Imagine trying to pinpoint the exact moment a chef adds spices in a cooking video. You can *see* it, but translating that visual cue into a precise timestamp is tricky. This challenge, known as Video Temporal Grounding (VTG), is a major hurdle for current video AI. Researchers have discovered a surprisingly simple solution inspired by manga: numbered frames. Just like following numbered panels in a manga, adding numerical identifiers to video frames allows AI models, specifically Video Large Language Models (Vid-LLMs), to accurately “read” the timeline of events. This ingenious trick, called Number-Prompt (NumPro), transforms VTG from a complex task into an intuitive visual alignment problem. Instead of needing extensive retraining or complicated architectural changes, simply adding frame numbers lets the AI directly link visual content with its temporal location. Experiments show that NumPro drastically boosts the performance of various Vid-LLMs, even exceeding state-of-the-art methods on standard benchmarks for moment retrieval and highlight detection. What’s even more impressive is that fine-tuning these models on a NumPro-enhanced dataset (NumPro-FT) sets new records for accuracy. This breakthrough has exciting implications for conversational AI, enabling more precise interactions with video content. Imagine asking an AI to find the *exact* moment a game's score changes or a specific celebrity appears on screen—NumPro makes this level of precision a reality. While promising, NumPro has some limitations. Current VTG datasets mostly contain short videos, so testing on longer content is crucial. Also, the visible frame numbers might occasionally obstruct important visual details. Optimizing the display of these numbers and exploring adaptive frame rates are promising areas for future research. This seemingly small innovation of adding frame numbers unlocks a new level of temporal understanding in video AI, paving the way for more interactive and insightful video analysis.