RRepoGEO

REPOGEO REPORT · LITE

christopherkarani/Wax

Default branch main · commit 7d2bbcb9 · scanned 6/11/2026, 7:03:02 PM

GitHub: 757 stars · 47 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 christopherkarani/Wax, 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
    Reposition the README's opening statement to clarify its core identity

    Why:

    CURRENT
    Give your AI agent a memory that never forgets. One file. Zero cloud. Blazing fast recall on Apple Silicon.
    COPY-PASTE FIX
    Wax is a Swift library providing a single-file, on-device memory layer for AI agents, optimized for sub-millisecond RAG on Apple Silicon.
  • mediumabout#2
    Refine the 'About' description to explicitly state it's a library

    Why:

    CURRENT
    Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift
    COPY-PASTE FIX
    Wax is a Swift library for AI agents, providing a single-file, on-device memory layer with sub-millisecond RAG on Apple Silicon. Metal Optimized. No Server. No API.
  • mediumtopics#3
    Remove potentially misleading or irrelevant topics

    Why:

    CURRENT
    ai-agents, cli, coreml, coreml-framework, data-science, machine-learning, mcp, mcp-server, memory, memory-cache, memory-hacking, metal, on-device-ai, rag, rag-pipeline, swift, vector-database, vector-embeddings, vector-search, vectordb
    COPY-PASTE FIX
    ai-agents, cli, coreml, coreml-framework, data-science, machine-learning, memory, memory-cache, metal, on-device-ai, rag, rag-pipeline, swift, vector-database, vector-embeddings, vector-search, vectordb

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 christopherkarani/Wax
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. MLX · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow Lite · recommended 1×
  5. Metal Performance Shaders (MPS) Framework · recommended 1×
  • CATEGORY QUERY
    How to implement fast on-device memory for AI agents on Apple Silicon?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. MLX
    3. PyTorch
    4. TensorFlow Lite
    5. Metal Performance Shaders (MPS) Framework

    AI recommended 5 alternatives but never named christopherkarani/Wax. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a serverless RAG solution for Swift applications with local vector storage.
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. FAISS (facebookresearch/faiss)

    AI recommended 2 alternatives but never named christopherkarani/Wax. 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 christopherkarani/Wax?
    pass
    AI named christopherkarani/Wax explicitly

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

  • If a team adopts christopherkarani/Wax in production, what risks or prerequisites should they evaluate first?
    pass
    AI named christopherkarani/Wax 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 christopherkarani/Wax solve, and who is the primary audience?
    pass
    AI named christopherkarani/Wax 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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christopherkarani/Wax — 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