# YOLOv8 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/yolov8
> Markdown URL: https://aitinkerers.org/technologies/yolov8.md
> Technology record last updated: 2026-02-23T20:57:53Z
> Generated: 2026-09-23T02:32:06Z

YOLOv8 is the state-of-the-art (SOTA) model from Ultralytics, delivering superior speed and accuracy across real-time object detection, instance segmentation, and image classification tasks.

This is the latest iteration in the You Only Look Once (YOLO) series, developed by Ultralytics and released in 2023. YOLOv8 leverages an anchor-free detection head and an enhanced PANet neck: this architectural shift simplifies the process, reducing hyperparameters and boosting performance over previous versions like YOLOv5. It provides a unified, highly efficient framework supporting multiple vision AI tasks (detect, segment, classify, pose), making it ideal for demanding, real-world applications (e.g., autonomous vehicle systems, advanced security). The model is easily accessible via a robust Python package and a clean Command Line Interface (CLI).

- Official technology site: https://docs.ultralytics.com/yolov8/
- Public AI Tinkerers demos and talks: 5
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Agent mail system as a replacement for software engineering talent](https://st-louis.aitinkerers.org/talks/rsvp_jfOTexBJhv4)

I'll "show the code" of how I use the MCP Agent Mail system to monitor my wildlife detection computer vision continuous retraining loop. The DeerAITrackingResponse project is too broad to cover, so the presentation will focus on how learning to use headless agents and MCP Agent Mail helped me ensure the quality and continuous operation of my sprawling computer vision project. By creating three agents - "Night Watchman", "Day Watchman", and "Model Training Master" and monitoring their communications in the project mail inbox I was able to lean more on my middle management muscles instead of my very weak software engineering muscles. I will introduce the MCP, https://github.com/Dicklesworthstone/mcp_agent_mail, and walk through the prompt that gives Night Watchman his 6 tasks to do during his nightly patrol.

- Event context: AI Tinkerers – St. Louis Meetup: February 4, 2026 — 2026-02-04 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_jfOTexBJhv4

### [MindServe AI: Real-Time GPU-Powered Computer Vision + RAG Coaching Pipeline](https://nyc.aitinkerers.org/talks/rsvp_GzawB-Spyz0)

MindServe AI is a fully-deployed real-time inference system that analyzes tennis match video on GPU and streams structured coaching insight back to the user. The backend runs YOLOv8 + MediaPipe pose models in parallel, detects rallies using a state machine, and pushes frame overlays via WebSockets as processing happens. At the same time, structured match data is fed into a retrieval-augmented coaching engine using Pinecone + LLM reasoning to provide mental-performance feedback. This talk will walk through the full infrastructure stack — from GPU scheduling to async model serving to vector-database-driven reasoning — all demonstrated live with running code.

- Event context: The Future of AI Infrastructure (Ft. Oracle & NVIDIA) — 2025-12-09 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_GzawB-Spyz0

### [Calibrating YOLO Models for More Reliable Waste Detection](https://lausanne.aitinkerers.org/talks/rsvp_zkdIO1YRTIs)

I will explain the issues with traditional waste management in recycling factories and how we can leverage AI to streamline and optimize these processes. Then I will discuss how waste detection models can suffer from miscalibration and why addressing this is important. To tackle miscalibration, I apply temperature scaling, a widely used and effective method for improving calibration. I will explain why temperature scaling is a good fit for this problem and how it helps reduce confidence errors in our predictions.

- Event context: AI Tinkerers Lausanne December 2025 Meetup — 2025-12-03 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_zkdIO1YRTIs

### [Teaching AI to read ancient Maya Glyphs](https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I)

The ancient Maya developed one of the most beautiful, complex, and intriguing writing systems in human history. For centuries, these glyphs recorded stories, rituals, dynasties, and the deep relationship between people, time, and the cosmos. Yet this knowledge was silenced — burned, buried, and forgotten for nearly a thousand years. Even today, only a few thousand people in the world can read Maya writing. I will the development of an AI agent designed to help bridge that gap. Using computer vision and deep learning, the system can detect glyph blocks, identify individual signs, and suggest possible readings by comparing them to a vast epigraphic corpus. It is not perfect — but it is a powerful first step toward using artificial intelligence to preserve and reactivate knowledge that was nearly lost forever. In doing so, we demonstrate how AI can be more than just code — it can be a force for memory, preservation, and cultural resilience. I will be presenting a live demo.

