add gosslens to Machine Learning Framework - #263
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A real-time vision engine in Zig whose ONNX inference engine is written from scratch rather than wrapping a runtime: quantization, control flow and allocation-free inference, running published models on device.
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Priority: ⬇️ Low Estimated code review effort: 1 (Trivial) | ~2 minutes Change: Other Merge Risk: ⚪ Minimal · up to The listing update includes the required AI-assisted attribution and introduces no identified merge risk. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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Inline comments:
In `@README.md`:
- Line 579: Update the myzonerocks/gosslens entry in the project list to
describe its documented camera and AR engine scope—beauty, tracking, AR effects,
and capture across iOS, Android, and web—and remove unsupported claims about a
frozen C ABI, from-scratch ONNX engine, quantization, control flow,
allocation-free inference, and on-device published-model execution.
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README.md
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Please add AI attribution type according to https://github.com/zigcc/awesome-zig/blob/main/contributing.md#ai-attribution |
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Added, and trimmed the entry to one sentence. |
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gosslens is a real-time vision engine in Zig, and its ONNX inference engine is written from scratch rather than wrapping a runtime: quantization, control flow, and allocation-free inference, running published models (MobileNetV2, SSD-MobileNet, Depth Anything V2, PP-OCRv4) on device.
The section currently lists an ONNX Runtime wrapper; this is a Zig implementation of the interpreter itself, which is why it seemed worth adding here rather than only under Image and Video Processing.
Summary by CodeRabbit
myzonerocks/gosslensentry in the README’s Machine Learning Framework section.