RRepoGEO

REPOGEO REPORT · LITE

RightNow-AI/picolm

Default branch main · commit cf3f2dfc · scanned 5/10/2026, 8:24:21 AM

GitHub: 1,597 stars · 199 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 RightNow-AI/picolm, 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.

OVERALL DIRECTION
  • highreadme#1
    Reinforce core value proposition and parameter count in README opening

    Why:

    CURRENT
    Run a 1-billion parameter LLM on a $10 board with 256MB RAM.
    COPY-PASTE FIX
    PicoLM enables running a 1-billion parameter LLM on a $10 board with 256MB RAM, making advanced AI inference accessible on extremely low-resource embedded systems and edge devices. This is a 1-billion parameter model, uniquely optimized for minimal footprint and zero dependencies.
  • mediumtopics#2
    Expand topics to improve category visibility

    Why:

    CURRENT
    arm, embedded, inference, llm, openclaw, picoclaw, quantization, raspberry-pi, risc-v
    COPY-PASTE FIX
    arm, embedded, inference, llm, openclaw, picoclaw, quantization, raspberry-pi, risc-v, edge-ai, offline-llm, on-device-ai, low-power, tiny-ml, c-language, bare-metal, resource-constrained
  • mediumcomparison#3
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., 'PicoLM vs. Other Local LLM Frameworks,' that highlights PicoLM's unique advantages like 'Pure C. Zero dependencies. One binary. No Python. No cloud.' and its ability to run a 1-billion parameter model on extremely low-RAM hardware, contrasting it with frameworks that might have higher dependency counts or resource requirements for similar model sizes.

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 RightNow-AI/picolm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TinyLlama
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TinyLlama · recommended 1×
  2. karpathy/nanogpt · recommended 1×
  3. mlc-ai/mlc-llm · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    What are options for running an LLM inference model on extremely low-resource embedded systems?
    you: not recommended
    AI recommended (in order):
    1. TinyLlama
    2. NanoGPT (karpathy/nanogpt)
    3. MLC LLM (mlc-ai/mlc-llm)
    4. TensorFlow Lite Micro (tensorflow/tensorflow)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. Edge Impulse

    AI recommended 6 alternatives but never named RightNow-AI/picolm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a lightweight, pure C solution for offline LLM inference on cheap, low-RAM hardware.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. GGML
    3. TinyGrad
    4. ONNX Runtime
    5. TFLite

    AI recommended 5 alternatives but never named RightNow-AI/picolm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 RightNow-AI/picolm?
    pass
    AI named RightNow-AI/picolm explicitly

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

  • If a team adopts RightNow-AI/picolm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named RightNow-AI/picolm 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 RightNow-AI/picolm solve, and who is the primary audience?
    pass
    AI named RightNow-AI/picolm 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite