Spring break travel and staffing shortages at the Transportation Security Administration have hit US airports with a devastating one-two punch this week, particularly at the security checkpoints. Personal accounts on social media have reported wait times of eight hours or more.

Understaffed airports across the US are now being assisted by Immigration and Customs Enforcement. The White House deployed ICE agents on Monday to help ease the staffing shortages caused by TSA officers who have quit or stopped showing up for work. TSA employees have already missed a full paycheck as a result of the partial government shutdown, and now they could miss a second.

Don’t be surprised if you get to the airport and see the security line trailing out into the parking lot. It’s easy to check security wait lines before you leave for the airport, so you know how long it will take to get from check-in to your gate. If you have spring break or other planned air travel coming soon, learn how you can check security line wait times so you can better plan your trip to the airport.

Check the official TSA app

The TSA maintains an app for mobile devices called MyTSA (iOS and Android) that lists security line wait times for airports around the US. The app is fairly basic and now includes a warning that “this website is not actively managed” due to the pause in federal funding, but it does include plenty of official TSA information about airline travel.

To check the wait times for specific US airports, tap the My Airports tab at the bottom of the app, then tap “Search Airports.” You can scroll through the alphabetical list of airports or type in an airport name or code in the search bar at the top.

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Tap through to the airport of your choice, and you’ll see the estimated security wait time at the top of the screen.

When I checked some of the major US airports Tuesday afternoon — Los Angeles (LAX), Chicago O’Hare (ORD), Hartsfield-Jackson in Atlanta (ATL), JFK in New York (JFK) and Ronald Reagan in DC (DCA) — most had relatively low wait times of 0 to 15 or 15 to 30 minutes. Only JFK had an estimate of 30 to 45 minutes.

Those estimates are a far cry from the two to four hours that airports are advising travelers to allow, but the times on the MyTSA app mostly matched the times listed on airport websites (see below). The exception was Hartsfield-Jackson in Atlanta, which showed an estimate of 0-15 minutes on the MyTSA app, but slightly longer times on the airport website.

The MyTSA app also includes historical averages for each airport’s security line wait times by time of day.

Check the airport website

When I tested the TSA app, it didn’t list specific terminals at any airports. It only listed a time range for “All Terminals.” If you want that sort of detailed information, your best bet is to use the official airport websites. Most major ones now offer estimated security wait times. Some airports put those estimated times front and center on their websites; others require a little more exploration.

Most airport websites will break out the times for specific terminals. At some bigger airports, there’s often quite a disparity between the terminals. Here are the web pages for estimated security wait lines for some of the most frequently traveled airports in the US:

I wasn’t able to find security line wait times on the websites for two of the busiest airports: O’Hare (ORD) in Chicago and Harry Reid International Airport (LAS) in Las Vegas. For those, you’ll need to use the TSA app.

Save your spot in the security line

Numerous airports now allow travelers to reserve a specific time in the security line. At SeaTac Airport in Seattle, you make a Spot Saver reservation and go to a specially marked entrance to the security checkpoint listed on your reservation. An employee scans the barcode you were emailed, and you’re ushered to the front. At SeaTac, you can be up to 15 minutes before or after your Spot Saver reservation, since airport timing is tough to estimate.

Here’s a list of some of those reservation sites. You can search for your airport name and “reserve security line spot” or something similar to see if your airport also has a program.

• Denver International Airport (DEN): DEN Reserve
• Orlando International Airport (MCO): MCO Reserve
• Seattle-Tacoma International Airport (SEA): Spot Saver
• Phoenix Sky Harbor International Airport (PHX): PHX Reserve
• Minneapolis-St. Paul International Airport (MSP): MSP Reserve

There was a time when subscriptions felt like a novelty. Those were times of (digital) peace. A seamless payment feature on cool new apps that brought unlimited access (for the month). You paid for Netflix, and maybe Spotify, and that was usually about it.

Now, it’s streaming, cloud storage, fitness apps, editing apps, AI chatbots, random free trials you forgot to kill three weeks ago, and so much more. The subscription economy didn’t just grow — it exploded, with the blast radius covering nearly every corner of the digital space.

It has become so pervasive that there’s a real subscription fatigue, which feels even worse in 2026. People thought they weren’t spending a lot, but many clearly are. The system with recurring payments has become small (in price), automatic, and easy to forget.

People have begun treating $5.99 or $10.00 charges as harmless when, in reality, the charges are piling up into something uglier over time. Subscription hell isn’t just about greed or convenience. It is also about invisibility.

Why subscription fatigue feels worse than normal spending

You often think twice before making a big purchase, and sometimes that one-off payment can sting a little. But this feeling dissipates over time. Subscriptions do the opposite. You won’t notice as they are hidden, sitting quietly in a corner, billing you for small amounts that you’ll barely notice. So they end up feeling like a bigger drain than a single large purchase. While the pain is less dramatic, it’s always around.

While clearer cancellation rules can reduce the subscription traps, reports point out that behavioral habits like inertia and auto-renewal still keep people paying for services long after they’ve stopped caring. Visibility can help; people don’t need more guilt or another sermon about “being better with money.”

Making them all visible

Want to know how you can make your life easier? The answer is rather simple: round them all up. It’s like gathering your bills, but it’s more convenient with a smartphone. Once all your subscriptions live in one place, they stop feeling abstract and start looking like real financial patterns.

We’ve seen apps that track your bill payments and overall spending, but there are even dedicated apps to track all your subscriptions. These break down the walls that they hide behind, arranging them neatly for you to scrutinize.

And, you may not like what you see.

Apple offers a similar function on a basic level, allowing users to cancel any Apple Store subscriptions directly. But many of these recurring payments live outside of the App Store. So if you’re trying to clean house, you may need some professional help.

The best subscription apps are not the flashiest ones

Most people would turn away from bloated finance dashboards, so there is a real demand for simple, focused, and low-friction tools. So with that in mind, here are a few names that often pop up when talking about good subscription managers:

• Subpli first got our attention for being a free app, which is also ad-free. It is available without the mandatory sign-up. It offers renewal reminders, category filters, monthly and yearly totals, and even a guest mode.
• Bobby has been around for a while now and is easily one of the better-known options for iPhones. Its App Store listing highlights hundreds of built-in subscription templates, due-date notifications, and a clearer overview of fixed monthly costs.
• Rocket Money, on the other hand, takes a more aggressive, finance-first approach than the simpler tracker apps. But it pitches itself as a service that will identify subscriptions for you. So you won’t need to manually log recurring payments, while also giving you a concierge-style flow to help cancel some unwanted expenses. That makes it more appealing for people who want a broader money-management tool.
• Subby is another solid app if you want an Android-specific option. It is pretty straightforward, focusing on what matters, like tracking subscriptions and recurring bills in one dashboard, sending cancellation reminders before renewals, and supporting multiple currencies. There are even some extras like widgets and Google Drive backup for Pro users.

It’s even becoming a policy problem

The subscription fatigue isn’t just a personal finance issue anymore. In the UK, the government has already proposed tougher rules aimed at “subscription traps,” including clearer information before signing up, renewal reminders, a 14-day cooling-off period after free trials, and easier cancellation processes. The government says unwanted subscription costs UK consumers about £1.6 billion a year through nearly 10 million of the country’s 155 million active subscriptions that are deemed unwanted.

The consumer data paints a similarly familiar picture. Surveys backed by multiple other findings suggest that US adults spend about $91 a month on subscriptions, while nearly half have forgotten to cancel a free trial. Younger users are also more likely to fall into that trap.

Subscription hell isn’t going away, but it’s time to step up

Companies love the recurring-revenue model, and with consumers still hooked to the convenience, this model is here to stay. But the real question is whether users can claw back some control.

The answer is yes, and it’s only by making it harder on yourself. Taking small steps like checking Apple’s built-in subscription page, searching your inbox for renewal emails, and using a tracker app brings more power to you. Basic visibility is what subscription culture and modern apps are engineered to take away. So seeing the damage clearly might be the only real counter to it.

Photo credit: Red Bull Racing, Zero-G BTS stills by Denis Klero
By the end of the 2019 season, Red Bull Racing set numerous world records for fastest pit stops. However, as expected, the team was still eager to push itself to new heights, literally. They decided to put their skills to the test in a completely new setting, or more specifically, changing an F1 car’s four tires while floating around in the center of an aircraft.

They chose the 2005 RB1 chassis as their test car. Its narrower form and lightweight construction made it an ideal fit for the Ilyushin Il-76 MDK, a jet that typically conducts cosmonaut training and was receiving assistance from the Russian space program. The team arrived at the Yuri Gagarin Cosmonaut Training Center just outside Star City, where the flight plans had been finalized and they were ready to go. The pilots would climb the plane at a sharp angle to around 33,000 feet before cutting power just as they reached the crest of the arc, allowing the jet to glide downward in freefall. Inside the airplane cabin, those moments spent in freefall provided the team with a whooping 22 seconds of weightlessness, a brief wonderful period in which everyone drifted around freely. To acquire as much quality time in as possible, the crew squeezed in 7 flights and around 80 of these freefall arcs over the course of many days.

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You have to remember that time was of the essence here; weightlessness lasts only a little while, so every second counts. Once the team was in place and the equipment was ready to begin, the usable window of time was reduced to roughly 15 or 20 seconds. To make matters even more difficult, a mock-up of the complete apparatus was constructed on the ground so that everyone could acquire a feel for the movements before flying to the skies. Consider how much harder it will be if you are not tethered to the floor.

As expected, things did not go smoothly from the start. Finally, it came down to basic physics. On the ground, the mechanics would press against the floor to offset the twisting force of the wheel cannons, but in mid-air, the anchor was removed, causing the mechanics to spin around rather than turn the nut. Add in the possibility that stray tools or wheels would float away and disrupt the following phase, and you have a formula for disaster.

Photo credit: Denis Klero
It didn’t take long for the team to realize that the only way to make this work was to divide their crew and have half of them work on the car from underneath while the other half worked from what seemed like the ceiling. The car would be inverted relative to half of the group, resulting in a balance of pushes and pulls. The only thing holding them down were their footstraps, so the mechanics had to flex their ankles to stay in place.

Photo credit: Denis Klero
The execution was completed in a relatively short period of time, with 16 crew members moving into predetermined places near the car, all of which were connected to a bespoke frame built to endure the severe ascent forces and float phases. They’d operate in a planned sequence, loosening the old nuts with the wheel guns before pulling the tires off and slipping the new ones in. To avoid spinning anyone out of control, the final tightening had to be steady and gentle. Every motion stayed choreographed or something would float out of reach.

Photo credit: Denis Klero
In the end, they did it, completing a full tire change in about 20 seconds, a real world record in the middle of the air, and, as if you needed to be reminded, the timing was all spot on, with the sequence matching the exact order of a track pit stop, the only difference being the constant adjustments required due to the lack of weight.
[Source]

• Two researchers have produced a clear nail polish that turns your nail into a stylus
• It should help solve issues with ‘zombie fingers’ and for folks with long nails
• It’s not yet ready for store shelves right now

If you love looking your best with long fingernails or have calloused fingertips from years of working as a musician or carpenter, you know how awkward touchscreens can be to use — and so might want to hear about this new nail polish that turns your nail into a stylus.

This coating was developed at Centenary College of Louisiana by student Manasi Desai and her research supervisor Joshua Lawrence (via LiveScience).

As Desai explained in a statement, “Our final, clear polish could be put over any manicure or even bare nails, which could help people with calluses on their fingertips too.” This means you can still enjoy your preferred nail art and use touchscreens, or if you don’t paint your nails normally, you can coat them in this polish without it being obvious.

Article continues below

Modern touch screens rely on a thin, imperceptible grid of wires carrying a subtle electrical charge. Because skin can slightly conduct electricity — unlike the glass of your phone screen — the electrical charge at the point your finger touches the screen sticks around on your fingertip.

When Jensen Huang told 30,000 attendees at GTC last week that the future data centre is a “token factory,” he was describing a world that a small Israeli startup has been quietly building toward for months. NeuReality, the Caesarea-based company behind the NR-NEXUS inference operating system, has appointed Shalini Agarwal, a product management director at Google Labs, as a strategic adviser charged with shaping how NR-NEXUS reaches enterprise buyers, according to a press release distributed on Monday.

The hire signals a shift in ambition for a company that began life designing custom silicon for AI inference and has since pivoted toward software that promises to turn fragmented GPU clusters into production-grade inference engines.

Agarwal brings roughly two decades of experience in product strategy across major technology companies. At Google Labs, she has directed product management for AI-focused initiatives. Before that, she spent nearly a decade at eBay, according to publicly available professional records, and holds a degree in computer science, electrical engineering, and management science from MIT. Her appointment is advisory rather than operational, but it places a recognisable Silicon Valley name alongside NeuReality’s existing leadership: co-founder and CEO Moshe Tanach and president Hiren Majmudar, a former GlobalFoundries and Intel Capital executive who joined in September 2024.

The timing is deliberate. On 12 March, NeuReality unveiled NR-NEXUS, describing it as a hardware-agnostic operating system for what the company calls AI factories. The platform disaggregates prefill and decode tasks across heterogeneous hardware, including GPUs, CPUs, and network interface cards, aiming to squeeze more useful work out of expensive accelerators that often sit partially idle. Beta customers are already running the software, according to the company, though NeuReality has not disclosed which organisations are in the programme.

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The product arrives at a moment when inference economics have become one of the most closely watched metrics in enterprise AI. Deloitte estimates that inference workloads accounted for half of all AI compute in 2025 and will reach two-thirds this year. Hyperscalers are responding with enormous capital expenditure, with Amazon projecting $200 billion in 2026 spending and Google budgeting between $175 billion and $185 billion, according to recent earnings disclosures. Yet much of that investment flows through a small number of vertically integrated stacks, leaving enterprises that want to run inference across mixed hardware with limited options.

That gap is where NeuReality is placing its bet. NR-NEXUS is designed to work across any CPU, GPU, or NIC, including NVIDIA’s forthcoming Vera Rubin architecture, and targets three buyer categories: neocloud providers, enterprises building their own inference capacity, and semiconductor vendors looking to offer a complete software layer atop their chips.

The company has raised approximately $70 million to date. A $35 million Series A in late 2022, led by Samsung Ventures with participation from OurCrowd and SK Hynix among others, was followed by a $20 million round in March 2024 anchored by the European Innovation Council Fund and existing investors. That EU backing positioned NeuReality as part of a broader European push to develop sovereign AI infrastructure, though the company’s engineering centre remains in Israel.

Agarwal’s advisory role appears focused on go-to-market strategy rather than product engineering, a recognition that building an inference operating system is only half the challenge. The other half is persuading infrastructure buyers, many of whom have deep relationships with NVIDIA’s own software ecosystem, that a startup’s orchestration layer is worth the integration effort.

Whether NR-NEXUS can gain traction will depend on execution in a market that is attracting well-funded competition. Modal Labs is raising at a reported $2.5 billion valuation. Baseten announced a $300 million round at $5 billion. Fireworks AI secured $250 million. Each approaches inference optimisation from a slightly different angle, but all are chasing the same fundamental opportunity: as AI moves from training to deployment, whoever controls the inference layer controls a growing share of the value chain.

For NeuReality, the appointment of an adviser with Google-grade product instincts may be a modest move on paper. In practice, it is a bet that the next phase of AI infrastructure will reward companies that can bridge the gap between silicon and the enterprises that need to run models at scale, efficiently, and across hardware they already own.

The BGIS 2026 Grand Finals will be the final stage of India’s biggest BGMI esports tournament happening this year. As the ultimate stage of the tournament, the Grand Finals events determine this year’s ultimate champion. The Grand Finals match will be held between March 27 and March 29, 2026, at the Chennai Trade Centre, making it a must-watch event for esports fans. It is a LAN event. That means all participating teams will play the game live on stage before an audience.

BGIS 2026 Grand Finals Format

The format of the BGIS 2026 Grand Finals is 16 teams, 18 matches over 3 days. 6 matches per day means that every match is critical. Therefore, consistency throughout the event will determine the champion.

Furthermore, the BGIS 2026 will feature 16 teams. 8 teams qualified through the Semi-Finals (March 12–15), and the other 8 teams made it through the Survival Stage (March 16–17). These teams will face each other in the finals scheduled from March 27 to 29, 2026.

Qualified Teams List

• Team Soul
• Orangutan
• Genesis Esports
• Learn From Past (LEFP)
• Reckoning Esports
• Revenant XSpark
• Victores Sumus
• Meta Ninza
• GodLike Esports
• Welt Esports
• Nebula Esports
• Myth Official
• Wyld Fangs
• K9 Esports
• Team Tamilas
• Vasista Esports

Prize Pool Breakdown

BGIS 2026 has a record-breaking ₹4 Crore prize pool, making it the biggest BGMI event so far. The champions will receive ₹1 Crore, while other teams will also earn significant rewards.

How to Watch BGIS 2026 Grand Finals Live

You can watch all BGIS 2026 Grand Finals matches live on the official Krafton India Esports YouTube channel starting at 2:30 PM IST. In addition, if you want to attend the event in person, tickets are available through Swiggy Scenes. BGIS 2026 was played across different stages before reaching the final round.

