Octomind
Same model as opencode, same endpoint, same prices — octomind solved 24/25 to its 19/25, at half the cost. Open-source agent runtime: one binary, any model, runs unattended in CI. Apache 2.0.
Fun real-world computer vision demos for you!
AI translation
Fun real-world computer vision demos!
Developer toolsPythonGitHub @jeremyiparkSelf-installOwner unconfirmed
Paste into Claude or ChatGPT for installation instructions suited to your environment.
Have you tried it? One tap is enough. No sign-in needed.
No updates detected yet. GitHub releases will appear here.
Owners of public repositories under a personal GitHub account can sign in to verify ownership and manage the introduction themselves. No administrator approval is needed.
Sign in, generate a verification file, and commit it to this repository’s default branch within 30 minutes. Files on other branches or forks do not count. Then check the file below to manage the project without administrator approval. Keep the file to retain your access.
If needed, you will go to sign-in. After signing in, return here and press your chosen verification button again.
Installable projects can be verified without a deployment domain. Use GitHub sign-in for a personal repository, or the repository verification file above for an organization repository.
Ask about other verification options
The maker's introduction consists of the line above. Read the repository README for more details. Repository README ↗
Loading the full README.
Read the full README ↗Real-world computer vision demos.
| Project | What it does | Key model |
|---|---|---|
| dance_sync | Compares dancers' sync performing the same choreography and computes a similarity metric. | vitpose-plus-large |
| chin_ups | Counts chin-up reps from a clip and times the ascent and descent of each one. | vitpose-plus-large |
| pull_ups | Counts pull-up reps, times each pull, and grades each rep's range of motion as full or partial against the athlete's own calibrated dead hang. | vitpose-plus-large |
| push_ups | Counts push-up reps from a side-on clip, times each push, and grades each rep for full range of motion and a straight back. | vitpose-plus-large |
| rock_climbing | Segments bouldering holds, returns which holds the climber used and in what order, and compares attempts at the same route. | sam3.1 + vitpose-plus-large |
| rock_climbing_3d | Reads the same route from a video recorded with depth (iPhone LiDAR), placing the wall, holds and climber in 3D and measuring the route in meters. | sam3.1 + vitpose-plus-large |
| running | Measures a runner's cadence, times every foot strike, and averages the knee shape at contact. | vitpose-plus-large |
| deadlift | Counts deadlift reps and classifies each rep's back as straight or rounded, with the probability. | sam3.1 + vitpose-plus-large + gemma-4-26b-a4b-it |
| basketball | Calls every shot a make or a miss by following the ball through the rim, and plots each shot on a court from above with the FG%. | sam3.1 + vitpose-plus-large |
Each project has its own README with setup and instructions. To try them in a browser instead:
conda env create -f environment.yml && conda activate vision-demos # or: pip install -r requirements.txt
python app.py # -> http://127.0.0.1:7860
Upload a clip, trim it, analyze. No API key needed (anonymous, rate-limited tier); paste a VLM Run key or set VLMRUN_API_KEY for higher limits. rock_climbing_3d isn't in the app (it needs a LiDAR capture). push_ups and basketball aren't in the app either. ffmpeg is required.