REPOGEO REPORT · LITE
wangzhaode/mnn-llm
Default branch master · commit 63c01a43 · scanned 5/22/2026, 8:47:03 PM
GitHub: 1,615 stars · 178 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 wangzhaode/mnn-llm, 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#1Clarify project status and relationship with MNN in README
Why:
CURRENT**该项目代码已经Merge到MNN.**
COPY-PASTE FIXThis project's core functionality has been officially merged into the MNN repository. This repository now serves as an archive and a reference for the original `mnn-llm` development, demonstrating efficient LLM deployment on edge devices using MNN.
- highreadme#2Reposition README H1 and opening paragraph for LLM inference on edge devices
Why:
CURRENT# mnn-llm
COPY-PASTE FIX# mnn-llm: Lightweight LLM Inference for Mobile & Edge Devices with MNN This project provides a highly optimized, cross-platform framework for deploying Large Language Models (LLMs) on resource-constrained mobile and edge devices. It leverages Alibaba's MNN inference engine to deliver efficient, hardware-accelerated LLM inference across various platforms, including Android, iOS, Linux, macOS, and Windows.
- mediumhomepage#3Add a homepage URL to the repository About section
Why:
COPY-PASTE FIXhttps://github.com/alibaba/MNN
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 1×
- TensorRT · recommended 1×
- OpenVINO · recommended 1×
- MLC LLM · recommended 1×
- llama.cpp · recommended 1×
- CATEGORY QUERYHow to deploy large language models efficiently on mobile devices using C++?you: not recommended
Show full AI answer
- CATEGORY QUERYSeeking a framework for optimized, cross-platform LLM inference on various hardware.you: not recommendedAI recommended (in order):
- ONNX Runtime
- TensorRT
- OpenVINO
- MLC LLM
- llama.cpp
- DeepSpeed
- vLLM
AI recommended 7 alternatives but never named wangzhaode/mnn-llm. 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 wangzhaode/mnn-llm?passAI did not name wangzhaode/mnn-llm — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts wangzhaode/mnn-llm in production, what risks or prerequisites should they evaluate first?passAI named wangzhaode/mnn-llm 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 wangzhaode/mnn-llm solve, and who is the primary audience?passAI did not name wangzhaode/mnn-llm — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of wangzhaode/mnn-llm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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wangzhaode/mnn-llm — 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