RRepoGEO

REPOGEO REPORT · LITE

DeepWism/DeepWism-R2

Default branch main · commit bdc27729 · scanned 5/23/2026, 7:52:57 PM

GitHub: 1,016 stars · 154 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 DeepWism/DeepWism-R2, 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
    Reposition the README's introduction to clearly state its core innovation and category

    Why:

    CURRENT
    ## 1. Introduction
We introduce DeepWism® R2(Research&Report), a revolutionary next-generation AI system based on the agents framework - a Thin-Thick-Thin Crowd Entropy Dynamics System (T3CEDS) that establishes entropy reduction as the fundamental mechanism underlying crowd intelligence.
    COPY-PASTE FIX
    Replace the current "## 1. Introduction" section with: "DeepWism R2 is a revolutionary next-generation AGI system built on the T3CEDS framework (Thin-Thick-Thin Crowd Entropy Dynamics System), which redefines intelligence as a process of entropy reduction rather than attention modeling. This project provides a novel approach to building multi-agent AI systems that leverage crowd intelligence for complex tasks, moving beyond traditional attention-based models."
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository. Consult legal counsel to choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's goals and then add the chosen license text to the file.

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 DeepWism/DeepWism-R2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
deepmind/alphafold
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. deepmind/alphafold · recommended 1×
  2. numenta/nupic · recommended 1×
  3. tensorflow/probability · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. google/edward2 · recommended 1×
  • CATEGORY QUERY
    What AI frameworks use entropy reduction instead of attention for intelligence modeling?
    you: not recommended
    AI recommended (in order):
    1. AlphaFold (deepmind/alphafold)
    2. Numenta Platform for Intelligent Computing (NuPIC) (numenta/nupic)
    3. TensorFlow Probability (tensorflow/probability)
    4. PyTorch (pytorch/pytorch)
    5. Edward2 (google/edward2)
    6. scikit-learn (scikit-learn/scikit-learn)

    AI recommended 6 alternatives but never named DeepWism/DeepWism-R2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for building multi-agent AI systems that leverage crowd intelligence for complex tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. CrewAI
    3. AutoGen
    4. OpenAI Assistants API
    5. Haystack
    6. Mesa
    7. SPADE

    AI recommended 7 alternatives but never named DeepWism/DeepWism-R2. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 DeepWism/DeepWism-R2?
    pass
    AI named DeepWism/DeepWism-R2 explicitly

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

  • If a team adopts DeepWism/DeepWism-R2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DeepWism/DeepWism-R2 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 DeepWism/DeepWism-R2 solve, and who is the primary audience?
    pass
    AI named DeepWism/DeepWism-R2 explicitly

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

Embed your GEO score

Drop this badge into the README of DeepWism/DeepWism-R2. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/DeepWism/DeepWism-R2.svg)](https://repogeo.com/en/r/DeepWism/DeepWism-R2)
HTML
<a href="https://repogeo.com/en/r/DeepWism/DeepWism-R2"><img src="https://repogeo.com/badge/DeepWism/DeepWism-R2.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

DeepWism/DeepWism-R2 — 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
DeepWism/DeepWism-R2 — RepoGEO report