offbeat

A blast from the past greets commuters

BORK!BORK!BORK! We’re big fans of retro computing here at Vulture Central, and so it is with a certain delight that we can report XP-era Windows has been spotted disgracing itself on London’s Docklands Light Railway.

Spotted by Register reader Tim Hayward, the wonderfully named DaisySignApp.exe has thrown up an application error. While the Windows shell might be shorn of all of XP’s fripperies, the Recycle Bin icon hints at the operating system’s origins. Hayward reckoned that XP was stalking the DLR, but it could also be Windows Server 2003.

Support for Windows Server 2003 finally ended in 2015. XP was sunset in 2014, so the DLR display is rather out of date. Then again, as any IT administrator would admit, if something isn’t broken, there’s no point fixing it, no matter how much Microsoft would encourage them to.

In this case, it is unlikely that the operating system is at fault (although one could argue that it should handle a misbehaving application more discreetly), and DaisySignApp.exe should be dealing with its own dirty laundry rather than throwing an exception in commuters’ faces at Limehouse station.

Limehouse connects London’s Docklands Light Railway (DLR) to the UK’s National Rail services. It was one of the first DLR stations and predates the borked operating system by more than a decade.

Indeed, at the time of the DLR’s opening in 1987, Microsoft was preparing to inflict Windows 2.0 upon the world – the delights of later versions and the company’s GUI dominance were still a few years in the future.

The DLR also seemed like a glimpse into the future back in the 1980s. However, a fair chunk of its underpinnings, such as formerly disused railway viaducts, hark back to an earlier era.

Anyone looking at today’s iteration of Windows might wonder how much of it dates back to what’s on display at Limehouse. ®

A coalition of state attorneys general has opened an investigation into OpenAI.

The company was served with a subpoena from New York’s attorney general on Friday, according to The Wall Street Journal. That subpoena sought documents related to a broad range of topics including the company’s advertising, user engagement and retention, model sycophancy, handling of consumer data and health data, and treatment of minors and seniors.

“AI is a new and powerful technology, and we work every day to safely bring its benefits to people in a responsible way,” an OpenAI spokesperson said in a statement. “We take the concerns raised by state attorneys general seriously and intend to engage constructively with their offices.”

The spokesperson also said, “Today’s ChatGPT includes a more protective experience for minors and people experiencing difficult situations, with safeguards that direct them to real-world resources and trusted human contacts. We believe kids should be treated like kids, which is why we built age prediction, released parental tools to guide their children’s use of AI, and disallowed advertising that targets kids.”

The company did not specify which states are involved in the investigation or share more details about what information was requested. TechCrunch has also reached out to New York attorney general’s office for confirmation.

OpenAI recently defeated its co-founder Elon Musk in a high-profile trial, after Musk accused the company of violating its founding agreement. (Musk’s lead attorney said he will appeal the decision.)

However, OpenAI still faces lawsuits over everything from alleged copyright infringement to ChatGPT’s alleged role in user suicides. Earlier this month, Florida Attorney General James Uthmeier sued OpenAI and its CEO Sam Altman, claiming that OpenAI and Altman “ignored internal and external safety warnings, put children at great risk, and allowed a dangerous product to reach millions of Floridians.”

Altman recently apologized to the community of Tumbler Ridge, Canada after a mass shooting; he acknowledged that OpenAI failed to alert law enforcement after the company flagged and banned the suspected shooter’s ChatGPT account.

The company announced this week that it has filed confidentially to go public.

This post has been updated with a statement from an OpenAI spokesperson.

On a recent evening in suburban Chicago, a group of parents, teachers and administrators gathered to talk about something that, until recently, rarely drew this level of public scrutiny: the role of technology in their schools.

The meeting was part of a three-session tech and learning focus group organized by Mary Jane (MJ) Warden, chief technology officer of Community Consolidated School District 15, in conjunction with the Teaching, Learning and Assessments Department.

The district, which serves 11,000 preK-8 students, spent the past several years — like so many others — adding digital tools. Now, with budgets tightening and concerns about screen time rising, it was time to take stock.

A re-examination of digital tools was already happening with curriculum reviews and tightening budgets after the pandemic. And then the screen time concerns arose.

Participants discussed everything from screen time to what district technology use looks like at home. Out of those conversations came something new: a “Portrait of a Digital Learner,” derived from the district’s Portrait of a Graduate, meant to develop clear expectations around what skills students need and, by extension, which technologies are worth keeping and how technology would be used by students toward positive learning outcomes.

