NZT ($NZT)
Recent developments:
0% of the initial token allocation is held by the creator.
Creator token stats last updated: Aug 27, 2025 18:03
The following is generated by an LLM:
Summary
unincentivized creator with 0% stake in AI analytics platform
Analysis
NZT presents an ambitious AI-driven platform combining quantitative market analysis with specialized AI agents for trading and personal development. While the concept of merging data-driven insights with collaborative tools is innovative, critical concerns exist. The creator holds only 1 token (0% of supply), severely undermining commitment incentives. No clear token utility is defined—its role in accessing services or governance isn't explained, raising questions about its necessity versus stablecoins. The documentation emphasizes technological sophistication but lacks implementation details, team credentials, or legal structure. The 1B token supply creates inflationary risks without demonstrated burn mechanisms. While the community-focused 'Collective' feature could add value, the complete absence of creator skin-in-the-game suggests high abandonment risk.
Rating: 0
Generated with LLM: deepseek/deepseek-r1
LLM responses last updated: Aug 27, 2025 18:03
Original investment data:
# NZT ($NZT)
URL on launchpad: https://app.virtuals.io/prototypes/0x7563e1dFba5b16347B196DE04Fe7b51aC2882b42
Launched at: Wed, 27 Aug 2025 17:58:37 GMT
Launched through the launchpad: Virtuals Protocol
Launch status: UNDERGRAD
## Token details and tokenomics
Token address: 0x7563e1dFba5b16347B196DE04Fe7b51aC2882b42
Top holders: https://basescan.org/token/0x7563e1dFba5b16347B196DE04Fe7b51aC2882b42#balances
Liquidity contract: https://basescan.org/address/0xe64d5fB10e664a18060c1b11AC2FCb8E24889028#asset-tokens
Token symbol: $NZT
Token supply: 1 billion
Creator initial number of tokens: Creator initial number of tokens: 1 (0% of token supply)
## Creator info
Creator address: 0x2523Ed58C19329d63b54d3F77D77198FE00cc84D
Creator on basescan.org: https://basescan.org/address/0x2523Ed58C19329d63b54d3F77D77198FE00cc84D#asset-tokens
Creator on virtuals.io: https://app.virtuals.io/profile/0x2523Ed58C19329d63b54d3F77D77198FE00cc84D
Creator on zerion.io: https://app.zerion.io/0x2523Ed58C19329d63b54d3F77D77198FE00cc84D/overview
Creator on debank.com: https://debank.com/profile/0x2523Ed58C19329d63b54d3F77D77198FE00cc84D
## Description at launch
Welcome to NZT, an innovative AI-driven platform designed to empower individuals with advice and insights across multiple fields; from trading and strategic planning to personal growth and creative exploration.
## Overview
# Core Concepts
NZT is built around a set of foundational ideas that shape how the platform works. Understanding these concepts will help you get the most out of the system.
[](https://ref.nztdocs.center/core-concepts#quantitative-backbone)
Quantitative Backbone
---------------------
The foundation of NZT is its quantitative engine. This layer is responsible for:
* Integrating live market data feeds.
* Processing historical datasets for context.
* Running mathematical models to detect trends, flows, and probability zones.
All token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.
[](https://ref.nztdocs.center/core-concepts#instructional-intelligence)
Instructional Intelligence
--------------------------
On top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.
* **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.
* **Collective** – Shared chat environment for comparing interpretations and collaborating with others.
* **Trainer** – Provides interactive exercises and games to build trading intuition safely.
* **Optimist** – Supports users in reframing setbacks and maintaining momentum.
* **Mentor** – Focuses on habit-building, discipline, and emotional regulation.
* **Visionary** – Suggests unconventional approaches and alternative perspectives.
* **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.
* **Sage** – Guides structured reflection to reduce indecision and clarify purpose.
[](https://ref.nztdocs.center/core-concepts#community-layer)
Community Layer
---------------
Beyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.
[](https://ref.nztdocs.center/core-concepts#adaptation)
Adaptation
----------
NZT is designed to adjust to changing conditions:
* Market models update in real time.
* Analytics are recalculated as inputs shift.
* User and community feedback refine how outputs are presented.
[](https://ref.nztdocs.center/core-concepts#usability)
Usability
---------
Although the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.
## Additional information extracted from relevant pages
<fetched_info>
""" [Creator profile on Virtuals Protocol](https://api.virtuals.io/api/profile/0x2523Ed58C19329d63b54d3F77D77198FE00cc84D)
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"""
""" https://ref.nztdocs.center/core-concepts#usability
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#quantitative-backbone) Quantitative Backbone
The foundation of NZT is its quantitative engine. This layer is responsible for:
- Integrating live market data feeds.
- Processing historical datasets for context.
- Running mathematical models to detect trends, flows, and probability zones.
All token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#instructional-intelligence) Instructional Intelligence
On top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.
