensions, in these kinds of nuanced dividends that AI is unable to comprehend.

The finance professionals are involved in much more than just crunching numbers. They`re interpreting signals and weighing qualitative factors in making a judgment on uncertain matters. When two competing investment strategies require a decision by a client, the things that the machines cannot render yet are trust, experience, and strategic contemplation.

AI models are only as good as what they are trained on. In cases when markets are either very volatile or quite new, the models may provide unreliable predictions. In those instances, particularly when big amounts of money are invested in high-stakes financial decisions, human expertise will fill the gap.

The CFA Designation: Human Intelligence Enhanced by AI

This is where the CFA charter earns its distinction. The CFA designation has often been considered the gold standard in investment management, imparting profound ethical and practical knowledge encompassing equity research, portfolio management, and financial analysis.

Today's CFA curriculum is keeping pace with the shifting landscape of finance. FinTech, AI applications, and data analytics are all interwoven into core study materials. The overriding aim? To equip professionals for the future, to work in harmony with technology, not to compete against it.

This is important because AI isn't so much taking over jobs as it is cherry-picking tasks. Remember the analysts who were bound to hours of spreadsheet work? Those same analysts now leverage their time on higher-order cognitive skills: scenario planning, customer engagement, and strategic forecasting.

AI isn't making finance irrelevant; instead, it hastens the process for those with good analytical and decision-making skills.

What This Means for Aspiring Finance Professionals

For those breaking into the field, this is a make-or-break situation. The in-demand skills are no longer purely technical; they are hybrid. Employers want to see knowledge of financial modeling with machine learning, portfolio theory with data ethics.

This has brought about a booming demand for specialized, less-structured ways of education. A notable emerging trend is the online CFA course, which enables you to obtain highly recognized credentials while continuing your career without interruption. These are not only convenient; they also match how finance works today-in real-time, tech-enabled environments.

Being able to learn and change course quickly is becoming an important differentiator. With AI accelerating change, it is becoming apparent that just having fixed knowledge is inadequate. Continuous learning is now more important than ever, especially in programs that balance financial rigor with practical application.

Conclusion: A Future Where AI Amplifies, Not Replaces

Does AI replace finance professionals? Probably yes for those adverse to change. But for CFA charter-holders and future-focused professionals, AI is a partner and not a threat.

The future of finance belongs to those who can merge human judgment with machine intelligence: analysts who can validate models, strategists who can translate data into direction, advisors who can build trust while wielding technology wisely.

This transformation is increasingly visible in regions that are experiencing rapid financial growth like the Middle East, where many professionals now spend on credentials to keep pace with global trends and increasingly resort to flexible, high-quality learning formats such as online CFA course in UAE-not because they want to catch up with everybody but rather, so as to get ahead.

Reshaping it with human insight is how success will be found in this new age, not resisting technology.

[dankusner]

D-FW family wants Oak Lawn development to be their ‘legacy project’

A Dallas-Fort Worth family known for its business and philanthropic work plans to transform an Oak Lawn strip mall into a mixed-use project anchored by a high-rise residential tower.

It wasn’t always in the plans, but the stars aligned just right for the Halperin family.

They’ve owned the roughly 2-acre site for two decades.

Jim Halperin, a well-known philanthropist and co-founder of Heritage Auctions, began buying properties in the neighborhood after he relocated the firm’s office to Maple Avenue in the early 2000s.

He had no designs to knock down a shopping center and build something new when he bought it.

His son, Dave, told The Dallas Morning News that his old man is simply “a collector at heart.”

But things changed in the last two years.

The family decided to move forward with 2615 Oak Lawn Ave., their first real estate venture of this magnitude.

The Halperins have hired GFF Design as the architect for the project.

The group plans to build 310 apartments in the residential high-rise. The average unit size will be around 1,000 square feet and the group’s current height request is a maximum of 270 feet.

“[Dad] wanted to have a connection to the neighborhood,” Dave Halperin said.

“We’ve held onto this land for 20 years. We intend to stay over here for the long term. I mean, this is our baby.”

A rendering of 2615 Oak Lawn Ave. The Halperin family wants to turn the site into a…

A rendering of 2615 Oak Lawn Ave. The Halperin family wants to turn the site into a residential high-rise with restaurants.

The development will also feature two to three restaurant spaces, including a two-story feature restaurant with a roof terrace on the development’s west side.

The project is still in its earliest stages.

The group presented plans to the Oak Lawn Committee last month, where they received unanimous support.

Support from the body isn’t required like approvals from the city’s zoning and council bodies.

However, it is a way to drum up support for a planned development.

Halperin and company want the project to be a “gateway,” bringing walkable urban design and quality housing as the renovated Old Parkland brings more workers looking to live in the neighborhood.

The average sidewalk width will be 10 feet along Oak Lawn, curb cuts will be consolidated to one cut and site landscaping will encourage walkability.