“We’re trying to get much [clearer] about what this is going to address,” says Warden. “What do we need students to learn, and which tools will help us understand where they are?”

Across the country, district leaders are asking similar questions. After years of rapid expansion, many are now engaged in a quieter but more consequential phase: reassessing what stays, what goes and how to decide.

From Buying Tools To Proving Value

For much of the past decade, edtech decisions often began with the product. A new platform promised to boost engagement or personalize learning; districts piloted it, added it to an already crowded ecosystem and moved on.

That approach is no longer sustainable, says Erin Mote, CEO of InnovateEDU, a nonprofit focused on systems change in special education, talent development and data modernization in schools.

“We’re seeing a shift from ‘Does this look cool?’ to ‘Does this work?’” she says. “Districts have less money now; they have to be smarter.”

The end of pandemic-era federal funding has intensified that pressure. Technology leaders are now expected not only to manage infrastructure and compliance, but also to demonstrate what Mote calls a return on instructional impact.

In practice, that is changing how districts approach procurement. Instead of starting with vendor demos, many are beginning with specific learning needs.

“If you need to improve third-grade reading comprehension, you start there,” Mote says. “Then you ask: Which tool can move that needle?”

New Playbook For Evaluation

As districts rethink their approach, a more structured and more skeptical evaluation process is emerging.

One major shift is toward tracking actual usage. Platforms like ClassLink and Clever now give districts detailed analytics on which tools students and teachers are accessing, how often they’re used and, in some cases, how much time is spent in each application. That data has helped uncover what some leaders call “zombie licenses,” products that continue to be renewed despite minimal use.

At Joliet Public Schools in Illinois, technology leaders review usage data each spring alongside feedback from a districtwide technology committee.

“If we’re not getting usage or we have another product that does it better, we start asking hard questions,” says John Armstrong, chief officer for technology and innovation.

But usage alone is not enough. Districts are also weighing cost, redundancy and alignment with instructional goals.

During the pandemic, many schools layered new tools on top of existing ones. Now, leaders are working to simplify.

“We had so many products that teachers were going to four different places to run a lesson,” says Kelly Ronnebeck, associate superintendent for student achievement in East Moline School District 37 in Illinois. “We’re trying to get back to a slower, more intentional process.”

That often means replacing several standalone tools with a single platform that can do multiple jobs — even if it means giving up some features teachers value. In some cases, a newer system can replace several standalone tools at a lower cost but may not match each one’s individual strengths.

“It’s not always a perfect swap,” admits Armstrong. “Someone gives up something.”

At the same time, districts are placing greater emphasis on interoperability and data privacy. Tools must integrate with existing systems like learning management platforms and single sign-on tools, and vendors have to be willing to sign increasingly stringent data privacy agreements.

“If a company can’t meet those requirements, that’s a red flag right away,” says Phil Hintz, CTO of Niles Township District 219 in Illinois.

The Challenge Of Proving What Works

Even as districts adopt more rigorous processes, it remains stubbornly difficult to determine whether edtech tools actually improve learning.

“It’s such a huge challenge,” says Naomi Hupert, director of the Center for Children & Technology at the Education Development Center. “We see so much that doesn’t seem to make a difference but costs a lot of money.”

Part of the difficulty lies in the sheer breadth of what “edtech” encompasses, everything from learning management systems to specialized math platforms to communication tools. Each category has different goals, users and measures of success.

“It’s like asking whether ‘books’ work,” says Hupert. “It depends on the book, the context and how it’s used.”

District leaders have to piece together evidence from multiple sources: vendor-provided analytics, small pilot studies, teacher feedback and, occasionally, external research. But those data points don’t always align.

Jason Schmidt, director of technology in Oshkosh Area School District in Wisconsin, describes his approach as “trust but verify.”

“I know vendors are collecting tons of data, and they have to, but I still need to talk to teachers and understand how the tool is actually being used,” he says.

Even then, results can be uneven. A platform might show strong engagement overall but fail to support certain groups of students — or vice versa.

In Alexandria City Public Schools in Virginia, leaders are developing a formal framework to evaluate both edtech and nontech programs. But defining “value” has proven complex.

“It’s not just usage and cost,” says CIO Emily Dillard. In a district with a high number of English learners, some tools play a critical role for students who need targeted or specialized support.

“You might have a tool that isn’t working for most students — or takes time to show results — but for a small group, it’s the best thing we have. We have to think about what’s best for them, too,” says Dillard.

