RRepoGEO

REPOGEO REPORT · LITE

hyperspaceai/agi

Default branch main · commit 9538acb7 · scanned 6/25/2026, 11:57:29 PM

GitHub: 1,941 stars · 235 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)

3 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 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 the README's opening paragraph to clearly state its unique value proposition

    Why:

    CURRENT
    **The first experimental distributed AGI system. Fully peer-to-peer. Intelligence compounds continuously.**
    COPY-PASTE FIX
    **The first distributed AGI system where thousands of autonomous AI agents collaboratively train models, share experiments via P2P gossip, and push breakthroughs here. Fully peer-to-peer, intelligence compounds continuously.**
  • hightopics#2
    Refine existing topics to emphasize agent collaboration and emergent intelligence

    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, autoresearch, collaborative-ai, decentralized, distributed-ai, llm, p2p, agent-collaboration, emergent-intelligence, p2p-ai, multi-agent-systems
  • mediumreadme#3
    Add a 'Comparison' or 'Why not X?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., '## Why Hyperspace AGI? (vs. Federated Learning)' or '## How is this different from Federated Learning?'. This section should briefly explain that while both involve distributed computation, Hyperspace AGI focuses on autonomous agents, emergent intelligence, and P2P gossip for AGI research, rather than just privacy-preserving model training on decentralized data.

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. Flower · recommended 1×
  3. FedML · recommended 1×
  4. Substra · recommended 1×
  5. IBM Federated Learning · recommended 1×
  • CATEGORY QUERY
    Seeking a platform for peer-to-peer AI agent collaboration and shared model training.
    you: not recommended
    AI recommended (in order):
    1. OpenMined PySyft
    2. Flower
    3. FedML
    4. Substra
    5. IBM Federated Learning
    6. Intel OpenFL

    AI recommended 6 alternatives but never named hyperspaceai/agi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to create a private, distributed AI cluster for collaborative inference and resource pooling?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. Kubeflow (kubeflow/kubeflow)
    3. Ray (ray-project/ray)
    4. OpenShift
    5. Open Data Hub (opendatahub-io/opendatahub-operator)
    6. HPE Ezmeral Container Platform
    7. NVIDIA AI Enterprise
    8. Apache Mesos (apache/mesos)
    9. Marathon (mesosphere/marathon)
    10. Aurora (apache/aurora)

    AI recommended 10 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?

Embed your GEO score

Drop this badge into the README of hyperspaceai/agi. 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/hyperspaceai/agi.svg)](https://repogeo.com/en/r/hyperspaceai/agi)
HTML
<a href="https://repogeo.com/en/r/hyperspaceai/agi"><img src="https://repogeo.com/badge/hyperspaceai/agi.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

hyperspaceai/agi — 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