- **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.
- **Collective** – Shared chat environment for comparing interpretations and collaborating with others.
- **Trainer** – Provides interactive exercises and games to build trading intuition safely.
- **Optimist** – Supports users in reframing setbacks and maintaining momentum.
- **Mentor** – Focuses on habit-building, discipline, and emotional regulation.
- **Visionary** – Suggests unconventional approaches and alternative perspectives.
- **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.
- **Sage** – Guides structured reflection to reduce indecision and clarify purpose.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#community-layer) Community Layer
Beyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#adaptation) Adaptation
NZT is designed to adjust to changing conditions:
- Market models update in real time.
- Analytics are recalculated as inputs shift.
- User and community feedback refine how outputs are presented.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#usability) Usability
Although the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.
[PreviousWelcome](https://ref.nztdocs.center/) [NextTokenomics](https://ref.nztdocs.center/tokenomics)
Last updated 3 days ago
"""
""" https://ref.nztdocs.center/core-concepts#instructional-intelligence
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#quantitative-backbone) Quantitative Backbone
The foundation of NZT is its quantitative engine. This layer is responsible for:
- Integrating live market data feeds.
- Processing historical datasets for context.
- Running mathematical models to detect trends, flows, and probability zones.
All token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#instructional-intelligence) Instructional Intelligence
On top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.
- **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.
- **Collective** – Shared chat environment for comparing interpretations and collaborating with others.
- **Trainer** – Provides interactive exercises and games to build trading intuition safely.
- **Optimist** – Supports users in reframing setbacks and maintaining momentum.
- **Mentor** – Focuses on habit-building, discipline, and emotional regulation.
- **Visionary** – Suggests unconventional approaches and alternative perspectives.
- **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.
- **Sage** – Guides structured reflection to reduce indecision and clarify purpose.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#community-layer) Community Layer
Beyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#adaptation) Adaptation
NZT is designed to adjust to changing conditions:
- Market models update in real time.
- Analytics are recalculated as inputs shift.
- User and community feedback refine how outputs are presented.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#usability) Usability
Although the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.
[PreviousWelcome](https://ref.nztdocs.center/) [NextTokenomics](https://ref.nztdocs.center/tokenomics)
Last updated 3 days ago
"""
""" https://ref.nztdocs.center/core-concepts#quantitative-backbone
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#quantitative-backbone) Quantitative Backbone
The foundation of NZT is its quantitative engine. This layer is responsible for:
- Integrating live market data feeds.
- Processing historical datasets for context.
- Running mathematical models to detect trends, flows, and probability zones.
All token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#instructional-intelligence) Instructional Intelligence
On top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.
- **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.
- **Collective** – Shared chat environment for comparing interpretations and collaborating with others.
- **Trainer** – Provides interactive exercises and games to build trading intuition safely.
- **Optimist** – Supports users in reframing setbacks and maintaining momentum.
- **Mentor** – Focuses on habit-building, discipline, and emotional regulation.
- **Visionary** – Suggests unconventional approaches and alternative perspectives.
- **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.
- **Sage** – Guides structured reflection to reduce indecision and clarify purpose.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#community-layer) Community Layer
Beyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#adaptation) Adaptation
NZT is designed to adjust to changing conditions:
- Market models update in real time.
- Analytics are recalculated as inputs shift.
- User and community feedback refine how outputs are presented.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#usability) Usability
Although the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.
[PreviousWelcome](https://ref.nztdocs.center/) [NextTokenomics](https://ref.nztdocs.center/tokenomics)
Last updated 3 days ago
"""
""" https://ref.nztdocs.center/core-concepts#adaptation
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#quantitative-backbone) Quantitative Backbone
The foundation of NZT is its quantitative engine. This layer is responsible for:
- Integrating live market data feeds.
- Processing historical datasets for context.
- Running mathematical models to detect trends, flows, and probability zones.
All token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#instructional-intelligence) Instructional Intelligence
On top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.
- **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.
- **Collective** – Shared chat environment for comparing interpretations and collaborating with others.
- **Trainer** – Provides interactive exercises and games to build trading intuition safely.
- **Optimist** – Supports users in reframing setbacks and maintaining momentum.
- **Mentor** – Focuses on habit-building, discipline, and emotional regulation.
- **Visionary** – Suggests unconventional approaches and alternative perspectives.
- **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.
- **Sage** – Guides structured reflection to reduce indecision and clarify purpose.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#community-layer) Community Layer
Beyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#adaptation) Adaptation
NZT is designed to adjust to changing conditions:
- Market models update in real time.
- Analytics are recalculated as inputs shift.
- User and community feedback refine how outputs are presented.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#usability) Usability
Although the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.
[PreviousWelcome](https://ref.nztdocs.center/) [NextTokenomics](https://ref.nztdocs.center/tokenomics)
Last updated 3 days ago
"""
""" https://ref.nztdocs.center/core-concepts#community-layer
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#quantitative-backbone) Quantitative Backbone
The foundation of NZT is its quantitative engine. This layer is responsible for:
- Integrating live market data feeds.