• The tournament started with the Grind from January 17 to February 1, 2026.
• After that, teams competed in the Semi-Finals from March 12 to 15 in Hyderabad, where the top teams secured their spots.
• Next, teams played the Survival Stage on March 16 and 17 in Hyderabad to earn the remaining slots.
• Finally, the top teams will compete in the Grand Finals from March 27 to 29 in Chennai to decide the champion.

If you were anywhere near a computer in the mid-to-late 1990s, you almost certainly encountered a Zip drive. That distinctive purple peripheral, with its satisfying clunk as you slotted in a cartridge, was as much a fixture of the era as beige tower cases and CRT monitors. Iomega, the company behind it, went from an obscure Utah outfit to a multi-billion-dollar darling of Wall Street in the span of about two years. And then, almost as quickly, it all fell apart.

The story of Iomega is one of genuine engineering innovation and the fickle nature of consumer technology. As with so many other juggernauts of its era, Iomega was eventually brought down by a new technology that simply wasn’t practical to counter.

The House That Bernoulli Built

Iomega was founded in Utah, in 1980, by Jerome Paul Johnson, David Bailey, and David Norton. The company soon developed a novel approach to removable magnetic storage based on the Bernoulli effect. The Bernoulli Box arrived in 1982, which was a drive relying on PET film disks spun at 1500 RPM inside a rigid, removable cartridge. The airflow generated by the spinning disk pulled the media down toward the read/write head thanks to the eponymous Bernoulli effect. While spinning, the disk would float a mere micron above the head surface on a cushion of air. If the power cut out or the drive otherwise failed, the disk simply floated away from the head rather than crashing into it—a boon over contemporary hard drives for which head crashes were a real risk. The Bernoulli Box made them essentially impossible.

Early Bernoulli Box drives offered 10 MB and 20 MB of removable storage at a time when a fixed hard drive might hold 30 MB. Bernoulli Boxes were never really aimed at the home market, but found a devoted following among power users—publishers, CAD users, and anyone who needed to move serious amounts of data between machines. Sales were strong, and by 1983, Iomega hit the stock market running with an initial public offering raising $21.7 million.

As hard drive prices continued to dive over time due to economies of scale, though, the expensive Bernoulli Box became a less attractive proposition even despite its portability and greater storage. By 1986, Iomega had sold over 70,000 units and more than a million cartridges, but sales had peaked. The company had racked up serious debt and slow sales left the company saddled with undesirable inventory that wouldn’t move. Upgrades came thick and fast as Iomega pushed to keep up with the rapidly-changing storage market, which was enough to keep Iomega relevant if not flourishing. By 1993, the largest Bernoulli carts could hold 230 MB if you had a suitable model drive to read them, though the expensive drives mostly remained the domain of large corporate and government users.

Zipping To The Top

The next phase for Iomega saw the company reach its greatest peak. The Zip drive launched in March 1995, and aimed to be a more affordable solution to high-capacity removable storage. It hit the market with 100 MB cartridges priced at $19.95 each, in an era when the standard 3.5” floppy could only hold 1.44 MB. For anyone regularly shuffling large files between home and office, or backing up a hard drive that might only hold a few hundred megabytes, it was a great leap forward. The iconic external model became popular in businesses, universities, and homes, and before long, OEMs like Apple, Dell, and Gateway started offering internal Zip drives as factory options. It became as close to a defacto standard for removable storage as a proprietary storage solution could ever be.

When the Zip drive hit, the sales numbers were staggering. Iomega’s revenue leapt from $362 million in 1995 to $1.2 billion in 1996. At its peak, Iomega was valued at nearly $7 billion. The company’s stock became a darling of investors addicted to massive gains. For a time, they appeared to be an unstoppable tech juggernaut, hanging on to a sizable chunk of the removable storage market without any obvious competitors on the horizon.

Iomega chased the success of the Zip drive with the even higher-capacity Jaz drive, which could store 1 GB in early models on hefty cartridges that contained rigid drive platters not dissimilar from those in contemporary hard disks. They were a great solution for power users moving what was then considered a lot of data, but their higher price meant they were never a consumer-grade darling like the cheaper Zip drive itself. The later “Clik” or “PocketZip” drive came along later in 1999, with a diminutive form factor and 40 MB disks. It too failed to gain the foothold of Zip, however, with a low install base limiting the usefulness of the removable format.

It wasn’t all smooth sailing, of course. A serious blow to Iomega’s reputation came from its own engineering. Some Zip drives developed a fault that came to be known as the “Click of Death.” The term referred to a clicking sound of the drive heads bouncing off their end stops when they became misaligned. In extreme cases, misaligned heads in a bad drive could damage disks, which would then damage the next drive they were used in. It was a mark against the technology that was supposed to be robust enough to be used as mobile storage. A class action lawsuit was filed in September 1998 and eventually settled in 2001, but the reputational damage remained.

Downfall

It wasn’t the Click of Death debacle that doomed Iomega, though. It was merely the march of competing technologies that made its storage solutions less attractive over time. CD-R drives, which had been expensive curiosities in the mid-1990s, became dirt cheap just a few years later. By 2000, blank CD-Rs were retailing for as little as fifty cents each, and they held 650 MB a pop— more than six times the capacity of a Zip disk, on media that cost a fraction of the price and didn’t require proprietary hardware. They were so cheap, the write-once nature almost failed to matter. It was far more attractive to many customers to just burn another cheap CD that anyone could read than to go out and buy a Zip drive, an expensive 100 MB disk, and hope that whoever you were sending the disk to also had a drive that could read it. The CD-RW followed soon enough after, and writable DVDs would then take storage capacities well into the multi-gigabyte range. Zip drives jumped to 250 MB and then 750 MB, while the Jaz line was upgraded to 2 GB, but by and large, consumers were choosing writable optical discs over Iomega’s proprietary solutions.

USB flash drives would then prove to be the final nail in the coffin. They were compact and cheap, and required no special hardware whatsoever. You could just plug them into any USB port on any computer and your files were right there. They too would become cheap enough to be disposable, in a way that Iomega’s bespoke drives and mechanically-complicated cartridges would never be.

By 2002, the Jaz drive was dead, and the Zip drive followed soon after in 2003. It was CD burners that did the most damage, with the leap to DVD and the rising prominence of the USB drive that promised there would be no way back for removable magnetic cartridge media. These solutions were far less mechanically complex and a lot cheaper in terms of cost per megabyte.

Iomega was, at this point, a lumbering corporation with hundreds of employees, a dying product line, and a bleak future ahead. The company pivoted to other storage solutions, like selling rebranded optical disc drives, external hard drives, and network-attached storage devices. However, none of these products were particularly unique or competitive, as Iomega went from dominating a specific niche to fighting in a market segment where it had no particular competitive advantage. They became a small, sickly fish in a big pond, competing against dozens of other established storage brands that were far more renowned in their fields.

The end came in April 2008, when EMC Corporation announced plans to acquire Iomega for $213 million — a tiny fraction of the company’s peak valuation. EMC saw lingering value in Iomega’s small office and home office customer base, and kept the brand alive for a few years, slapping the Iomega name on NAS boxes and media adapters. These weren’t iconic products unique to the brand, so much as middle-of-the-road options that had no technical edge or promise to speak of. In 2013, EMC formed a joint venture with Lenovo called LenovoEMC. Iomega’s remaining products were rebadged accordingly, and the brand effectively ceased to exist. There was no reason to continue Iomega, because what it was built to do was simply no longer relevant in the modern marketplace.

The Iomega story is, in many ways, the archetypal cautionary tale of the consumer technology industry. In the 1990s, the company identified a genuine need—affordable, portable, high-capacity removable storage. It nailed this brief with the Zip drive, which propelled the company’s fortunes into the stratosphere. However, the entire business hinged on a product category that had a shelf life measured in years. Iomega simply couldn’t hold on to its edge in removable storage against so many competitors that were both cheaper and more practical. It’s the same death that Blockbuster died—fail to see the future, and you will inevitably succumb to it.

Getting AI agents to perform reliably in production — not just in demos — is turning out to be harder than enterprises anticipated. Fragmented data, unclear workflows, and runaway escalation rates are slowing deployments across industries.

“The technology itself often works well in demonstrations,” said Sanchit Vir Gogia, chief analyst with Greyhound Research. “The challenge begins when it is asked to operate inside the complexity of a real organization.” 

Burley Kawasaki, who oversees agent deployment at Creatio, and team have developed a methodology built around three disciplines: data virtualization to work around data lake delays; agent dashboards and KPIs as a management layer; and tightly bounded use-case loops to drive toward high autonomy.