The group will move the high-rise several steps back on the north side to prevent it from casting shadows over nearby apartment complexes.

They aren’t trying to change everything, though.

Halperin is a big fan of Open Sesame Lebanese Grill.

He wants to keep it around, and Halperin hopes they’ll open in the new building.

“We want a project that really benefits everybody in the surrounding neighborhood,” said Evan Beattie, chairman and CEO of GFF.

“That is our architectural vision.”

The next step for the project is to submit a zoning change with the city of Dallas, which could take up to a year and a half.

Then comes the design and permitting.

The project could be two and a half years from breaking ground, the group said.

“I want this to be our family’s legacy project,” Halperin said. “I want this to be a neighborhood amenity.”

Data banks at the QTS Data Center in Irving in 2019.

Kansas-based QTS is adding to its D-FW holdings, according to state filings.

Miznon is known internationally for its pita. The Deep Ellum restaurant has closed, but…

The brothers who operated it are still expanding Miznon across Texas. Just in smaller spaces.

[onpremiseerpvsclouderp]

Details About Cloud ERP Vs On Premise ERP

Enterprise resource planning systems have evolved significantly, offering businesses the choice between traditional setups and modern cloud-based solutions. When you compare on premise erp vs cloud erp, decision-makers must assess long-term needs and scalability. Companies trying to find flexibility and lower upfront costs often go for scalable cloud erp, which allows seamless expansion without heavy infrastructure investments. Cloud solutions offer remote accessibility, automatic updates, and improved collaboration, making them a compelling option for businesses of all sizes. However, some organisations prefer on-premise ERP for greater control over security and customisation, balancing long-term operational strategies with current needs. A major consideration when evaluating cloud erp vs on premise erp is how each system supports growth and adapts to changing demands. Businesses requiring rapid expansion benefit from cloud erp scalability, as cloud solutions allow for quick integration of new functionalities and users. On-premise ERP systems, while offering stability and control, may involve significant costs and resources for upgrades. Scalability and cloud erp go turn in hand, ensuring companies can evolve without being restricted by hardware limitations. Choosing the right system is dependent upon whether the organisation prioritises flexibility, security, or control over its IT infrastructure.

One of the biggest advantages of cloud-based systems is their ability to leverage advanced data analytics. The role of business intelligence in erp is now increasingly important, helping businesses transform raw data into actionable insights. Cloud solutions integrate seamlessly with bi erp system tools, enabling companies to produce informed decisions centered on real-time data. On-premise solutions also can incorporate business intelligence, nevertheless they often require additional infrastructure and investment. The capability to access and process vast levels of information efficiently gives cloud-based ERP a proper edge in a increasingly data-driven business environment. When evaluating cloud based erp vs on premise, businesses should also consider integration capabilities. Cloud-based ERP systems are created to connect effortlessly with other digital tools, streamlining operations and enhancing productivity. Bi and erp integration allows organisations to automate reporting, monitor performance, and anticipate market trends with greater accuracy. On-premise ERP, while offering customisation, may face challenges in adapting to modern analytics solutions without extensive reconfiguration.

For companies looking to keep agile and competitive, cloud ERP presents a far more adaptable and cost-effective method of enterprise resource planning. Another critical aspect of ERP selection is how well the system handles large datasets and complex analytics. The connection between big data and erp has reshaped business intelligence, rendering it required for organisations to select a system that supports high-volume data processing. Cloud ERP solutions give you the computational power needed to deal with massive data sets efficiently, whereas on-premise ERP may require additional investments in hardware. The capacity to leverage erp and data analytics effectively can drive better forecasting, risk management, and strategic decision-making, ultimately impacting business success. Selecting between cloud and on-premise ERP takes a clear comprehension of an organisation's long-term goals, infrastructure capabilities, and security preferences. While scalable cloud erp solutions offer greater flexibility, cost savings, and advanced analytics, some industries can still require the control and security of on-premise systems. Businesses must carefully weigh these factors to make certain their ERP investment aligns with future growth and operational efficiency. A well-informed decision ensures that companies can maximise their ERP's potential, improving overall productivity and competitiveness in a evolving digital landscape.

[nursingwriter]