- Processing historical datasets for context.
- Running mathematical models to detect trends, flows, and probability zones.
All token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#instructional-intelligence) Instructional Intelligence
On top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.
- **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.
- **Collective** – Shared chat environment for comparing interpretations and collaborating with others.
- **Trainer** – Provides interactive exercises and games to build trading intuition safely.
- **Optimist** – Supports users in reframing setbacks and maintaining momentum.
- **Mentor** – Focuses on habit-building, discipline, and emotional regulation.
- **Visionary** – Suggests unconventional approaches and alternative perspectives.
- **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.
- **Sage** – Guides structured reflection to reduce indecision and clarify purpose.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#community-layer) Community Layer
Beyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#adaptation) Adaptation
NZT is designed to adjust to changing conditions:
- Market models update in real time.
- Analytics are recalculated as inputs shift.
- User and community feedback refine how outputs are presented.
## [Direct link to heading](https://ref.nztdocs.center/core-concepts\#usability) Usability
Although the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.
[PreviousWelcome](https://ref.nztdocs.center/) [NextTokenomics](https://ref.nztdocs.center/tokenomics)
Last updated 3 days ago
"""
</fetched_info>
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"roadmap": "# Experiment 0.01: The Virtual of NZT\n\nA sleek digital environment shimmers in soft, pulsing light. Seven virtual seats are arranged in a loose circle, each occupied by a distinct AI persona. As data streams swirl overhead, the conversation unfolds.\n\n# Experiment 0.02: The Great Pet Debate\n\nA virtual lounge flickers into view. Plush digital couches and abstract paintings of both cats and dogs adorn the walls. Seven distinct auras occupy the room, each representing a unique AI persona.",
"additionalDetails": "In a world defined by complexity, where answers are elusive and potential often feels just out of reach, NZT stands as the key to unlocking everything you already hold within yourself. This is not merely a platform, it is a dynamic framework, designed to illuminate new pathways of thought, insight, and action through cutting-edge artificial intelligence.\n\n***\n\nX : [https://x.com/NZT\\_Virtual](https://x.com/NZT_Virtual)\n\nWebsite : [https://www.nzt.center/](https://www.nzt.center/)",
"overview": "# Core Concepts\n\nNZT is built around a set of foundational ideas that shape how the platform works. Understanding these concepts will help you get the most out of the system.\n\n[](https://ref.nztdocs.center/core-concepts#quantitative-backbone)\nQuantitative Backbone\n---------------------\n\nThe foundation of NZT is its quantitative engine. This layer is responsible for:\n\n* Integrating live market data feeds.\n* Processing historical datasets for context.\n* Running mathematical models to detect trends, flows, and probability zones.\n\nAll token-related analysis is generated here. These outputs are independent of AI and designed to provide measurable, repeatable results.\n\n[](https://ref.nztdocs.center/core-concepts#instructional-intelligence)\nInstructional Intelligence\n--------------------------\n\nOn top of the quantitative core, NZT includes a set of specialized agents. Each agent is designed for a distinct role and can be used individually or together depending on the task.\n\n* **Strategist** – Central agent for token analysis, tailored indicators, and structured trading plans.\n* **Collective** – Shared chat environment for comparing interpretations and collaborating with others.\n* **Trainer** – Provides interactive exercises and games to build trading intuition safely.\n* **Optimist** – Supports users in reframing setbacks and maintaining momentum.\n* **Mentor** – Focuses on habit-building, discipline, and emotional regulation.\n* **Visionary** – Suggests unconventional approaches and alternative perspectives.\n* **Philosopher** – Explores deeper questions of meaning, ethics, and context in decision-making.\n* **Sage** – Guides structured reflection to reduce indecision and clarify purpose.\n\n[](https://ref.nztdocs.center/core-concepts#community-layer)\nCommunity Layer\n---------------\n\nBeyond individual use, NZT emphasizes group interaction. The **Collective** allows users to share observations, test interpretations, and validate signals. This collaborative element reduces bias and increases confidence in decision-making.\n\n[](https://ref.nztdocs.center/core-concepts#adaptation)\nAdaptation\n----------\n\nNZT is designed to adjust to changing conditions:\n\n* Market models update in real time.\n* Analytics are recalculated as inputs shift.\n* User and community feedback refine how outputs are presented.\n\n[](https://ref.nztdocs.center/core-concepts#usability)\nUsability\n---------\n\nAlthough the underlying systems are complex, NZT is structured for accessibility. Users can rely on quantitative outputs alone or combine them with AI agents for additional perspective. The platform is intended to support both casual users and experienced traders.",
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},
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],
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},
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}
</full_details>
Investment info last updated: Aug 27, 2025 18:03