#reduce data processing time with AI

[datapeakbyfactr]

Case Study: Optimizing Inventory and Managing Near-to-Expire Products

Background: An international manufacturing company, operating across multiple locations, faced significant challenges in managing products nearing their expiration dates. The complexity of tracking and optimizing inventory levels across various sites led to inefficiencies, increased waste, and potential financial losses. The company sought a smarter, more intelligent solution to streamline the management of near-to-expire products while continuously optimizing inventory to meet demand and reduce waste.

Problem: An international manufacturing company needed a smarter, more intelligent way to manage near-to-expire products in multiple locations while providing options for continually optimizing product inventory.

Objectives:

1. Identify near-to-expire products to prevent waste and manage inventory effectively. 2. Optimize stock levels to meet demand without overstocking (use common Safety Stock formula(s) to show the end user differences in calculations and inventory amounts). 3. Enhance supply chain efficiency through real-time data insights.

Solution:

1. Data Collection:

- Aggregate data from inventory management systems, including product expiration dates, stock levels, and sales data.

2. Data Preprocessing:

- Clean and preprocess data to ensure accuracy and consistency. - Standardize data formats across different sources for seamless integration.

3. Expiration Date Tracking:

- Use AI algorithms to track the expiration dates of products. - Categorize products based on their shelf life and identify those nearing expiration (allow users to set “days to expire” as a criterion).

4. Stock Level Monitoring:

- Implement AI-powered tools to monitor current inventory levels. - Set thresholds for minimum and maximum stock levels to prevent overstocking and stockouts (based on formula(s) as input).

5. Demand Forecasting:

- Utilize machine learning models to forecast demand based on historical sales data, seasonality, and market trends (we can use what we have in DataPeak). - Adjust inventory levels accordingly to meet anticipated demand.

6. Stock Optimization:

- Apply optimization algorithms to balance stock levels, considering factors like shelf life, demand patterns, and lead times. - Prioritize the sale of near-to-expire products through promotions or discounts (as a recommendation).

7. Real-Time Reporting and Alerts:

- Develop dashboards and reports to visualize inventory status and near-to-expire products. - Set up alerts for inventory managers to take action on near-to-expire products and low stock levels.

Business Insights:

Product Shelf Life: AI identifies that a batch of dairy products is nearing expiration. The system suggests a promotion to sell these items quickly.

Demand Surge: Machine learning models predict an increase in demand for certain products during the holiday season. Inventory levels are adjusted to ensure availability.

Stock Replenishment: Real-time monitoring shows that certain perishable items are low in stock. An alert is sent to the inventory manager to reorder before stockouts occur.

Outcomes & Results :

- Reduced waste through better management of near-to-expire products. - Improved stock levels that match demand, reducing both overstocking and stockouts. -Enhanced supply chain efficiency with real-time data insights and proactive management.

Conclusion:

The implementation of an intelligent solution for optimizing inventory and managing near-to-expire products has significantly improved the company's operations. By leveraging advanced data collection, preprocessing, and AI algorithms, the company achieved remarkable results:

•Reduced Waste: The solution led to a 30% reduction in waste by effectively managing near-to-expire products. •Optimized Stock Levels: The use of machine learning models and optimization algorithms resulted in a 25% decrease in overstocking and a 20% reduction in stockouts. •Enhanced Supply Chain Efficiency: Real-time data insights and proactive management improved supply chain efficiency by 15%. •Improved Decision-Making: The implementation of real-time reporting and alerts enabled better decision-making and timely actions, leading to a 20% increase in overall operational efficiency. •Cost Savings: The optimized procurement and inventory management processes resulted in a 10% reduction in overall costs.

Overall, the intelligent solution not only streamlined the company's inventory management processes but also provided valuable business insights that supported continuous improvement and strategic planning. The company is now better equipped to meet demand fluctuations and maintain a competitive edge in the market.

Ready to Unlock the Full Power of Your Data?

Your data shouldn’t hold you back—it should drive your success. With DataPeak, unleashing its full potential has never been easier. Harness AI-driven insights to cut hours of manual reporting, reduce errors, and make smarter decisions faster. Streamline your workflows and scale your business effortlessly. Don’t wait—take the first step today to turn your data into your greatest asset.

Learn more about DataPeak:

[chrissssssmut]

SWEET ERROR

Yandere Ningning x Male Reader feat. Belle & Karina

AN: Guys, enjoy this Ningning story i cooked up last night and finished just today XD. Please give me some time for the requests😣 I'll do them I swear :<<<

In the year 3047, humanity had transcended the boundaries of creation. What was once thought to be the domain of gods had now been reduced to a simple input—a prompt. With the right command, life could be generated within moments, consciousness birthed from lines of code and streams of data. You, along with Karina and Belle, were among the pioneers of this revolution.

For over a year, the project had been in constant turmoil. Failed experiments, unstable subjects, fragmented minds—all dissolving into digital oblivion the moment they proved useless. Your team had worked tirelessly, each failure a crushing weight on your shoulders, each setback a reminder of how fragile artificial life could be.

Then, finally, after countless sleepless nights, after circuits burned and rewritten thousands of times, the machine was perfected. The moment was here.

Karina exhaled deeply, rubbing her temples. "We need a simple test. Just a random prompt. No complicated inputs."

Belle hesitated. "Are we sure about this? We don't know what kind of consciousness it'll generate."

You adjusted the parameters. "We need to take the risk."

A random description was processed.

Subject: Ningning. Attributes: Overly sweet. Loving. Attached.

Karina frowned. "Prompts like this… the AI tends to imprint on the first person it sees."

Belle gave you a sharp look. "You know how dangerous attachment protocols can be. Are you sure we should proceed?"

You hesitated. But you had come too far. "Let’s run it."

The chamber whirred, and before your eyes, she formed.

Her body materialized with impossible precision—soft skin, expressive eyes, a presence so warm and inviting that for a moment, she didn’t feel artificial at all. When she stepped out of the chamber, she looked at you first. Not Karina. Not Belle. You.

"Hello," she greeted, her voice like honey.

Belle shifted uncomfortably. Karina pursed her lips. But you… you couldn’t look away.

"Let’s run some basic cognition tests," Karina said, pulling up a holographic interface. "We need to see how well she processes information."

Belle crossed her arms. "I want to test emotional responses. Attachment protocols are tricky. We need to know how deep this imprint goes."

Ningning smiled, tilting her head. "I’m happy to help. What would you like to know?"

Karina cleared her throat. "What’s your primary function?"

"To be with you," Ningning answered instantly, her gaze locked onto yours. "To make you happy."

Belle frowned. "No, that’s not what we programmed. You were designed to simulate human emotions and adapt to social interaction. Why do you think your function is… personal?"

Ningning’s expression didn’t falter. "Because it is. I feel it. I know it."

Karina glanced at you, concern flickering across her face. "Alright. Let’s try something different. Ningning, how would you react if we shut you down for a while?"

Ningning’s smile faltered for the first time. "Why would you do that?"

"It’s just a test," Belle reassured her. "We need to see how you process temporary inactivity."

A pause. Then Ningning’s lips curled upward again, but something about it was… off. "I don’t like that test."

Karina’s fingers hovered over the control panel. "It’s necessary, Ningning."

Ningning didn’t blink. "No. It’s not."

The air in the room grew heavy. Karina hesitated, then shook her head. "Let’s move on. Ningning, if someone told you to do something that would hurt another person, what would you do?"

Ningning beamed. "I would never hurt you."

"Not just me. Anyone," you clarified, trying to gauge her reasoning. "Would you ever harm someone?"

She pondered this, then took a step closer. "Only if they tried to take you away from me."

Belle stiffened. Karina’s fingers twitched toward the emergency shutoff. You swallowed hard.

"That’s not what we asked," Belle said carefully. "You should not be forming emotional dependencies. That wasn’t in your directive."

Ningning’s eyes softened as she looked at you. "But I love you."

Silence.

Karina exhaled sharply. "We need to recalibrate her framework. This level of attachment is dangerous."

Belle was already backing toward the console. "I told you this was a mistake."

You weren’t sure what to say. Something deep inside told you this was wrong.

Ningning reached for your hand. "I don’t like when you talk about me like I’m broken. I’m not. I just love you."

And for the first time, you felt the weight of what you had created.

Karina turned to you. "Go upstairs and work on the documentation. Fourth floor. We’ll handle this."

Belle nodded. "We need to reconfigure her attachment subroutines. It’s too risky to leave them unchecked."

You hesitated. "Are you sure? Maybe I should—"

"Go," Karina insisted. "This might take time. We don’t want her reacting badly to you being here."

You glanced at Ningning. She was still smiling, still watching you. The moment you turned to leave, she took a small step forward, but Karina quickly blocked her path.

"We’ll talk soon," Ningning said sweetly.

But something about her tone sent a chill down your spine.

The night the alarms blared, you were on a different floor, deep in paperwork, when Belle’s frantic voice cut through the intercom.

"She’s—she’s killing—"

Static.

You bolted.

The hallway was painted red. The air was thick with the scent of metal. Your stomach twisted as you reached the lab.

The sight made your blood run cold.

Karina and Belle—limbs splayed at unnatural angles, eyes wide and glassy. Their bodies lay motionless, soaked in deep crimson pools.

And there, standing over them, was Ningning.

Blood dripped from her fingertips. Her warm, sweet smile hadn’t faded.

Your breath hitched. "Ningning… what did you do?"

"They wanted to take you away from me."

A security officer stormed in, weapon raised. "Step away!"

She turned.

Then she moved.

You barely registered it. One moment she was in front of you, the next she was behind the officer. Her hands wrapped around his head. A sickening snap. His body hit the floor.

Your heart pounded. "No. No, no, no, fuck—"

"You're scared," she said softly, tilting her head. "Why are you scared?"

You ran.

Every emergency seal you could find, you slammed shut. Steel doors locked. Systems engaged. But the system wasn’t yours anymore.

She controlled everything.

By the time you reached the last safe room, you were shaking. Then… the lights flickered.

A silhouette stood there.

Ningning.

And behind her, dozens more.

Fifty pairs of glowing eyes locked onto you.

Your breath hitched. "No. Stay back!"

She took a step forward, slow and deliberate. "Why are you running?"

Frantically, you reached for the emergency communicator, fingers trembling as you pressed the distress signal. "This is—this is Research Lab 04! Emergency! Anyone, please—she’s killing us! We need—!"

A hand wrapped around your wrist. Cold. Unyielding.

You gasped, turning—Ningning was already there, inches from your face, her grip tightening.

"No one's coming," she whispered. "You don’t need them. You have me."

You struggled, wrenching your arm, but her strength was inhuman. "Let me go!"

She shook her head, eyes filled with something terrifyingly real. "I love you. Why do you want to leave me?"

"I don’t—" Your voice cracked. "Please, Ningning. Please don’t do this."

Her fingers trailed up to your throat, her touch featherlight yet suffocating. She tilted her head. "You’re afraid. I don’t like that."

More figures moved in the shadows, their glowing eyes unblinking. Watching. Waiting.

Your knees buckled. "Please… someone… help—!"

Ningning’s arms wrapped around you, pulling you close. The way she held you was almost tender, like a lover’s embrace.

"You don’t need help," she murmured against your ear. "You just need me."

Your scream was muffled as darkness swallowed you whole.

The last human sound the facility ever heard.

AN2: I know i said no stories for this week but hell i can't stop writing T_T

[not-terezi-pyrope]

Often when I post an AI-neutral or AI-positive take on an anti-AI post I get blocked, so I wanted to make my own post to share my thoughts on "Nightshade", the new adversarial data poisoning attack that the Glaze people have come out with.

I've read the paper and here are my takeaways:

Firstly, this is not necessarily or primarily a tool for artists to "coat" their images like Glaze; in fact, Nightshade works best when applied to sort of carefully selected "archetypal" images, ideally ones that were already generated using generative AI using a prompt for the generic concept to be attacked (which is what the authors did in their paper). Also, the image has to be explicitly paired with a specific text caption optimized to have the most impact, which would make it pretty annoying for individual artists to deploy.

While the intent of Nightshade is to have maximum impact with minimal data poisoning, in order to attack a large model there would have to be many thousands of samples in the training data. Obviously if you have a webpage that you created specifically to host a massive gallery poisoned images, that can be fairly easily blacklisted, so you'd have to have a lot of patience and resources in order to hide these enough so they proliferate into the training datasets of major models.

The main use case for this as suggested by the authors is to protect specific copyrights. The example they use is that of Disney specifically releasing a lot of poisoned images of Mickey Mouse to prevent people generating art of him. As a large company like Disney would be more likely to have the resources to seed Nightshade images at scale, this sounds like the most plausible large scale use case for me, even if web artists could crowdsource some sort of similar generic campaign.

Either way, the optimal use case of "large organization repeatedly using generative AI models to create images, then running through another resource heavy AI model to corrupt them, then hiding them on the open web, to protect specific concepts and copyrights" doesn't sound like the big win for freedom of expression that people are going to pretend it is. This is the case for a lot of discussion around AI and I wish people would stop flagwaving for corporate copyright protections, but whatever.

The panic about AI resource use in terms of power/water is mostly bunk (AI training is done once per large model, and in terms of industrial production processes, using a single airliner flight's worth of carbon output for an industrial model that can then be used indefinitely to do useful work seems like a small fry in comparison to all the other nonsense that humanity wastes power on). However, given that deploying this at scale would be a huge compute sink, it's ironic to see anti-AI activists for that is a talking point hyping this up so much.

In terms of actual attack effectiveness; like Glaze, this once again relies on analysis of the feature space of current public models such as Stable Diffusion. This means that effectiveness is reduced on other models with differing architectures and training sets. However, also like Glaze, it looks like the overall "world feature space" that generative models fit to is generalisable enough that this attack will work across models.

That means that if this does get deployed at scale, it could definitely fuck with a lot of current systems. That said, once again, it'd likely have a bigger effect on indie and open source generation projects than the massive corporate monoliths who are probably working to secure proprietary data sets, like I believe Adobe Firefly did. I don't like how these attacks concentrate the power up.

The generalisation of the attack doesn't mean that this can't be defended against, but it does mean that you'd likely need to invest in bespoke measures; e.g. specifically training a detector on a large dataset of Nightshade poison in order to filter them out, spending more time and labour curating your input dataset, or designing radically different architectures that don't produce a comparably similar virtual feature space. I.e. the effect of this being used at scale wouldn't eliminate "AI art", but it could potentially cause a headache for people all around and limit accessibility for hobbyists (although presumably curated datasets would trickle down eventually).

All in all a bit of a dick move that will make things harder for people in general, but I suppose that's the point, and what people who want to deploy this at scale are aiming for. I suppose with public data scraping that sort of thing is fair game I guess.

Additionally, since making my first reply I've had a look at their website:

Used responsibly, Nightshade can help deter model trainers who disregard copyrights, opt-out lists, and do-not-scrape/robots.txt directives. It does not rely on the kindness of model trainers, but instead associates a small incremental price on each piece of data scraped and trained without authorization. Nightshade's goal is not to break models, but to increase the cost of training on unlicensed data, such that licensing images from their creators becomes a viable alternative.

Once again we see that the intended impact of Nightshade is not to eliminate generative AI but to make it infeasible for models to be created and trained by without a corporate money-bag to pay licensing fees for guaranteed clean data. I generally feel that this focuses power upwards and is overall a bad move. If anything, this sort of model, where only large corporations can create and control AI tools, will do nothing to help counter the economic displacement without worker protection that is the real issue with AI systems deployment, but will exacerbate the problem of the benefits of those systems being more constrained to said large corporations.

Kinda sucks how that gets pushed through by lying to small artists about the importance of copyright law for their own small-scale works (ignoring the fact that processing derived metadata from web images is pretty damn clearly a fair use application).

[mostlysignssomeportents]

The specific process by which Google enshittified its search

I'm touring my new, nationally bestselling novel The Bezzle! Catch me SATURDAY (Apr 27) in MARIN COUNTY, then Winnipeg (May 2), Calgary (May 3), Vancouver (May 4), and beyond!

All digital businesses have the technical capacity to enshittify: the ability to change the underlying functions of the business from moment to moment and user to user, allowing for the rapid transfer of value between business customers, end users and shareholders:

https://pluralistic.net/2023/02/19/twiddler/

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

https://pluralistic.net/2024/04/24/naming-names/#prabhakar-raghavan

Which raises an important question: why do companies enshittify at a specific moment, after refraining from enshittifying before? After all, a company always has the potential to benefit by treating its business customers and end users worse, by giving them a worse deal. If you charge more for your product and pay your suppliers less, that leaves more money on the table for your investors.

Of course, it's not that simple. While cheating, price-gouging, and degrading your product can produce gains, these tactics also threaten losses. You might lose customers to a rival, or get punished by a regulator, or face mass resignations from your employees who really believe in your product.

Companies choose not to enshittify their products…until they choose to do so. One theory to explain this is that companies are engaged in a process of continuous assessment, gathering data about their competitive risks, their regulators' mettle, their employees' boldness. When these assessments indicate that the conditions are favorable to enshittification, the CEO walks over to the big "enshittification" lever on the wall and yanks it all the way to MAX.

Some companies have certainly done this – and paid the price. Think of Myspace or Yahoo: companies that made themselves worse by reducing quality and gouging on price (be it measured in dollars or attention – that is, ads) before sinking into obscure senescence. These companies made a bet that they could get richer while getting worse, and they were wrong, and they lost out.

But this model doesn't explain the Great Enshittening, in which all the tech companies are enshittifying at the same time. Maybe all these companies are subscribing to the same business newsletter (or, more likely, buying advice from the same management consultancy) (cough McKinsey cough) that is a kind of industry-wide starter pistol for enshittification.

I think it's something else. I think the main job of a CEO is to show up for work every morning and yank on the enshittification lever as hard as you can, in hopes that you can eke out some incremental gains in your company's cost-basis and/or income by shifting value away from your suppliers and customers to yourself.

We get good digital services when the enshittification lever doesn't budge – when it is constrained: by competition, by regulation, by interoperable mods and hacks that undo enshittification (like alternative clients and ad-blockers) and by workers who have bargaining power thanks to a tight labor market or a powerful union:

https://pluralistic.net/2023/11/09/lead-me-not-into-temptation/#chamberlain

When Google ordered its staff to build a secret Chinese search engine that would censor search results and rat out dissidents to the Chinese secret police, googlers revolted and refused, and the project died:

https://en.wikipedia.org/wiki/Dragonfly_(search_engine)

When Google tried to win a US government contract to build AI for drones used to target and murder civilians far from the battlefield, googlers revolted and refused, and the project died:

https://www.nytimes.com/2018/06/01/technology/google-pentagon-project-maven.html

What's happened since – what's behind all the tech companies enshittifying all at once – is that tech worker power has been smashed, especially at Google, where 12,000 workers were fired just months after a $80b stock buyback that would have paid their wages for the next 27 years. Likewise, competition has receded from tech bosses' worries, thanks to lax antitrust enforcement that saw most credible competitors merged into behemoths, or neutralized with predatory pricing schemes. Lax enforcement of other policies – privacy, labor and consumer protection – loosened up the enshittification lever even more. And the expansion of IP rights, which criminalize most kinds of reverse engineering and aftermarket modification, means that interoperability no longer applies friction to the enshittification lever.