In simpler use cases, Kawasaki says these practices have enabled agents to handle up to 80-90% of tasks on their own. With further tuning, he estimates they could support autonomous resolution in at least half of use cases, even in more complex deployments.

“People have been experimenting a lot with proof of concepts, they’ve been putting a lot of tests out there,” Kawasaki told VentureBeat. “But now in 2026, we’re starting to focus on mission-critical workflows that drive either operational efficiencies or additional revenue.”

Why agents keep failing in production

Enterprises are eager to adopt agentic AI in some form or another — often because they’re afraid to be left out, even before they even identify real-world tangible use cases — but run into significant bottlenecks around data architecture, integration, monitoring, security, and workflow design. 

The first obstacle almost always has to do with data, Gogia said. Enterprise information rarely exists in a neat or unified form; it is spread across SaaS platforms, apps, internal databases, and other data stores. Some are structured, some are not. 

But even when enterprises overcome the data retrieval problem, integration is a big challenge. Agents rely on APIs and automation hooks to interact with applications, but many enterprise systems were designed long before this kind of autonomous interaction was a reality, Gogia pointed out. 

This can result in incomplete or inconsistent APIs, and systems can respond unpredictably when accessed programmatically. Organizations also run into snags when they attempt to automate processes that were never formally defined, Gogia said. 

“Many business workflows depend on tacit knowledge,” he said. That is, employees know how to resolve exceptions they’ve seen before without explicit instructions — but, those missing rules and instructions become startlingly obvious when workflows are translated into automation logic.

The tuning loop

Creatio deploys agents in a “bounded scope with clear guardrails,” followed by an “explicit” tuning and validation phase, Kawasaki explained. Teams review initial outcomes, adjust as needed, then re-test until they’ve reached an acceptable level of accuracy. 

That loop typically follows this pattern: 

• Design-time tuning (before go-live): Performance is improved through prompt engineering, context wrapping, role definitions, workflow design, and grounding in data and documents. 

• Human-in-the-loop correction (during execution): Devs approve, edit, or resolve exceptions. In instances where humans have to intervene the most (escalation or approval), users establish stronger rules, provide more context, and update workflow steps; or, they’ll narrow tool access. 

• Ongoing optimization (after go-live): Devs continue to monitor exception rates and outcomes, then tune repeatedly as needed, helping to improve accuracy and autonomy over time. 

Kawasaki’s team applies retrieval-augmented generation to ground agents in enterprise knowledge bases, CRM data, and other proprietary sources. 

Once agents are deployed in the wild, they are monitored with a dashboard providing performance analytics, conversion insights, and auditability. Essentially, agents are treated like digital workers. They have their own management layer with dashboards and KPIs.

For instance, an onboarding agent will be incorporated as a standard dashboard interface providing agent monitoring and telemetry. This is part of the platform layer — orchestration, governance, security, workflow execution, monitoring, and UI embedding —  that sits “above the LLM,” Kawasaki said.

Users see a dashboard of agents in use and each of their processes, workflows, and executed results. They can “drill down” into an individual record (like a referral or renewal) that shows a step-by-step execution log and related communications to support traceability, debugging, and agent tweaking. The most common adjustments involve logic and incentives, business rules, prompt context, and tool access, Kawasaki said. 

The biggest issues that come up post-deployment: 

• Exception handling volume can be high: Early spikes in edge cases often occur until guardrails and workflows are tuned. 

• Data quality and completeness: Missing or inconsistent fields and documents can cause escalations; teams can identify which data to prioritize for grounding and which checks to automate.

• Auditability and trust: Regulated customers, particularly, require clear logs, approvals, role-based access control (RBAC), and audit trails.

“We always explain that you have to allocate time to train agents,” Creatio’s CEO Katherine Kostereva told VentureBeat. “It doesn’t happen immediately when you switch on the agent, it needs time to understand fully, then the number of mistakes will decrease.” 

“Data readiness” doesn’t always require an overhaul

When looking to deploy agents, “Is my data ready?,” is a common early question. Enterprises know data access is important, but can be turned off by a massive data consolidation project. 

But virtual connections can allow agents access to underlying systems and get around typical data lake/lakehouse/warehouse delays. Kawasaki’s team built a platform that integrates with data, and is now working on an approach that will pull data into a virtual object, process it, and use it like a standard object for UIs and workflows. This way, they don’t have to “persist or duplicate” large volumes of data in their database. 

This technique can be helpful in areas like banking, where transaction volumes are simply too large to copy into CRM, but are “still valuable for AI analysis and triggers,” Kawasaki said.

Once integrations and virtual objects are established, teams can evaluate data completeness, consistency, and availability, and identify low-friction starting points (like document-heavy or unstructured workflows). 

Kawasaki emphasized the importance of “really using the data in the underlying systems, which tends to actually be the cleanest or the source of truth anyway.” 

Matching agents to the work

The best fit for autonomous (or near-autonomous) agents are high-volume workflows with “clear structure and controllable risk,” Kawasaki said. For instance, document intake and validation in onboarding or loan preparation, or standardized outreach like renewals and referrals.

“Especially when you can link them to very specific processes inside an industry — that’s where you can really measure and deliver hard ROI,” he said. 

For instance, financial institutions are often siloed by nature. Commercial lending teams perform in their own environment, wealth management in another. But an autonomous agent can look across departments and separate data stores to identify, for instance, commercial customers who might be good candidates for wealth management or advisory services.

“You think it would be an obvious opportunity, but no one is looking across all the silos,” Kawasaki said. Some banks that have applied agents to this very scenario have seen “benefits of millions of dollars of incremental revenue,” he claimed, without naming specific institutions. 

However, in other cases — particularly in regulated industries — longer-context agents are not only preferable, but necessary. For instance, in multi-step tasks like gathering evidence across systems, summarizing, comparing, drafting communications, and producing auditable rationales.

“The agent isn’t giving you a response immediately,” Kawasaki said. “It may take hours, days, to complete full end-to-end tasks.” 

This requires orchestrated agentic execution rather than a “single giant prompt,” he said. This approach breaks work down into deterministic steps to be performed by sub-agents. Memory and context management can be maintained across various steps and time intervals. Grounding with RAG can help keep outputs tied to approved sources, and users have the ability to dictate expansion to file shares and other document repositories.

This model typically doesn’t require custom retraining or a new foundation model. Whatever model enterprises use (GPT, Claude, Gemini), performance improves through prompts, role definitions, controlled tools, workflows, and data grounding, Kawasaki said. 

The feedback loop puts “extra emphasis” on intermediate checkpoints, he said. Humans review intermediate artifacts (such as summaries, extracted facts, or draft recommendations) and correct errors. Those can then be converted into better rules and retrieval sources, narrower tool scopes, and improved templates. 

“What is important for this style of autonomous agent, is you mix the best of both worlds: The dynamic reasoning of AI, with the control and power of true orchestration,” Kawasaki said.

Ultimately, agents require coordinated changes across enterprise architecture, new orchestration frameworks, and explicit access controls, Gogia said. Agents must be assigned identities to restrict their privileges and keep them within bounds. Observability is critical; monitoring tools can record task completion rates, escalation events, system interactions, and error patterns. This kind of evaluation must be a permanent practice, and agents should be tested to see how they react when encountering new scenarios and unusual inputs. 

“The moment an AI system can take action, enterprises have to answer several questions that rarely appear during copilot deployments,” Gogia said. Such as: What systems is the agent allowed to access? What types of actions can it perform without approval? Which activities must always require a human decision? How will every action be recorded and reviewed?

“Those [enterprises] that underestimate the challenge often find themselves stuck in demonstrations that look impressive but cannot survive real operational complexity,” Gogia said. 

In Steal a Brainrot, Brainrots, or characters are the main way you earn money in the game. You can collect them, place them in your base, and watch them earn money automatically. The game features different rarity levels, and higher-tier Brainrots can boost your earnings significantly. Let’s explore all the Brainrots (characters) you can find in Steal a Brainrot.

1. Common Brainrots

When you begin playing, Common Brainrots characters will be everywhere. They don’t give high earnings, but they’re the easiest to get your hands on. The best option here is Pipi Kiwi, which earns $13 per second. It’s basic, but useful in the early stage.

