Not everyone had the money for the original Neo Geo Advanced Entertainment System when it released in the ’90s, but there’s still a chance to experience it as an adult with disposable income. SNK and Plaion Replai, who is also behind the all-black remake of the Commodore 64, announced a faithful remake of the high-end retro console, called the Neo Geo AES+.

To bring the original console into the modern day, the collaborating companies added HDMI compatibility for resolutions up to 1080p and DIP switches on the bottom of the console to allow for language selection, overclocking and switching display modes. Rounding out the upgrades, SNK and Plaion Replai included a permanent way to retain high scores on a memory card and a low-power usage mode. For the purists out there, the Neo Geo AES+ still works on those chunky CRT displays since it has the original AV output.

Preorders are currently open for two versions of the Neo Geo AES+, including an all-white 35th anniversary edition bundle that includes an Arcade Stick, a limited-edition Metal Slug game cartridge and a memory card, for $349.99. The standard edition in classic black will only come with an arcade stick, but will be available for $249.99. Coinciding with the console release, Replai Plaion will release 10 modernized game cartridges, including Metal Slug, The King of Fighters 2002 and other classics, for $89.99 each. If you think those prices are high, don’t forget the original Neo Geo AES’ release price was $649.99. The Neo Geo AES+ is set to start shipping on November 12.

Looking for the most recent regular Connections answers? Click here for today’s Connections hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle and Strands puzzles.

Today’s Connections: Sports Edition gets a bit wild in the blue and purple categories. If you’re struggling with today’s puzzle but still want to solve it, read on for hints and the answers.

Connections: Sports Edition is published by The Athletic, the subscription-based sports journalism site owned by The Times. It doesn’t appear in the NYT Games app, but it does in The Athletic’s own app. Or you can play it for free online.

Read more: NYT Connections: Sports Edition Puzzle Comes Out of Beta

Hints for today’s Connections: Sports Edition groups

Here are four hints for the groupings in today’s Connections: Sports Edition puzzle, ranked from the easiest yellow group to the tough (and sometimes bizarre) purple group.

Yellow group hint: Play ball!

Green group hint: Gridiron roles.

Blue group hint: Like Ted Lasso.

Purple group hint: LA team that came from Brooklyn.

Answers for today’s Connections: Sports Edition groups

Yellow group: AL East teams.

Green group: First words of football positions.

Blue group: Premier League managers.

Purple group: Nicknames for the Dodgers franchise, over time.

Read more: Wordle Cheat Sheet: Here Are the Most Popular Letters Used in English Words

What are today’s Connections: Sports Edition answers?

The yellow words in today’s Connections

The theme is AL East teams. The four answers are Blue Jays, Orioles, Rays and Yankees.

The green words in today’s Connections

The theme is first words of football positions. The four answers are defensive, running, tight and wide.

The blue words in today’s Connections

The theme is Premier League managers. The four answers are Emery, Guardiola, Moyes and Slot.

The purple words in today’s Connections

The theme is nicknames for the Dodgers franchise, over time. The four answers are Bridegrooms, Dodgers, Robins and Superbas.

In short: Nvidia CEO Jensen Huang warned on the Dwarkesh Podcast that DeepSeek optimising its AI models for Huawei’s Ascend chips instead of American hardware would be “a horrible outcome” for the United States, as the Chinese AI lab prepares to launch its V4 foundation model on Huawei’s Ascend 950PR processor. The migration from Nvidia’s CUDA to Huawei’s CANN framework threatens to break the software-hardware dependency underpinning American AI dominance, even as US lawmakers push to place DeepSeek on the entity list for export control.

Nvidia CEO Jensen Huang said on the Dwarkesh Podcast on Wednesday that if DeepSeek optimised its new AI models to run on Huawei chips rather than American hardware, it would be “a horrible outcome” for the United States. The warning frames the emerging partnership between China’s most capable AI lab and its most advanced chipmaker as a direct threat to the technological leverage that has underpinned American AI dominance for the past decade.

“If future AI models are optimised in a very different way than the American tech stack,” Huang said, and as “AI diffuses out into the rest of the world” with Chinese standards and technology, China “will become superior to” the US. The statement is notable because it comes from the CEO of the company that has benefited most from the current arrangement, in which virtually every frontier AI model in the world is trained on Nvidia GPUs using Nvidia’s CUDA software framework.

What DeepSeek is building

DeepSeek is preparing to launch V4, a multimodal foundation model expected later this month. The Information reported earlier in April that V4 would run on Huawei’s latest Ascend 950PR processor, while a separate Reuters report suggested the model had been trained on Nvidia’s Blackwell chips, which would constitute a violation of US export controls. The two claims are not necessarily contradictory: a model can be trained on one set of hardware and deployed for inference on another.