Diabetes in Australia The Australian government and the relevant Health agencies have for many years strived to put the diabetes menace under close observation and management. There have been massive researches and huge sums directed towards good management and possible elimination of diabetes at the national levels. This commitment is exhibited by the specialized funds and efforts like the Juvenile Diabetes Research Fund (JDRF) that has been committed to striving to mitigate the effects of diabetes from the render age of the Australians. Since diabetes is such a big challenge to Australia as a whole, diabetes mellitus was declared a National Health Priority Area in 1996 during the Australian Health Minister's Conference and this was as recognition to the high levels of diabetes prevalence within Australia, the mortality rates that were due to it, the impact it had on morbidity and the possibility of the health improvements that can be achieved from the various prevention and treatment programs that were thereafter launched (Australian Government, Department of Health and Ageing, 2012). What then is diabetes? This disease is described by Department of Health (ditto) as "a chronic disease characterized by high levels of glucose in the blood." Normally, the blood sugar is controlled by insulin that is dispensed into the blood and body system in general and this insulin is a hormone produced by the pancreas. Diabetes therefore occurs when the pancreas, that should be producing the sufficient quantities of insulin, is then unable to produce enough of it, or when the human body grows resistance to insulin and does not utilize it as required, or in some instances both cases above. There are three main types of diabetes commonly referred to; Type 1 diabetes where the body immune system attacks the cells of the pancreas responsible for production of insulin. Type 2 that is activated by lifestyle and eating habits and the third is the gestational diabetes that occurs mainly during pregnancy (Diabetes Australia, 2013). Prevalence of diabetes in Australia Diabetes in Australia has been closely followed, the trends keenly recorded in terms of the age and regional demographics. Regular data is collected, analyzed and presented in the most basic and easy to underrated modes as possible through the various agencies and government websites. This is in a bid to enable as many people as possible to have access and easy understanding of the trends that diabetes takes hence contribute their share in controlling it as well as educate others on the ways that it can be managed effectively. According to the statistics presented to the Australian government and consequently hosted on various websites by the Australian Institute of Health and Welfare (2011), in 2007-2008, there was an estimated 898,800 Australians diagnosed with diabetes and this figure excluded the gestational diabetes. It also indicates that at a given time in their life, a significant 87,100 people had been diagnosed with Type 1 diabetes and another 787,500 with Type 2 diabetes. It is also estimated that 56% of the people diagnosed in this case with diabetes in general were male and the other 44% were female. It is also indicated within the same source that 96% of the people diagnosed with diabetes in this case were 35 years of age and above and yet another 43% were seen to be 65 years old and above. The general spread of the prevalence was found to be similar in all states and territories and that is after the difference in age has been catered for. It is also noted that after accounting for the age differences, the proportion of people with diabetes was similar in major cities, regional areas as well as the remote areas. Out of the total number of people with diabetes I Australia, an estimated 557,700 which represented 52% of this population were found to be living in Australia's major cities. Another 230,700 a representative of 26% were studied to be in the inner regional areas and yet another 110, 400 which represent 12% were seen to be living in the outer regional areas or the remote areas. The Australian Bureau of Statistics -National Health Survey did not however include the very remote areas. Also worth noting in the regional proportionality in the prevalence is that, there was a similarity in proportion between the inner regional areas (with 5.2% of the population having diabetes) and the outer regional or remote areas (with 5.1% of the population living there having diabetes) but a slight decrease in proportionality detected in the major cities with 4.0% of the population suffering diabetes. It is worth noting however here that, statistically speaking, after the difference in age has been accounted for, the difference in prevalence between the major cities and the inner regions as well as the outer regions was not statistically significant. This was an indication that the difference between the city and the other regions was partly due to the fact that the dwellers of the other two regions apart from the city were relatively older on average. In terms of the two major types of diabetes, 87,000 have been diagnosed with Type 1 diabetes which is representative of 0.4% of all Australians. Of this population, 53% of them were seen to be 45 years of age and above. 53% of the Type 1 diabetes patients were male with 47% remaining for the females for the data of 2007-2008. The geographic spread of the Type 1 diabetes was found to be similar between people living within the cities and those living in the remote areas. From these numbers in the Type 1 diabetes, it is worth noting that the number of Australians suffering the Type 1 diabetes is very low as compared against the total number of people suffering from diabetes in general. Indeed even this number is presumed to be higher than it should be since there is possibility that some respondents suffering Type 2 diabetes might have presumed they suffer Type 1 diabetes merely from the fact that they were using insulin to control the effects. Those diagnosed with Type 2 diabetes was pegged at 787,500 in Australia based on the self reports within the 2007-2008 report as above. Of this number, 444,300 were observed to be males, representing 56% and the remaining 343,200 to be females. It was also noted that 92% of those suffering the Type 2 diabetes were 45 years old and above and yet out of this, another 45% being 65 years and above. It is indicated that 3.8% of all Australians were diagnosed with the Type 2 diabetes with a higher proportion being the males. 4.3% of the Australian males were diagnosed with Type 2 diabetes and 2.2% of the females diagnosed with the same. It is also noted that the proportion of people with Type 2 diabetes increased with the increase in age, this give 0.1% of ages 0-34 years suffering Type 2 diabetes to a high of 14.7% of ages 65-69. Amazingly the proportion goes down again to 12.4% among the ages 80 years and above. These are outstanding statistics and numbers that should attract ever y person's attention and hence the government has in a major way gone out of its way to ensure there is control of the disease. This effort must be kept up bearing the mortality rates fro diabetes related complications and will be analyzed hereafter. Similarities and differences between Type 1 and Type 2 diabetes Being that diabetes is divided into the two types as indicated above, there are various reasons for the categorization hence the differences despite the fact that they are both diabetes, hence the observable similarities between the. Similarities; according to Diabetic Supplies Inc. (2013) Both types are caused by insufficiency in insulin within the body system hence the rise in blood glucose levels. The use of injection of insulin to control the disease can and does apply to both types. Both Type 1 and Type 2 diabetes predispose the victims to development of cardiovascular diseases. Both types can and have led to Retinopathy, which is the leading cause of blindness of Australians between the age of 30-69 according to Baker IDI, Heart & Diabetes Institute (2013). Retinopathy is known to affect one in every six diabetes victims. The other similarity is that both types can cause impaired kidney function and this is at the rate of three times the likelihood of having kidney function impairment among the diabetes patients as compared to those without. Diabetes is known to be