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Today’s Connections: Sports Edition is a tough one. As an old-school Minnesota Twins fan, I was excited to see the last name of our most legendary player on the grid. 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: QB is another one.

Green group hint: Hit it out of the park.

Blue group hint: Great gridiron signal-callers.

Purple group hint: Half of a thousand.

Answers for today’s Connections: Sports Edition groups

Yellow group: Football positions, abbreviated.

Green group: Members of the 500-HR club.

Blue group: First names of QBs to throw 500 career TDs.

Purple group: ____500.

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 football positions, abbreviated. The four answers are CB, OT, S and TE.

The green words in today’s Connections

The theme is members of the 500-HR club. The four answers are Banks, Bonds, Foxx and Killebrew.

The blue words in today’s Connections

The theme is first names of QBs to throw 500 career TDs. The four answers are Aaron, Drew, Peyton and Tom.

The purple words in today’s Connections

The theme is ____500. The four answers are ATP, Daytona, Indy and WTA.

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If you’ve ever wanted a way to keep all of your family’s health records in place, Fitbit may have come up with a solution. Fitbit, well-known for its fitness wearables, announced the launch of its own health care system on Wednesday.  

Luffu, which translates to the Old English word for «love,» uses AI to create what it calls an «intelligent family care system.» The platform allows family members to share all their health information through an app.

It’s unclear when Luffu will be officially available, but you can sign up for the waitlist to get access to the limited public beta. Pricing or other details have not been announced.

Luffu will allow families to keep track of everyone’s doctor’s appointments, test results, vaccine records, medications, symptoms, diet and more. The platform uses AI to learn your family’s health history and patterns, and to alert you to any changes that should be addressed, such as missed medications or abnormal vitals. The AI function organizes the data submitted into the system. The app will also connect to third-party apps and wearables, such as the Fitbit.

Luffu is meant to lighten the mental load of family care by organizing all this health data in one place, its co-founder said.

«I was caring for my parents from across the country, trying to piece together my mom’s health care across various portals and providers, with a language barrier that made it hard to get a complete, timely context from her about doctor visits,» said Luffu co-founder James Park.

Luffu will include alerts and a space to log health and medication information via voice, text, photos, and other health portals and devices. The key medical information can be shared across the platform with spouses, caregivers and parents.

A representative for Fitbit did not immediately respond to a request for comment.

Sometimes you want to transcribe something, but don’t want it to be hanging out on the internet for any hacker to see. Maybe it’s a conversation with your doctor or lawyer. Maybe you’re a journalist, and it’s a sensitive interview. Privacy and control are important.

That desire for privacy is one reason the French developer Mistral AI built its latest transcription models to be small enough to run on devices. They can run on your phone, on your laptop or in the cloud.

Voxtral Mini Transcribe 2, one of the new models announced Wednesday, is «super, super small,» Pierre Stock, Mistral’s vice president of science operations, told me. Another new model, Voxtral Realtime, can do the same thing but live, like closed captioning. 

Privacy is not the only reason the company wanted to build small open-source models. By running right on the device you’re using, these models can work faster. No more waiting on files to find their way through the internet to a data center and back. 

«What you want is the transcription to happen super, super close to you,» Stock said. «And the closest we can find to you is any edge device, so a laptop, a phone, a wearable like a smartwatch, for instance.»

The low latency (read: high speed) is especially important for real-time transcription. The Voxtral Realtime model can generate with a latency of less than 200 milliseconds, Stock said. It can transcribe a speaker’s words about as quickly as you can read them. No more waiting two or three seconds for the closed captioning to catch up.

The Voxtral Realtime model is available through Mistral’s API and on Hugging Face, along with a demo where you can try it out.

In some brief testing, I found it generated fairly quickly (although not as fast as you’d expect if it were on device) and managed to capture what I said accurately in English with a little bit of Spanish mixed in. It’s capable of handling 13 languages right now, according to Mistral. 

Voxtral Mini Transcribe 2 is also available through the company’s API, or you can play around with it in Mistral’s AI Studio. I used the model to transcribe my interview with Stock.

I found it to be quick and pretty reliable, although it struggled with proper names like Mistral AI (which it called Mr. Lay Eye) and Voxtral (VoxTroll). Yes, the AI model got its own name wrong. But Stock said users can customize the model to understand certain words, names and jargon better if they’re using it for specific tasks.

The challenge of building small, fast AI models is that they also have to be accurate, Stock said. The company touted the models’ performance on benchmarks showing improved error rates compared to competitors. 

«It’s not enough to say, OK, I’ll make a small model,» Stock said. «What you need is a small model that has the same quality as larger models, right?»