2. Rare Brainrots

Among Rare Brainrots characters, Pipi Avocado stands out as the best, earning $70 per second. These show up more often than higher-tier ones, making them a solid choice for boosting your early income.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	Trippi Troppi	$2,000	$15/s
	Tung Tung Tung Sahur	$3,000	$25/s
	Gangster Footera	$4,000	$30/s
	Bandito Bobritto	$4,500	$35/s
	Boneca Ambalabu	$5,000	$40/s
	Cacto Hipopotamo	$6,500	$50/s
	Ta Ta Ta Ta Sahur	$7,500	$55/s
	Tric Trac Baraboom	$9,000	$65/s
	Frogo Elfo	$9,200	$67/s
	Pipi Avocado	$9,500	$70/s
	Pengolino Nuvoletto	$9,600	$72/s

3. Epic Brainrots

Epic Brainrot characters are where your income really improves in Steal a Brainrot. Salamino Penguino earns $250 per second, making it a solid choice. These Brainrots are perfect for progressing through the middle stages of the game.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	Cupcake Koala	$8,000	$60/s
	Pinealotto Fruttarino	$9,700	$75/s
	Cappuccino Assassino	$10,000	$75/s
	Bandito Axolito	$12,500	$90/s
	Brr Brr Patapim	$15,000	$100/s
	Avocadini Antilopini	$17,500	$115/s
	Trulimero Trulicina	$20,000	$125/s
	Bambini Crostini	$22,500	$135/s
	Malame Amarele	$23,500	$140/s
	Bananita Dolphinita	$25,000	$150/s
	Perochello Lemonchello	$27,500	$160/s
	Brri Brri Bicus Dicus Bombicus	$30,000	$175/s
	Avocadini Guffo	$35,000	$225/s
	Ti Ti Ti Sahur	$37,500	$225/s
	Mangolini Parrocini	$38,500	$235/s
	Frogato Pirato	$39,000	$240/s
	Salamino Penguino	$40,000	$250/s
	Gato Celesto	$40,000	$250/s
	Doi Doi Do	$41,000	$260/s
	Penguin Tree	$42,000	$270/s
	Wombo Rollo	$42,500	$275/s
	Penguino Cocosino	$45,000	$300/s
	Mummio Rappitto	$47,500	$325/s

4. Legendary Brainrots

Steal a Brainrot gives Legendary Brainrot characters every 5 minutes, making it easier to grow your income. Sigma Boy leads this tier with $1,300 per second, which is far higher than what Common Brainrots offer.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	Burbaloni Loliloli	$35,000	$200/s
	Chimpanzini Bananini	$50,000	$300/s
	Tirilikalika Tirilikalako	$75,000	$450/s
	Ballerina Cappuccina	$100,000	$500/s
	Chef Crabracadabra	$150,000	$600/s
	Lionel Cactuseli	$175,000	$650/s
	Glorbo Fruttodrillo	$200,000	$750/s
	Quivioli Ameleonni	$225,000	$1,000/s
	Blueberrinni Octopusini	$250,000	$1,000/s
	Clickerino Crabo	$250,000	$1,000/s
	Caramello Filtrello	$255,000	$1,000/s
	Pipi Potato	$256,000	$1,100/s
	Strawberrelli Flamingelli	$275,000	$1,100/s
	Cocosini Mama	$285,000	$1,200/s
	Pandaccini Bananini	$300,000	$1,200/s
	Quackula	$310,000	$1,200/s
	Pi Pi Watermelon	$315,000	$1,200/s
	Signore Carapace	$320,000	$1,300/s
	Sigma Boy	$325,000	$1,300/s
	Buho del Cielo	$325,000	$1,300/s
	Sigma Girl	$325,000	$1,800/s
	Chocco Bunny	$327,500	$1,400/s
	Puffaball	$330,000	$1,500/s
	Sealo Regalo	$342,000	$1,800/s
	Buho de Fuego	$345,000	$1,400/s
	Seraphino Gruyero	$347,500	$1,900/s

5. Mythic Brainrots

Every 15 minutes, a Mythic Brainrot character is guaranteed to appear in Steal a Brainrot. Ganganzelli Trulala is the best among them, bringing in $9,000 per second. It performs even better when it gets a Trait.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	Frigo Camelo	$300,000	$1,200/s
	Harpuccino	$347,500	$14,000/s
	Orangutini Ananassini	$400,000	$1,700/s
	Rhino Toasterino	$450,000	$2,100/s
	Bombardiro Crocodilo	$500,000	$2,500/s
	Brutto Gialutto	$600,000	$3,000/s
	Spioniro Golubiro (Lucky Block)	$750,000	$3,500/s
	Bombombini Gusini	$1,000,000	$5,000/s
	Zibra Zubra Zibralini (Lucky Block)	$1,000,000	$6,000/s
	Tigrilini Watermelini (Lucky Block)	$1,000,000	$7,500/s
	Avocadorilla	$2,000,000	$7,500/s
	Cavallo Virtuoso	$2,500,000	$7,500/s
	Te Te Te Sahur	$2,500,000	$9,500/s
	Gorillo Subwoofero	$2,700,000	$7,700/s
	Gorillo Watermelondrillo	$3,000,000	$8,000/s
	Stoppo Luminino	$3,000,000	$8,000/s
	Tracoducotulu Delapeladustuz	$3,000,000	$12,000/s
	Tob Tobi Tobi	$3,500,000	$8,500/s
	Lerulerulerule	$3,500,000	$8,750/s
	Ganganzelli Trulala	$4,000,000	$9,000/s
	Magi Ribbitini	$4,000,000	$12,000/s
	Rhino Helicopterino	$4,100,000	$11,000/s
	Jingle Jingle Sahur	$4,300,000	$12,200/s
	Los Noobinis	$4,300,000	$12,500/s
	Cachorrito Melonito	$4,400,000	$13,000/s
	Spongini Quackini	$4,400,000	$15,000/s
	Carloooo (Lucky Block)	$4,500,000	$13,500/s
	Elefanto Frigo	$4,500,000	$14,000/s
	Berenjello Angello	$4,600,000	$18,000/s
	Carrotini Brainini (Lucky Block)	$4,700,000	$15,000/s
	Centrucci Nuclucci	$4,800,000	$15,500/s
	Toiletto Focaccino	$4,800,000	$16,000/s
	Jacko Spaventosa	$4,800,000	$16,200/s
	Bananito Bandito	$4,900,000	$16,500/s
	Tree Tree Tree Sahur	$4,900,000	$17,000/s

