Looking for the most recent 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, Connections: Sports Edition and Strands puzzles.

Today’s NYT Connections puzzle is a real challenge. The purple category is another one where you have to hunt inside other words for four words that have some kind of connection. Read on for clues and today’s Connections answers.

The Times has a Connections Bot, like the one for Wordle. Go there after you play to receive a numeric score and to have the program analyze your answers. Players who are registered with the Times Games section can now nerd out by following their progress, including the number of puzzles completed, win rate, number of times they nabbed a perfect score and their win streak.

Read more: Hints, Tips and Strategies to Help You Win at NYT Connections Every Time

Hints for today’s Connections groups

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

Yellow group hint: Pretty sly.

Green group hint: Different plans.

Blue group hint: Elementary, my dear Watson.

Purple group hint: Hidden anatomy words.

Answers for today’s Connections groups

Yellow group: Move stealthily, with “in.” 

Green group: Kinds of schemes.

Blue group: Detective movies.

Purple group: Body parts surrounded by two letters.

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

What are today’s Connections answers?

The yellow words in today’s Connections

The theme is move stealthily, with “in.” The four answers are creep, slip, sneak and steal.

The green words in today’s Connections

The theme is kinds of schemes. The four answers are color, Ponzi, pyramid and rhyme.

The blue words in today’s Connections

The theme is detective movies. The four answers are Chinatown, Knives Out, Seven and Vertigo.

The purple words in today’s Connections

The theme is body parts surrounded by two letters. The four answers are elegy (leg), karma (arm), keyed (eye) and shandy (hand).

Aussie maker Turnah81 wanted to know whether an ordinary person could create a functional car airbag at home. He documented every step in a recent video and followed through with an actual crash test on a homemade rig. The results offer a clear window into both the ingenuity behind the build and the reasons professionals handle these systems.

Turnah81 began with the fundamental problem that all airbags must solve. In a crash, the car comes to a quick stop, but the driver continues forward because a bag must inflate and get in the way of items such as the steering wheel or dashboard before the driver’s body can close the space. Commercial versions use sensors, CPUs, and special inflators to achieve split-second timing, but he believed he could do better by stripping everything back.

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The trigger was an inertial fuel cut off switch, which is what mechanics use to shut down the fuel pump after a severe impact. He set it up to close an electrical circuit when it received the correct kind of jolt. His concept lacked microcontrollers and accelerometers, instead opting for a traditional mechanical system that could be built in a workshop by one person. Next came the airbag module, which he salvaged off a wrecked car’s steering wheel. He got the gas generator, but he put the chemical one away and replaced it with a standard pressurized gas cylinder with an electronic valve. When the inertial switch activated, current flowed to the valve, opening it, and the gas simply pushed it into the bag. The cylinder generated a lot of pressure without causing any terrible explosive reactions like the chemical ones. Commercial gas generators are small, only a canister that can be hidden in a steering column, which saves space, but the compressed gas cylinder he used was large and cumbersome. When you see the entire thing in place, it becomes clear why manufacturers prefer the chemical approach; it simply will not fit neatly into a car.

He utilized a pillow case as the airbag material, with reinforcement at the stress areas and some careful sewing to prevent all that gas from rushing in. He put it all on a crash test rig, which is supposed to give you a realistic impression of what happens in a crash without putting a real automobile or person at risk. It works by detecting a collision and opening the valve, allowing gas to enter and inflate the pillow case. The whole thing moves quickly enough to see what’s happening on on camera, but it also demonstrates the practical reality, as it has some significant restrictions. Measuring the timing reveals that the bag inflates just as quickly as the actual thing, but the bag itself is different, as is the pressure profile, which is due to the fact that the gas is cool and expands slower.