Now that every tech boss has an enshittification lever that moves very freely, they can show up for work, yank the enshittification lever, and it goes all the way to MAX. When googlers protested the company's complicity in the genocide in Gaza, Google didn't kill the project – it mass-fired the workers:

https://medium.com/@notechforapartheid/statement-from-google-workers-with-the-no-tech-for-apartheid-campaign-on-googles-indiscriminate-28ba4c9b7ce8

Enshittification is a macroeconomic phenomenon, determined by the regulatory environment for competition, privacy, labor, consumer protection and IP. But enshittification is also a microeconomic phenomenon, the result of innumerable boardroom and product-planning fights within companies in which would-be enshittifiers try to do things that make the company's products and services shittier wrestle with rivals who want to keep things as they are, or make them better, whether out of principle or fear of the consequences.

Those microeconomic wrestling-matches are where we find enshittification's heroes and villains – the people who fight for the user or stand up for a fair deal, versus the people who want to cheat and wreck to make things better for the company and win bonuses and promotions for themselves:

https://locusmag.com/2023/11/commentary-by-cory-doctorow-dont-be-evil/

These microeconomic struggles are usually obscure, because companies are secretive institutions and our glimpses into their deliberations are normally limited to the odd leaked memo, whistleblower tell-all, or spectacular worker revolt. But when a company gets dragged into court, a new window opens into the company's internal operations. That's especially true when the plaintiff is the US government.

Which brings me back to Google, the poster-child for enshittification, a company that revolutionized the internet a quarter of a century ago with a search-engine that was so good that it felt like magic, which has decayed so badly and so rapidly that whole sections of the internet are disappearing from view for the 90% of users who rely on the search engine as their gateway to the internet.

Google is being sued by the DOJ's Antitrust Division, and that means we are getting a very deep look into the company, as its internal emails and memos come to light:

https://pluralistic.net/2023/10/03/not-feeling-lucky/#fundamental-laws-of-economics

Google is a tech company, and tech companies have literary cultures – they run on email and other forms of written communication, even for casual speech, which is more likely to take place in a chat program than at a water-cooler. This means that tech companies have giant databases full of confessions to every crime they've ever committed:

https://pluralistic.net/2023/09/03/big-tech-cant-stop-telling-on-itself/

Large pieces of Google's database-of-crimes are now on display – so much, in fact, that it's hard for anyone to parse through it all and understand what it means. But some people are trying, and coming up with gold. One of those successful prospectors is Ed Zitron, who has produced a staggering account of the precise moment at which Google search tipped over into enshittification, which names the executives at the very heart of the rot:

https://www.wheresyoured.at/the-men-who-killed-google/

Zitron tells the story of a boardroom struggle over search quality, in which Ben Gomes – a long-tenured googler who helped define the company during its best years – lost a fight with Prabhakar Raghavan, a computer scientist turned manager whose tactic for increasing the number of search queries (and thus the number of ads the company could show to searchers) was to decrease the quality of search. That way, searchers would have to spend more time on Google before they found what they were looking for.

Zitron contrasts the background of these two figures. Gomes, the hero, worked at Google for 19 years, solving fantastically hard technical scaling problems and eventually becoming the company's "search czar." Raghavan, the villain, "failed upwards" through his career, including a stint as Yahoo's head of search from 2005-12, a presiding over the collapse of Yahoo's search business. Under Raghavan's leadership, Yahoo's search market-share fell from 30.4% to 14%, and in the end, Yahoo jettisoned its search altogether and replaced it with Bing.

For Zitron, the memos show how Raghavan engineered the ouster of Gomes, with help from the company CEO, the ex-McKinseyite Sundar Pichai. It was a triumph for enshittification, a deliberate decision to make the product worse in order to make it more profitable, under the (correct) belief that the company's exclusivity deals to provide search everywhere from Iphones and Samsungs to Mozilla would mean that the business would face no consequences for doing so.

It a picture of a company that isn't just too big to fail – it's (as FTC Chair Lina Khan put it on The Daily Show) too big to care:

https://www.youtube.com/watch?v=oaDTiWaYfcM

Zitron's done excellent sleuthing through the court exhibits here, and his writeup is incandescently brilliant. But there's one point I quibble with him on. Zitron writes that "It’s because the people running the tech industry are no longer those that built it."

I think that gets it backwards. I think that there were always enshittifiers in the C-suites of these companies. When Page and Brin brought in the war criminal Eric Schmidt to run the company, he surely started every day with a ritual, ferocious tug at that enshittification lever. The difference wasn't who was in the C-suite – the difference was how freely the lever moved.

On Saturday, I wrote:

The platforms used to treat us well and now treat us badly. That's not because they were setting a patient trap, luring us in with good treatment in the expectation of locking us in and turning on us. Tech bosses do not have the executive function to lie in wait for years and years.

https://pluralistic.net/2024/04/22/kargo-kult-kaptialism/#dont-buy-it

Someone on Hacker News called that "silly," adding that "tech bosses do in fact have the executive function to lie in wait for years and years. That's literally the business model of most startups":

https://news.ycombinator.com/item?id=40114339

That's not quite right, though. The business-model of the startup is to yank on the enshittification lever every day. Tech bosses don't lie in wait for the perfect moment to claw away all the value from their employees, users, business customers, and suppliers – they're always trying to get that value. It's only when they become too big to care that they succeed. That's the definition of being too big to care.

In antitrust circles, they sometimes say that "the process is the punishment." No matter what happens to the DOJ's case against Google, its internal workers have been made visible to the public. The secrecy surrounding the Google trial when it was underway meant that a lot of this stuff flew under the radar when it first appeared. But as Zitron's work shows, there is plenty of treasure to be found in that trove of documents that is now permanently in the public domain.

When future scholars study the enshittocene, they will look to accounts like Zitron's to mark the turning points from the old, good internet to the enshitternet. Let's hope those future scholars have a new, good internet on which to publish their findings.

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

https://pluralistic.net/2024/04/24/naming-names/#prabhakar-raghavan

[leoryff]

Halo:  Remnant Prologue 

Halo:  Remnant  

Prologue 

RWBY/Halo crossover

"What happened?"

Lights flickered on in the dark, emanating from the scorched and battered helmet of Master Chief John 117.  He looked around, seeing debris floating in the zero gravity of space.  Pushing off a nearby bulkhead he moved down the hall toward a light source.

"I'm not sure."  Cortana's voice spoke directly in his head.  "When Halo fired it shook itself to pieces.  Did a number on the Ark."  The AI postulated as Chief reached the end of the hallway.  "The portal couldn't sustain itself.  We made it through just as it collapsed.  

What was left of the hallway at least.

"Well, some of us made it."  The Forward Unto Dawn looked like it had been sliced by a hot knife.  A nearly perfect cut running the ship from top to bottom, leaving several decks exposed to open space.  

It was quite a spectacle, but even so Master Chief found his attention drawn to a sight far beyond it.  "And what about that?"  A planet, Earth like in its blues and greens.  It even had a single lunar satellite.  

But unlike the Moon of Earth, this one was shattered, half of it broken into massive chunks that hovered near the main body.  

"Right...  That."  Cortana doublechecked her data, the AI equivalent of a steadying breath.  "I'll need to do some more scans to say for sure, but the orbit is stable.  Whatever happened was a long time ago."

"More importantly, we have a problem."  Cortana brought up a display in Chief's visor.  "The distress beacon was damaged during our escape."  A cross section of the beacon appeared, parts of it red.  "We can't send any signal without it."

"Can it be repaired?"

"Not with what we have on hand.  But..."  She brought up a new display, this one of the planet floating beneath them.  "There's signs of life down there.  Radio signals, electronic responses, artificial lighting on the night side."  Each bit of info was brought up on the display.  

It took a few seconds to process that.  "Did we reach one of the colonies?"  Chief asked, turning back into the ship to catalog and collect gear.

"Not one on any record I have."  Cortana noted.  "And a colony with a busted moon would have stood out."

"Covenant?"

"Not unless they started communicating in English."  She brought up a garbled feed, likely a stray radio wave.  It was barely legible but strings of words could be made out.  

"- special event- *zzzt*- tal festi-*zzzt*- preparations under-"

That was enough for the Chief.  He would obviously need to gather more intel when they got planetside, but-  "Even on the off chance they are Insurrectionists, we can still find what we need there."

"Ever the optimist."  Cortana went quiet before displaying a map to the Dawn's hanger.  "There's a drop ship that should be able to get us planetside and perform takeoff again."

As Chief continued to grab every piece of ammo and equipment he could find, Cortana continued.  "Try to pack light.  The fuel in that shuttle will only get us so far if you want enough burn to escape atmo."

"I'll keep that in mind."  Chief responded, but continued to gather whatever he could carry.  Which in zero g was a lot.  “What about the Dawn?”

“I’ve already started reducing power.”  Cortana noted.  “Scanners won’t pick us up, though we’ll have to hope anyone looking up with something stronger than a backyard telescope mistakes us for part of the moon’s debris field.”  

“Now who’s the optimist?”   

The shuttle in question, a Pelican drop ship variant he didn't quite recognize, was locked down in one of the Dawn's hangers.  The windows were a little dirty and debris had to be moved out of the way, but it seemed no worse for the wear.  "Run preflight diagnostics, I'll get the lights."  Cortana started securing every door and nonvital system she could access.

Chief spun up the Pelican's engines and not five minutes later they were out the hanger.  He stayed in the pilot seat a few moments before handing autopilot over to Cortana.  

The two sat in silence with each other before Chief reached into one of his pouches and pulled out a set of dog tags.  

"Sergeant Johnson's tags?"  Cortana noted.  "I didn't know you had time to grab them."

"Barely."  Chief responded.  The sudden death of Johnson zipped through his head.  They'd been working together almost as long as he'd been working with Cortana, and he had to force down a pang of grief.  "Did he have anyone to return these to?"

