RRepoGEO

REPOGEO REPORT · LITE

antirez/iris.c

Default branch main · commit 9873887d · scanned 5/28/2026, 4:18:31 PM

GitHub: 1,949 stars · 141 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

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 antirez/iris.c, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    Flux 2 image generation model pure C inference
    COPY-PASTE FIX
    Pure C inference pipeline for text-to-image diffusion models (FLUX.2, Z-Image-Turbo).
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/antirez/iris.c

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.

Recall
0 / 2
0% of queries surface antirez/iris.c
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/onnxruntime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/onnxruntime · recommended 2×
  2. openvinotoolkit/openvino · recommended 2×
  3. ggerganov/llama.cpp · recommended 1×
  4. ggerganov/stable-diffusion.cpp · recommended 1×
  5. Tencent/ncnn · recommended 1×
  • CATEGORY QUERY
    Looking for a pure C library to run text-to-image diffusion models for inference.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. stable-diffusion.cpp (ggerganov/stable-diffusion.cpp)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. OpenVINO (openvinotoolkit/openvino)
    5. ncnn (Tencent/ncnn)
    6. libtorch (pytorch/pytorch)

    AI recommended 6 alternatives but never named antirez/iris.c. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform image synthesis from text prompts using an efficient C inference pipeline?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit (openvinotoolkit/openvino)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TensorRT (NVIDIA/TensorRT)
    4. GGML (ggerganov/ggml)
    5. DirectML (microsoft/DirectML)

    AI recommended 5 alternatives but never named antirez/iris.c. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

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 antirez/iris.c?
    pass
    AI named antirez/iris.c explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts antirez/iris.c in production, what risks or prerequisites should they evaluate first?
    pass
    AI named antirez/iris.c 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 antirez/iris.c solve, and who is the primary audience?
    pass
    AI named antirez/iris.c explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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antirez/iris.c — 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