Building Systems for Quality

Recognizing these challenges, a growing coalition of organizations is working to create clearer signals of quality in the edtech marketplace.

Through the Edtech Quality Collaborative, 1EdTech, CAST, CoSN, Digital Promise, InnovateEDU, ISTE, and SETDA are developing a shared framework built around five indicators: safety, evidence, inclusivity, interoperability and usability.

The goal, says Korah Wiley, senior director of edtech R&D at Digital Promise, is to reduce the noise.

“Right now, there are a lot of certifications and labels, and it’s hard for districts to know what to trust,” says Wiley. “We want to brighten the signal of what quality looks like.”

The initiative includes a planned directory of vetted validators, an implementation guide for districts and a central hub to connect educators with high-quality tools. Leaders hope it will help districts make decisions more confidently and push developers to meet clearer standards.

“This is the cost of doing business in education,” says Mote. “If you want to be in classrooms, you need to be building evidence and demonstrating impact.”

What Happens When Tools Are Cut

For all the talk of frameworks and data, the hardest part of reassessment often comes when districts decide to let a tool go.

Those decisions can affect classroom routines, teacher preferences and even student outcomes. And they are rarely straightforward.

In some cases, tools are phased out because of cost or low usage. In others, they are replaced by more comprehensive platforms. Sometimes, they no longer align with district priorities.

But even when the rationale is clear, the transition can be difficult.

“Teachers build practices around these tools,” says Warden. “We have to be thoughtful about how we support them through change.”

Districts are increasingly pairing those decisions with professional development, clearer communication and, in some cases, community engagement. In Warden’s district, the focus groups that helped define the “Portrait of a Digital Learner” are also shaping how the district explains its choices to families.

“We want to be transparent about what we’re using and why,” she says.

A More Intentional Future

As districts move into this new phase, many leaders describe it as a reset that is forcing them to be more deliberate about how technology fits into teaching and learning.

That includes pushing back on broader narratives that treat all screen time as equal.

“There’s a big difference between passive consumption and purposeful edtech and we need to be clear about this,” says Mote.

It also requires clearer alignment between technology decisions and instructional goals. Without that, even the best tools can fall short.

“If you don’t know what you want teaching and learning to look like, it’s very hard to decide what tools you need,” says Keith Krueger, CEO of CoSN.

Back in District 15, Warden and her colleagues are trying to build that alignment. The conversations sparked by their focus groups are informing not just which tools they keep, but how they define success.

“We’re still digging out from COVID, when we had to move fast and add a lot. Now we have an opportunity to be more strategic.”

For district leaders across the country, that shift may be the most important change of all. The future of edtech, they suggest, will not be defined by the number of tools schools use, but by how thoughtfully they choose them.

Anthropic’s sudden move to suspend access to its newest AI models following a U.S. government directive has raised fresh questions across the global technology industry. In India, the decision has reignited a long-running debate over whether one of the world’s largest AI markets can afford to rely on technologies built and controlled elsewhere.

The announcement came late Friday, when Anthropic said it had received the U.S. government directive requiring it to suspend access to its recently launched Fable 5 and Mythos 5 models for all foreign nationals, including its own foreign national employees. The move came shortly after the company announced a partnership with Indian IT services giant Tata Consultancy Services to expand enterprise AI adoption in India, underlining how closely the country’s AI ambitions have become tied to technologies developed and governed in the U.S.

While the broader implications remain unclear, some reports said the initial security concerns were first reported to the government by Amazon CEO Andy Jassy. And The Information said the White House is unlikely to extend similar restrictions to other AI companies and is privately blaming Anthropic’s handling of alleged jailbreak vulnerabilities. Anthropic has disputed the government’s characterization and argued the action should not have been taken.

Regardless, the development has triggered debate among Indian founders, investors, and policy experts over whether the country should accelerate efforts to build domestic AI capabilities, deepen investment in open-source alternatives, or continue relying on a handful of U.S. frontier model providers. For some, the episode is a wake-up call on technological dependence. For others, it is a reminder that access to increasingly critical AI systems can be shaped by geopolitical decisions beyond India’s control.

India has become one of the most important markets for frontier AI companies. Anthropic and OpenAI have both described the South Asian nation as their second-largest market after the U.S., reflecting its growing importance in the global AI race. The companies have already set up their offices in India, expanded local hiring, partnerships, and enterprise initiatives in recent months, betting on India’s vast base of developers, startups, and businesses to accelerate adoption of their latest technologies.

