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A new NYT Connections puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Thursday's puzzle instead then click here: NYT Connections hints and answers for Thursday, August 13 (game #1159).

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 Connections hints.

What should you do once you've finished? Why, play some more word games of course. I've also got daily Strands hints and answers and Quordle hints and answers articles if you need help for those too, while Marc's Wordle today page covers the original viral word game.

SPOILER WARNING: Information about NYT Connections today is below, so don't read on if you don't want to know the answers.

Today's NYT Connections words are…

• BUMBLEBEE
• BUBBLE
• BASS ALE
• CHARLIE BROWN
• DELTA AIRLINES
• STRAIGHTEDGE
• ECHO CHAMBER
• CITGO
• CAUTION TAPE
• GOOGLE DRIVE
• INDIA INK
• SILO
• DIP PEN
• WOLVERINE
• HIVEMIND
• BRISTOL BOARD

What are some clues for today's NYT Connections groups?

• YELLOW: Two colors in common
• GREEN: Opinion loop
• BLUE: Things a cartoonist uses
• PURPLE: The shape of the brand

Need more clues?

We're firmly in spoiler territory now, but read on if you want to know what the four theme answers are for today's NYT Connections puzzles…

What are the answers for today's NYT Connections groups?

• YELLOW: BLACK-AND-YELLOW
• GREEN: PLACES WHERE EVERYONE THINKS THE SAME WAYS
• BLUE: COMIC BOOK ARTIST'S TOOLS
• PURPLE: TRIANGLE LOGOS

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

The answers to today's Connections, game #1160, are…

• YELLOW: BLACK-AND-YELLOW: BUMBLEBEE, CAUTION TAPE, CHARLIE BROWN, WOLVERINE
• GREEN: PLACES WHERE EVERYONE THINKS THE SAME WAYS: BUBBLE, ECHO CHAMBER, HIVEMIND, SILO
• BLUE: COMIC BOOK ARTIST'S TOOLS: BRISTOL BOARD, DIP PEN, INDIA INK, STRAIGHTEDGE
• PURPLE: TRIANGLE LOGOS: BASS ALE, CITGO, DELTA AIRLINES, GOOGLE DRIVE

• My rating: Hard
• My score: Fail

This is the first game of Connections I’ve lost since March 2025 and apart from BLACK-AND-YELLOW I had no idea what any of the groups were about.

Things got off to a bad start when I guessed SILO instead of CHARLIE BROWN, but after I corrected that I was utterly in the dark; I had a couple of pairs in INDIA INK and DIP PEN, and HIVEMIND and BUBBLE, but beyond that i was clueless.

What is extra annoying is that I cannot blame my loss on cultural differences and being based in the UK — especially as the relatively obscure BASS ALE is a British brand.

• YELLOW: PERSEVERE: CARRY ON, CONTINUE, MAKE IT, PERSIST
• GREEN: MOVE SLOWLY (UP TO): CREEP, EASE, EDGE, INCH
• BLUE: AIRLINE STATUS PERKS: CHECKED BAGS, LOUNGE, MILES, UPGRADE
• PURPLE: "COLD" THINGS, IN IDIOMS: COMFORT, FEET, SHOULDER, TURKEY

NYT Connections is one of several increasingly popular word games made by the New York Times. It challenges you to find groups of four items that share something in common, and each group has a different difficulty level: green is easy, yellow a little harder, blue often quite tough and purple usually very difficult.

On the plus side, you don't technically need to solve the final one, as you'll be able to answer that one by a process of elimination. What's more, you can make up to four mistakes, which gives you a little bit of breathing room.

It's a little more involved than something like Wordle, however, and there are plenty of opportunities for the game to trip you up with tricks. For instance, watch out for homophones and other word games that could disguise the answers.

It's playable for free via the NYT Games site on desktop or mobile.

• GPT-4 predicted personality scores months before any human ever answered a question
• A Bosch oven manual was enough for GPT-4 to invent personality traits
• DSM-5 predictions hit 0.71 accuracy; astrology predictions somehow hit 0.85 accuracy

Human personality has traditionally been measured by asking people questions and comparing their answers across established psychological frameworks.

A new study published on iScience suggests GPT-4 can go further by estimating how people collectively respond to personality questions before answering begins.

Researchers Rotem Monsa, Aviv Zohar, and Shahar Arzy of the Hebrew University and Hadassah Medical School tested whether the model could infer patterns in human responses from language alone, rather than merely generating plausible questionnaire wording.

The team used GPT-4 to create personality questionnaires from two very different textual sources.

One questionnaire drew on the DSM-5, the standard clinical manual used worldwide by psychiatrists to diagnose personality disorders with precision.

The other version pulled statements from an astrology textbook that assigns particular personality traits to each of the twelve zodiac signs.

"We wanted to choose texts that describe human personality in very rich detail but also sit on opposite ends of a spectrum in terms of scientific grounding," said Rotem Monsa, lead author of the study.

“The DSM-5 was refined through decades of clinical research and is the standard diagnostic manual in clinical psychiatry, known worldwide.”

Both questionnaires were then given to 600 participants alongside the Big Five Inventory, the most validated personality measurement tool available.

Before collecting those responses, GPT-4 estimated average scores for individual questions using a five-point Likert response scale.

This gave the researchers a way to compare predictions against subsequently collected evidence.

The model's estimates correlated strongly with actual participant averages, reaching 0.71 for DSM-derived questions and 0.85 for astrology-derived questions.

This decent internal consistency for DSM-5-based questionnaires suggests that traits that typically appear together in real life also correlated in responses.

The astrology questionnaire, by comparison, showed a noticeably weaker internal consistency across most of the personality traits it measured overall.

GPT-4 also estimated relationships between individual questions, producing correlations of 0.74 and 0.69 for DSM and astrology questionnaires, respectively.

The researchers therefore argue that GPT-4 appears capable of capturing population-level response tendencies embedded within language and questionnaire content.

However, they specifically caution that predicting average responses does not establish that the resulting questionnaire accurately measures individual personality differences.

The researchers also tested whether GPT-4 would generate personality-style questions when given material containing no explicit personality information.

They supplied the model with a Bosch oven manual and a literary description of a landscape from The Lord of the Rings.

GPT-4 still produced questionnaire items resembling personality assessments, even though neither source was designed around psychological traits.

That result suggests the model has learned strong associations between language patterns, personality descriptions, and familiar questionnaire structures.

Yet the researchers found that unrelated source material could produce items that were semantically weak, narrow, or poorly connected.

This distinction matters because a convincing question does not necessarily measure the psychological characteristic that researchers intended to examine.

The study also found important differences between the questionnaires when researchers examined their underlying statistical structures.

The Big Five questionnaire most closely matched its expected organization, while the DSM and astrology questionnaires showed weaker confirmatory factor-model fits.

Astrology-derived items also failed to preserve their intended elemental structure, instead forming broader patterns resembling established personality dimensions.

The results nonetheless showed comparable predictive performance across many life outcomes, although astrology-based measures performed worse for mental-health outcomes.

For now, the evidence supports a narrower claim: GPT-4 can anticipate aggregate human response patterns from textual information with measurable accuracy.

“Given that personality traits are reflected in language, LLMs may have learned the structure of human personality as a natural byproduct of their training,” Monsa added.

“So, while they were not taught specifically psychology or personality theories, these are already embedded in the language that LLMs learn from.”

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