the leading cause of kidney failure. Both types are known to predispose the patients to neuropathy which is a peripheral nerve disease, as well as blood vessels damage that may lead to foot problems, ulcers and eve amputation in some cases. Diabetes is known to be 15 times more the cause of amputation among those with diabetes as compared to those without diabetes. Both types can also cause erective dysfunction as well as complications during birth. Both types are also known to be the cause for the premature mortality rates with an estimated 12-14 years of life lost. In both cases, the other similarity they have is that early detection, effective treatment and continued good management can significantly lead to reduction in the diabetic related complications and even death. Differences; according to Rheem G., (2013) While Type 1 diabetes is basically an auto-immune disease where the cells responsible for producing insulin in the pancreas are attacked by the immune system and the person has no control over this, the Type 2 diabetes is caused by predisposition to risk factors as docile living conditions, poor diet and obesity. While Type 1 diabetes renders the patient dependent on lifelong injection of insulin into the body to help manage the blood glucose levels, the Type 2 diabetes can be managed through change of lifestyle. Type 1 diabetes occurs predominantly among children and young adults, yet the Type 2 diabetes is predominant among the ages of 40 years and above. The victims of Type 1 diabetes are usually of normal weight or thin yet those of Type 2 are usually obese or overweight. In type 1 diabetes, the body produces no insulin or too little to be of significant function for the body while in Type 2 the body produces insulin but does not utilize it effectively. The onset of the Type 1 diabetes is usually observed to be sudden while that of Type 2 is observed to be gradual and develops over years to reach full scale. Type 2 diabetes and Chronic Kidney Disease It has been noted that the number one cause of Chronic Kidney Disease (CKD) is diabetes which causes damage to the tiny blood vessels in the kidney over a number of years (National Kidney Foundation, 2007). This is a condition that is caused by both types of diabetes and not restricted to Type 2 diabetes alone. Basically, the kidney is made up of up to one million nephrons which are the filter units of the kidneys. Within the nephrons are the glomerulus which are tiny blood vessels. Damage to these filters is what is referred to as the CKD or diabetic nephropathy. At this state the kidney cannot filer blood as efficiently as it should. Early detection and treatment can help save the kidneys from further damages. Since Type 2 diabetes is characterized by high blood glucose levels, the sugar in the blood, passing through the kidney can cause the blood vessels within the nephrons to be clogged hence get narrower than usual. This deprives the kidney of blood hence damaging the kidney. Therefore, albumin, a type of protein, passes through the kidney and the filters ending up in the urine yet this should not be case. Diabetes is also known to damage the nerves within the body, yet it is the nerves that are responsible for the passing of information between the brain and the various body sections, the bladder included. The nerves informs the brain when the bladder is full and hence the need to empty it. Upon the damage of the nerves, such information on the bladder is not passed to the brain hence continued retention of the urine within the body. This builds up pressure and this pressure from the bladder can damage the kidney. The other effect is that, when the urine stays for abnormally long time within the bladder, it may cause urinary tract infection due to the bacteria in the urine. Diabetes is a predisposing factor to CKD since it is estimated that a third of the people with diabetes end up with CKD. However, there are other factors that can contribute to the development of the disease such as the age (65 years and onwards), if one has high blood pressure, family history of CKD and also race such as if one is African-American, Hispanic-American, Pacific Islander or American Indian then the risks are higher than the others. It is important generally to keep the blood sugar low and to manage the blood pressure too. The symptoms of CKD are not very specific though one of the most predominant is the fluid buildup within the body. Other symptoms observed are stomach upset, poor appetite, loss of sleep, weakness and lack of concentration (Kidney Health Australia, 2008). Management of Diabetes Being that it is a disease that can predispose the person to other forms of diseases, it is significant for the person to know how to manage the diabetes, having known the type, so that he can avoid further complications. Of particular case study here is the Type 2 diabetes and how to manage it, bearing the fact that it is the most prevalent type yet it is the one that can be avoided and even managed effectively till it clears off. Management of Type 2 diabetes can take two major approaches among other; Diet modification- The patient needs to know that the excessive sugar in the blood is responsible for the Type 2 diabetes. Hence, the tight control of sugar levels within the diet of the individual will reduce significantly the risk of microalbuminuria. The patient should also be encouraged to take very low protein diet. This is in recognition of the fact that a lot of protein strains the kidney and the kidney has to work extra hard hence subsequent ineffectiveness (American Diabetes Association, 2013). Foods that are high on fats should also be avoided since they lead to weight gain and that is a predisposing factor to diabetes. The patient should also be encouraged to eat high fiber carbohydrates instead of the refined ones that have no fiber. Lifestyle changes -- the docile lifestyle is a central factor to people gaining weight hence predisposing themselves to diabetes. Type 2 diabetes can easily be managed by regular exercises. The regular exercises ensures the control of the blood sugar as it gets metabolized in the process and also, taking into account the weight of most Type 2 diabetes victims, to prevent the possibility of cardiovascular disease (The Global Diabetes Community, 2013). The central reasoning behind the exercise and active lifestyle is that, the muscles that are active and are working use up more glucose that docile muscles. The strained and exercising muscles lead to higher uptake of sugar by the muscle cells and the consequent lowering of the blood sugar. References American Diabetes Association, (2013). Kidney Disease (Nephropathy). Retrieved May 13, 2013 from http://www.diabetes.org/living-with-diabetes/complications/kidney-disease-nephropathy.html Australian Government, Department of Health and Ageing, (2012). Diabetes. Retrieved May 13, 2013 from http://www.health.gov.au/internet/main/publishing.nsf/Content/pq-diabetes Australian Institute of Health and Welfare, (2011). Diabetes Prevalence in Australia Detailed estimates for 2007 -- 08. Retrieved May 13, 2013 from http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=10737419307 Baker IDI, Heart & Diabetes Institute (2013). Diabetes: The Silent Pandemic and its Impact on Australia. Retrieved May 13, 2013 from http://www.diabetesaustralia.com.au/Documents/DA/WhatsNew/12.03.14DiabetesmanagementbookletFINAL.pdf Diabetes Australia, (2013). Diabetes -- the Facts. Retrieved May 13, 2013 from http://www.diabetesaustralia.com.au/PageFiles/1615/thefactsDAFINAL2011.pdf Diabetic Supplies Inc. (2013). Similarities in Type 1, 2 Diabetes. Retrieved May 13, 2013 from http://www.diabeticsuppliesinc.com/type2diabetic/29-similaritiestype12diabetes.html Kidney Health Australia, (2008). Diabetic Kidney Disease. Retrieved May 13, 2013 from http://www.kidney.org.au/ForPatients/Management/DiabetesandCKD/tabid/704/Default.aspx National Kidney Foundation, (2007). Diabetes and Chronic Kidney Disease. Retrieved May 13, 2013 from http://www.kidney.org/atoz/pdf/diabetes.pdf Rheem G., (2013). Types 1 and 2 Diabetes: A Comparison and Contrast. Retrieved May 13, 2013 from http://amarris.homestead.com/Type_I_and_II_diabetes.htm The Global Diabetes Community, (2013). Diabetes and Exercise. Retrieved May 13, 2013 from http://www.diabetes.co.uk/exercise-for-diabetics.html Read the full article