For many in India’s technology sector, Anthropic’s Friday announcement was about more than just one AI company. It reopened questions about the country’s long-term AI strategy and whether India could afford to remain dependent on a small number of foreign frontier AI providers.

“It completely changes things,” said Aakrit Vaish, founder of Indian AI venture platform Activate, referring to Anthropic’s decision. “I think this materially changes the way all of us should be thinking about sovereign AI in India.”

Vaish told TechCrunch that he woke up on Saturday morning “shocked and confused” by the announcement and said it strengthened the case for developing domestic AI capabilities. He expects startups to increasingly turn to open-source models and plans to encourage companies in his portfolio to reduce their dependence on a small number of frontier AI providers.

For some founders, the bigger concern was what restrictions on frontier AI access could mean for competitiveness. Vijay Rayapati, co-founder and CEO of Atomicwork, told TechCrunch that the episode highlighted the risks facing startups whose teams span multiple countries if access to advanced AI systems increasingly becomes subject to geopolitical restrictions.

Atomicwork has around 25 employees in the U.S., though much of its product engineering team is based in Bengaluru, India.

“If your AI team is not made up entirely of U.S. citizens, you are at a competitive disadvantage,” Rayapati said, arguing that unequal access to frontier AI models could give some companies a significant edge over rivals.

The concern comes as parts of India’s tech sector are already grappling with questions about how AI could reshape the economics of global talent. This week, U.S. real estate technology company Opendoor shut its India office less than two years after expanding in the country, with CEO Kaz Nejatian citing a push to bring operational work closer to customers in the U.S. and a shift toward smaller AI-native teams.

While Opendoor did not specify how much of the decision was driven by AI-related efficiencies, the move added to a broader debate about how advances in AI could affect the future of global technology work and what that might mean for India’s position as an engineering talent hub.

Beyond Anthropic

In addition to startups and AI builders, the Anthropic episode also prompted a broader debate among India’s technology leaders about dependence on foreign AI infrastructure.

Sridhar Vembu, founder of Indian SaaS company Zoho, said the move showed that “technology is the ultimate weapon” and urged Indian organizations to increasingly embrace smaller and open-source models.

“What can our government do right now? Ensure that orgs in India embrace smaller models, both Indian and Chinese open source ones,” Vembu wrote on X.

Investor and former Infosys executive Mohandas Pai responded to Vembu on X, arguing that the development highlighted the need for a far more ambitious national AI strategy and calling on the government to substantially increase investments in AI, computing infrastructure, and deep technology.

“We are way behind and need a national mission to get going quickly,” Pai wrote, urging the government to create an annual ₹500 billion (about $5 billion) fund for AI and deep tech, alongside a ₹2 trillion (around $21 billion) credit guarantee program to support cloud infrastructure, hardware, and semiconductor development.

Pai’s proposal would dwarf India’s existing AI efforts. In 2024, New Delhi approved the IndiaAI Mission with an outlay of ₹103.72 billion (about $1.2 billion) over five years, aimed at expanding compute infrastructure, supporting startups, and developing indigenous AI capabilities.

Despite growing interest in AI and New Delhi’s push to develop domestic capabilities, India remains a relatively small player in frontier model development. Only a handful of startups are pursuing foundational AI models, including Sarvam, which released open-source models earlier this year. However, another high-profile AI startup, Krutrim, pivoted toward cloud and AI infrastructure services after initially positioning itself around foundational model development.

Much of India’s AI ecosystem has instead concentrated on applications and specialized models built on top of existing foundation models. Recent examples include Avataar AI, which launched a video-generation model earlier this week aimed at providing a lower-cost alternative to offerings from rivals including Google’s Veo, Kling, Luma, and Runway.

Not everyone agrees that the primary challenge is a lack of capital. Responding to Pai’s comments, Lightspeed partner Hemant Mohapatra argued that the biggest constraints to building globally competitive AI companies are talent, access to computing resources, and execution, rather than simply the size of investment commitments.

Mohapatra estimated that training a frontier AI model could cost anywhere from hundreds of millions to several billion dollars, depending on the approach, but said successful AI companies have historically scaled their capital requirements over time as adoption grew.

Yet for some policy observers, the implications extend well beyond AI startups or model providers.

Prasanto Roy, a New Delhi-based technology policy expert who advises multinational companies, said the episode would likely reinforce concerns within the Indian government about strategic autonomy, comparing it to the lesson many countries drew from Russia’s loss of access to SWIFT and other parts of the global financial system following its invasion of Ukraine.

He told TechCrunch that the move was likely to provoke a significant nationalist backlash in India and described it as a poorly considered decision by Washington, with consequences extending far beyond Anthropic itself.

“Even if this is corrected or reversed, the Anthropic episode shows there’s no such thing as a geopolitically neutral foreign LLM,” Roy said. “American AI models are bound to American geopolitics.”

Australia and Turkey will face off in a pivotal World Cup 2026 Group D clash at BC Place in Vancouver, Canada, with both sides eyeing a strong start in one of the tournament’s most open groups. Alongside the United States and Paraguay, both teams will believe qualification is a possibility, and so we can expect high intensity from the opening whistle.

Turkey head into the match as slight favourites thanks to their wealth of European talent. Vincenzo Montella’s side boasts exciting stars such as Arda Güler, Kenan Yildiz, and Kerem Aktürkoğlu, and arrives on the back of a five-match winning streak in competitive qualifiers.

Looking for the most recent Strands answer? Click here for our daily Strands hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle, Connections and Connections: Sports Edition puzzles.

If you have a British royal family obsession, like me, today’s NYT Strands puzzle will be a royal breeze. Some of the answers are difficult to unscramble, so if you need hints and answers, read on.

I go into depth about the rules for Strands in this story. 

If you’re looking for today’s Wordle, Connections and Mini Crossword answers, you can visit CNET’s NYT puzzle hints page.

Read more: NYT Connections Turns 1: These Are the 5 Toughest Puzzles So Far

Hint for today’s Strands puzzle

Today’s Strands theme is: Peer group

If that doesn’t help you, here’s a clue: Titles

Clue words to unlock in-game hints

Your goal is to find hidden words that fit the puzzle’s theme. If you’re stuck, find any words you can. Every time you find three words of four letters or more, Strands will reveal one of the theme words. These are the words I used to get those hints but any words of four or more letters that you find will work:

• SOUR, MARE, RANT, RANTS, DIRT, DIRTS, LEAR, COUNT, QUID, QUIDS

Answers for today’s Strands puzzle

These are the answers that tie into the theme. The goal of the puzzle is to find them all, including the spangram, a theme word that reaches from one side of the puzzle to the other. When you have all of them (I originally thought there were always eight but learned that the number can vary), every letter on the board will be used. Here are the nonspangram answers:

• EARL, LADY, DUCHESS, LORD, VISCOUNT, BARON, MARQUESS

Today’s Strands spangram

Today’s Strands spangram is NOBILITY. To find it, start with the N that is three letters to the right on the top row, and wind down.

Networks

Honey, I flattened the datacenter network

Amazon has developed a new networking topology that’s up to a third faster and up to 40 percent more energy efficient than traditional hierarchical network designs.

The novel architecture, called Resilient Network Graphs (RNG), is based on random graph theory.

“Traditional networks have always been hierarchical,” explained Matt Rehder, VP of global network engineering at AWS, in a recent interview. “They’re sort of like an org chart where one network device will talk to the boss network device which will talk to the next boss network device and you gotta go up the chain of command in order to talk to someone else in another department.”

There are reasons for that, Rehder said. Hierarchy creates structure and makes data routing rules simpler. “You don’t have to know how to talk to everyone in the organization, you just talk to the person above you,” he said.

But that creates inefficiencies. The tree-like structure creates points of contention where data flow bottlenecks can occur. At the same time, other parts of the network may be underutilized. 

Rehder said that academics in 2012 proposed a random graph topology for networks. 

But that design, as detailed [PDF] by Amazon researchers, had issues. The reimagined network structure, dubbed Jellyfish, relied on truly random graphs and called for removing routers from server racks and locating them centrally to simplify cabling. But that approach ended up increasing latency between servers within a rack.

Rehder said no one has been able to put that design into production. 

“It requires much more complicated routing rules to figure out how to program every device – you can’t just program every device to know who everyone is, they have limited memory space,” he said. “And then the other [issue] is that the cabling actually is very complicated. Part of that hierarchy is about simplifying how you build the network in the datacenter and with a random graph it’s literally random and you can’t just have cable spaghetti all over a datacenter. So you could build it in a lab but you could never really do it at scale.”

Nonetheless, said Rehder, AWS has been solving these problems over the past few years.

