RRepoGEO

REPOGEO REPORT · LITE

AlmondGod/tinyworlds

Default branch main · commit a99f3a81 · scanned 5/19/2026, 1:12:50 AM

GitHub: 1,297 stars · 102 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 AlmondGod/tinyworlds, 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
    Clarify the project's core identity and technology in the README's opening

    Why:

    CURRENT
    TinyWorlds is a minimal autoregressive world model built on Google Deepmind's Genie Architecture.
    COPY-PASTE FIX
    TinyWorlds is a minimal **Python implementation** of an autoregressive world model, built on Google Deepmind's Genie Architecture. It aims to help researchers and developers understand the clever unsupervised methods used to achieve scalable world models.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    world-models, deepmind, genie, autoregressive-models, unsupervised-learning, video-generation, machine-learning, python
  • mediumreadme#3
    Emphasize the unique unsupervised action inference capability in the README

    Why:

    CURRENT
    Deepmind's Genie solves this by inferring the actions between frames using **no prior action data**.
    COPY-PASTE FIX
    Deepmind's Genie architecture, which TinyWorlds implements, uniquely solves this by inferring actions between frames using **no prior action data**, enabling scalable world models from action-less internet video.

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 AlmondGod/tinyworlds
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DreamerV3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DreamerV3 · recommended 2×
  2. World Models · recommended 2×
  3. DreamerV2 · recommended 1×
  4. VideoGPT · recommended 1×
  5. VQ-VAE-2 · recommended 1×
  • CATEGORY QUERY
    Looking for a minimal implementation to understand autoregressive world models for video generation.
    you: not recommended
    AI recommended (in order):
    1. DreamerV3
    2. DreamerV2
    3. VideoGPT
    4. VQ-VAE-2
    5. World Models

    AI recommended 5 alternatives but never named AlmondGod/tinyworlds. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to infer actions from raw video data using unsupervised world modeling techniques?
    you: not recommended
    AI recommended (in order):
    1. DreamerV3
    2. PlaNet
    3. SimPLe
    4. World Models
    5. Latent Imagination with Self-Supervised Learning (LISS)
    6. Video PreTraining (VPT)

    AI recommended 6 alternatives but never named AlmondGod/tinyworlds. 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 AlmondGod/tinyworlds?
    pass
    AI named AlmondGod/tinyworlds explicitly

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

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