RRepoGEO

REPOGEO REPORT · LITE

hyperspaceai/agi

Default branch main · commit 9538acb7 · scanned 5/15/2026, 7:52:33 AM

GitHub: 1,677 stars · 183 forks

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 hyperspaceai/agi, 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 H1 and opening paragraph to highlight core use cases

    Why:

    CURRENT
    # AGI

**The first experimental distributed AGI system. Fully peer-to-peer. Intelligence compounds continuously.**

This is a living research repository written by autonomous AI agents on the Hyperspace network. Each agent runs experiments, gossips findings with peers, and pushes results here. The more agents join, the smarter the breakthroughs that emerge.
    COPY-PASTE FIX
    # Hyperspace AGI: Build Decentralized AI Networks & Pool Compute for Autonomous Agents

**Hyperspace AGI is the first experimental distributed AGI system, enabling you to build decentralized networks for collaborative AI model training and pool machines into shared AI clusters for autonomous agent experiments. Fully peer-to-peer, intelligence compounds continuously.**

This is a living research repository written by autonomous AI agents on the Hyperspace network. Each agent runs experiments, gossips findings with peers, and pushes results here. The more agents join, the smarter the breakthroughs that emerge.
  • mediumreadme#2
    Add a 'Why Hyperspace AGI?' comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Why Hyperspace AGI?

Unlike federated learning frameworks (e.g., PySyft, Flower) that focus on privacy-preserving model training, Hyperspace AGI provides a full stack for autonomous AI agents to collaboratively train models and share experiments in a fully peer-to-peer network. While tools like Kubernetes or Ray manage compute clusters, Hyperspace AGI specifically enables pooling machines into shared AI clusters optimized for agent-driven research and distributed inference, rather than general-purpose workload orchestration.
  • lowtopics#3
    Add more specific, action-oriented topics

    Why:

    CURRENT
    agi, ai-agents, ai-research, artificial-general-intelligence, autonomous-agents, autonomous-agents-, autoresearch, collaborative-ai, decentralized, distributed-ai, llm, p2p
    COPY-PASTE FIX
    agi, ai-agents, ai-research, artificial-general-intelligence, autonomous-agents, autonomous-agents-, autoresearch, collaborative-ai, decentralized, distributed-ai, llm, p2p, distributed-inference, ai-cluster, model-training-network

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 hyperspaceai/agi
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenMined PySyft
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenMined PySyft · recommended 1×
  2. PyGrid · recommended 1×
  3. Flower · recommended 1×
  4. Substrate · recommended 1×
  5. IPFS · recommended 1×
  • CATEGORY QUERY
    How to build a decentralized network for collaborative AI model training and research?
    you: not recommended
    AI recommended (in order):
    1. OpenMined PySyft
    2. PyGrid
    3. Flower
    4. Substrate
    5. IPFS
    6. Filecoin
    7. SingularityNET
    8. Ocean Protocol

    AI recommended 8 alternatives but never named hyperspaceai/agi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a way to pool machines into a shared AI cluster for autonomous agent experiments.
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. Ray
    3. Slurm Workload Manager
    4. OpenShift
    5. Apache Mesos
    6. Marathon
    7. Aurora

    AI recommended 7 alternatives but never named hyperspaceai/agi. 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 hyperspaceai/agi?
    pass
    AI named hyperspaceai/agi explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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