Turnah81 conducted a number of studies and openly released the findings, with each trial supporting the premise that the system could be deployed on demand. The accompanying video captures that exact moment when the rig ploughs ahead and the bag whacks the bag where a driver’s chest would normally be. Although the findings are encouraging, the configuration demonstrates why a DIY airbag would always fall short of commercial kit. You have compressed gas cylinders that require constant pressure monitoring and temperature adjustments, which a basic valve cannot do on its own. If you set up the system on a cold winter morning, it will be underpowered. In warmer conditions, however, the bag will overinflate, rendering it incapable of providing adequate cushioning. The inertial switch detects head-on impacts well, but it may miss glancing blows or crashes at an angle, which commercial sensor suites can detect precisely.
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The University of Michigan invested 20 million dollars in OpenAI before ChatGPT existed, before Microsoft committed billions, and before the company was worth more than some countries. Court documents from the Musk v. Altman trial revealed this week that the stake carries a target redemption value of two billion dollars. A university endowment made a hundred-to-one return on an artificial intelligence company that was, at the time of investment, a nonprofit research laboratory with no commercial product.

The investment appeared in an exhibit filed in the federal trial in Oakland, California, where Elon Musk is suing OpenAI and its leadership for 150 billion dollars, alleging that the company’s conversion from nonprofit to for-profit corporation constituted theft from a charity. The document listing early investors was not the focus of the trial. But the line item, 20 million dollars from the University of Michigan, has become the most consequential revelation for anyone interested in who saw the AI revolution coming and who actually wrote a cheque.

The bet

Michigan’s investment arrived in one of OpenAI’s earliest fundraising rounds, alongside Khosla Ventures at 50 million dollars, Reid Hoffman’s Aphorism Foundation at 50 million, a Y Combinator fund at 10 million, and the trust of Google’s Paul Buchheit at three million. The round predated Microsoft’s initial one billion dollar investment in 2019 and the public release of ChatGPT in November 2022. At the time, OpenAI was a nonprofit whose mission was to ensure that artificial general intelligence benefits all of humanity. It had no revenue model, no consumer product, and no path to a public listing.

University endowments invest in venture capital and early-stage companies as part of their alternative asset allocation, typically committing capital through fund-of-funds structures or direct investments managed by the endowment’s chief investment officer. Michigan’s endowment, which totalled approximately 17.9 billion dollars at the end of fiscal 2025, has been more aggressive than most in its AI allocation. The 20 million dollar commitment to OpenAI was not a rounding error in a portfolio of that size. But it was a bet on a nonprofit research lab at a time when the commercial potential of large language models was understood by almost nobody outside the organisations building them.

The relationship

The OpenAI investment was not Michigan’s only connection to Sam Altman. In 2023, the university committed 75 million dollars to Hydrazine Capital, a venture fund led by Altman. By 2024, Michigan had increased that commitment to 180 million dollars. The Hydrazine investments are separate from the OpenAI stake, distinct vehicles with different structures and return profiles. But the combined exposure, 200 million dollars across a direct investment and a venture fund both connected to the same individual, represents an unusual concentration of a university endowment’s capital in one network.

The Michigan Daily, the university’s student newspaper, reported in 2024 that the endowment had increased its allocation to AI and cryptocurrency investments, generating returns that outperformed the broader market. An opinion column in the same publication argued that the university should scale back its AI investments, citing ethical concerns about the technology the endowment was profiting from.

Musk called himself “a fool” on the stand for funding OpenAI, a characterisation that applies to his own contributions of approximately 50 million dollars to the same nonprofit that Michigan invested in. The difference is that Musk’s contributions were donations to a nonprofit. Michigan’s investment, through the for-profit conversion, became equity in a company now valued at 852 billion dollars.

The conversion

OpenAI’s transformation from nonprofit to for-profit is the mechanism that turned Michigan’s 20 million dollar investment into a two billion dollar stake. In October 2025, OpenAI restructured into OpenAI Group PBC, a public benefit corporation. The OpenAI Foundation retained a 26 per cent stake. Microsoft held 27 per cent. Early investors, including Michigan, saw their positions converted into equity in an entity that could pursue a public listing.