[visionaryvogues03]

How Fintech Disruption is Reshaping Traditional Banking: Opportunities and Challenges

The Fintech disruption underlies banking products & processes for savings, lendings, & business services renders the sector ripe for disruption. This disruption in banking has brought us enhanced financial tools, online payments, & crowdfunding with InsurTech (insurance technology) solutions. All of this was done by brilliant minds that came together to understand the peculiarity of design thinking & created services in an environment where banks struggled. The fintech innovators deeply understood the real struggle of customers when they need to perform banking services.

The Evolution of Fintech Disruption

Fintech disruption is not a new phenomenon, but its acceleration over the past decade has been remarkable. The rise of mobile banking, peer-to-peer lending, and digital wallets has fundamentally changed customer expectations. Traditional banks, once the sole gatekeepers of financial services, now face increasing competition from agile fintech startups that offer innovative, customer-centric solutions.

The COVID-19 pandemic further fueled this shift, with digital adoption skyrocketing as consumers and businesses sought seamless, contactless financial solutions. Today, banking transformation is not just a trend—it is a defining force shaping the future of banking.

Opportunities Created by Fintech Disruption

While fintech disruption presents challenges for legacy institutions, it also opens doors for strategic innovation. Some of the most notable opportunities include:

1. Enhanced Customer Experience

One of the key drivers of fintech disruption is its ability to deliver superior customer experiences. Traditional banks, often hindered by outdated systems and bureaucratic processes, struggle to match the seamless, intuitive interfaces of fintech platforms. Digital-only banks and neobanks offer faster onboarding, personalized services, and real-time transaction capabilities, raising customer expectations across the board.

Fintech firms also leverage big data analytics and artificial intelligence to personalize financial products, tailoring services based on user behavior. This hyper-personalization is something traditional banks must rapidly adopt to remain competitive in the modern landscape.

2. Expansion of Financial Inclusion

Digital banking disruption has enabled access to financial services for previously underserved populations. Mobile banking and digital lending platforms have reached millions of unbanked individuals worldwide, particularly in emerging markets. By leveraging AI-driven credit scoring models, fintech companies can provide financial products to consumers without traditional credit histories, fostering economic growth and inclusion.

In many developing nations, fintech solutions are bridging the gap between financial exclusion and accessibility, allowing small businesses and individuals to gain access to capital and transactional capabilities that were previously unattainable.

3. Cost Efficiency and Automation

Automation powered by artificial intelligence and machine learning has allowed financial institutions to reduce operational costs while improving efficiency. Chatbots, robo-advisors, and AI-driven risk assessments streamline banking services, minimizing human intervention and enhancing accuracy. Traditional banks that embrace these technologies can significantly cut costs and improve their bottom line.

Additionally, fintech startups are leveraging cloud-based solutions to eliminate the need for costly infrastructure, offering leaner and more agile banking alternatives that operate with greater efficiency.

4. Blockchain and Decentralized Finance (DeFi)

Blockchain technology, a major driver of fintech disruption, is reshaping the banking sector through decentralized finance (DeFi). Smart contracts, tokenized assets, and decentralized exchanges eliminate intermediaries, offering faster and more transparent financial transactions. Banks that integrate blockchain technology can improve security, reduce fraud, and enhance transactional efficiency.

The impact of DeFi is extending beyond simple transactions, introducing new lending models, staking rewards, and innovative credit mechanisms that challenge the very foundation of traditional banking practices.

Challenges Posed by Fintech Disruption

Despite the immense opportunities, fintech-driven disruption also brings significant challenges for traditional banks and regulators.

1. Regulatory Uncertainty and Compliance Issues

The rapid growth of fintech has outpaced regulatory frameworks in many regions. Traditional banks operate under strict compliance requirements, while fintech startups often function in more loosely regulated environments. As governments worldwide attempt to regulate fintech disruption, banks and startups alike must navigate complex and evolving legal landscapes.

Regulatory frameworks also differ across countries, creating additional hurdles for fintech firms looking to expand globally. Banks must adapt to these variations while ensuring compliance with local financial laws and data protection regulations.

2. Cybersecurity Risks and Data Privacy Concerns

With fintech disruption comes an increased risk of cyber threats and data breaches. Digital-first financial services rely heavily on cloud computing, open banking APIs, and third-party integrations, which create vulnerabilities. Banks and fintech companies must invest heavily in cybersecurity measures to protect consumer data and maintain trust.

Cybercriminals are continually evolving their tactics, making it essential for financial institutions to stay ahead with robust security protocols, biometric authentication, and end-to-end encryption.

3. Competition and Market Saturation

The fintech ecosystem is rapidly expanding, with thousands of startups entering the market each year. Traditional banks not only compete with these disruptors but also need to differentiate themselves from the growing number of fintech players. Partnerships between banks and fintech firms have become a strategic necessity to stay competitive in an increasingly saturated market.

The competition also forces traditional banks to innovate rapidly, sometimes leading to rushed digital transformation efforts that may not be fully optimized or secure.

4. Legacy Infrastructure and Digital Transformation Challenges

Many traditional banks still rely on outdated legacy systems that are not built for the agility of modern fintech solutions. Transitioning to a digital-first model requires significant investment, cultural shifts, and technological overhauls. Banks that fail to adapt risk becoming obsolete in the face of rapid digital banking disruption.

Financial institutions must undergo comprehensive digital transformation, modernizing their IT infrastructure, training employees, and rethinking customer engagement strategies to align with new-age banking trends.

The Future of Banking in a Fintech-Driven World

Looking ahead, traditional banks must embrace digital banking disruption as an opportunity rather than a threat. The future of banking will likely involve greater collaboration between legacy institutions and fintech innovators. Banks that successfully integrate fintech capabilities—whether through in-house innovation, strategic partnerships, or acquisitions—will be better positioned to thrive in this evolving landscape.

Regulators, too, play a crucial role in shaping the future of financial services. Balanced regulatory frameworks must encourage innovation while ensuring consumer protection and financial stability. Governments worldwide are actively working on establishing clearer fintech regulations to bridge the gap between innovation and compliance.

The integration of AI, blockchain, and big data analytics will continue to redefine the banking industry, creating new avenues for personalized financial services, automated wealth management, and secure digital transactions.

Conclusion

Fintech disruption is undeniably reshaping traditional banking, presenting both groundbreaking opportunities and formidable challenges. From enhancing customer experience and financial inclusion to navigating regulatory complexities and cybersecurity threats, the financial industry is at a critical juncture.