What makes the Huawei integration significant is the software migration behind it. DeepSeek has spent months rewriting its core code to work with Huawei’s CANN framework, moving away from the CUDA ecosystem that Nvidia has spent two decades building into the foundation of AI development. CUDA’s dominance has functioned as a second layer of American control over AI, beyond the chips themselves. Export restrictions can limit which Nvidia hardware reaches China, but as long as Chinese labs wrote their software for CUDA, they remained dependent on the Nvidia ecosystem even when using alternative processors. DeepSeek’s move to CANN breaks that dependency.

DeepSeek’s V3 model, launched in late 2024, was trained on 2,048 Nvidia H800 GPUs, a chip tailor-made for the Chinese market that was itself banned from sale to China in 2023. The company has already demonstrated that it can produce frontier-competitive models with fewer resources than its American rivals. Its R1 reasoning model matched or exceeded the performance of models that cost orders of magnitude more to train. V4 would extend that approach by proving the company can do it without American hardware at all.

The hardware gap and why it may not matter

On raw performance, Huawei’s chips are not competitive with Nvidia’s best. The Ascend 910C, the predecessor to the 950PR, delivers roughly 60% of the inference performance of Nvidia’s H100, a chip that is itself two generations behind Nvidia’s current best. American chips are approximately five times more powerful than their Chinese equivalents today, and that gap is projected to widen to 17 times by 2027. Huawei is targeting 750,000 AI chip shipments in 2026, but its total production represents only 3 to 5% of Nvidia’s aggregate computing power.

But Huang’s concern is not about the current performance gap. He said on the podcast that even if China had inferior chips, it could still catch up with the US in AI development given its “abundant energy” and “large pool of AI researchers.” The implication is that raw hardware performance is only one variable, and that software optimisation, researcher talent, and energy availability can compensate for silicon disadvantages. If V4 performs well on Ascend chips, it validates an alternative path for AI development that does not depend on Nvidia at any point in the supply chain.

The export control paradox

The situation exposes a tension at the centre of American chip export policy. Nvidia restarted production of the H200, a more powerful chip, for sale in China, as Huang confirmed in March. But China has been blocking H200 imports to protect Huawei’s domestic chip business, and Nvidia’s CFO has said the company has recorded no revenue from China H200 sales. The controls designed to limit China’s AI capabilities are instead accelerating the development of a Chinese alternative.

DeepSeek’s experience with its R2 model illustrates both the promise and the limits of the Huawei path. R2 was repeatedly delayed because of training failures on Huawei hardware. Chinese authorities had urged DeepSeek to train on domestic chips, but the company encountered stability issues that forced it to revert to Nvidia GPUs for training while using Huawei chips only for inference. The distinction matters: training is the most compute-intensive phase of AI development, and the fact that Huawei chips could not handle it reliably suggests the hardware gap is real. But inference, the phase where models serve users, is where commercial value is generated, and Huawei’s chips appear adequate for that purpose.

Meanwhile, US lawmakers are pushing to tighten restrictions further. On Thursday, lawmakers and experts accused China of buying “what they can” and stealing “what they cannot” in the AI industry, and called for the government to evaluate placing DeepSeek, Moonshot AI, and MiniMax on the entity list for export control.

What Huang is really warning about

Huang’s warning is ultimately about software-hardware co-design. Nvidia’s dominance rests not just on making the fastest chips but on CUDA’s position as the default development environment for AI. When researchers write code, they write it for CUDA. When startups build products, they build them on CUDA. When governments invest in AI infrastructure, they buy Nvidia GPUs because that is what the software requires. DeepSeek’s migration to CANN threatens to create a parallel ecosystem in which none of that applies.

The scale of Nvidia’s business makes the stakes concrete. The company’s market capitalisation exceeds $3 trillion. Its data centre revenue grew 93% year over year in its most recent quarter. Its chips power the training runs for virtually every major AI model outside China. If the most capable Chinese AI lab demonstrates that competitive models can be built without Nvidia, the argument for maintaining export controls weakens, the argument for buying Nvidia weakens, and the geopolitical assumptions that have shaped AI policy for the past three years come under pressure.

None of this means Huawei is about to overtake Nvidia. The performance gap is large and growing. The R2 training failures demonstrate that Chinese hardware is not yet ready for the most demanding AI workloads. But Huang is not warning about today. He is warning about a trajectory in which DeepSeek proves the concept, other labs follow, and the CUDA moat that has made Nvidia the most valuable company in the AI supply chain begins to erode.

The fact that the CEO of Nvidia is the one making this argument publicly suggests he believes the risk is no longer theoretical. DeepSeek’s V4 will be the first major test. If a multimodal foundation model runs competitively on Huawei silicon, the warning Huang issued on Wednesday will look less like corporate lobbying and more like the most consequential forecast in the AI chip war so far.

