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Snack Prompt: The Reddit of AI Prompts

In a recent episode by Boldpush on how Generative AI will impact the events industry, Chase went on to detail how Snack Prompt started, gave example prompts that event organizers could use to leverage generative AI in their events planning, and gave us some pro-tips on prompt engineering. In this post, I will discuss three key attributes of Snack Prompt, and share the prompt engineering advice I received.

About Snack Prompt

an image of the snack prompt website banner
snackprompt.com

A Community-Driven Platform

I might be the only one who thinks its genius, but Snack Prompt thrives on a community-model where users actively participate by upvoting or downvoting prompts. This democratic approach ensures that the most intriguing and thought-provoking prompts rise to the surface, captivating the attention of AI enthusiasts and creators. This collective approach of community input creates an immediate measurement of the “usefulness” of posts shared. It not only shapes the use prominence of prompts but also fosters an environment where users collaborate to refine and elevate each other’s ideas. Getting feedback on prompts can help users improve them, snowballing into the best collaborative efforts in prompt engineering.

Organized by Topic

In classic forum style, you can easily navigate your prompt search by knowing what goal you want to achieve. Prompts designed for ChatGPT or MidJourney or Claude.ai can be filtered on, as well as further refined by the topic of your search or what you want to achieve with your prompt. Its wonderfully organized, and this makes it so much easier to navigate.

A New Marketplace for Creators

What I particularly appreciate about the Snack Prompt platform is how it has introduced a marketplace where creators can monetize their exceptional prompt designs. Those who craft particularly captivating and sought-after prompts have the opportunity to sell them to other users. This encourages creativity while also rewarding ingenuity, providing an incentive for users to create and share high-quality prompts that captivate the community. I think this is brilliant, and has strong potential as a staple platform for prompt engineers in future.

On Prompt Engineering

a picture of html code on a smartphone's screen
Photo by Caspar Camille Rubin / Unsplash

Dabbling with prompts myself, I paid extra attention to some of the feedback that Chase gave attendees on their questions of how to improve their prompting to get better results from whichever generative AI they are using. Here are my key takeaways:

  1. Be Clear and Concise: Being able to communicate well is a rare skill. If you want to write good prompts, you need to know how to structure your communication. Presenting a problem, your expectations of the solution, and providing an example or expected word count in the response is key to receiving good output.
  2. Give context: This one we surely know already, but giving context is not just about the information-environment that you’re working in. It includes small tricks such as starting a prompt with a verb instead of request words. For example “Create a seven day event schedule for a rock music concert”  versus “Can you create a concert schedule for seven days?”. Providing an example of what you are looking for can further refine your prompt and clarify the length of text and the tone that you would like to communicate.
  3. Monitor your feedback: Monitor the feedback you give a bot to refine on the output you are receiving. While you may be tempted to respond saying “that was awful”  or “you are not doing a good job” – this can risk the AI disregarding previous input information from you so as to readjust its processing and improve on the output. But this disregard of contextual information does not help refine the AI’s process. Instead, correct unwanted outputs by saying “I need you to adjust (x)”  or “I am looking for more detail in (y) instead of (x)”.

If you’re an AI enthusiast, from novice to experienced, I invite you to checkout the Snack Prompt community and, who knows, maybe you’ll end up contributing to it too