To stay relevant in an era of fintech disruption, traditional banks must prioritize digital transformation, adopt emerging technologies, and foster strategic collaborations. By doing so, they can not only survive but thrive in this dynamic and rapidly evolving financial ecosystem.

The future of banking belongs to those who can seamlessly blend the best of both worlds—leveraging the trust and stability of traditional banking with the agility and innovation of digital banking disruption.

Uncover the latest trends and insights with our articles on Visionary Vogues

[britesparc]

Weekend Top Ten #679

Top Ten Games of the 2010s

Once again I return to the whole decade-based ranking thing, this time turning my beady eye Sauron-like onto the world of videogames. This is a good week to do so, too, because of the announcement of the Videogame BAFTA nominations; a nice list, I thought, that celebrated a lot of great British talent (including the exquisitely British Thank Goodness You’re Here, which I really must play soon).

However, we’re not here to talk about the present; no, we’re journeying back in time, like Liev Schreiber in sci-fi classic Kate & Leopold. But rather than cock up the invention of the elevator and get sectioned under the mental health act, I’m instead going to rank videogames that were released in the 2010s. That is, to be irritatingly clear, the decade that bestraddles the years 2010-2019 inclusive. Come on, it’s not that hard, I did the same thing about films a couple of weeks ago.

This feels like the decade when, as much as I retained a vested interest in gaming, the amount of time I could actually spend on the things began to seriously dwindle. I remember when Mass Effect 3 came out, which coincided more or less with the birth of Daughter #1, that it took me about eighteen months to complete it. Back then, I thought it was insane that it took that long; nowadays, it’s pretty normal for me to spend a year or more playing a large story-based game. I’m trying to think about what the last really big RPGs I completed were, and how long it took; look back at my data (yes I have data), Starfield took me about fourteen months, and Cyberpunk 2077 about eleven months; and this is when the kids are old enough to put themselves to bed! And when I say “completed” for a game like this, I’m just talking about finishing the story more. There’s loads I could still do in these games, and whilst I’ll be going back to finish the Cyberpunk DLC at some point this year, I doubt I’ll ever play Starfield again (sorry guys).

Also, I feel this is the decade where the fact that I never play PlayStation games and rarely play Nintendo ones, and the fact that there are entire franchises I just basically don’t touch, becomes increasingly evident. There’s no The Last of Us on this list, no God of War. Similarly, there’s no GTA, because I played GTA IV too close to Crackdown, and the fact you couldn’t hop from one roof to another or throw cars at people just rendered Rockstar’s effort way too dull for my refined palette. As such, I had no interest in GTA V. And then there are other games that either didn’t tickle my fancy or I just never got round to: everything from Dark Souls to Destiny, Fallout to Fortnite. What I’m trying to say is, if you’re a big gamer, chances are you’ll notice some big games missing from this list.

Before I start talking about some tremendous classics, however, I did want to sort of tip my hat to a few games that I think could, or should, or even nearly were, on this list. Games like Witcher III, Spider-Man, Life is Strange, and especially Breath of the Wild. These are games that I’ve played, and enjoyed, but for various reasons never quite got into them; certainly, never came close to completing them. I’m giving myself a pass with Spider-Man as I’ve only got it on PC, and I tend not to enjoy playing action games on PC anymore, and also I’ve not really had it very long; but Zelda I’ve had for about five years and barely scratched the surface. I’m not sure why, as it “fixes” (for want of a better word) almost every issue that’s caused me to bounce off previous Zelda games; and yet here we are. I think it’s probably because I tend to play the Switch handheld, and like playing an action game on the PC, I find it difficult to get emersed in a dense RPG-esque game when I’m staring at my hands. Pokémon Sword was okay, but then again Pokémon Sword was, well, only okay; but generally I find the Switch more suitable for games like Mario + Rabbids or Animal Crossing or Civilization. So, look, Breath of the Wild is a classic and a masterpiece and all the rest; but I have to confess that, late at night, when I could have played anything in the world, I instead chose to fire up the Xbox and play Crackdown 3. I don’t know what to tell you.

Crackdown 3 is also not on the list.

Sid Meier’s Civilization VI (2016): my love for the whole Civ endeavour has abated somewhat, due to the developers – in my opinion – ditching everything I love about the franchise with Civ VII (so drastic a gameplay change that it’s genuinely put me off the older games). however, I can’t ignore the vice-like grip this game has grasped me in for nearly a decade. It’s possibly my most-played game of all time; I’ve completed it I don’t know how many times, probably in the hundreds. As far as these sorts of games go, I think this one is exceedingly accessible; the gameplay tweaks (districts built outside the city for instance) work really well to its advantage. The colourful graphics allow for really easy sight-reading of situations. And it’s fun. It’s funny. You get to tell your own alternative history across the millennia, crafting a civilization that feels exclusively yours. It’s a masterpiece, it really is. One of the greatest games of all time. The fact they’ve ditched the specific thing about it that I love the most makes me so sad.