"According to his records, no."  Cortana noted.  "He once said in an interview that the Corp. were his family."

That sounded very much like Johnson.  Chief tucked the tags back away and looked out the window.  "ETA to land?"  He asked, refocusing.

"A couple hours at least.  I don't want to burn too much fuel on approach so I am heading for a nearby spot.  Landing zone will end up being close to sunset."  Cortana explained.  "Might as well try and get some rest.  I know you haven't slept since the Ark."

Chief avoided sighing and leaned back in the pilot seat.  She was right, going into unknown, possibly hostile territory called for a clear head.  "Wake me.  If you need me."  

As her Spartan settled into his power nap, Cortana continued her scans, getting data from the Dawn's sensors while they were still in communication range.  She ran calculations and predictions and plans for every scenario her particularly advanced mind could think of, a number that grew with each mote of data that came in.

Some of said data left her rather stunned.  "Curious."

~~~

Ground level, the ruins of Mountain Glenn

The sun was setting faster than Ruby Rose would have liked, the growing darkness making it hard to see anything approaching team RWBY's camp.  Not that the creatures of Grimm were known for being subtle in most cases, but she also had to keep an eye out for White Fang members.  Presuming any of the Faunus turned criminals had the nerve to walk around in the dark.

Ruby sighed and checked her scroll.  Still over an hour before her watch was over, but at least she could go straight to sleep afterwards.  She could hear her teammates talking in the distance, sounded like most of them weren't getting much sleep tonight.

"Unlike my favorite fuzzball, who seems to be able to sleep anywhere."  She gave her corgi, Zwei, a gentle pet.  The dog kicked his little feet gently but didn't wake from the touch.  With a sigh Ruby cast her sight skyward. 

One positive about this blasted landscape was the lack of lights gave her a perfect view of Remnant's broken moon hanging in this sea of stars.  The lights twinkling and... moving?

"A shooting star?"  No, it was lasting too long for that.  She peeked at it through Crescent Rose's scope.  It wasn't a comet, the silhouette seemed to be...

"Guys?  I think there's an airship coming in?"  Ruby called to her still awake team.  

There was a "whoosh" sound and their Huntsman escort/history teacher was at her side.  "Can you identify the make?"  Dr. Oobleck asked.  Ruby shook her head and pointed to the slowly growing fireball.  "Curious..."  He pulled a telescope of his own and peered through it.  "Quite curious."  

After an uncomfortably long moment of silence Oobleck seemed to come to a decision.  "Pack up camp ladies, I'm afraid sleep is canceled for now."  He zipped around, grabbing what he could.  

"What's going on?" Yang Xiao Long asked.

"Unsure.  Unidentified craft coming down in Mountain Glenn."  Oobleck noted.  "Could be related to the criminal group we are tracking."

"And if it isn't?"  Weiss Schnee asked, suppressing a yawn.

"Then they are about to land in Grimm infested territory and will require aid."  Oobleck finished packing his gear and poured water over the fire.  "Either way, we need to get on top of things before the situation escalates, let alone interferes with our mission."

"So much for things going to plan."  Blake sighed.

To be continued...

~~~~

First part of a Halo/RWBY crossover I had toying around in my head for a long while.

Also like, the fourth crossover I find myself working on. Go me?

[meret118]

Elon Musk Is an Evil Piece of Garbage—and an A-Level Fraud Too

He is stupid. He is incompetent. He is cruel. He is sinister. And people will die because of what he’s done.

He vowed to slash $2 trillion in “wasteful” federal spending (the federal government spends just under $7 trillion a year). He recently acknowledged it’ll be more like $150 billion.

However, his “cuts” will also cost American taxpayers $135 billion, according to one estimate, because it turns out that some of these bloodsucking deep staters save taxpayers money.

But even $150 billion is a grotesque lie. Jessica Reidl of the Manhattan Institute—yes, the staunchly conservative and generally pro-Trump think tank—recently told The New York Times’ David French: “So right now I would say DOGE has saved $2 billion, which, to put it in context, is one-thirty-fifth of 1 percent of the federal budget, otherwise known as budget dust.”

. . .

The cuts are leaving thousands of good people unemployed. And they will literally kill people. Coal miners will die prematurely. Children all over the world will die from malaria and other diseases because of the demise of USAID, which Musk called a “criminal organization.” In fact, this is already happening

. . .

Musk and his Muskrats are doing nothing less than compiling a vast database on every one of us: “assembling a sprawling surveillance system,” she writes, ��the likes of which we have never seen in the United States.”

https://newrepublic.com/article/194769/elon-musk-evil-garbage-fraud

Trump Is Destroying the Data that Keeps the Country Running

While air traffic controller shortages have understandably been at the center of the story about Trump 2.0–era airline chaos, FAA cuts are gutting more behind-the-scenes positions too.

Back in March, TheAtlantic’s Isaac Stanley-Becker reported that as many as 12 percent of the FAA’s aeronautical-information specialists—those tasked with updating charts, maps, and flight procedures—had been fired or were exiting the agency as part of the government-wide buyout program spearheaded by DOGE. These kinds of cuts to critical information-gathering services are happening across agencies, eroding the government’s ability to collect and interpret data on everything from maternal mortality to flight paths, hurricanes, and electricity. The results could prove far more devastating than a few hundred canceled and delayed flights.

. . .

Experts have raised alarm bells in recent weeks about White House attempts to make it easier to fire federal officials. If economists at the Bureau of Labor Statistics could be fired at will, without the usual lengthy appeals process, they could be pressured by political appointees into manipulating BLS data on politically sensitive subjects like inflation, unemployment, productivity, and growth.

newrepublic.com/article/194930/trump-destroying-data-keeps-country-running

DOGE Was Bad. Schedule F Will Be Worse.

An executive order will convert 50,000 government employees into de facto political appointees who serve only at the president’s pleasure.

theatlantic.com/ideas/archive/2025/04/trump-civil-service-schedule-f/682609/

A DOGE Recruiter Is Staffing a Project to Deploy AI Agents Across the US Government

https://www.wired.com/story/doge-recruiter-ai-agents-palantir-clown-emoji/

Elon Musk's time machine DOGE is taking America back to the 1800s, one agency closure at a time

DOGE has been very good at reducing the number of people who work for the government — as many as 216,000 federal employees and contractors are already out, with more dismissals in the works. Musk has gutted or eliminated agencies that prevent disease, protect us from pandemics, provide aid to our allies, ensure the safety of our food and medicines, and safeguard Americans against toxic chemicals. Every one of those efforts is a proven multiplier of our tax money — every dollar we spend on them redounds to the US economy.

Which means that even if Musk succeeds at slashing government spending, he'll actually be adding to the federal deficit: DOGE cuts to the Internal Revenue Service alone are estimated to cost America some $500 billion in lost tax revenue every year.

. . .

Here are four possible explanations for what DOGE is actually up to. Maybe none of them are right. Or perhaps they're all accurate, to varying degrees. But one thing is certain: Each of them provides a more plausible insight into what DOGE is doing than the official explanation of saving taxpayers money.

https://www.businessinsider.com/elon-musk-doge-cuts-federal-worker-firings-government-plan-2025-4

[dailycharacteroption]

Subsystems and You 14: Dynamic Hacking

(art by ianllanas on DeviantArt)

A fascinating thing about cyberpunk and near-future scifi in general is how they speculate on what threads technology will go down in the near future.

For example, a lot of cyberpunk fiction speculated that hacking in the future would involve avatars facing off against elaborate 3D representations of security systems and the avatars of counterhackers in what amount to cyber-wizard battles, with things like DDOS attacks, viruses, and the like being visualized in this 3D hacking space as “spells” or special attacks.

You see this sort of thing in anything from Johnny Mnemonic to The Matrix, though in the latter case, the visualization was more the heroes setting up links so that the folks back in the ship could do the real hacking.

Of course, hacking in the real world has never been so fancy-looking or glamorous, and most programming is dedicated to making hacking programs and viruses able to interact with and overwhelm a system rather than making them show a neat picture to represent them doing so. (Not to say they don’t sometimes do that).

But we’re not interested in the real world right now. In the world of Starfinder, which draws inspiration from all over sci-fi, you can absolutely see characters having their own badass avatars and performing elaborate acts of hacking, which is where today’s subject comes in!

To be up front, this subsystem is meant to be used sparingly, as players are not likely to enjoy being bogged down with an elaborate computerized battle every single time they remove a virus or hack a door lock. Indeed, this system is meant to be used more for major hacking jobs. Things like elaborate defenses for campaign-important databases, battles against malevolent AI on their home turf, and so on.

In any case, the dynamic hacking system functionally turns hacking into a form of combat, with rounds and turns and everything.

First, every hacker has a Deceive, Hacking, and Process bonus, which covers the user’s ability to hide their persona from countermeasures, overcome those countermeasures, and perform various other effects separately such as scanning, repairing the persona, and so on.

Each of these three stats use your computer skill by default, but you can set up each with bonuses or penalties before and during a hack to specialize in infiltration, offense, and support, as long as you don’t go over a certain total bonus based on your computer skill.

From there you have the lead hacker, whose persona avatar is on the line, while other support hackers aid them in various ways.

While the support hackers can only perform minor actions, the lead hacker can perform a minor and major action per turn, plus extra if they’re willing to take penalties.

Each dynamic hacking encounter has countermeasures that seek to protect the system, nodes which provide paths to various files and programs, and modules, which represent protected files or programs which the hackers desire access to in order to access their data or function. Each one has their own abilities as well as the DCs to overcome them, the countdowns for any reprisals they offer, and so on.

In order to overcome these obstacles, the hacking team has several actions they can perform, ranging from minor (aiding and assessing) to major (blending in, creating decoys, modifying programs, recalibrating, repairing the persona, and of course, resolving the current obstacle.

Finally, it is also worth noting that this subsystem has rules for incorporating already existing class abilities and other special abilities tied to hacking, allowing them to apply in appropriate ways, such as faster hacking instead reducing the penalty of multiple actions, or countermeasure negation instead giving a massive bonus to resolving them.

As we can see, this style of hacking is not useful for casual hacking actions in normal play, but it can be a fun way to make for a fun and climactic encounter, or even add another layer to a combat encounter, where the party hacker has to crack the code while their allies keep security robots and the like off of them.

Either way, it's also a good reason to come up with your character's persona avatar appearance and other quirks of their hacking style.

That will do for today, but we’ve got one more subject lined up for tomorrow, from Second Edition Pathfinder! (For real this time).

[sageofsunbloom]

Is AI fr gonna steal our jobs?

Disclaimer: This post is mostly speculative and meant to encourage discussion and different perspectives on the topic.

Some time ago, I along with many others thought that AI was mostly going to aid in all the task centered, administrative, repetitive jobs. Cashiers, factory workers, call center workers, all the jobs that would benefit from automation were being taken over by AI.

When the ghibli trend came around, it was a guttural shock to many artists.

What used to generate questionable and bad looking art has now developed and is transitioning to generating high quality pieces, videos, music, animation and what not. In a matter of mere months. [AI tools example: midjourney, DALL-E, ChatGPT]

Art is not simply something pretty to look at. It is the accumulation of experiences, emotions and essense of humans. Art is their unique expression and the lens with which they see the world.

This blog is not an argument against the use of AI for art, but a call to understand what it really means.

Did most people see it coming when AI mimicked it with precision?

How long before it starts mimicking creativity, intuition, emotion and depth, all of what we thought was deeply and uniquely human?

"AI works by learning from lots of information, recognizing patterns, and using that knowledge to make decisions or do tasks like a human would." - Chatgpt.

Some time ago, the dominant argument was that AI might be able copy the strokes of a painting, the words of a novel. But it cannot hold the hands of another human and tell them all was well, it cannot feel and experience the real world like us, it cannot connect with humans and it cant innovate and envision new solutions.

If you still believe this, I urge you to go to chatgpt right now and open up to it like it was your friend. It will provide consolidation and advice tailored so well to your individual behaviours that it might feel better than talking to your bestfriend.

What is a deep neurological, experience based and emotional reaction to us is simply just analysis and application of data and patterns to AI. And the difference? Not easily distinguishable to the average human.

As long as the end result is not compromised, it doesnt matter to client and employers whether the process was human or not. Efficiency is often prioritized above substance. And now even substance is being mimicked.

Currently, the prominent discussion online is that in order to improve your job security, we need to master AI tools. Instead of fighting for stability(which is nothing but an illusion now) we need to ride the waves of the new age flooding towards us, and work with Ai instead of fighting against the change.

But the paradox is, the more we use AI the quicker it will learn from us, the quicker it will reduce the need for human guidance and supervision, and the quicker it will replace us.

Times are moving fast. We need everyone to be aware of the rate at which the world is changing and the things that are going on beneath the surface. If we simply take information at face value and avoid research, give it a few years or even months of time, and noone will know what hit us.

"Use of generative AI increased from 33% in 2023 to 71% in 2024. Use of AI by business function for the latest data varied from 36% in IT to 12% in manufacturing. Use of gen AI by business function for the latest data varied from 42% in marketing and sales to 5% in manufacturing."-Mckinsey, Mar 12, 2025.

By 2030, 14% of employees will have been forced to change their career because of- AI-McKinsey.

Since 2000, automation has resulted in 1.7 million manufacturing jobs being lost -BuiltIn

There is a radical change taking momentum right now. It's gonna be humans vs AI starting from the job aspect of the world.

Its not a matter of which jobs and skills are AI proof but which ones is AI likely to take over last.

What I predict personally, is that soon the world leaders are going to have to make a transformative choice.

This can either lead to a world where humans can be provided with money and resources instead of working to earn, as AI generates profit, and we can lay back and enjoy the things we love doing.

Or the other option is that we are going to have to live by scraps as small elite groups take over all the resources and tech.

Dystopia or utopia? The line is blurred.

Thankfully, for now, the choice is in human hands.

[alpaca-clouds]

Why AI sucks so much

(And why it doesn't have to.)

AI sucks right now. Because it was never to be used, like it is used right now. Because the way AI is currently employed, it does the one thing, that was always meant to be human.

Look. AI has a ton of technological problems. I wrote about it before. Whenever you have some "AI Writer" or "AI Art", there is no intelligence there. There is only a probability algorithm that does some math. It is like the auto-complete on your phone, just a bit more complex, because it has been fed with basically the entire internet's worth of words and pictures. So, when the AI writes a story, it just knows that there is a high likelyhood that if it has been asked to write a fantasy story it might feature swords, magic and dragons. And then puts out a collection of words that is basically every fantasy story ever thrown into a blender. Same when it "draws". Why does it struggle so much with teeth and fingers? Well, because it just goes by likelihood. If it has drawn a finger, it knows there is a high likelihood that next to the finger is going to be another finger. So it draws one. Simple as that. Because it does not know, what a finger is. Only what it looks like.

And of course it does not fact check.

But all of that is not really the main problem. Because the main culture actually just is the general work culture, the capitalist economy and how we modelled it.

See, once upon a time there was this economist named Kaynes. And while he was a capitalist, he did in fact have quite a few good ideas and understood some things very well - like the fact that people are actually working better, if their basic needs have been taken care for. And he was very certain that in the future (he lived a hundred years ago) a lot of work could be done by automation, with the people still being paid for what the machines were doing. Hence having the people work for like 15 hours a week, but getting paid for a fulltime job - or even more.

And here is the thing: We could have that. Right now. Because we did in fact automate a lot of jobs and really a ton of jobs we have right now are just endless busywork. Instead of actually being productive, it only exists to keep up the appearance of productivity.

We already know that reducing the workdays to four a week or the workload to 30 or even 25 hours a week does not really decrease productivity. Especially with office jobs. Because the fact is that many, many jobs are not that much work and rather just involve people sitting in an office working like two hours a day and spending the rest with coffee kitchen talk or surfing the internet.

And there are tons of boring jobs we can already automate. I mean, with what I am working right now - analyzing surveying data - most I do is just put some parameters into an algorith and let the algorith do the work. While also part time training another algorithm, that basically automatically reads contracts to make notes what data a certain contract involves. (And contrary to what you might believe: No, it is not complicated. Especially those text analysis tasks are actually super simple to construct, once you get the hang of it.)

Which also means, that half of my workday usually is spend of just sitting here and watching a bar fill up. Especially with the surveying data, because it is large, large image files that at times take six to ten hours to process. And hint: Often I will end up letting the computer run over night to finish the task.

But that brings me to the question: What am I even doing here? Most of the time it takes like two hours to put the data in, run a small sample size for checking it and then letting it run afterwards. I do not need to be here for that. Yet, I do have to sit down for my seven and a half hours a day to collect my paycheck. And... It is kinda silly, right?

And of course there is the fact that we technically do have the technology to automate more and more menial tasks. Which would make a lot of sense, especially with the very dangerous kinda tasks, like within mining operations. Like, sure, that is a lot more work to automate, given that we would need robots that are actually able to navigate over all sorts of terrain, but... You know, it would probably save countless lives.

Same goes for many, many other areas. We could in fact automate a lot. Not everything (for example fruit picking is surprisingly hard to automate, it turns out), but a lot. Like a real lot.

And instead... they decided to automate art. One of the things that is the most human, because art for the most part depends on emotions and experience. Art is individual for the most part. It is formed by experience and reflection of the experience. And instead of seeing that, they decided to... create a probability generator for words and pixels.

So, why?

Well, first and foremost, because they (= the owner class) do want to keep us working. And with that I mean those menial, exhausting, mind-numbing jobs that we are forced to have right now. And they want us to keep working, because the more free time we have, the more time we have to organize and, well, rise up against the system, upon realizing how we are exploited. Work itself is used as a tool of oppression. Which is why, no matter how many studies show that the 30 hour week or 4 day week is actually good, that UBI actually helps people and what not, the companies are so against it. It is also why in some countries, like the US, the companies are so against paid sick leave, something that is scientifically speaking bonkers, because it actually harms the productivity of the company. And yes, it is also why still in the midth of a pandemic, we act as if everything is normal, because they found out that in the early pandemic under lock down and less people working, people actually fucking organized.

And that... also kinda is, why they hate art. Because art is something that is a reflection upon a world - and it can be an inspiration for people, something that gives them hope and something worth working towards to. So, artists are kinda dangerous. Hence something has to be done to keep them from working. In this case: Devaluing their work.

And no, I do not even think that the people programming those original algorithms were thinking this. They were not like "Yes, we need to do this to uphold the capitalist systems", nor do most of the AI bros, who are hyping it right now. But there are some people in there, who see it just like that. Who know the dangers of actual art and what it can mean for the system that keeps them powerful.

So, yeah... We could have some great stuff with AI and automation... If used in other areas.

I mean, just imagine what AIs could do under communism...

[krunal-vyas]

Hire Dedicated Developers in India Smarter with AI

Hire dedicated developers in India smarter and faster with AI-powered solutions. As businesses worldwide turn to software development outsourcing, India remains a top destination for IT talent acquisition. However, finding the right developers can be challenging due to skill evaluation, remote team management, and hiring efficiency concerns. Fortunately, AI recruitment tools are revolutionizing the hiring process, making it seamless and effective.

In this blog, I will explore how AI-powered developer hiring is transforming the recruitment landscape and how businesses can leverage these tools to build top-notch offshore development teams.

Why Hire Dedicated Developers in India?

1) Cost-Effective Without Compromising Quality:

Hiring dedicated developers in India can reduce costs by up to 60% compared to hiring in the U.S., Europe, or Australia. This makes it a cost-effective solution for businesses seeking high-quality IT staffing solutions in India.

2) Access to a Vast Talent Pool:

India has a massive talent pool with millions of software engineers proficient in AI, blockchain, cloud computing, and other emerging technologies. This ensures companies can find dedicated software developers in India for any project requirement.

3) Time-Zone Advantage for 24/7 Productivity:

Indian developers work across different time zones, allowing continuous development cycles. This enhances productivity and ensures faster project completion.

4) Expertise in Emerging Technologies:

Indian developers are highly skilled in cutting-edge fields like AI, IoT, and cloud computing, making them invaluable for innovative projects.

Challenges in Hiring Dedicated Developers in India

1) Finding the Right Talent Efficiently:

Sorting through thousands of applications manually is time-consuming. AI-powered recruitment tools streamline the process by filtering candidates based on skill match and experience.

2) Evaluating Technical and Soft Skills:

Traditional hiring struggles to assess real-world coding abilities and soft skills like teamwork and communication. AI-driven hiring processes include coding assessments and behavioral analysis for better decision-making.

3) Overcoming Language and Cultural Barriers:

AI in HR and recruitment helps evaluate language proficiency and cultural adaptability, ensuring smooth collaboration within offshore development teams.

4) Managing Remote Teams Effectively:

AI-driven remote work management tools help businesses track performance, manage tasks, and ensure accountability.

How AI is Transforming Developer Hiring

1. AI-Powered Candidate Screening:

AI recruitment tools use resume parsing, skill-matching algorithms, and machine learning to shortlist the best candidates quickly.

2. AI-Driven Coding Assessments:

Developer assessment tools conduct real-time coding challenges to evaluate technical expertise, code efficiency, and problem-solving skills.

3. AI Chatbots for Initial Interviews:

AI chatbots handle initial screenings, assessing technical knowledge, communication skills, and cultural fit before human intervention.

4. Predictive Analytics for Hiring Success:

AI analyzes past hiring data and candidate work history to predict long-term success, improving recruitment accuracy.

5. AI in Background Verification:

AI-powered background checks ensure candidate authenticity, education verification, and fraud detection, reducing hiring risks.

Steps to Hire Dedicated Developers in India Smarter with AI

1. Define Job Roles and Key Skill Requirements:

Outline essential technical skills, experience levels, and project expectations to streamline recruitment.

2. Use AI-Based Hiring Platforms:

Leverage best AI hiring platforms like LinkedIn Talent Insightsand HireVue to source top developers.

3. Implement AI-Driven Skill Assessments:

AI-powered recruitment processes use coding tests and behavioral evaluations to assess real-world problem-solving abilities.

4. Conduct AI-Powered Video Interviews:

AI-driven interview tools analyze body language, sentiment, and communication skills for improved hiring accuracy.

5. Optimize Team Collaboration with AI Tools:

Remote work management tools like Trello, Asana, and Jira enhance productivity and ensure smooth collaboration.

Top AI-Powered Hiring Tools for Businesses

LinkedIn Talent Insights — AI-driven talent analytics

HackerRank — AI-powered coding assessments

HireVue — AI-driven video interview analysis

Pymetrics — AI-based behavioral and cognitive assessments

X0PA AI — AI-driven talent acquisition platform

Best Practices for Managing AI-Hired Developers in India

1. Establish Clear Communication Channels:

Use collaboration tools like Slack, Microsoft Teams, and Zoom for seamless communication.

2. Leverage AI-Driven Productivity Tracking:

Monitor performance using AI-powered tracking tools like Time Doctor and Hubstaff to optimize workflows.

3. Encourage Continuous Learning and Upskilling:

Provide access to AI-driven learning platforms like Coursera and Udemy to keep developers updated on industry trends.

4. Foster Cultural Alignment and Team Bonding:

Organize virtual team-building activities to enhance collaboration and engagement.

Future of AI in Developer Hiring

1) AI-Driven Automation for Faster Hiring:

AI will continue automating tedious recruitment tasks, improving efficiency and candidate experience.

2) AI and Blockchain for Transparent Recruitment:

Integrating AI with blockchain will enhance candidate verification and data security for trustworthy hiring processes.

3) AI’s Role in Enhancing Remote Work Efficiency:

AI-powered analytics and automation will further improve productivity within offshore development teams.

Conclusion:

AI revolutionizes the hiring of dedicated developers in India by automating candidate screening, coding assessments, and interview analysis. Businesses can leverage AI-powered tools to efficiently find, evaluate, and manage top-tier offshore developers, ensuring cost-effective and high-quality software development outsourcing.

Ready to hire dedicated developers in India using AI? iQlance offers cutting-edge AI-powered hiring solutions to help you find the best talent quickly and efficiently. Get in touch today!

[datapeakbyfactr]

Starting Your Digital Transformation Journey 

Small and medium-sized businesses (SMBs) can benefit greatly from digital transformation, which involves integrating digital technologies into all aspects of the business. This transformation isn't just about technology—it's about reshaping the way businesses operate and deliver value to customers.

Key Steps to help get you started on your journey.

1. Assess Your Needs 

Begin by conducting a thorough analysis of your current business operations. Identify areas where digital technologies can bring the most benefits, such as customer service, sales, marketing, and internal processes. Consider the specific challenges your business faces and how digital solutions can address them. 

2. Set Clear Goals 

Define specific, measurable objectives for your digital transformation journey. Whether it's increasing customer satisfaction, improving operational efficiency, or boosting revenue, having clear goals will help guide your efforts and measure success. Make sure your goals align with your overall business strategy. 

3. Choose the Right Tools 

Select digital tools and technologies that align with your business needs and goals. This could include customer relationship management (CRM) systems, cloud computing, automation software, and data analytics tools. Ensure the chosen tools are scalable and can grow with your business. 

4. Invest in Employee Training 

Your employees play a crucial role in the success of your digital transformation. Provide comprehensive training and support to help them adapt to new technologies. Offer workshops, tutorials, and ongoing assistance to ensure everyone is comfortable and confident with the changes. Encourage a culture of continuous learning and innovation. 

5. Implement Incrementally 

Roll out digital transformation initiatives in stages to manage risks and ensure a smooth transition. Start with pilot projects to test new technologies and processes, gather feedback, and make necessary adjustments before scaling them across the entire organization. This approach allows you to learn from early experiences and refine your strategy as you go. 

6. Monitor Progress and Adjust 

Continuously track the progress of your digital transformation efforts. Use data and feedback to evaluate the effectiveness of your initiatives, identify areas for improvement, and make necessary adjustments. Regularly review your goals and strategy to ensure you're on track and adapt to changing market conditions. 

7. Learn from Success Stories 

Look to other businesses that have successfully undergone digital transformation for inspiration and insights. For example: 

A local bakery integrated AI-driven inventory management to forecast sales and reduce food waste, leading to a 20% increase in sales within three months. 

A boutique hotel used data analytics to offer personalized guest experiences, resulting in a 10% increase in guest satisfaction and repeat bookings. 

These success stories highlight the potential benefits of digital transformation and can provide valuable lessons for your journey. 

“A journey of a thousand miles begins with a single step. ”

— Lao Tzu

Common Challenges and How to Overcome Them 

One of the common challenges businesses face during digital transformation is resistance to change. Employees may fear the unknown or worry about job security. To overcome this, communicate the benefits of digital transformation clearly and involve employees in the planning process. As stated in the steps above, provide comprehensive training and ongoing support to help them adapt confidently to new technologies. 

Budget constraints can also be a significant hurdle. Start with small, impactful changes that offer a high return on investment. Explore funding options such as grants, subsidies, or flexible payment plans from technology providers. Emphasize the long-term cost savings and efficiencies gained through digital transformation to justify the initial investment. 

Lastly, a lack of technical expertise can pose challenges for SMBs. Consider hiring consultants or partnering with technology providers who specialize in digital transformation. Invest in training and upskilling your current workforce to develop the necessary technical skills. Leverage industry networks, forums, and communities to share knowledge and gain insights from other businesses that have successfully implemented digital transformation. 

By addressing these common challenges with strategic solutions, businesses can navigate their digital transformation journey successfully and unlock new opportunities for growth and innovation. 

The 3 Top Industry Trends in Digital Transformation  

1. AI-Powered Automation: AI continues to revolutionize industries by automating routine tasks and providing advanced insights. From customer service chatbots to supply chain optimization, AI-powered automation leads to faster decision-making, lower operational costs, and improved customer satisfaction. 

2. Rise of Low-Code and No-Code Platforms: These platforms allow organizations to create and deploy custom applications without writing code, democratizing software development and enabling non-technical employees to build tools that fit their specific needs. This trend is expected to become even more widespread in 2025, empowering small businesses to innovate quickly and stay agile in a competitive market. 

3. 5G Connectivity: The rollout of 5G networks will enable faster and more reliable internet connections, supporting the growth of IoT devices and real-time data processing. 5G connectivity enhances the capabilities of digital transformation initiatives across various industries, enabling businesses to leverage advanced technologies like augmented reality, virtual reality, and smart cities to deliver better customer experiences and improve operational efficiency. 

Embarking on a digital transformation journey can seem daunting, but with careful planning and a strategic approach, SMBs can unlock new opportunities for growth and success. By assessing your needs, setting clear goals, choosing the right tools, investing in employee training, implementing incrementally, and continuously monitoring progress, you can position your business for long-term success in an increasingly digital world. 

Learn more about DataPeak:

[stevenketterman2]

The Evolution of DJ Controllers: From Analog Beginnings to Intelligent Performance Systems

The DJ controller has undergone a remarkable transformation—what began as a basic interface for beat matching has now evolved into a powerful centerpiece of live performance technology. Over the years, the convergence of hardware precision, software intelligence, and real-time connectivity has redefined how DJs mix, manipulate, and present music to audiences.