For the past year, early adopters of autonomous AI agents have been forced to play a murky game of chance: keep the agent in a useless sandbox or give it the keys to the kingdom and hope it doesn’t hallucinate a catastrophic “delete all” command.

To unlock the true utility of an agent—scheduling meetings, triaging emails, or managing cloud infrastructure—users have had to grant these models raw API keys and broad permissions, raising the risk of their systems being disrupted by an accidental agent mistake.

That tradeoff ends today. The creators of the open source sandboxed NanoClaw agent framework — now known under their new private startup named NanoCo — have announced a landmark partnership with Vercel and OneCLI to introduce a standardized, infrastructure-level approval system.

By integrating Vercel’s Chat SDK and OneCLI’s open source credentials vault, NanoClaw 2.0 ensures that no sensitive action occurs without explicit human consent, delivered natively through the messaging apps where users already live.

The specific use cases that stand to benefit most are those involving high-consequence “write” actions. That is, in DevOps, an agent could propose a cloud infrastructure change that only goes live once a senior engineer taps “Approve” in Slack.

For finance teams, an agent could prepare batch payments or invoice triaging, with the final disbursement requiring a human signature via a WhatsApp card.

Technology: security by isolation

The fundamental shift in NanoClaw 2.0 is the move away from “application-level” security to “infrastructure-level” enforcement. In traditional agent frameworks, the model itself is often responsible for asking for permission—a flow that Gavriel Cohen, co-founder of NanoCo, describes as inherently flawed.

“The agent could potentially be malicious or compromised,” Cohen noted in a recent interview. “If the agent is generating the UI for the approval request, it could trick you by swapping the ‘Accept’ and ‘Reject’ buttons.”

NanoClaw solves this by running agents in strictly isolated Docker or Apple Containers. The agent never sees a real API key; instead, it uses “placeholder” keys. When the agent attempts an outbound request, the request is intercepted by the OneCLI Rust Gateway. The gateway checks a set of user-defined policies (e.g., “Read-only access is okay, but sending an email requires approval”).

If the action is sensitive, the gateway pauses the request and triggers a notification to the user. Only after the user approves does the gateway inject the real, encrypted credential and allow the request to reach the service.

Product: bringing the ‘human’ into the loop

While security is the engine, Vercel’s Chat SDK is the dashboard. Integrating with different messaging platforms is notoriously difficult because every app—Slack, Teams, WhatsApp, Telegram—uses different APIs for interactive elements like buttons and cards.

By leveraging Vercel’s unified SDK, NanoClaw can now deploy to 15 different channels from a single TypeScript codebase. When an agent wants to perform a protected action, the user receives a rich interactive card on their phone. “The approval shows up as a rich, native card right inside Slack or WhatsApp or Teams, and the user taps once to approve or deny,” said Cohen. This “seamless UX” is what makes human-in-the-loop oversight practical rather than a productivity bottleneck.

The full list of 15 supported messaging apps/channels contains many favored by enterprise knowledge workers, including:

• Slack

• WhatsApp

• Telegram

• Microsoft Teams

• Discord

• Google Chat

• iMessage

• Facebook Messenger

• Instagram

• X (Twitter)

• GitHub

• Linear

• Matrix

• Email

• Webex

Background on NanoClaw

NanoClaw launched on January 31, 2026, as a minimalist and security-focused response to the “security nightmare” inherent in complex, non-sandboxed agent frameworks.

Created by Cohen, a former Wix.com engineer, and marketed by his brother Lazer, CEO of B2B tech public relations firm Concrete Media, the project was designed to solve the auditability crisis found in competing platforms like OpenClaw, which had grown to nearly 400,000 lines of code.

By contrast, NanoClaw condensed its core logic into roughly 500 lines of TypeScript—a size that, according to VentureBeat, allows the entire system to be audited by a human or a secondary AI in approximately eight minutes.

The platform’s primary technical defense is its use of operating system-level isolation. Every agent is placed inside an isolated Linux container—utilizing Apple Containers for high performance on macOS or Docker for Linux—to ensure that the AI only interacts with directories explicitly mounted by the user.

As detailed in VentureBeat’s reporting on the project’s infrastructure, this approach confines the “blast radius” of potential prompt injections strictly to the container and its specific communication channel.

In March 2026, NanoClaw further matured this security posture through an official partnership with the software container firm Docker to run agents inside “Docker Sandboxes”.

This integration utilizes MicroVM-based isolation to provide an enterprise-ready environment for agents that, by their nature, must mutate their environments by installing packages, modifying files, and launching processes—actions that typically break traditional container immutability assumptions.