Mass Effect 2 (2010): playing Avowed recently has reminded me how much I love this style of open but quest-based adventure-RPG, and at the time Mass Effect was clearly the pinnacle. Building on the success of both Knights of the Old Republic and the first Mass Effect, BioWare gave us a glorious sci-fi universe of wonderful aliens, with gameplay systems that were easy to understand but also delightfully deep. More than anything else, though, it’s the writing that sticks; a cast of amazing characters who you really wanted to spend time with. Knowing how much you’d just like chatting with these guys, the game rewards you for your investment, giving you structured missions built around each character. One of the first times a game felt like a prestige streaming drama, years before those things became popular.

Mario Kart 8 Deluxe (2017): I find Mario Kart to be one of those series that’s so ubiquitous you forget about its greatness. But doesn’t everyone love Mario Kart? It’s a bright and colourful racer, easy to understand but with hidden quirk and complexities for the purist. This version is expansive, with a squillion characters and tons of options. At its core remains the incredibly fun gameplay experience that’s been there since the start, tinkering with and refined over generations with new concepts (bikes, gliders, hills). Arguably the greatest multiplayer game of all time. There might be a new one this year. Callooh callay.  

Slay the Spire (2019): technically I don’t think I played this until this decade, but it came out in 2019 so it totally counts. These deck-building rogue-like games are increasingly popular, and I’ve discovered – to some mild surprise, I’ll admit – that they are absolutely my jam. Battling baddies by dealing cards with different abilities is a nuanced and tactical affair, but there’s always the accursed spectre of luck to kick your legs from under you. Slay the Spire’s art style and fairly stripped-back approach to its decks and its battles – less complex or varied than, say, Monster Train – is, I think, the thing that keeps me coming back for more. It feels like an almost infinite number of possibilities with each encounter, making starting a new game a joy.

Lego Marvel Super Heroes (2013): it feels like my adoration of the Lego games might be something that’s sliding into the rearview mirror – Skywalker Saga was great in a lot of ways but also flawed in others – and this one remains the overall best of the bunch. The Marvel universe is a perfect fit for the blasting and brawling these games present, giving us a larger and more varied roster of abilities than Star Wars, with less nerfing of the over-powered than you have to do with DC. The open world of Marvel New York gives you a great canvas for play, as well as much to discover and do; but the missions themselves are brilliantly conceived and designed, and also serve as a cool showcase for characters and powers. And, of course, these things are a delight to play with children.

Forza Horizon 4 (2018): the Horizon series has taken Project Gotham’s crown as my go-to arcade racer (outside of the goofiness of Mario Kart, natch). The open worlds they present, in glorious fidelity, coupled with the score-attack nature of the game’s points sytem, and the accessibility presented by being able to rewind time if you make a mistake, all adds up to one incredibly fun package. Truth be told, the individualities of each iteration kinda blur together; each one is great in its own way. This one’s probably my favourite, though, as it’s set in the North of England (and Scotland).

Doom (2016): the very idea of remaking the practically-perfect, genre-defining original Doom in the modern age seemed folly; after all, FPS games had come on immeasurably in the nearly twenty years since. But this Doom managed to bottle lightning. Retaining both the creepiness of its forbearer, as well as its penchant for incredibly fast-paced, gory gameplay, this nu-Doom innovated by forcing us players to get up close and personal, rewarding us for grisly melee kills, making it almost as much a puzzler as a blaster. Divine.

Portal 2 (2011): for me it somehow lacks the tiniest bit of magic from the first game, but still – blimey. Even more brain-frying puzzles to solve using, well portals (and lasers, and cubes, and pass-agg computers), with some incredibly funny writing and performances. Like its predecessor, it features sumptuous yet subtle world-building. A work of twisty genius.

Hellblade: Senua’s Sacrifice (2017): on the one hand, this is a brilliant effort from a very small team. A game of high fidelity featuring some brilliant effects and modelling, and a stupendous central performance, it has the trappings of a third-person hack-and-slash affair but is actually a complex puzzle game, requiring a great deal of environmental awareness and the ability to think beyond what you can clearly see. However, that’s not what makes it so great; it’s the phenomenal sound design, and how that contributes to an incredibly nuanced and moving story of trauma and mental health, as Senua battles with her own condition as much as she battles with gods. Prickly and powerful, like an interactive A-24 drama.  

Minecraft (2011): how could I not include this? One of the most influential and important games of all time. In its scope and complexity, it offers an entire world: both a robust survival RPG and a complex construction kit. However, it’s how accessible it is, how child-friendly, that really makes it shine. This is more than a playground, more than a sandbox; it’s basically an entire videogame ecosystem in one title. And kids have taken it to their heart, tearing it down and building it back up again. Attempts to diversify can be fun – Minecraft Dungeons – but they move too far away from the core of: what if Lego but also D&D and also Doom and also I am Legend and also and also and also…?