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Fundraising 5 hours ago

London-based AI laboratory Ineffable Intelligence has emerged from stealth with a $1.1 billion seed round at a $5.1 billion post-money valuation, the company confirmed on 27 April 2026. The financing is the largest seed round ever raised by a European company and one of the largest first-money-in rounds in the global history of artificial intelligence. The round was co-led by Sequoia Capital and Lightspeed Venture Partners. Participating investors included Nvidia, DST Global, Index Ventures, Google, and the UK Sovereign AI Fund, the British government’s recently established vehicle for backing strategic AI capacity on home soil. A bet on a different path to general intelligence Ineffable Intelligence was founded in 2025 by David Silver, the former Vice President of Reinforcement Learning at Google DeepMind and the principal architect of AlphaGo, AlphaZero and AlphaStar. He is joined by three further DeepMind alumni: Wojciech Czarnecki, Lasse Espeholt and Junhyuk Oh. All four have spent the past decade at the frontier of reinforcement learning research, the discipline behind some of the most consequential demonstrations of machine learning over the past ten years. The company describes its objective as building a “superlearner” — an AI system capable of acquiring knowledge directly from its own experience rather than from human-generated text or imagery. “Our mission is to make first contact with superintelligence,” Silver said in a statement accompanying the launch. “We are creating a superlearner that discovers all knowledge from its own experience, from elementary motor skills through to profound intellectual breakthroughs.” The framing is a deliberate departure from the dominant industry trajectory. Most leading AI laboratories, including OpenAI, Anthropic and Google DeepMind itself, have built large language models trained primarily on the corpus of the internet, then refined that training with human feedback. Ineffable’s wager is that the marginal returns on scaling text-based pretraining are diminishing and that the next leap in capability will come from agents that learn endlessly from the consequences of their own actions, in much the same way AlphaZero learnt the game of Go without studying any human matches. Why $1.1 billion at seed The size of the round is unusual even by the inflated standards of the 2026 AI capital cycle. Two factors appear to explain it. First, frontier reinforcement learning at the scale Ineffable describes is computationally extraordinarily expensive: the company will need to operate vast simulation environments and train very large models against them, an undertaking that consumes capital at a rate closer to physical R&D than to traditional software. Second, the round signals a strategic move by Europe’s investor and policy ecosystems to retain the most ambitious AI researchers on the continent. The presence of the UK Sovereign AI Fund alongside Sequoia, Lightspeed and Nvidia is the clearest expression of that intent. The British government has publicly framed the investment as a bet on breakthrough AI that “can discover new knowledge”, positioning the country as a willing co-investor in domestic frontier laboratories. For Ineffable, the implication is access not only to capital but to compute, regulatory engagement and the still-resilient academic talent base around UCL, Oxford, Cambridge and Imperial. Founder pledge of historic scale Alongside the funding announcement, Silver disclosed that he is committing 100 per cent of any personal proceeds from his Ineffable equity to charity via the Founders Pledge network — described by the organisation as the largest pledge in its history. At the round’s $5.1 billion valuation, that commitment could ultimately exceed several billion dollars if the company succeeds. It is a meaningful gesture in a sector where the reputational stakes around concentrated AI wealth are escalating, and one likely to be referenced in subsequent founder-led commitments. Implications for the European AI landscape Ineffable’s emergence reshapes the European AI map in three concrete ways. It establishes London as the home of the continent’s largest-ever seed-stage company, complicating Paris’s recent narrative of frontier-AI primacy after Mistral’s earlier rounds. It validates a thesis — that reinforcement learning, not transformer scaling, is the next frontier — that has lately been losing capital share to language-model incumbents. And it confirms that the UK government is now willing to act as a balance-sheet co-investor in domestic AI laboratories, a posture much closer to the French model than to the predominantly grant-based regimes elsewhere in Europe. The execution risk is non-trivial. Reinforcement learning at frontier scale has historically required years of careful environment design before producing competitive systems, and Ineffable’s “first contact” framing sets a high bar against which it will be judged. But for now, with a billion dollars on the balance sheet, four of the discipline’s most accomplished researchers in the founding team and a sovereign co-investor at its back, Ineffable Intelligence is the most heavily resourced new entrant in the European AI cycle. Sesamers covers European fundraising rounds across deeptech, fintech and AI. Source: tech.eu.

Fundraising 5 days ago

Belfast's Cloudsmith has raised $72M Series C led by TCV, with Insight Partners participating, to expand its artifact management platform and secure the AI-era software supply chain.

Fundraising 5 days ago

Berlin’s VREY has raised €3.3M seed led by Rubio Impact Ventures to roll out rooftop solar software for Germany’s multi-family buildings.

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