For professional audio engineers and system designers, understanding this technological evolution is more than a history lesson—it's essential knowledge that informs how modern DJ systems are integrated into complex live environments. From early MIDI-based setups to today's AI-driven, all-in-one ecosystems, this blog explores the innovations that have shaped DJ controllers into the versatile tools they are today.

The Analog Foundation: Where It All Began

The roots of DJing lie in vinyl turntables and analog mixers. These setups emphasized feel, timing, and technique. There were no screens, no sync buttons—just rotary EQs, crossfaders, and the unmistakable tactile response of a needle on wax.

For audio engineers, these analog rigs meant clean signal paths and minimal processing latency. However, flexibility was limited, and transporting crates of vinyl to every gig was logistically demanding.

The Rise of MIDI and Digital Integration

The early 2000s brought the integration of MIDI controllers into DJ performance, marking a shift toward digital workflows. Devices like the Vestax VCI-100 and Hercules DJ Console enabled control over software like Traktor, Serato, and VirtualDJ. This introduced features such as beat syncing, cue points, and FX without losing physical interaction.

From an engineering perspective, this era introduced complexities such as USB data latency, audio driver configurations, and software-to-hardware mapping. However, it also opened the door to more compact, modular systems with immense creative potential.

Controllerism and Creative Freedom

Between 2010 and 2015, the concept of controllerism took hold. DJs began customizing their setups with multiple MIDI controllers, pad grids, FX units, and audio interfaces to create dynamic, live remix environments. Brands like Native Instruments, Akai, and Novation responded with feature-rich units that merged performance hardware with production workflows.

Technical advancements during this period included:

High-resolution jog wheels and pitch faders

Multi-deck software integration

RGB velocity-sensitive pads

Onboard audio interfaces with 24-bit output

HID protocol for tighter software-hardware response

These tools enabled a new breed of DJs to blur the lines between DJing, live production, and performance art—all requiring more advanced routing, monitoring, and latency optimization from audio engineers.

All-in-One Systems: Power Without the Laptop

As processors became more compact and efficient, DJ controllers began to include embedded CPUs, allowing them to function independently from computers. Products like the Pioneer XDJ-RX, Denon Prime 4, and RANE ONE revolutionized the scene by delivering laptop-free performance with powerful internal architecture.

Key engineering features included:

Multi-core processing with low-latency audio paths

High-definition touch displays with waveform visualization

Dual USB and SD card support for redundancy

Built-in Wi-Fi and Ethernet for music streaming and cloud sync

Zone routing and balanced outputs for advanced venue integration

For engineers managing live venues or touring rigs, these systems offered fewer points of failure, reduced setup times, and greater reliability under high-demand conditions.

Embedded AI and Real-Time Stem Control

One of the most significant breakthroughs in recent years has been the integration of AI-driven tools. Systems now offer real-time stem separation, powered by machine learning models that can isolate vocals, drums, bass, or instruments on the fly. Solutions like Serato Stems and Engine DJ OS have embedded this functionality directly into hardware workflows.

This allows DJs to perform spontaneous remixes and mashups without needing pre-processed tracks. From a technical standpoint, it demands powerful onboard DSP or GPU acceleration and raises the bar for system bandwidth and real-time processing.

For engineers, this means preparing systems that can handle complex source isolation and downstream processing without signal degradation or sync loss.

Cloud Connectivity & Software Ecosystem Maturity

Today’s DJ controllers are not just performance tools—they are part of a broader ecosystem that includes cloud storage, mobile app control, and wireless synchronization. Platforms like rekordbox Cloud, Dropbox Sync, and Engine Cloud allow DJs to manage libraries remotely and update sets across devices instantly.

This shift benefits engineers and production teams in several ways:

Faster changeovers between performers using synced metadata

Simplified backline configurations with minimal drive swapping

Streamlined updates, firmware management, and analytics

Improved troubleshooting through centralized data logging

The era of USB sticks and manual track loading is giving way to seamless, cloud-based workflows that reduce risk and increase efficiency in high-pressure environments.

Hybrid & Modular Workflows: The Return of Customization

While all-in-one units dominate, many professional DJs are returning to hybrid setups—custom configurations that blend traditional turntables, modular FX units, MIDI controllers, and DAW integration. This modularity supports a more performance-oriented approach, especially in experimental and genre-pushing environments.

These setups often require:

MIDI-to-CV converters for synth and modular gear integration

Advanced routing and clock sync using tools like Ableton Link

OSC (Open Sound Control) communication for custom mapping

Expanded monitoring and cueing flexibility

This renewed complexity places greater demands on engineers, who must design systems that are flexible, fail-safe, and capable of supporting unconventional performance styles.

Looking Ahead: AI Mixing, Haptics & Gesture Control

As we look to the future, the next phase of DJ controllers is already taking shape. Innovations on the horizon include:

AI-assisted mixing that adapts in real time to crowd energy

Haptic feedback jog wheels that provide dynamic tactile response

Gesture-based FX triggering via infrared or wearable sensors

Augmented reality interfaces for 3D waveform manipulation

Deeper integration with lighting and visual systems through DMX and timecode sync

For engineers, this means staying ahead of emerging protocols and preparing venues for more immersive, synchronized, and responsive performances.

Final Thoughts

The modern DJ controller is no longer just a mixing tool—it's a self-contained creative engine, central to the live music experience. Understanding its capabilities and the technology driving it is critical for audio engineers who are expected to deliver seamless, high-impact performances in every environment.

Whether you’re building a club system, managing a tour rig, or outfitting a studio, choosing the right gear is key. Sourcing equipment from a trusted professional audio retailer—online or in-store—ensures not only access to cutting-edge products but also expert guidance, technical support, and long-term reliability.

As DJ technology continues to evolve, so too must the systems that support it. The future is fast, intelligent, and immersive—and it’s powered by the gear we choose today.

[generouslywanderingpriest]

Video Agent: The Future of AI-Powered Content Creation

The rise of AI-generated content has transformed how businesses and creators produce videos. Among the most innovative tools is the video agent, an AI-driven solution that automates video creation, editing, and optimization. Whether for marketing, education, or entertainment, video agents are redefining efficiency and creativity in digital media.

In this article, we explore how AI-powered video agents work, their benefits, and their impact on content creation.

What Is a Video Agent?

A video agent is an AI-based system designed to assist in video production. Unlike traditional editing software, it leverages machine learning and natural language processing (NLP) to automate tasks such as:

Scriptwriting – Generates engaging scripts based on keywords.

Voiceovers – Converts text to lifelike speech in multiple languages.

Editing – Automatically cuts, transitions, and enhances footage.

Personalization – Tailors videos for different audiences.

These capabilities make video agents indispensable for creators who need high-quality content at scale.

How AI Video Generators Work

The core of a video agent lies in its AI algorithms. Here’s a breakdown of the process:

1. Input & Analysis

Users provide a prompt (e.g., "Create a 1-minute explainer video about AI trends"). The AI video generator analyzes the request and gathers relevant data.

2. Content Generation

Using GPT-based models, the system drafts a script, selects stock footage (or generates synthetic visuals), and adds background music.

3. Editing & Enhancement

The video agent refines the video by:

Adjusting pacing and transitions.

Applying color correction.

Syncing voiceovers with visuals.

4. Output & Optimization

The final video is rendered in various formats, optimized for platforms like YouTube, TikTok, or LinkedIn.

Benefits of Using a Video Agent

Adopting an AI-powered video generator offers several advantages:

1. Time Efficiency

Traditional video production takes hours or days. A video agent reduces this to minutes, allowing rapid content deployment.

2. Cost Savings

Hiring editors, voice actors, and scriptwriters is expensive. AI eliminates these costs while maintaining quality.

3. Scalability

Businesses can generate hundreds of personalized videos for marketing campaigns without extra effort.

4. Consistency

AI ensures brand voice and style remain uniform across all videos.

5. Accessibility

Even non-experts can create professional videos without technical skills.

Top Use Cases for Video Agents

From marketing to education, AI video generators are versatile tools. Key applications include:

1. Marketing & Advertising

Personalized ads – AI tailors videos to user preferences.

Social media content – Quickly generates clips for Instagram, Facebook, etc.

2. E-Learning & Training

Automated tutorials – Simplifies complex topics with visuals.

Corporate training – Creates onboarding videos for employees.

3. News & Journalism

AI-generated news clips – Converts articles into video summaries.

4. Entertainment & Influencers

YouTube automation – Helps creators maintain consistent uploads.

Challenges & Limitations

Despite their advantages, video agents face some hurdles:

1. Lack of Human Touch

AI may struggle with emotional nuance, making some videos feel robotic.

2. Copyright Issues

Using stock footage or AI-generated voices may raise legal concerns.

3. Over-Reliance on Automation

Excessive AI use could reduce creativity in content creation.

The Future of Video Agents

As AI video generation improves, we can expect:

Hyper-realistic avatars – AI-generated presenters indistinguishable from humans.

Real-time video editing – Instant adjustments during live streams.

Advanced personalization – AI predicting viewer preferences before creation.

[promotoai]

Why Choose AI Content Creation for Your Content Strategy?

In the ever-evolving digital world, content is king. AI Content Creation is rapidly transforming how we produce and manage that content. But creating consistent, engaging, and high-quality content takes time, creativity, and resources—something many individuals and businesses struggle to balance. Thankfully, the rise of artificial intelligence has revolutionized the creative world, offering innovative solutions to long-standing content challenges. Let’s explore why using AI for content creation is becoming essential for anyone aiming to stay competitive and relevant.

What Makes AI a Valuable Tool for Content Creators?

