REPOGEO REPORT · LITE
microsoft/hummingbird
Default branch main · commit eb0a2353 · scanned 6/30/2026, 9:32:06 AM
GitHub: 3,537 stars · 292 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface microsoft/hummingbird, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition the README's introduction to emphasize faster GPU inference
Why:
CURRENT*Hummingbird* is a library for compiling trained traditional ML models into tensor computations. *Hummingbird* allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.
COPY-PASTE FIX*Hummingbird* is a library for compiling trained traditional ML models into tensor computations, enabling faster GPU inference and seamless integration with deep learning frameworks. It allows users to leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.
- mediumhomepage#2Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIXhttps://microsoft.github.io/hummingbird/
- mediumtopics#3Expand repository topics to include GPU inference and model optimization
Why:
CURRENTmachine-learning, neural-networks, pytorch, scikit-learn, tensor-computation
COPY-PASTE FIXmachine-learning, neural-networks, pytorch, scikit-learn, tensor-computation, gpu-inference, model-optimization, onnx, tvm
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- ONNX Runtime · recommended 2×
- skl2onnx · recommended 1×
- NVIDIA RAPIDS · recommended 1×
- cuML · recommended 1×
- Intel Extension for Scikit-learn · recommended 1×
- CATEGORY QUERYHow to speed up scikit-learn model inference using tensor operations?you: #8AI recommended (in order):
- ONNX Runtime
- skl2onnx
- NVIDIA RAPIDS
- cuML
- Intel Extension for Scikit-learn
- oneAPI
- oneDAL
- Hummingbird ← you
- PyTorch
- TensorFlow
- JAX
- Numba
Show full AI answer
- CATEGORY QUERYWhat tools convert trained machine learning models for faster GPU inference?you: not recommendedAI recommended (in order):
- NVIDIA TensorRT
- OpenVINO Toolkit
- ONNX Runtime
- TVM
- PyTorch JIT
- TensorFlow Lite
- MIGraphX
AI recommended 7 alternatives but never named microsoft/hummingbird. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of microsoft/hummingbird?passAI named microsoft/hummingbird explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts microsoft/hummingbird in production, what risks or prerequisites should they evaluate first?passAI named microsoft/hummingbird explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo microsoft/hummingbird solve, and who is the primary audience?passAI named microsoft/hummingbird explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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microsoft/hummingbird — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite