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PublicAI

FIRST TRAIN-AI-TO-EARN DAPP.

3.6 Store rating Β· 747 reviewsPublicAI
App Signal Score6/10
PublicAI is worth watching because its Store description highlights AI or agent automation, DePIN or mining utility, and reward or points loops and visible review snippets mention AI or agent automation, DePIN or mining utility, and reward or points loops.
Product context

Description

PublicAI is a distributed AI network enables every human to contribute to AI and share the benefits.

Product view

Screenshots

1 assets
Radar assessment

Why this score

6/10
  • Strong Store listing
  • Trust and permission coverage
  • Basic utility signal
  • Official public proof

Scores reflect listing quality, trust coverage, utility, public proof, and Store traction. Recency does not add points.

Read the scoring methodology
Community signalNo ratings yet
What to watch

Signals

  • Store description themes: AI or agent automation, DePIN or mining utility, and reward or points loops
  • Reviewer note: "PublicAI explores the Train-to-Earn model, rewarding users for contributing data and participating in AI deve..."
  • Reviewer note: "还可δ»₯。。。。。。。。。。。。。。。。。"
  • Review mix: 59% 4-5 star and 16% 1-2 star
  • Release-note focus: ""
  • Store media: 1 screenshot
  • APK metadata: 1.5 MB with SHA-256 hash
  • Publisher: PublicAI; 3 website/contact links
  • 3 policy/legal links captured
Radar follow-up

Next check

Recheck PublicAI after the next snapshot for rating, version, permission, reward, or media changes.

Public listing should be verified on-device before signing or depositing funds.
Catalog evidence

Source

Solana Mobile dApp Store GraphQL scrape, May 22, 2026

Open source
Version 1.0.0

Release notes

  • 1.0.0
Store package

Technical metadata

Package
com.publicai.app
Version
1.0.0 (1)
Minimum Android SDK
24
Languages
1
First seen
May 22, 2026
Android permissionsPermission review2 permissions
android.permission.INTERNETcom.publicai.app.DYNAMIC_RECEIVER_NOT_EXPORTED_PERMISSION
Store feedback

Recent reviews

PublicAI explores the Train-to-Earn model, rewarding users for contributing data and participating in AI development through a distributed network. The concept is promising, but its long-term success will depend on data quality, incentive design, anti-fraud mechanisms, and the ability to build a sustainable AI data ecosystem.

stevenhuhu.skr

还可δ»₯。。。。。。。。。。。。。。。。。

ohjsjsj.skr
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