Telekinesis Learning Library
Understand the architectures, mechanisms, and practical use of Physical AI models in the Telekinesis Agentic OS.
Technical tutorials
11 of 11 deep dives
Intermediate · 14 min readGrounding DINO solves the problem of locating objects described in ordinary language by using an open-vocabulary object detection model. Learn the architecture of Grounding DINO, the loss functions it was trained with, and how to use it in the Telekinesis Agentic OS.
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Intermediate · 19 min readQwen3-VL-4B-Instruct solves the problem of understanding images and video through natural-language instructions by using a four-billion-parameter vision-language model. Learn the architecture of Qwen3-VL-4B-Instruct, the loss functions it was trained with, and how to use it in the Telekinesis Agentic OS.
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Basic · 13 min readContour detection solves the problem of recovering the exact outline of a foreground region by tracing an ordered boundary through a binary image. Learn how contour detection works, how contours are measured, and how to use it in the Telekinesis Agentic OS.
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Intermediate · 14 min readRF-DETR solves the problem of detecting known objects accurately within a tight latency budget by using a real-time detection transformer. Learn the architecture of RF-DETR, the loss functions it was trained with, and how to use it in the Telekinesis Agentic OS.
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Intermediate · 12 min readYOLOX solves the problem of detecting and localizing known object classes quickly by using a one-stage, anchor-free object detection model. Learn the architecture of YOLOX, the loss functions it was trained with, and how to use it in the Telekinesis Agentic OS.
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Basic · 12 min readThe Classic Circle Hough Transform solves the problem of finding circular shapes from incomplete or noisy edges by using a deterministic voting algorithm. Learn how the algorithm works, the mathematics behind circle voting, and how to use it in the Telekinesis Agentic OS.
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Intermediate · 10 min readSAM 3 solves the problem of finding and precisely segmenting every image region that matches a text concept or visual prompt. Learn its promptable concept segmentation architecture, training losses, data engine, and how to use it in the Telekinesis Agentic OS.
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Basic · 8 min readRGB color segmentation solves the problem of separating pixels with known raw color values under controlled lighting. Learn its channel-thresholding mechanism, mathematical decision rule, engineering limits, and how to use it in the Telekinesis Agentic OS.
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Basic · 8 min readHSV color segmentation solves the problem of selecting pixels by color while separating hue from brightness. Learn the RGB-to-HSV mechanism, circular hue thresholds, practical failure modes, and how to use it in the Telekinesis Agentic OS.
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Basic · 7 min readLAB color segmentation solves the problem of selecting pixels with bounds that better reflect perceived lightness and color differences. Learn the CIELAB conversion, opponent channels, threshold geometry, and how to use it in the Telekinesis Agentic OS.
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Intermediate · 9 min readBiRefNet solves the problem of separating a detailed foreground subject from its background without a prompt. Learn its localization and reconstruction modules, bilateral references, training losses, and how to use it in the Telekinesis Agentic OS.
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