Operationally, NanoClaw rejects the traditional “feature-rich” software model in favor of a “Skills over Features” philosophy. Instead of maintaining a bloated main branch with dozens of unused modules, the project encourages users to contribute “Skills”—modular instructions that teach a local AI assistant how to transform and customize the codebase for specific needs, such as adding Telegram or Gmail support.

This methodology, as described on NanoClaw’s website and in VentureBeat interviews, ensures that users only maintain the exact code required for their specific implementation.

Furthermore, the framework natively supports “Agent Swarms” via the Anthropic Agent SDK, allowing specialized agents to collaborate in parallel while maintaining isolated memory contexts for different business functions.

Licensing and open source strategy

NanoClaw remains firmly committed to the open source MIT License, encouraging users to fork the project and customize it for their own needs. This stands in stark contrast to “monolithic” frameworks.

NanoClaw’s codebase is remarkably lean, consisting of only 15 source files and roughly 3,900 lines of code, compared to the hundreds of thousands of lines found in competitors like OpenClaw.

The partnership also highlights the strength of the “Open Source Avengers” coalition.

By combining NanoClaw (agent orchestration), Vercel Chat SDK (UI/UX), and OneCLI (security/secrets), the project demonstrates that modular, open-source tools can outpace proprietary labs in building the application layer for AI.

Community reactions

As shown on the NanoClaw website, the project has amassed more than 27,400 stars on GitHub and maintains an active Discord community.

A core claim on the NanoClaw site is that the codebase is small enough to understand in “8 minutes,” a feature targeted at security-conscious users who want to audit their assistant.

In an interview, Cohen noted that iMessage support via Vercel’s Photon project addresses a common community hurdle: previously, users often had to maintain a separate Mac Mini to connect agents to an iMessage account.

The enterprise perspective: should you adopt?

For enterprises, NanoClaw 2.0 represents a shift from speculative experimentation to safe operationalization.

Historically, IT departments have blocked agent usage due to the “all-or-nothing” nature of credential access. By decoupling the agent from the secret, NanoClaw provides a middle ground that mirrors existing corporate security protocols—specifically the principle of least privilege.

Enterprises should consider this framework if they require high-auditability and have strict compliance needs regarding data exfiltration. According to Cohen, many businesses have not been ready to grant agents access to calendars or emails because of security concerns. This framework addresses that by ensuring the agent structurally cannot act without permission.

Enterprises stand to benefit specifically in use cases involving “high-stakes” actions. As illustrated in the OneCLI dashboard, a user can set a policy where an agent can read emails freely but must trigger a manual approval dialog to “delete” or “send” one.

Because NanoClaw runs as a single Node.js process with isolated containers , it allows enterprise security teams to verify that the gateway is the only path for outbound traffic. This architecture transforms the AI from an unmonitored operator into a supervised junior staffer, providing the productivity of autonomous agents without forgoing executive control.

Ultimately, NanoClaw is a recommendation for organizations that want the productivity of autonomous agents without the “black box” risk of traditional LLM wrappers. It turns the AI from a potentially rogue operator into a highly capable junior staffer who always asks for permission before hitting the “send” or “buy” button.

As AI-native setups become the standard, this partnership establishes the blueprint for how trust will be managed in the age of the autonomous workforce.

• Nuclear clocks promise accuracy far beyond existing atomic timekeeping systems
• Thorium 229 offers a rare pathway to practical nuclear time measurement
• Ultraviolet breakthrough reduces one of the hardest barriers in nuclear clock development

A new crystal developed by Chinese scientists has broken the world record for ultraviolet light conversion, bringing nuclear clock technology closer to reality.

The fluorinated borate compound pushes laser light to a wavelength of 145.2nm, beating the previous benchmark of 150nm set by a Chinese crystal from the 1990s.

Cerebras Systems, a startup building what CEO Andrew Feldman describes as “the fastest AI hardware for training and inference,” has filed to go public.

The company previously filed for an initial public offering in 2024, but that was delayed due to a federal review of an investment from Abu Dhabi-based G42 and was ultimately withdrawn. Cerebras raised a $1.1 billion Series G last year, followed by a $1 billion Series H in February at a $23 billion valuation, according to the Wall Street Journal.

In recent months, the company announced an agreement with Amazon Web Services to use Cerebras chips in Amazon data centers, as well as a deal with OpenAI reportedly worth more than $10 billion.

In a recent interview with the WSJ, Feldman boasted, “Obviously, [Nvidia] didn’t want to lose the fast inference business at OpenAI, and we took that from them.”

Cerebras brought in $510 million in revenue in 2025, according to the filing, with a net income of $237.8 million (excluding certain one-time items, it was a non-GAAP net loss of $75.7 million).

A company has not disclosed how much it hopes to raise in the IPO. A spokesperson said the offering is planned for mid-May.