The reviews for Hyundai’s little electric SUV that could are trickling in, and it’s clear that the Inster is a delightful way to move about town — regardless of its lack of quickness compared to other similarly-sized EVs. The Inster’s top speed for the long-range version is about 93 miles per hour (or 150 km/h), and it has a zero to 62 mph (100km/h) acceleration in 10.6 seconds, according to the specs Hyundai published today.

Hyundai also revealed more details about the Inster’s price, with European reviewers saying it’s expensive compared to similar competition at £23,495 (about $25,477). In the US, however, that’s a price we can only dream about since our most affordable options include the $35,000 Chevy Equinox EV or the hope Tesla will deliver a cheaper car for around $25,000.

One newer compact EV that has made it to the US is the Fiat 500e. At 143 inches in length, it’s only about 7 inches shorter than the Inster at 150.59 inches. However, the Inster has more internal storage since it’s SUV-shaped, plus it has a range of about 230 miles WTLP on the long range 49 kWh battery compared to the 500e’s, which is under 200 miles.

Good morning! Let’s play Connections, the NYT’s clever word game that challenges you to group answers in various categories. It can be tough, so read on if you need clues.

What should you do once you’ve finished? Why, play some more word games of course. I’ve also got daily Wordle hints and answers, Strands hints and answers and Quordle hints and answers articles if you need help for those too.

Strands is a brand new daily puzzle from the New York Times. A trickier take on the classic word search, you’ll need a keen eye to solve this puzzle.

Like Wordle, Connections, and the Mini Crossword, Strands can be a bit difficult to solve some days. There’s no shame in needing a little help from time to time. If you’re stuck and need to know the answers to today’s Strands puzzle, check out the solved puzzle below.

How to play Strands

Hint for today’s Strands puzzle

Today’s Strand answers

Today’s spanagram

We’ll start by giving you the spangram, which might help you figure out the theme and solve the rest of the puzzle on your own:

Today’s Strands answers

• RULER
• PENCILS
• SCISSORS
• PRINTER
• STAPLER

In the US, Google TV products have access to over 150 free (ad-supported) channels, which is great. Recently, Google improved the experience of enjoying these channels with a new Google TV Freeplay, which includes a useful live guide. However, the Google TV Freeplay app has been disabled due to unexpected technical issues.

Google TV Freeplay app’s new features were causing crashes, so Google disabled it

Google began the rollout of the revamped Freeplay app in early September. The update made it much easier to use than before, where the experience of searching for channels and shows was quite cumbersome. The mere presence of a programming guide totally changed the reality of the app. Sadly, it seems that the guide was causing crashes on some devices.

As spotted by 9to5Google, the Google TV Freeplay app disappeared from all Google TV devices this week. The only product where it is still active and working without problems is the latest Google TV Streamer. The reason for the app’s disappearance was unknown, as not everyone was aware of the crash issues. However, an official statement from Google shed more light on what’s going on.

“While rolling out the new Google TV Freeplay guide, we discovered an issue that can increase crashes for some users. We have disabled access to the new guide on affected devices until the fix is ​​in place. The fix will begin rolling out soon, and the updated guide will be available for all Google TV devices in the coming months,” a company spokesperson said.

Freeplay could be back soon without the live guide

So, given the facts, by “affected devices” Google is referring to all Google TV products except the latest Streamer. While the company claims that the fix will be available “soon,” they also mention that the guide will be available “in the coming months.” So, possibly the Google TV Freeplay app will return in a few days, but without the latest improvements to the user experience. Let’s hope that a fixed version of the app with all the new features won’t take long to arrive.

A new update for Instagram posted earlier today could fix one of the most frustrating problems for creators. Adam Mosseri, the head of Instagram, announced a new filtering update on Instagram for creators’ inboxes.

Instagram users with a creator-designated account can now filter message requests in their inbox based on its sender in a similar way to Gmail’s labels. Creators can still sort their messages by the most “recent” received and by the “number of followers” but they can now filter out certain messages. The new filters include requests and messages from “verified accounts,” “businesses,” “creators” and “subscribers.”

The update also includes a way to sort all of your story replies on Instagram. If you go to the top of your inbox, you can also sort and filter your story replies “in case you just wanna get to these requests really quickly and easily,” Mosseri says.

“Now there’s a lot more to do to improve the inbox for creators and requests but hopefully this is one step in the right direction,” Mosseri adds in his video. He also said this feature was one a lot of creators were asking for, so hopefully Instagram will be adding more inbox tools in the near future to make that part of the app a bit cleaner.

Instagram has been toying with new ways to update its platform for higher profile users and creators for a long time now. The company started testing its creator account concept in 2018 that allowed celebrities and more famous social media stars to filter their direct messages and track stats of their followers.

Hugging Face today has released SmolLM2, a new family of compact language models that achieve impressive performance while requiring far fewer computational resources than their larger counterparts.

The new models, released under the Apache 2.0 license, come in three sizes — 135M, 360M and 1.7B parameters — making them suitable for deployment on smartphones and other edge devices where processing power and memory are limited. Most notably, the 1.7B parameter version outperforms Meta’s Llama 1B model on several key benchmarks.

Small models pack a powerful punch in AI performance tests

“SmolLM2 demonstrates significant advances over its predecessor, particularly in instruction following, knowledge, reasoning and mathematics,” according to Hugging Face’s model documentation. The largest variant was trained on 11 trillion tokens using a diverse dataset combination including FineWeb-Edu and specialized mathematics and coding datasets.

This development comes at a crucial time when the AI industry is grappling with the computational demands of running large language models (LLMs). While companies like OpenAI and Anthropic push the boundaries with increasingly massive models, there’s growing recognition of the need for efficient, lightweight AI that can run locally on devices.

The push for bigger AI models has left many potential users behind. Running these models requires expensive cloud computing services, which come with their own problems: slow response times, data privacy risks and high costs that small companies and independent developers simply can’t afford. SmolLM2 offers a different approach by bringing powerful AI capabilities directly to personal devices, pointing toward a future where advanced AI tools are within reach of more users and companies, not just tech giants with massive data centers.

Edge computing gets a boost as AI moves to mobile devices

SmolLM2’s performance is particularly noteworthy given its size. On the MT-Bench evaluation, which measures chat capabilities, the 1.7B model achieves a score of 6.13, competitive with much larger models. It also shows strong performance on mathematical reasoning tasks, scoring 48.2 on the GSM8K benchmark. These results challenge the conventional wisdom that bigger models are always better, suggesting that careful architecture design and training data curation may be more important than raw parameter count.

The models support a range of applications including text rewriting, summarization and function calling. Their compact size enables deployment in scenarios where privacy, latency or connectivity constraints make cloud-based AI solutions impractical. This could prove particularly valuable in healthcare, financial services and other industries where data privacy is non-negotiable.

Industry experts see this as part of a broader trend toward more efficient AI models. The ability to run sophisticated language models locally on devices could enable new applications in areas like mobile app development, IoT devices, and enterprise solutions where data privacy is paramount.

The race for efficient AI: Smaller models challenge industry giants

However, these smaller models still have limitations. According to Hugging Face’s documentation, they “primarily understand and generate content in English” and may not always produce factually accurate or logically consistent output.

The release of SmolLM2 suggests that the future of AI may not solely belong to increasingly large models, but rather to more efficient architectures that can deliver strong performance with fewer resources. This could have significant implications for democratizing AI access and reducing the environmental impact of AI deployment.

The models are available immediately through Hugging Face’s model hub, with both base and instruction-tuned versions offered for each size variant.