Brockman’s own journals, introduced at trial, described the nonprofit mission as “a lie,” language that Musk’s legal team used to argue that the conversion was premeditated. The conversion is the central issue in the trial. For Michigan’s endowment, it is the event that crystallised the return. Without the for-profit conversion, the 20 million dollar investment would have remained a contribution to a nonprofit with no liquidity path.

OpenAI closed a 122 billion dollar funding round in March 2026 at a post-money valuation of 852 billion dollars. The round included commitments from SoftBank, Andreessen Horowitz, Amazon, and Nvidia. An IPO is anticipated, with internal targets discussed for a filing in the second half of 2026 and a listing that could value the company at one trillion dollars. If Michigan holds its position through a public offering at that valuation, the return would exceed a hundred to one.

The pattern

Stanford’s James Zou is targeting a one billion dollar valuation for an AI physiology startup backed by research published in Nature, one example of a university-to-company pipeline that has produced some of the most valuable AI companies. Google emerged from Stanford. OpenAI’s founding team included researchers from Berkeley and Stanford. The university endowments that invested earliest in these networks have generated returns that dwarf their conventional portfolios.

Michigan’s 20 million dollar investment is exceptional in magnitude but not in kind. University endowments have been allocating to venture capital for decades. Yale’s endowment, under the late David Swensen, pioneered the model of heavy alternative asset allocation that most large endowments now follow. What distinguishes Michigan’s OpenAI bet is not the strategy but the timing and the target. The endowment committed capital to an AI nonprofit before the technology had demonstrated commercial viability, before the industry had attracted mainstream venture capital at scale, and before the word “ChatGPT” existed in any language.

The AI industry’s trajectory in 2025 confirmed what Michigan’s investment office apparently understood years earlier: that large language models would become the most valuable technology platform since the smartphone. The 20 million dollars is now worth two billion. The university that wrote the cheque will have to decide, when OpenAI eventually goes public, whether to take the return or hold the position in a company that is losing 14 billion dollars a year while generating 25 billion in annualised revenue. The bet was prescient. The exit will determine whether it was also wise.

There’s another type of digital scam to be aware of, as per the BBC. It’s called “reservation hijacking.”

The name gives you a clue as to how it works. Essentially, scammers use details about a booking you’ve placed (perhaps with a hotel or airline) to trick you into sending money somewhere you shouldn’t.

While this type of scam isn’t brand new, a recent data breach at Booking.com has raised the risk of people being caught out. With data about you and your reservation, a far more convincing setup can be put in place—why wouldn’t you believe that someone purporting to be an employee from a spa you’ve got a reservation with is telling the truth about who they are, especially if they know the dates of your trip, your phone number, and your email address?

According to Booking.com, no financial information was exposed in the April 2026 hack. However, names, email addresses, phone numbers, and booking details have been leaked. The travel portal says affected customers have been emailed about the heightened risk of scams, so that’s the first thing to check for when it comes to staying safe.

Minimizing the risk of getting scammed by a reservation hijack involves many of the same security precautions you may already be following, and just being aware that this is a way you might be targeted will make a difference.

How Reservation Hijacks Work

Scammers can get hold of your booking details.

Courtesy of David Nield

We’ve already outlined the basics of a reservation hijack, but it can take several forms. As with other types of scams, it tends to evolve over time. The basic premise is that someone will get in touch with you claiming to be from a place you have a reservation with, whether it’s a car rental company or a hotel.

The scammers will try to pull together as much information as they can on you and your booking. Sometimes they’ll target employees of the place you’ve got the reservation with in order to get access to their systems, and other times they may take advantage of a wider data breach (as with the recent Booking.com hack).

They might also get information through other means. Maybe they’ve somehow got access to your email, or to some of your social media posts (where you’ve shared your next vacation destination and a countdown of how many days are left to go). Don’t be caught out if you find yourself speaking to someone who knows a lot about your travel plans.