RRepoGEO

REPOGEO REPORT · LITE

tanishqkumar/ssd

Default branch main · commit d7eb8fa0 · scanned 6/4/2026, 1:13:32 AM

GitHub: 939 stars · 70 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 tanishqkumar/ssd, 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
  • highreadme#1
    Add an explicit disambiguation for "SSD" in the README's opening

    Why:

    CURRENT
    The current README does not explicitly state it is *not* an object detection project.
    COPY-PASTE FIX
    Add a sentence immediately after the main title, such as: "This project focuses on Speculative Speculative Decoding (SSD) for Large Language Model (LLM) inference, *not* Single Shot MultiBox Detector for computer vision."
  • mediumreadme#2
    Reorder README content to immediately highlight the project's core purpose

    Why:

    CURRENT
    The current README places a Borges quote before the core statement "SSD is a new LLM inference algorithm."
    COPY-PASTE FIX
    Move the sentence "**SSD is a new LLM inference algorithm. It is exact, and it is extremely fast.**" to appear immediately after the main `<h1>` title and paper link, before any quotes or further introductory text.

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 tanishqkumar/ssd
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. Megatron-LM · recommended 2×
  3. Google's Speculative Decoding · recommended 1×
  4. Medusa · recommended 1×
  5. Lookahead Decoding · recommended 1×
  • CATEGORY QUERY
    How to speed up large language model inference using advanced speculative decoding techniques?
    you: not recommended
    AI recommended (in order):
    1. Google's Speculative Decoding
    2. Medusa
    3. Lookahead Decoding
    4. DistilSpec
    5. Block-Recurrent Autoregressive (BRA) Decoding
    6. Self-Speculative Decoding

    AI recommended 6 alternatives but never named tanishqkumar/ssd. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for parallelizing LLM inference to reduce token generation latency?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. Megatron-LM
    3. DeepSpeed
    4. Megatron-LM
    5. Hugging Face Transformers
    6. T5X
    7. JAX
    8. vLLM
    9. NVIDIA Triton Inference Server
    10. Hugging Face Optimum
    11. NVIDIA TensorRT-LLM
    12. PyTorch DistributedDataParallel
    13. Hugging Face Accelerate

    AI recommended 13 alternatives but never named tanishqkumar/ssd. 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 tanishqkumar/ssd?
    pass
    AI named tanishqkumar/ssd explicitly

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

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

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

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tanishqkumar/ssd — 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