- Event context: AI Tinkerers Montreal – Spooky October 2025 Meetup — 2025-10-21 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I

### [Aura: A Locally Hosted AI Gaming Companion](https://dc.aitinkerers.org/talks/rsvp_PPJAfIPNvoQ)

Aura is a locally hosted AI companion that observes live gameplay, interprets on-screen activity, and interacts with players through voice-based commentary. It combines screen capture, computer vision, speech recognition, and voice synthesis into a modular system designed for real-time operation without relying on cloud services. Aura adapts to different games, learns from user feedback, and allows dynamic personality and voice customization

- Event context: AI Tinkerers - DC Metro Meetup (July 10th 2025) — 2025-07-10 — DC
- Public talk page: https://dc.aitinkerers.org/talks/rsvp_PPJAfIPNvoQ

## Related Technologies

- [Claude](https://aitinkerers.org/technologies/claude) ([Markdown](https://aitinkerers.org/technologies/claude.md)) — 174 public demos
- [Pinecone](https://aitinkerers.org/technologies/pinecone) ([Markdown](https://aitinkerers.org/technologies/pinecone.md)) — 25 public demos
- [Annoy](https://aitinkerers.org/technologies/annoy) ([Markdown](https://aitinkerers.org/technologies/annoy.md)) — 1 public demo
- [Chroma](https://aitinkerers.org/technologies/chroma) ([Markdown](https://aitinkerers.org/technologies/chroma.md)) — 8 public demos
- [COCO dataset](https://aitinkerers.org/technologies/coco-dataset) ([Markdown](https://aitinkerers.org/technologies/coco-dataset.md)) — 1 public demo
- [Coqui TTS](https://aitinkerers.org/technologies/coqui-tts) ([Markdown](https://aitinkerers.org/technologies/coqui-tts.md)) — 1 public demo
- [Cron](https://aitinkerers.org/technologies/cron) ([Markdown](https://aitinkerers.org/technologies/cron.md)) — 2 public demos
- [Docker](https://aitinkerers.org/technologies/docker) ([Markdown](https://aitinkerers.org/technologies/docker.md)) — 147 public demos
- [FAISS](https://aitinkerers.org/technologies/faiss) ([Markdown](https://aitinkerers.org/technologies/faiss.md)) — 17 public demos
- [Fine-tuning](https://aitinkerers.org/technologies/fine-tuning) ([Markdown](https://aitinkerers.org/technologies/fine-tuning.md)) — 20 public demos
- [Flask-SocketIO](https://aitinkerers.org/technologies/flask-socketio) ([Markdown](https://aitinkerers.org/technologies/flask-socketio.md)) — 1 public demo
- [GitHub](https://aitinkerers.org/technologies/github) ([Markdown](https://aitinkerers.org/technologies/github.md)) — 74 public demos
- [Grad-CAM](https://aitinkerers.org/technologies/grad-cam) ([Markdown](https://aitinkerers.org/technologies/grad-cam.md)) — 1 public demo
- [hnswlib](https://aitinkerers.org/technologies/hnswlib) ([Markdown](https://aitinkerers.org/technologies/hnswlib.md)) — 3 public demos
- [Llama 3](https://aitinkerers.org/technologies/llama-3) ([Markdown](https://aitinkerers.org/technologies/llama-3.md)) — 38 public demos
- [MCP](https://aitinkerers.org/technologies/mcp) ([Markdown](https://aitinkerers.org/technologies/mcp.md)) — 129 public demos
- [MCP Agent Mail](https://aitinkerers.org/technologies/mcp-agent-mail) ([Markdown](https://aitinkerers.org/technologies/mcp-agent-mail.md)) — 1 public demo
- [Milvus](https://aitinkerers.org/technologies/milvus) ([Markdown](https://aitinkerers.org/technologies/milvus.md)) — 3 public demos