“Awesome ChatGPT Prompts” is exactly what it sounds like: a curated list of 203 imaginative prompt examples, from “act as a Linux terminal” to “play the football commentator”. Originally created for ChatGPT and later expanded to work with other AI models (Claude, Google’s Gemini, you name it), this crowd-sourced prompt compendium showcases the art of prompt engineering in full bloom. It was first compiled by the user known as fka (who also turned it into a GitHub repo and even wrote an e-book on prompts – talk about hustle!).

So why is an old-school prompt list topping the charts in mid-2025? Chalk it up to real-world relevance and community hype. As new open-source large language models keep emerging, users (from novices to savvy devs) are grabbing this dataset to jumpstart conversations and test model capabilities. It’s a quick way to make an AI pretend to be an advertiser, a poet, or even a pirate – no fine-tuning needed. The list’s popularity (over 8,000 likes on HF and counting) signaled this week that even in the era of trillion-token training sets (see our Model Review this week), know-how is still gold. In fact, being able to coax clever output via great prompts remains a coveted skill. The buzz around Awesome ChatGPT Prompts highlights a broader trend: the AI community isn’t just chasing bigger models; it’s also obsessed with making AI useful. This dataset stands out because it’s practical, fun, and immediately usable (fancy that!). Its resurgence suggests that after all the complex model releases, folks are circling back to the human element – crafting the right ask for the AI. Consider it a gentle reminder that sometimes a well-phrased question beats a mountain of data.

The rest of last week’s trending list is filled with heavy-hitters that couldn’t be more different from our prompt champion. Here’s a quick rundown of the noteworthy runners-up, and why they’re turning heads:

• Institutional Books 1.0 – 242 billion tokens of library literature. This mammoth text dataset comes courtesy of Harvard’s libraries and the Institutional Data Initiative, offering a curated trove of public domain books for AI research. It’s an “early-access” release with an academic license, complete with cautions about potential offensive content in old texts. Why the trend? It’s new, enormous, and high-quality – basically catnip for anyone training large language models. The buzz here signals a push for ethically sourced, richly curated data (no web-scrape grunge, just well-scanned books) and a broader movement by institutions to open up data responsibly. In an age concerned with data provenance and bias, Institutional Books 1.0 is a poster child for transparent, careful curation – and the community is paying attention.

  
  

• Essential-Web v1.0 – The Internet on steroids (24 trillion tokens!). If Harvard’s dataset is a curated library, Essential-Web is a mega-sized encyclopedia of the open web. Dropped by the startup Essential AI (founded by Transformer luminaries, no less), this dataset boasts 24 trillion tokens of web data annotated with a rich 12-category taxonomy. In plain English: every document is tagged by topic, format, complexity level, and quality, so researchers can slice and dice the data with simple filters. The creators claim their organized web corpus outperforms or matches the usual web crawl datasets on domain-specific model training. It’s trending because it represents the next-gen approach to “big data” – not just scraping everything, but structuring and filtering it smartly. The community’s excitement here is all about scale and control: Essential-Web v1.0 hints at a future where we feed models better chow, not just more. This popularity underscores an industry truth: we’re entering a data quality arms race, and everyone wants a piece of that 24T-token pie for their next model.

  
  

• Facebook’s Seamless Interaction – Multimodal social intelligence, anyone? Leave it to Meta (which is, if you can believe it, in fact, Facebook on Hugging Face!) to bring a 4,000+ hour video dataset of human-to-human interactions into the spotlight. Seamless Interaction is a massive collection of face-to-face conversation footage from over 4,000 people in diverse scenarios. It’s multimodal (video + audio) and aimed at teaching AI to understand the subtleties of human communication – think body language, tone, and all the “unspoken” signals. Why the buzz: it’s a treasure trove for embodied AI and HCI research, enabling advances in virtual agents, AR/VR telepresence, and more. Basically, if you want an AI that can nod, shrug, or interrupt at just the right moment, this dataset is a goldmine. The fact that it’s trending alongside text behemoths shows a key divergence in AI data trends: not all roads are text-only. The community is clearly jazzed about multimodal datasets that push AI beyond words into understanding interaction. It also doesn’t hurt that Meta open-sourced it – researchers love free high-res data like kids love candy. The takeaway: alongside giant text corpora, there’s growing emphasis on datasets that capture human experience in 3D Technicolor.

This week's zeitgeist was scale, quality, an expansion to new modalities (video, audio, images – the full human experience), and a recognition that better data means better models (we're biased on that one). At the same time, the surprise popularity of Awesome ChatGPT Prompts adds a wink of truth: even as we engineer ever-larger datasets, sometimes small and clever beats big and brute-force. It’s a reminder that AI is ultimately a human collaboration – our creative prompts and high-quality data together teaching machines to be smarter, fairer, and maybe a tad more fun.

TL;DR: This week’s top trending Hugging Face dataset is an “Awesome” list of ChatGPT prompts – a community-favorite trove of 203 creative instructions that’s trending again, proving prompt engineering never goes out of style. Meanwhile, other trending datasets include Harvard’s 242B-token book corpus and Essential AI’s 24-trillion-token web dataset, both signaling a race for massive, curated training data, as well as Meta’s 4,000-hour Seamless Interaction video collection pushing AI into truly multimodal understanding. In short, the AI world is hyped about better data (bigger, cleaner, more diverse) – but it hasn’t forgotten that a clever prompt can still steal the show.