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

• BAND
• CHOIR
• ORCHESTRA
• DRAMA
• DEBATE
• YEARBOOK

This year, social media companies have been in the spotlight of the authorities. Lawsuits have hit big names like Meta and TikTok for their failure to adequately protect underage users. Under all the pressure, some, like Instagram, have been implementing harsh privacy measures on teen accounts. Now, Meta has offered insight into its new AI-powered adult classifier.

For months now, underage accounts (users under 16) on Instagram have received the “teen account” label. Profiles labeled as such have the most restrictive privacy restrictions by default. This should prevent children or teens from directly contacting potential bad actors or predators. Because these restrictions may limit features, some teens may try to bypass them.

Meta offers more details about the AI-powered adult classifier that Instagram will get

One way that minors might try to get around teen account restrictions is to create a new profile with a fake birth date. With that in mind, Meta announced in September that it will launch an AI-powered adult classifier tool to automatically detect such cases. Now Allison Hartnett, Meta’s director of product management for youth and social impact, has revealed more details about how it will work.

According to Hartnett, the tool will analyze multiple parameters to make a decision. These include the accounts a user follows in particular and the type of content they tend to interact with. Meta’s systems will also be on the lookout for potentially suspicious behavior when creating a new Instagram account, for example, using an email associated with an existing profile or even obtaining the device ID. This way, they can make a more accurate decision about who is creating a new profile.

Meta’s AI adult classifier will be able to label accounts suspected of belonging to minors as “teens,” automatically applying all restrictions. Accounts with those restrictions cannot have them removed without prior authorization from a parent. The company promises to provide an appeal tool if it incorrectly labels an account as “teen.” However, there is no date yet for the appeal tool’s availability.

Instagram will ask for valid IDs or AI-powered facial analysis when trying to change age

There may also be cases of teenagers trying to remove restrictions by changing their date of birth. Here, Instagram will ask for a valid government-issued ID. Users will also have the option to upload a selfie video through Yoti’s technology. The latter offers advanced AI-powered recognition services that can even determine a person’s age. Meta has already turned to Yoti to verify the age of users of Facebook’s dating option.

Meta is opening up its Llama AI models to government agencies and contractors working on national security, the company said in . The group includes more than a dozen private sector companies that partner with the US government, including Amazon Web Services, Oracle and Microsoft, as well as defense contractors like Palantir and Lockheed Martin.

Mark Zuckerberg hinted at the move last week during Meta’s earnings call, when the company was “working with the public sector to adopt Llama across the US government.” Now, Meta is offering more details about the extent of that work.

Oracle, for example, is “building on Llama to synthesize aircraft maintenance documents so technicians can more quickly and accurately diagnose problems, speeding up repair time and getting critical aircraft back in service.” Amazon Web Services and Microsoft, according to Meta, are “using Llama to support governments by hosting our models on their secure cloud solutions for sensitive data.”

Meta is also providing similar access to Llama to governments and contractors in the UK, Canada, Australia and New Zealand, Bloomberg . In a blog post, Meta’s President of Global Affairs, Nick Clegg, suggested the partnerships will help the US compete with China in the global arms race over artificial intelligence. “We believe it is in both America and the wider democratic world’s interest for American open source models to excel and succeed over models from China and elsewhere,” he wrote. “As an American company, and one that owes its success in no small part to the entrepreneurial spirit and democratic values the United States upholds, Meta wants to play its part to support the safety, security and economic prosperity of America – and of its closest allies too.”

A team of computer scientists has developed a method that helps artificial intelligence understand when to use tools versus relying on built-in knowledge, mimicking how human experts solve complex problems.

The research from the University of California San Diego and Tsinghua University demonstrates a 28% improvement in accuracy when AI systems learn to balance internal knowledge with external tools — a critical capability for deploying AI in scientific work.

How scientists taught AI to make better decisions

“While integrating LLMs with tools can increase reliability, this approach typically results in over-reliance on tools, diminishing the model’s ability to solve simple problems through basic reasoning,” the researchers write in their paper. “In contrast, human experts first assess problem complexity using domain knowledge before choosing an appropriate solution approach.”

The new method, called “Adapting While Learning,” uses a two-step process to train AI systems. First, the model learns directly from solutions generated using external tools, helping it internalize domain knowledge. Then, it learns to categorize problems as either “easy” or “hard” and decides whether to use tools accordingly.

Small AI model outperforms larger systems on complex tasks

What makes this development significant is its efficiency-first approach. Using a language model with just 8 billion parameters — far smaller than industry giants like GPT-4 — the researchers achieved a 28.18% improvement in answer accuracy and a 13.89% increase in tool usage precision across their test datasets. The model demonstrated particular strength in specialized scientific tasks, outperforming larger models in specific domains.

This success challenges a fundamental assumption in AI development: that bigger models necessarily yield better results. Instead, the research suggests that teaching AI when to use tools versus rely on internal knowledge — much like training a junior scientist to know when to trust their calculations versus consult specialized equipment — may be more important than raw computational power.

The rise of smaller, smarter AI models

This research aligns with a broader industry shift toward more efficient AI models in 2024. Major players including Hugging Face, Nvidia, OpenAI, Meta, Anthropic, and H2O.ai have all released smaller but highly capable models this year.