6. Brainrot God

If you want big earnings before Secrets, Brainrot God units are the way to go. Piccione Macchina generates $225,000 per second, which is huge. These show up when server goals are reached, so playing during busy periods gives you a better chance.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	Fizzy Soda	$4,900,000	$17,200/s
	Cocofanto Elefanto	$5,000,000	$10,000/s
	Antonio	$6,000,000	$18,500/s
	Girafa Celestre	$7,500,000	$20,000/s
	Gattatino Nyanino	$7,500,000	$35,000/s
	Gattatino Neonino	$7,500,000	$20,000/s
	Chihuanini Taconini	$8,500,000	$45,000/s
	Matteo	$10,000,000	$50,000/s
	Tralalero Tralala	$10,000,000	$50,000/s
	Los Crocodillitos	$12,500,000	$55,000/s
	Tigroligre Frutonni (Lucky Block)	$15,000,000	$60,000/s
	Odin Din Din Dun	$15,000,000	$75,000/s
	Orcalero Orcala (Lucky Block)	$15,000,000	$100,000/s
	Money Money Man	$17,500,000	$65,000/s
	Alessio	$17,500,000	$65,000/s
	Unclito Samito	$20,000,000	$65,000/s
	Statutino Libertino	$20,000,000	$75,000/s
	Tipi Topi Taco	$20,000,000	$75,000/s
	Tralalita Tralala	$20,000,000	$100,000/s
	Tukanno Banana	$22,500,000	$100,000/s
	Extinct Ballerina	$23,500,000	$125,000/s
	Vampira Cappucina	$24,500,000	$125,000/s
	Espresso Signora	$25,000,000	$70,000/s
	Trenozosturzzo Turbo 3000	$25,000,000	$150,000/s
	Bulbito Bandito Traktorito (Lucky Block)	$25,000,000	$205,000/s
	Urubini Flamenguini	$30,000,000	$150,000/s
	Jacko Jack Jack	$30,000,000	$150,000/s
	Trippi Troppi Troppa Trippa	$30,000,000	$175,000/s
	Capi Taco	$31,000,000	$155,000/s
	Los Chihuaninis	$32,000,000	$160,000/s
	Divino Platypio	$32,000,000	$160,000/s
	Gattito Tacoto	$32,500,000	$165,000/s
	Las Capuchinas	$32,500,000	$185,000/s
	Ballerino Lololo	$35,000,000	$200,000/s
	Los Tungtungtungcitos	$37,500,000	$210,000/s
	Pakrahmatmamat	$37,500,000	$215,000/s
	Ballerina Peppermintina	$37,500,000	$215,000/s
	Piccione Macchina	$40,000,000	$225,000/s
	Pakrahmatmatina	$40,500,000	$225,000/s
	Los Bombinitos	$42,500,000	$220,000/s
	Tractoro Dinosauro	$42,500,000	$230,000/s
	Brr Es Teh Patipum	$45,000,000	$225,000/s
	Cacasito Satalito	$45,000,000	$240,000/s
	Orcalita Orcala	$45,000,000	$240,000/s
	Aquanaut	$45,500,000	$245,000/s
	Tartaruga Cisterna	$45,000,000	$250,000/s
	Snailenzo	$45,000,000	$250,000/s
	Corn Corn Corn Sahur	$45,000,000	$250,000/s
	Squalanana	$45,000,000	$250,000/s
	Mummy Ambalabu	$45,000,000	$250,000/s
	Los Orcalitos	$45,000,000	$310,000/s
	Dug Dug Dug	$45,500,000	$255,000/s
	Ginger Globo	$45,700,000	$257,500/s
	Yeti Claus	$45,700,000	$267,500/s
	Crabbo Limonetta	$46,000,000	$230,000/s
	Los Tipi Tacos	$46,000,000	$260,000/s
	Tootini Shrimpini	$46,000,000	$260,000/s
	Granchiello Spiritell	$46,000,000	$260,000/s
	Frio Ninja	$46,500,000	$265,000/s
	Piccionetta Macchina	$47,000,000	$270,000/s
	Boba Panda	$47,000,000	$270,000/s
	Mastodontico Telepiedone (Lucky Block)	$47,500,000	$275,000/s
	Los Gattitos	$47,500,000	$275,000/s
	Bambu Bambu Sahur	$47,500,000	$275,000/s
	Chrismasmamat	$47,700,000	$277,500/s
	Anpali Babel	$48,000,000	$280,000/s
TBA	Astrolero Cervalero	$48,000,000	$280,000/s
	Cappuccino Clownino	$48,000,000	$285,000/s
	Luv Luv Luv	$48,300,000	$282,000/s
	Bombardini Tortinii	$50,000,000	$225,000/s
	Brasilini Berimbini	$55,500,000	$285,000/s
	Belula Beluga	$60,000,000	$290,000/s
	Krupuk Pagi Pagi	$60,000,000	$290,000/s
	Skull Skull Skull	$60,000,000	$290,000/s
	Cocoa Assassino	$60,000,000	$291,000/s
	Tentacolo Tecnico	$62,500,000	$295,000/s
	Ginger Cisterna	$63,500,000	$293,500/s
	Dolphini Jetskini	$64,500,000	$294,500/s
	Pop Pop Sahur	$65,000,000	$295,000/s
	Noo La Polizia	$67,000,000	$280,000/s
	Karkerheart Luvkur	$67,500,000	$297,500/s
	Dumborino Miracello	$75,000,000	$315,000/s

