I ve been working on NiceShot AI for the last 26 months as a way to experiment with computer vision for gameplay analysis.The basic idea is to take a recorded long gameplay session and automatically turn it into structured gameplay information and highlights. Bridging the performance analysis gap between match stats (too short) lifetime stats (too long).The current pipeline is:Gameplay video → YOLO detection → event tracking → OCR/context filtering → event timestamps → clips → highlight compilation → session statisticsFor example, I can fine-tune a YOLO model to recognize game-specific HUD elements and then configure the pipeline to interpret those detections as events such as kills, deaths, or medals.One thing I wanted to avoid was making the entire system game-specific. Supporting another game should mostly require a new detector/model and configuration rather than rewriting the whole pipeline. And currently, I am working on that.I also use OCR for states that shouldn t be counted as gameplay events. For example, in Call of Duty games a kill indicator can appear during a KillCam or Spectating state, so the OCR layer can be used to filter those cases.The system can then produce clips around detected events and compile them into highlight reels, including vertical versions for Shorts/TikTok-style content.Recently, I have also been experimenting with using a lightweight Video LLM after event detection. Instead of sending the entire multi-hour gameplay session to the Video LLM model, I take the extracted short clips around an interesting event and ask the model to explain what happened. The goal is to eventually turn this into a lightweight conversational gameplay coach.The project currently runs locally on NVIDIA GPUs. Have experimented with an old GTX1650 4GB VRAM laptop and it works fine but slow ofcourse.Finally, I know that real-time event detection should be the way to go and this was my original goal, but I chose offline processing because I didn t want computer vision inference to interfere with the player s actual game performance.I d also be interested in hearing how others would approach making the detector/inference layer fast enough for real-time gameplay without causing noticeable GPU/CPU overhead. Keeping in mind, I input 1080p frames into the model.The project is still evolving, so technical criticism and suggestions are very welcome.Thanks.