“The only reason we were able to even think about tackling them is that 15-year history of iteratively improving our hardware development and software ownership of our network,” he said. 

Less random

Inspired by other academic networking research, AWS managed to succeed with random network topology by making it not entirely random. RNG relies on a flat graph where routers interconnect through a mix of deterministic and randomized cabling.

RNG began taking shape three years ago when Seshadhri Comandur, an Amazon Scholar and professor at the University of California, Santa Cruz, answered an internal Slack message from Ratul Mahajan, a fellow Amazon Scholar, datacenter networking expert, and professor at the University of Washington, who was looking for an expert on graph theory and routing.

With help from AWS principal applied scientist Giacomo Bernardi and other colleagues, AWS has become the first company to deploy a flat datacenter network at scale. AWS expects the technology will offer better performance and reliability for Amazon customers while also saving billions of dollars in hardware and reducing CO₂ emissions.

The reimagined network structure was referred to as Penrose internally because the original design involved Penrose tiles. But as the project evolved, AWS settled on Resilient Network Graphs “to reflect the customer benefit and that primarily is a more resilient and performant network,” as a company spokesperson put it.

RNG relies on a routing algorithm called Spraypoint to identify node paths and an optical device called a Shufflebox for mixing connections between routers. 

Rehder said the Shufflebox is one of the pieces of magic that makes RNG work.

“In a random graph network you don’t have that hierarchical structure where you can have all the cables neatly aligned,” he explained. “So how do you do that? How do you basically make a random network feel more structured? Well, you have the Shufflebox and the idea is that you plug fiber in here and inside of this it will randomize or basically scramble the fiber. So the ports you plug in get scrambled around and come out on some random port around the other side.”

RNG is AWS’s new network for its core database servers. Machine learning hardware uses the company’s UltraServer network, because the machine learning workloads need full bandwidth. 

“The core server networks can be oversubscribed more efficiently,” said Rehder. “Everyone’s not talking to each other at the same time.”

RNG has been rolled out in Ireland, Germany, and Spain, and the plan is to deploy it in the majority of company datacenters by the end of the year. ®

This month saw the release of Vim Classic 8.3, the first stable version of a new long-term support fork of Vim maintained without generative AI tools. Linuxiac reports:

The release is based on Vim 8.2.0148 and includes selected bug fixes and patches backported from later upstream Vim releases. Vim Classic was first announced by [SourceHut’s CEO/founder] Drew DeVault in March 2026 after he objected to LLM-assisted development in Vim and Neovim. In his announcement, DeVault said he no longer wanted to use software developed with LLM assistance and introduced Vim Classic as a fork for users who want to continue using Vim without that involvement… Vim Classic follows Vim’s charityware model and continues to direct users toward Bram Moolenaar’s long-running support for children in Uganda. The release is distributed as a signed source tarball from SourceHut, while future important announcements are expected through the project’s mailing list.

“Vim is important to me…” DeVault wrote in March. (DeVault even tattooed “hjkl” on his right arm.) “[A]lmost every word I have ever committed to posterity, through this blog, in my code, all of the docs I’ve written, emails I’ve sent, and more, almost all of it has passed through Vim.”

But DeVault wrote that he also cares about AI’s impact on air pollution, fresh water supplies, global supply chains, and the working conditions of miners in African companies:
And at a moment when the climate demands immediate action to reduce our footprint on this planet, the AI boom is driving data centers to consume a full 1.5% of the world’s total energy production in order to eliminate jobs and replace them with a robot that lies… All this to enrich the few, centralize power, reduce competition, and underwrite an enormous bubble that, once it bursts, will ruin the lives of millions of the world’s poor and marginalized classes.

I don’t think it’s cute that someone vibe coded “battleship” in VimScript. I think it’s more important that we stop collectively pretending that we don’t understand how awful all of this is. I don’t want to use software which has slop in it. I do what I can to avoid it, and sadly even Vim now comes under scrutiny in that effort as both Vim and NeoVim are relying on LLMs to develop the software… To keep my conscience clear, and continue to enjoy the relationship I have with this amazing piece of software, I have forked Vim…

Since forking from this base, I have backported a handful of patches, most of which address CVEs discovered after this release, but others which address minor bug fixes. I also penned a handful of original patches which bring the codebase from this time up to snuff for building it on newer toolchains…

I invite you to use Vim Classic, if you feel the same way as me, and to maintain it with me, contributing the patches you need to support your own use cases.

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