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Commit Art
LumiGap
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LumiGap is a macOS AI vision workspace for poker table recognition.
It reads visible online poker tables from your screen, uses OCR and Core ML to recognize names, stacks, bets, cards, board, pot, and table regions, then turns everything into structured data you can review, correct, export, and use for training custom models.
Build your own poker vision datasets, annotate cards and player tokens, tune recognition thresholds, connect external detectors, convert manifests into model-ready datasets, and test how your models perform on real table layouts.
LumiGap is built for poker researchers, ML experimenters, coaches, and advanced players who want to create their own recognition pipeline instead of relying only on generic trackers or manual screenshots.
It is designed for training, research, dataset creation, and post-session analysis. Users are responsible for following the rules of any poker platforms they use.
Commit Art LumiGapNo features have been listed yet.
LumiGap's answer:
LumiGap is not just a poker tracker or a note-taking app. It is an AI vision workspace for poker table recognition: capture the table from your screen, recognize cards, stacks, bets, names, board and pot, correct the results, build datasets, train custom models, and export structured table state for research workflows.
LumiGap's answer:
Most poker tools focus on hand histories, solvers, or finished analytics. LumiGap focuses on the recognition pipeline itself: screen capture, OCR, Core ML, table-region mapping, annotation, custom datasets, model testing, and export. It is for users who want to build and improve their own AI-powered poker research workflow.
LumiGap's answer:
LumiGap is for advanced poker players, coaches, poker researchers, ML builders, data-driven analysts, and macOS users who want to recognize poker table state visually, create custom datasets, and train models for their own layouts and research needs.
LumiGap's answer:
LumiGap started from a simple gap: serious poker work often depends on screenshots, manual notes, hand histories, and tools that cannot easily be adapted to your own table layouts or model experiments. LumiGap was built to turn visible table states into structured data, datasets, and custom AI recognition workflows.
LumiGap's answer:
Native macOS stack: Swift, SwiftUI, ScreenCaptureKit, Vision OCR, Core ML, local data storage, annotation tools, dataset converters, external detector support, and live export APIs.
LumiGap's answer: