Bored Waiting for LLM Responses? A Developer Built a Mini-Game to Fill the Gap

Someone Finally Addressed the Pain of Waiting for LLM Responses

Anyone who has used a large language model knows the feeling — after entering a complex prompt, all that remains on screen is a spinning loading icon, and the wait of several seconds or even tens of seconds breeds anxiety. Recently, a developer shared an ingenious solution on Hacker News's "Show HN" section: giving users a mini-game to play during the wait while the LLM processes their request.

The core philosophy of the project is simple yet hits the nail on the head — if waiting is inevitable, why not make it fun?

From 'Blank Waiting' to 'Engaging Experience'

The project's design philosophy stems from a fundamental UX principle: perceived wait time matters far more than actual wait time. This concept has proven successful in other domains. For instance, mirrors beside elevators were originally installed to give people something to do while waiting, thereby reducing complaints about wait times. The dinosaur runner game that appears in Google Chrome when the internet is disconnected follows the same logic.

Today, with the explosive growth of AI applications, LLM response latency has become one of the most prominent weak points in user experience. Especially when complex reasoning, long-text generation, or multimodal processing is involved, wait times can stretch from a few seconds to over a minute. By embedding lightweight mini-games into the waiting interface, the developer transformed this "dead time" into "interactive time," effectively reducing user churn and anxiety.

Community Buzz: Great Concept, but Details Need Work

The Hacker News community engaged in a lively discussion about the project. Supporters considered it a highly creative UX optimization, particularly suited for AI application scenarios with longer inference times, such as code generation and deep analysis.

However, some developers raised pragmatic concerns:

• Attention-switching costs: If users become immersed in the game, they might miss the notification that the LLM has finished responding, actually reducing work efficiency.
• Context appropriateness: Embedding mini-games in serious enterprise applications could appear unprofessional.
• Streaming output is already mainstream: Most LLM applications now use streaming technology to display generated content word by word, allowing users to read partial results during the wait, which diminishes the necessity of a mini-game.

Other community members offered more constructive suggestions, such as replacing mini-games with task-relevant "knowledge cards" or "fun quizzes" that kill time without completely diverting the user's attention.

The Deeper Question: The Economics of Waiting in the AI Era

This seemingly lighthearted project actually touches on a topic worth serious consideration in AI application development — how to design the "waiting experience."

As AI models grow increasingly powerful, inference computation demands continue to climb. OpenAI's o1 series models and Anthropic's Claude both require extended "thinking" time when handling complex tasks. In the future, as agent workflows and multi-step reasoning become more widespread, the waiting windows users face could grow even longer.

Under this trend, optimizing the waiting experience will become an important dimension of differentiation for AI products. Whether through progress visualization, stage-by-stage feedback, or playful designs like this project, all are directions worth exploring.

Looking Ahead

Although this is just a small open-source project, the design philosophy it represents deserves the attention of every AI application developer. While pursuing model performance, micro-innovations at the user experience level can also create tremendous value. After all, what determines whether users stay or leave is often not a model's MMLU score, but how they feel during those few seconds of waiting.

As AI grows ever smarter, we also need to make the experience of "waiting for AI to be smart" smarter in itself.