RRepoGEO

REPOGEO REPORT · LITE

elicit/machine-learning-list

Default branch main · commit e505c961 · scanned 5/21/2026, 12:43:23 AM

GitHub: 1,460 stars · 126 forks

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 elicit/machine-learning-list, 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 README opening to emphasize public curriculum utility

    Why:

    CURRENT
    The purpose of this curriculum is to help new Elicit employees learn background in machine learning, with a focus on language models.
    COPY-PASTE FIX
    This curriculum is a public, curated reading list designed to help anyone learn about foundation models, from foundational concepts to the research frontier, with a focus on language models.
  • highhomepage#2
    Remove or update misleading Homepage URL

    Why:

    CURRENT
    https://elicit.com/careers
    COPY-PASTE FIX
    (none)
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file to the repository root, specifying the intended open-source license (e.g., MIT, Apache-2.0, or CC-BY-4.0 for content).

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 elicit/machine-learning-list
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepLearning.AI's "Generative AI with Transformers" Specialization on Coursera
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepLearning.AI's "Generative AI with Transformers" Specialization on Coursera · recommended 1×
  2. Hugging Face's "NLP Course" · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. Stanford CS224N: Natural Language Processing with Deep Learning · recommended 1×
  5. fast.ai's "Practical Deep Learning for Coders" · recommended 1×
  • CATEGORY QUERY
    Where can I find a structured curriculum to learn about foundation models and transformers?
    you: not recommended
    AI recommended (in order):
    1. DeepLearning.AI's "Generative AI with Transformers" Specialization on Coursera
    2. Hugging Face's "NLP Course"
    3. Hugging Face Transformers library (huggingface/transformers)
    4. Stanford CS224N: Natural Language Processing with Deep Learning
    5. fast.ai's "Practical Deep Learning for Coders"
    6. "The Illustrated Transformer" by Jay Alammar
    7. Google's "Introduction to Generative AI" Learning Path on Google Cloud Skills Boost

    AI recommended 7 alternatives but never named elicit/machine-learning-list. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources help understand deploying machine learning models, especially large language models, in production?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Hugging Face Inference Endpoints
    3. Text Generation Inference (TGI)
    4. Hugging Face Optimum
    5. MLflow
    6. MLflow Models
    7. MLflow Tracking
    8. MLflow Model Serving
    9. Kubernetes
    10. KServe
    11. Seldon Core
    12. AWS SageMaker
    13. SageMaker Endpoints
    14. SageMaker Model Monitor
    15. SageMaker JumpStart
    16. Google Cloud Vertex AI
    17. Vertex AI Endpoints
    18. Vertex AI Model Monitoring
    19. Vertex AI Workbench
    20. NVIDIA Triton Inference Server
    21. Ray Serve

    AI recommended 21 alternatives but never named elicit/machine-learning-list. 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 elicit/machine-learning-list?
    pass
    AI named elicit/machine-learning-list explicitly

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

  • If a team adopts elicit/machine-learning-list in production, what risks or prerequisites should they evaluate first?
    pass
    AI named elicit/machine-learning-list 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 elicit/machine-learning-list solve, and who is the primary audience?
    pass
    AI did not name elicit/machine-learning-list — 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?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite