RRepoGEO

REPOGEO REPORT · LITE

WecoAI/aideml

Default branch main · commit 40dcf28f · scanned 7/1/2026, 3:32:22 AM

GitHub: 1,333 stars · 197 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
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 WecoAI/aideml, 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 clarify agentic ML R&D focus

    Why:

    CURRENT
    AIDE ML is the open‑source “reference build” of the AIDE algorithm, a tree‑search agent that autonomously drafts, debugs and benchmarks code until a user‑defined metric is maximised (or minimised). It ships as a *research‑friendly* Python package with batteries‑included utilities (CLI, visualisation, config presets) so that academics and engineer‑researchers can **replicate the paper, test new ideas, or prototyping ML pipelines**.
    COPY-PASTE FIX
    AIDE ML is the open‑source “reference build” of the AIDE algorithm, an **LLM-driven agent specifically designed for autonomous ML R&D**. It goes beyond traditional AutoML by autonomously drafting, debugging, and benchmarking machine learning code until a user‑defined metric is maximised (or minimised). This *research‑friendly* Python package provides batteries‑included utilities (CLI, visualisation, config presets) for academics and engineer‑researchers to **replicate the paper, test new ideas, or prototype advanced ML pipelines**.
  • mediumcomparison#2
    Add a 'Comparison' section to differentiate from AutoML and general LLM agents

    Why:

    COPY-PASTE FIX
    ## How AIDE ML Compares
    
    AIDE ML is an LLM-driven agent focused on automating the entire ML R&D lifecycle, from code generation to evaluation and improvement. This differentiates it from:
    
    *   **Traditional AutoML Platforms (e.g., Google Cloud AutoML, H2O.ai AutoML):** While AutoML platforms automate parts of the ML pipeline (like model selection or hyperparameter tuning), AIDE ML operates as an autonomous agent that writes, debugs, and optimizes *code itself*, enabling more flexible and complex research workflows.
    *   **General LLM Agents (e.g., AutoGPT, MetaGPT, CrewAI):** Unlike general-purpose agents that can perform a wide array of tasks, AIDE ML is specialized for machine learning engineering. Its core algorithm and tools are optimized for navigating the space of ML code, making it highly effective for ML-specific research and development.
  • lowreadme#3
    Complete and expand the 'Who should use it?' section

    Why:

    CURRENT
    ### Who should use it?
    
    Agent‑archite
    COPY-PASTE FIX
    ### Who should use it?
    
    AIDE ML is ideal for:
    
    *   **ML Researchers & Academics:** To replicate papers, test novel algorithmic ideas, and accelerate experimental workflows.
    *   **ML Engineers & Data Scientists:** For prototyping advanced ML pipelines, automating repetitive coding tasks, and exploring new model architectures more efficiently.
    *   **Agent Architects:** Those interested in LLM-driven autonomous agents specifically applied to complex code generation and optimization problems within machine learning.

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 WecoAI/aideml
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud AutoML
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud AutoML · recommended 1×
  2. H2O.ai AutoML · recommended 1×
  3. Azure Machine Learning automated ML · recommended 1×
  4. Optuna · recommended 1×
  5. Ray Tune · recommended 1×
  • CATEGORY QUERY
    How to automate machine learning model development and code optimization using AI agents?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud AutoML
    2. H2O.ai AutoML
    3. Azure Machine Learning automated ML
    4. Optuna
    5. Ray Tune
    6. Weights & Biases Sweeps
    7. AutoKeras
    8. NNI (Neural Network Intelligence)
    9. Snyk Code
    10. GitHub Copilot
    11. AlphaCode (DeepMind)

    AI recommended 11 alternatives but never named WecoAI/aideml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an LLM-powered agent to autonomously improve machine learning code and research workflows.
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. MetaGPT
    3. Smol Developer
    4. Open Interpreter
    5. CrewAI
    6. LangChain Agents

    AI recommended 6 alternatives but never named WecoAI/aideml. 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 WecoAI/aideml?
    pass
    AI named WecoAI/aideml explicitly

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

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

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

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WecoAI/aideml — 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