Hugging Face’s SmolLM2, with versions as small as 135 million parameters, can run directly on smartphones. H2O.ai’s compact document analysis models have outperformed tech giants’ larger systems on specialized tasks. Even OpenAI entered the small model arena with GPT-4o Mini, offering similar capabilities at a fraction of the cost.

This trend toward “AI downsizing” reflects growing recognition that bigger isn’t always better — specialized, efficient models can often match or exceed the performance of their larger counterparts while using far fewer computational resources.

The technical approach involves two distinct learning phases. During training, the model first undergoes what the researchers call “World Knowledge Distillation” (WKD), where it learns from solutions generated using external tools. This helps it build up internal expertise.

The second phase, “Tool Usage Adaptation” (TUA), teaches the system to classify problems based on its own confidence and accuracy in solving them directly. For simpler problems, it maintains the same approach as in WKD. But for more challenging problems, it learns to switch to using external tools.

Business impact: More efficient AI systems for complex scientific work

For enterprises deploying AI systems, this research addresses a fundamental challenge that has long plagued the industry. Current AI systems represent two extremes: they either constantly reach for external tools — driving up computational costs and slowing down simple operations — or dangerously attempt to solve everything internally, leading to potential errors on complex problems that require specialized tools.

This inefficiency isn’t just a technical issue — it’s a significant business problem. Companies implementing AI solutions often find themselves paying premium prices for cloud computing resources to run external tools, even for basic tasks their AI should handle internally. On the flip side, organizations that opt for standalone AI systems risk costly mistakes when these systems attempt complex calculations without proper verification tools.

The researchers’ approach offers a promising middle ground. By teaching AI to make human-like decisions about when to use tools, organizations could potentially reduce their computational costs while maintaining or even improving accuracy. This is particularly valuable in fields like scientific research, financial modeling, or medical diagnosis, where both efficiency and precision are crucial.

Moreover, this development suggests a future where AI systems could be more cost-effective and reliable partners in scientific work, capable of making nuanced decisions about when to leverage external resources — much like a seasoned professional who knows exactly when to consult specialized tools versus rely on their expertise.

The power of knowing when to ask for help

Beyond the immediate technical achievements, this research challenges the bigger-is-better paradigm that has dominated AI development. In demonstrating that a relatively small model can outperform its larger cousins by making smarter decisions about tool use, the team points toward a more sustainable and practical future for AI.

The implications extend far beyond academic research. As AI increasingly enters domains where mistakes carry real consequences – from medical diagnosis to climate modeling – the ability to know when to seek help becomes crucial. This work suggests a future where AI systems won’t just be powerful, but prudent – knowing their limitations just as skilled professionals do.

In essence, the researchers have taught AI something fundamentally human: sometimes the smartest decision is knowing when to ask for help.

GM is claiming the number two spot in EV sales in the US for the third quarter of this year, selling 32,000 electric vehicles. The automaker produces EVs across multiple brands running on the same platform, like Chevy’s Silverado, Blazer, and Equinox EVs, as well as the GMC Hummer EV and the Cadillac Lyriq.

GM says it has sold a total of 370,000 EVs in North America since 2016, including 300,000 in the US specifically. Tesla is still the undisputed leader, with more than 5 million vehicles sold since 2008.

In an email with The Verge, GM’s executive director of finance and sales communications James Cain wrote that sales have accelerated since the company built a dedicated EV platform (formerly known as Ultium) and began producing battery cells through its joint ventures with LG and Samsung SDI. GM’s third-quarter EV sales beat out rival Ford by about 8,600 units, according to Kelley Blue Book, as reported by The New York Times.

Meanwhile, Ford spokesperson Dan Barbossa claims the Blue Oval remains “America’s No. 2 best-selling EV brand behind Tesla.” In an email with The Verge, Barbossa wrote:

We remain the No. 2 brand. GM is adding every brand EV (Chevy, GMC, Cadillac, etc) they sell and making a different claim.

Still, GM has a ways to go before it achieves the goal of producing 1 million EVs, which it previously projected it would accomplish by 2025. The company later distanced itself from that target when it became clear that production troubles, charging difficulties, and high interest rates would slow down the rate of growth in EV sales in the US.

Ford had a strong early start with solid sales of its all-electric Mustang Mach-E, launched in 2019, and the F-150 Lightning electric truck in 2022. During that timeframe, GM only had the Chevy Bolt, built on an older battery platform. The Hummer EV truck launched in 2020, but overall EV sales were slow amid production troubles.

Ford also hit some snags along the way, including parts shortages. The company has lost billions of dollars in its Model e division, where revenues have not kept up with spending. Ford recently canceled a planned three-row SUV and has paused production of the F-150 Lightning until next year. Ford is placing a lot of its hopes on its skunkworks team in Silicon Valley, developing its next-gen platform for cheaper EVs.

Strands is the NYT’s latest word game after the likes of Wordle, Spelling Bee and Connections – and it’s great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my Wordle today, NYT Connections today and Quordle today pages for hints and answers for those games.