7. Secret Brainrots

Secret Brainrot characters don’t show up often in Steal a Brainrot, but they are the best money-makers in the game. Spawning one depends on luck, but once it happens, you’re in a great position.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	La Vacca Saturno Saturnita	$50,000,000	$250,000/s
	Pandanini Frostini	$64,000,000	$294,000/s
	Bisonte Giuppitere	$75,000,000	$300,000/s
	Blackhole Goat	$75,000,000	$400,000/s
	Jackorilla	$80,000,000	$315,000/s
	Agarrini Ia Palini	$80,000,000	$425,000/s
	Chachechi	$85,000,000	$400,000/s
	Karkerkar Kurkur	$100,000,000	$275,000/s
	Los Tortus	$100,000,000	$500,000/s
	Los Matteos	$100,000,000	$300,000/s
	Sammyni Spyderini	$100,000,000	$300,000/s
	Trenostruzzo Turbo 4000	$100,000,000	$310,000/s
	Chimpanzini Spiderini	$100,000,000	$325,000/s
	Boatito Auratito	$115,000,000	$525,000/s
	Fragola La La La	$125,000,000	$450,000/s
	Dul Dul Dul	$150,000,000	$375,000/s
	La Vacca Prese Presente	$160,000,000	$600,000/s
	Frankentteo	$175,000,000	$700,000/s
	Los Trios	$175,000,000	$700,000/s
	Karker Sahur	$185,000,000	$725,000/s
	Torrtuginni Dragonfrutini (Lucky Block)	$500,000,000	$350,000/s
	Los Tralaleritos	$100,000,000	$750,000/s
	Zombie Tralala	$100,000,000	$500,000/s
	La Cucaracha	$110,000,000	$475,000/s
	Vulturino Skeletono	$110,000,000	$500,000/s
	Guerriro Digitale	$120,000,000	$550,000/s
	Extinct Tralalero	$125,000,000	$450,000/s
	Yess My Examine	$130,000,000	$575,000/s
	Extinct Matteo	$140,000,000	$625,000/s
	Las Tralaleritas	$150,000,000	$650,000/s
	Rocco Disco	$150,000,000	$650,000/s
	Reindeer Tralala	$160,000,000	$600,000/s
	Las Vaquitas Saturnitas	$160,000,000	$750,000/s
	Pumpkin Spyderini	$165,000,000	$650,000/s
	Job Job Job Sahur	$175,000,000	$700,000/s
	Los Karkeritos	$200,000,000	$750,000/s
	Graipuss Medussi	$200,000,000	$1,000,000/s
	Santteo	$210,000,000	$800,000/s
	La Vacca Jacko Linterino	$225,000,000	$850,000/s
	Triplito Tralaleritos	$230,000,000	$875,000/s
	Trickolino	$235,000,000	$900,000/s
	Paradiso Axolottino	$235,000,000	$900,000/s
	Giftini Spyderini	$240,000,000	$999,900/s
	Los Spyderinis	$250,000,000	$450,000/s
	Love Love Love Sahur	$250,000,000	$1,000,000/s
	Perrito Burrito	$250,000,000	$1,000,000/s
	1x1x1x1	$255,500,000	$1,100,000/s
	Los Cucarachas	$300,000,000	$1,200,000/s
	Please My Present	$350,000,000	$1,300,000/s
	Cuadramat and Pakrahmatmamat	$400,000,000	$1,400,000/s
	Los Jobcitos	$500,000,000	$1,500,000/s
	Nooo My Hotspot	$500,000,000	$2,000,000/s
	Pot Hotspot (Lucky Block)	$500,000,000	$2,500,000/s
	Noo My Examine	$525,000,000	$1,700,000/s
	Telemorte	$550,000,000	$2,000,000/s
	La Sahur Combinasion	$550,000,000	$2,000,000/s
	List List List Sahur	$550,000,000	$2,000,000/s
	To To To Sahur	$575,000,000	$2,500,000/s
	Pirulitoita Bicicletaire	$600,000,000	$2,500,000/s
	25	$600,000,000	$2,500,000/s
	Santa Hotspot	$625,000,000	$2,600,000/s
	Horegini Boom	$650,000,000	$2,700,000/s
	Quesadilla Crocodila	$700,000,000	$3,000,000/s
	Pot Pumpkin	$700,000,000	$3,000,000/s
	Naughty Naughty	$700,000,000	$3,000,000/s
	Cupid Cupid Sahur	$715,000,000	$3,100,000/s
	Ho Ho Ho Sahur	$725,000,000	$3,200,000/s
	Mi Gatito	$725,000,000	$3,200,000/s
	Chicleteira Bicicleteira	$750,000,000	$3,500,000/s
	Cupid Hotspot	$750,000,000	$3,500,000/s
	Spaghetti Tualetti (Lucky Block)	$750,000,000	$6,000,000/s
	Esok Sekolah (Lucky Block)	$750,000,000	$3,000,000/s
	Quesadillo Vampiro	$750,000,000	$3,500,000/s
	Brunito Marsito	$750,000,000	$3,500,000/s
	Chill Puppy	$800,000,000	$4,000,000/s
	Burrito Bandito	$800,000,000	$4,000,000/s
	Chicleteirina Bicicleteirina	$850,000,000	$4,000,000/s
	Los Quesadillas	$875,000,000	$4,500,000/s
	Bunito Bunito Spinito	$900,000,000	$3,000,000/s
	Noo My Candy	$900,000,000	$5,000,000/s
	Los Nooo My Hotspotsitos	$1,000,000,000	$5,000,000/s
TBA	Serafinna Medusella	$1,000,000,000	$5,500,000/s
	La Grande Combinassion	$1,000,000,000	$10,000,000/s
	Rang Ring Bus	$1,100,000,000	$6,000,000/s
	Guest 666	$1,100,000,000	$6,600,000/s
	Los Mi Gatitos	$1,200,000,000	$6,500,000/s
	Los Chicleteiras	$1,200,000,000	$7,000,000/s
	67	$1,200,000,000	$7,500,000/s
	Donkeyturbo Express	$1,200,000,000	$7,500,000/s
	Mariachi Corazoni	$1,200,000,000	$12,500,000/s
	Los Burritos	$1,400,000,000	$8,500,000/s
	Los 25	$1,500,000,000	$10,000,000/s
	Tacorillo Crocodillo	$1,500,000,000	$12,500,000/s
	Swag Soda	$1,800,000,000	$13,000,000/s
	Noo my Heart	$1,800,000,000	$13,000,000/s
	Chimnino	$1,900,000,000	$14,000,000/s
	Los Combinasionas	$2,000,000,000	$15,000,000/s
	Chicleteira Noelteira	$2,000,000,000	$15,000,000/s
	Fishino Clownino	$2,100,000,000	$15,500,000/s
	Tacorita Bicicleta	$2,200,000,000	$16,500,000/s
	Los Sweethearts	$2,200,000,000	$16,500,000/s
	Spinny Hammy	$2,300,000,000	$17,000,000/s
	Nuclearo Dinosauro	$2,500,000,000	$15,000,000/s
	Las Sis	$2,500,000,000	$17,500,000/s
	Chicleteira Cupideira	$2,500,000,000	$17,500,000/s
	La Karkerkar Combinasion	$2,500,000,000	$17,500,000/s
	Chillin Chili	$2,500,000,000	$25,000,000/s
	Chipso and Queso	$2,500,000,000	$25,000,000/s
	Money Money Reindeer	$2,500,000,000	$25,000,000/s
	Money Money Puggy	$2,600,000,000	$21,000,000/s
	Celularcini Viciosini	$2,600,000,000	$22,500,000/s
	Los Planitos	$2,700,000,000	$18,500,000/s
	Los Mobilis	$2,700,000,000	$22,000,000/s
	Los 67	$2,700,000,000	$22,500,000/s
	Mieteteira Bicicleteira	$2,700,000,000	$26,000,000/s
	Tuff Toucan	$2,700,000,000	$26,000,000/s
	La Spooky Grande	$2,900,000,000	$24,500,000/s
	Los Spooky Combinasionas	$3,000,000,000	$20,000,000/s
	Cigno Fulgoro	$3,000,000,000	$20,000,000/s
	Los Candies	$3,000,000,000	$23,000,000/s
	Los Hotspositos	$3,000,000,000	$25,000,000/s
	Los Jolly Combinasionas	$3,000,000,000	$25,000,000/s
	Los Cupids	$3,000,000,000	$30,000,000/s
	Los Puggies	$3,000,000,000	$30,000,000/s
	W or L	$3,000,000,000	$30,000,000/s
	Tralalalaledon	$3,000,000,000	$37,500,000/s
	La Extinct Grande Combinasion	$3,200,000,000	$23,500,000/s
	Tralaledon	$3,500,000,000	$27,500,000/s
	La Jolly Grande	$3,500,000,000	$30,000,000/s
	Los Primos	$3,700,000,000	$31,000,000/s
	Bacuru and Egguru	$3,800,000,000	$24,000,000/s
	Eviledon	$3,800,000,000	$31,500,000/s
	Los Tacoritas	$4,000,000,000	$32,000,000/s
	Lovin Rose	$4,200,000,000	$32,500,000/s
	Tang Tang Kelentang	$4,500,000,000	$33,500,000/s
	Ketupat Kepat	$5,000,000,000	$35,000,000/s
	Los Bros	$6,000,000,000	$37,500,000/s
	Tictac Sahur	$6,000,000,000	$37,500,000/s
	La Romantic Grande	$7,000,000,000	$40,000,000/s
	Gingerat Gerat	$7,000,000,000	$40,000,000/s
	Orcaledon	$7,000,000,000	$40,000,000/s
	Ketchuru and Masturu	$7,500,000,000	$42,500,000/s
	Jolly Jolly Sahur	$8,000,000,000	$45,000,000/s
	Garama and Madundung	$10,000,000,000	$50,000,000/s
	Rosetti Tualetti	$10,000,000,000	$50,000,000/s
	Nacho Spyder	$10,000,000,000	$50,000,000/s
	Festive 67	$16,000,000,000	$67,000,000/s
	Sammyni Fattini	$20,000,000,000	$70,000,000/s
	Love Love Bear	$23,000,000,000	$140,000,000/s
	La Ginger Sekolah	$23,000,000,000	$75,000,000/s
	Spooky and Pumpky	$25,000,000,000	$80,000,000/s
	Lavadorito Spinito	$30,000,000,000	$45,000,000/s
	La Food Combinasion	$30,000,000,000	$0/s
	Los Spaghettis	$40,000,000,000	$70,000,000/s
	La Casa Boo	$40,000,000,000	$100,000,000/s
	Fragrama and Chocrama	$40,000,000,000	$100,000,000/s
	Los Sekolahs	$45,000,000,000	$110,000,000/s
	La Secret Combinasion	$50,000,000,000	$125,000,000/s
	Los Amigos	$55,000,000,000	$130,000,000/s
	Reinito Sleighito	$60,000,000,000	$140,000,000/s
	Ketupat Bros	$65,000,000,000	$145,000,000/s
	Burguro and Fryuro	$75,000,000,000	$150,000,000/s
	Cooki and Milki	$100,000,000,000	$155,000,000/s
	Capitano Moby	$125,000,000,000	$160,000,000/s
	Rosey and Teddy	$130,000,000,000	$165,000,000/s
	Popcuru and Fizzuru	$135,000,000,000	$170,000,000/s
	Celestial Pegasus	$150,000,000,000	$150,000,000/s
	Cerberus	$150,000,000,000	$175,000,000/s
	La Supreme Combinasion	$200,000,000,000	$200,000,000/s
	Dragon Cannelloni	$250,000,000,000	$250,000,000/s
	Dragon Gingerini	$300,000,000,000	$300,000,000/s
	Headless Horseman	$325,000,000,000	$325,000,000/s
	Hydra Dragon Cannelloni	$350,000,000,000	$350,000,000/s
	Griffin	$400,000,000,000	$400,000,000/s

8. OG Brainrots

OG Brainrot characters are the rarest units in Steal a Brainrot, even harder to find than Secrets. If one spawns, it starts an event and gains a strong mutation. Currently, only one OG exists.

Brainrot Icon	Brainrot Name	Cost	Income per Second
	Skibidi Toilet	$400,000,000,000	$400,000,000/s
	Meowl	$500,000,000,000	$500,000,000/s
	Strawberry Elephant	$750,000,000,000	$750,000,000/s

ByteDance, the Chinese tech giant behind TikTok, last month released what may be one of the most ambitious open-source AI agent frameworks to date: DeerFlow 2.0. It’s now going viral across the machine learning community on social media. But is it safe and ready for enterprise use?

This is a so-called “SuperAgent harness” that orchestrates multiple AI sub-agents to autonomously complete complex, multi-hour tasks. Best of all: it is available under the permissive, enterprise-friendly standard MIT License, meaning anyone can use, modify, and build on it commercially at no cost.

DeerFlow 2.0 is designed for high-complexity, long-horizon tasks that require autonomous orchestration over minutes or hours, including conducting deep research into industry trends, generating comprehensive reports and slide decks, building functional web pages, producing AI-generated videos and reference images, performing exploratory data analysis with insightful visualizations, analyzing and summarizing podcasts or video content, automating complex data and content workflows, and explaining technical architectures through creative formats like comic strips.

ByteDance offers a bifurcated deployment strategy that separates the orchestration harness from the AI inference engine. Users can run the core harness directly on a local machine, deploy it across a private Kubernetes cluster for enterprise scale, or connect it to external messaging platforms like Slack or Telegram without requiring a public IP.

While many opt for cloud-based inference via OpenAI or Anthropic APIs, the framework is natively model-agnostic, supporting fully localized setups through tools like Ollama. This flexibility allows organizations to tailor the system to their specific data sovereignty needs, choosing between the convenience of cloud-hosted “brains” and the total privacy of a restricted on-premise stack.

Importantly, choosing the local route does not mean sacrificing security or functional isolation. Even when running entirely on a single workstation, DeerFlow still utilizes a Docker-based “AIO Sandbox” to provide the agent with its own execution environment.

This sandbox—which contains its own browser, shell, and persistent filesystem—ensures that the agent’s “vibe coding” and file manipulations remain strictly contained. Whether the underlying models are served via the cloud or a local server, the agent’s actions always occur within this isolated container, allowing for safe, long-running tasks that can execute bash commands and manage data without risk to the host system’s core integrity.