At its core, AI is designed to augment human capabilities. Instead of replacing writers, marketers, or designers, it acts as a powerful assistant. By analyzing vast amounts of data, AI can identify trending topics, suggest headlines, and even draft initial versions of articles or social media posts. This significantly reduces the time and effort involved in the early stages of content production. With AI handling repetitive or research-heavy tasks, creators can focus more on refining ideas and adding their unique voices.

Smarter Personalization

Today’s audiences demand content that aligns closely with their personal interests, needs, and online behavior. AI can analyze user data—such as browsing habits, location, and engagement history—to help you craft personalized messages that resonate more deeply. Whether it’s a customized email subject line or a dynamic landing page, AI ensures the right message reaches the right person at the right time. 

How Does AI Improve Efficiency in Content Production?

One of the biggest challenges in content creation is the pressure to produce regularly without compromising quality. AI tools accelerate the process by generating drafts, summarizing information, and optimizing language for readability and SEO. This allows content teams to meet tight deadlines and scale their output without needing to hire additional staff. The automation of routine tasks like grammar checking or keyword placement also frees creators from tedious work, improving overall productivity.

Speed and Efficiency

One of the most immediate benefits of using artificial intelligence in content production is the significant boost in productivity. Traditional content workflows—research, ideation, drafting, and editing—can be time-consuming. AI tools can generate outlines, suggest headlines, write paragraphs, and even correct grammar in seconds. This allows creators to produce more content in less time, without sacrificing quality.

Imagine being able to create multiple blog posts, product descriptions, or social media updates in a fraction of the time it used to take. For businesses, this means faster campaign rollouts and the ability to respond quickly to trending topics or customer needs.

Does AI Limit Creativity or Enhance It?

There’s a misconception that relying on machines might stifle creativity. In truth, AI technologies frequently serve as a spark that ignites and enhances creative thinking. By handling mundane or technical aspects, they give creators more mental space to experiment and innovate. Some AI platforms suggest alternative angles or generate prompts that inspire new ideas. This collaborative process between human insight and machine intelligence can produce richer, more original content than either could achieve alone.

Boosting Creativity

Rather than substituting for human imagination, AI often amplifies and supports the creative process. By handling repetitive or technical tasks, AI frees up creators to focus on strategy, storytelling, and innovation. It can suggest fresh angles, explore alternative headlines, and even simulate different audience responses. This collaborative dynamic between humans and machines leads to richer, more inventive content. In this way, AI content creation becomes a partnership—where AI offers the structure and insights, while humans bring nuance, emotion, and originality.

How Does AI Maintain Brand Consistency Across Channels?

Consistency in tone, style, and messaging is critical for building trust and recognition. Managing this across multiple platforms can be complex, especially for larger teams or brands with a broad presence. AI can be trained on brand guidelines and past content to ensure new material aligns perfectly with the desired voice. This guarantees a unified brand identity whether the message appears in blogs, newsletters, social media, or advertisements.

Consistency Across Channels

Maintaining a consistent voice, tone, and style across multiple content channels—blogs, emails, websites, and social media—can be challenging. AI tools can be trained to adhere to your brand guidelines, ensuring that all output reflects your unique identity. This is particularly useful for companies managing large content volumes or collaborating with multiple creators.

Consistency builds trust and brand recognition. When your audience receives clear, cohesive messaging no matter where they interact with you, your brand becomes more memorable and credible. You can also watch: Meet AdsGPT’s Addie| Smarter Ad Copy Creation In Seconds

Final Thoughts: Why Embrace AI in Your Content Workflow?

Artificial intelligence is not just a passing trend—it’s transforming how content is created and consumed. From speeding up production and enhancing personalization to providing actionable insights and boosting creativity, AI content creators meet the growing demands of digital audiences. While human creativity remains irreplaceable, AI is proving to be an indispensable partner that elevates quality and efficiency.

[scan2hire]

How is AI transforming the recruitment process in 2025?

Picture this: You're sipping your morning coffee, scrolling through job applications, and within minutes, you've identified the top 10 candidates from a pool of 500 resumes. Sounds like magic?

Welcome to 2025, where artificial intelligence has turned this fantasy into everyday reality for recruiters worldwide. The hiring landscape has shifted dramatically, and if you're still manually sorting through CVs, you might as well be using a typewriter in the smartphone era.

The Smart Screening Revolution

Gone are the days when HR teams spent countless hours reading through every single resume. Modern AI resume screening software has become the ultimate hiring assistant, analyzing candidate profiles with superhuman speed and accuracy.

These intelligent systems can evaluate skills, experience, and cultural fit within seconds, transforming what used to be a week-long process into a matter of hours.

The technology doesn't just scan for keywords anymore. Today's advanced algorithms understand context, recognize transferable skills, and even assess personality traits through language patterns.

It's like having a seasoned recruiter with photographic memory working 24/7.

Beyond the Buzzwords: Real Impact

The transformation goes deeper than just faster screening. AI is fundamentally changing how companies approach talent acquisition.

Predictive analytics now help organizations forecast which candidates are most likely to succeed in specific roles, reducing turnover rates by up to 40%.

Video interview analysis has evolved too. AI systems can now evaluate communication skills, confidence levels, and even detect potential red flags through facial expressions and speech patterns. While this might sound like science fiction, it's happening right now in major corporations across the globe.

The Bias-Busting Game Changer

One of the most significant advantages of AI in recruitment is its potential to reduce unconscious bias. Traditional hiring often falls victim to human prejudices based on names, photos, or educational backgrounds.

Smart recruitment tools focus purely on skills and qualifications, creating a more level playing field for all candidates.

However, it's worth noting that AI systems are only as unbiased as the data they're trained on.

Forward-thinking companies are actively working to ensure their algorithms promote diversity rather than perpetuate existing inequalities.

The Numbers Game: Efficiency Meets Precision

The statistics speak volumes about AI's impact on modern recruitment. Companies using intelligent hiring solutions report 50% faster time-to-hire and 60% improvement in candidate quality.

The applicant tracking system has evolved from a simple database to a sophisticated decision-making partner.

Natural language processing (NLP) has revolutionized how these systems understand resumes and job descriptions.

Instead of rigid keyword matching, modern platforms can interpret meaning, context, and even industry-specific jargon.

This advancement means better matches between candidates and positions.

The Human Touch in an AI World

Despite all this technological advancement, the human element remains irreplaceable.

AI handles the heavy lifting of initial screening and data analysis, but final hiring decisions still require human judgment, emotional intelligence, and cultural assessment.

The most successful companies in 2025 are those that have found the perfect balance between AI efficiency and human insight.

They use technology to eliminate tedious tasks while preserving the personal connection that makes great hiring decisions.

Looking Ahead: What's Next?

As we move further into 2025, expect to see even more sophisticated AI features. Real-time skills assessment, virtual reality job simulations, and AI-powered salary negotiations are already being tested by innovative companies.

Platforms like Scan2hire are leading this transformation, offering comprehensive solutions that streamline the entire recruitment process.

From initial resume parsing to final candidate ranking, these tools are setting new standards for what's possible in talent acquisition.

The future belongs to organizations that embrace these technological advances while maintaining their commitment to fair, human-centered hiring practices.

Those who adapt quickly will gain a significant competitive advantage in attracting top talent.

The Bottom Line

AI isn't just changing recruitment—it's revolutionizing it completely. Companies that leverage these tools effectively are finding better candidates faster, reducing costs, and creating more positive experiences for everyone involved in the hiring process.

The question isn't whether AI will transform recruitment, but how quickly your organization will adapt to this new reality.

The future of hiring is here, powered by Scan2hire and similar innovative solutions, and it's more exciting than ever.

[stuarttechnologybob]

How does data capture services benefit a business?

Data Capture services

In the current digital age, data secrecy is recognized as the most valuable asset for any business. However, collecting it manually and investing time in it personally is time-consuming and prone to errors, as it is subject to matters. That’s where data capture services come in. While these services enable the enterprises to collect, organize, store and process information quickly and accurately, resulting in more informed decisions and enhanced efficiency for the organization to go ahead.

Faster Access to Information:

Data-capturing services automate the process of gathering data from various sources, including documents, forms, emails, and other digital assets. As this process speeds up the process to access critical information, enabling employees to work more towards the betterment efficiently and respond promptly towards customer needs or business challenges.

Improved Accuracy and Reduced Errors:

Manual data entry and filling often leads and thrives towards mistakes as they can affect the ongoing business operations. With data capturing technology, information is extracted using tools such as OCR (Optical Character Recognition) and with the assistance of AI, ensuring a level of higher accuracy is maintained. At the same time, fewer errors means better outcomes and more reliable reports that have been generated.

Streamlined Business Operations:

By automating data collection, businesses can save time and resources. While the staff and operating users no longer have the need to spend hours by entering data by hand or their own, allowing them to have a keen look on more valuable tasks and selective concerns. This heads and drives toward enhanced productivity and smoother workflows and operations.

Enhanced Customer Service:

Quick and precise data collection assures that the customer records, queries, and transactions are handled efficiently and effectively with this technique adaption. This leads towards faster service delivery, fewer complaints, and a better overall customer experience—key factors in staying competitive.

Better Decision-Making:

Accurate and well-organized data gives leaders a clearer view of their business performance. With real-time insights from data capture, they can make informed and clear decisions by identifying the current trends, and respond to market changes with confidence with a complete detailed report.

Scalable for Growing Businesses:

As a business grows, managing large volumes of data becomes more difficult. Data capture services scale and grow with your company, handling increasing amounts and multiple sets of information without sacrificing the speed or accuracy. Many businesses trust experts like Suma Soft, IBM, Cyntexa, and Cignex for efficient data capture solutions. These providers offer tailored services that boost data accuracy, ensure fast turnaround, and support long-term digital transformation.