Since its release last month, it has accumulated more than 39,000 stars (user saves) and 4,600 forks — a growth trajectory that has developers and researchers alike paying close attention.

Not a chatbot wrapper: what DeerFlow 2.0 actually is

DeerFlow is not another thin wrapper around a large language model. The distinction matters.

While many AI tools give a model access to a search API and call it an agent, DeerFlow 2.0 gives its agents an actual isolated computer environment: a Docker sandbox with a persistent, mountable filesystem.

The system maintains both short- and long-term memory that builds user profiles across sessions. It loads modular “skills” — discrete workflows — on demand to keep context windows manageable. And when a task is too large for one agent, a lead agent decomposes it, spawns parallel sub-agents with isolated contexts, executes code and Bash commands safely, and synthesizes the results into a finished deliverable.

It is similar to the approach being pursued by NanoClaw, an OpenClaw variant, which recently partnered with Docker itself to offer enterprise-grade sandboxes for agents and subagents.

But while NanoClaw is extremely open ended, DeerFlow has more clearly defined its architecture and scoped tasks: Demos on the project’s official site, deerflow.tech, showcase real outputs: agent trend forecast reports, videos generated from literary prompts, comics explaining machine learning concepts, data analysis notebooks, and podcast summaries.

The framework is designed for tasks that take minutes to hours to complete — the kind of work that currently requires a human analyst or a paid subscription to a specialized AI service.

From Deep Research to Super Agent

DeerFlow’s original v1 launched in May 2025 as a focused deep-research framework. Version 2.0 is something categorically different: a ground-up rewrite on LangGraph 1.0 and LangChain that shares no code with its predecessor. ByteDance explicitly framed the release as a transition “from a Deep Research agent into a full-stack Super Agent.”

New in v2: a batteries-included runtime with filesystem access, sandboxed execution, persistent memory, and sub-agent spawning; progressive skill loading; Kubernetes support for distributed execution; and long-horizon task management that can run autonomously across extended timeframes.

The framework is fully model-agnostic, working with any OpenAI-compatible API. It has strong out-of-the-box support for ByteDance’s own Doubao-Seed models, as well as DeepSeek v3.2, Kimi 2.5, Anthropic’s Claude, OpenAI’s GPT variants, and local models run via Ollama. It also integrates with Claude Code for terminal-based tasks, and with messaging platforms including Slack, Telegram, and Feishu.

Why it’s going viral now

The project’s current viral moment is the result of a slow build that accelerated sharply this week.

The February 28 launch generated significant initial buzz, but it was coverage in machine learning media — including deeplearning.ai’s The Batch — over the following two weeks that built credibility in the research community.

Then, on March 21, AI influencer Min Choi posted to his large X following: “China’s ByteDance just dropped DeerFlow 2.0. This AI is a super agent harness with sub-agents, memory, sandboxes, IM channels, and Claude Code integration. 100% open source.” The post earned more than 1,300 likes and triggered a cascade of reposts and commentary across AI Twitter.

A search of X using Grok uncovered the full scope of that response. Influencer Brian Roemmele, after conducting what he described as intensive personal testing, declared that “DeerFlow 2.0 absolutely smokes anything we’ve ever put through its paces” and called it a “paradigm shift,” adding that his company had dropped competing frameworks entirely in favor of running DeerFlow locally. “We use 2.0 LOCAL ONLY. NO CLOUD VERSION,” he wrote.

More pointed commentary came from accounts focused on the business implications. One post from @Thewarlordai, published March 23, framed it bluntly: “MIT licensed AI employees are the death knell for every agent startup trying to sell seat-based subscriptions. The West is arguing over pricing while China just commoditized the entire workforce.”

Another widely shared post described DeerFlow as “an open-source AI staff that researches, codes and ships products while you sleep… now it’s a Python repo and ‘make up’ away.”

Cross-linguistic amplification — with substantive posts in English, Japanese, and Turkish — points to genuine global reach rather than a coordinated promotion campaign, though the latter is not out of the question and may be contributing to the current virality.

The ByteDance question

ByteDance’s involvement is the variable that makes DeerFlow’s reception more complicated than a typical open-source release.

On the technical merits, the open-source, MIT-licensed nature of the project means the code is fully auditable. Developers can inspect what it does, where data flows, and what it sends to external services. That is materially different from using a closed ByteDance consumer product.

But ByteDance operates under Chinese law, and for organizations in regulated industries — finance, healthcare, defense, government — the provenance of software tooling increasingly triggers formal review requirements, regardless of the code’s quality or openness.

The jurisdictional question is not hypothetical: U.S. federal agencies are already operating under guidance that treats Chinese-origin software as a category requiring scrutiny.

For individual developers and small teams running fully local deployments with their own LLM API keys, those concerns are less operationally pressing. For enterprise buyers evaluating DeerFlow as infrastructure, they are not.

A real tool, with limitations

The community enthusiasm is credible, but several caveats apply.

DeerFlow 2.0 is not a consumer product. Setup requires working knowledge of Docker, YAML configuration files, environment variables, and command-line tools. There is no graphical installer. For developers comfortable with that environment, the setup is described as relatively straightforward; for others, it is a meaningful barrier.

Performance when running fully local models — rather than cloud API endpoints — depends heavily on available VRAM and hardware, with context handoff between multiple specialized models a known challenge. For multi-agent tasks running several models in parallel, the resource requirements escalate quickly.

The project’s documentation, while improving, still has gaps for enterprise integration scenarios. There has been no independent public security audit of the sandboxed execution environment, which represents a non-trivial attack surface if exposed to untrusted inputs.

And the ecosystem, while growing fast, is weeks old. The plugin and skill library that would make DeerFlow comparably mature to established orchestration frameworks simply does not exist yet.

What does it mean for enterprises in the AI transformation age?

The deeper significance of DeerFlow 2.0 may be less about the tool itself and more about what it represents in the broader race to define autonomous AI infrastructure.

DeerFlow’s emergence as a fully capable, self-hostable, MIT-licensed agentic orchestrator adds yet another twist to the ongoing race among enterprises — and AI builders and model providers themselves — to turn generative AI models into more than chatbots, but something more like full or at least part-time employees, capable of both communications and reliable actions.

In a sense, it marks the natural next wave after OpenClaw: whereas that open source tool sought to great a dependable, always on autonomous AI agent the user could message, DeerFlow is designed to allow a user to deploy a fleet of them and keep track of them, all within the same system.

The decision to implement it in your enterprise hinges on whether your organization’s workload demands “long-horizon” execution—complex, multi-step tasks spanning minutes to hours that involve deep research, coding, and synthesis. Unlike a standard LLM interface, this “SuperAgent” harness decomposes broad prompts into parallel sub-tasks performed by specialized experts. This architecture is specifically designed for high-context workflows where a single-pass response is insufficient and where “vibe coding” or real-time file manipulation in a secure environment is necessary.

The primary condition for use is the technical readiness of an organization’s hardware and sandbox environment. Because each task runs within an isolated Docker container with its own filesystem, shell, and browser, DeerFlow acts as a “computer-in-a-box” for the agent. This makes it ideal for data-intensive workloads or software engineering tasks where an agent must execute and debug code safely without contaminating the host system. However, this “batteries-included” runtime places a significant burden on the infrastructure layer; decision-makers must ensure they have the GPU clusters and VRAM capacity to support multi-agent fleets running in parallel, as the framework’s resource requirements escalate quickly during complex tasks.

Strategic adoption is often a calculation between the overhead of seat-based SaaS subscriptions and the control of self-hosted open-source deployments. The MIT License positions DeerFlow 2.0 as a highly capable, royalty-free alternative to proprietary agent platforms, potentially functioning as a cost ceiling for the entire category. Enterprises should favor adoption if they prioritize data sovereignty and auditability, as the framework is model-agnostic and supports fully local execution with models like DeepSeek or Kimi. If the goal is to commoditize a digital workforce while maintaining total ownership of the tech stack, the framework provides a compelling, if technically demanding, benchmark.

Ultimately, the decision to deploy must be weighed against the inherent risks of an autonomous execution environment and its jurisdictional provenance. While sandboxing provides isolation, the ability of agents to execute bash commands creates a non-trivial attack surface that requires rigorous security governance and auditability. Furthermore, because the project is a ByteDance-led initiative via Volcengine and BytePlus, organizations in regulated sectors must reconcile its technical performance with emerging software-origin standards. Deployment is most appropriate for teams comfortable with a CLI-first, Docker-heavy setup who are ready to trade the convenience of a consumer product for a sophisticated and extensible SuperAgent harness.