# 1st Report Of January 2025

<figure><img src="https://2664391676-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT5LtcFS1DoKk05KGaZdm%2Fuploads%2Ft6PbJbyUXbnq3s6hgUZv%2FWeb%201920%20220.png?alt=media&#x26;token=3cd20720-a44d-4b9a-870a-6b54d5bd9995" alt=""><figcaption></figcaption></figure>

<figure><img src="https://2664391676-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FT5LtcFS1DoKk05KGaZdm%2Fuploads%2FP2l3bN4p1yLbkaa7S6Zt%2Fbiwekltry.png?alt=media&#x26;token=4addd9f5-5a87-43a4-92cd-9a50fbdf35c9" alt=""><figcaption></figcaption></figure>

### Mainnet Performance

* Validator Nodes: 107
* Miner Nodes: 1,237
* New Wallets: +77

### Project Updates

1. **Large Model Initialization**

* **Optimization of Morpheus as the Base Large Model for AI Agents:** 12%
* * Expanding Morpheus's core capabilities, including multitask learning, agent behavior modeling, and reinforcement learning.
* **Language Understanding, Reasoning, and Communication Abilities:** 0%
* **Enhanced Features:** Introduction of foundational personality modules (logical, emotional, creative) for personalized agent development: 0%.
* **Integration of DeepSeek V3's Mixture of Experts (MOE) Framework:** 7%
* * Designing the integration of MOE models within Morpheus, including strategy for model partitioning, expert selection mechanisms, and allocation algorithms during training.
  * Determining hardware requirements and evaluating potential efficiency gains in training.

2. **Support for Distributed Privacy Computing**

* Expanding the MANTA computing framework to support privacy-preserving training and inference required by AI agents: 0%.
* Optimizing the MANTA computing framework for MOE model training and inference required by DeepSeek V3: 0%.

3. **Agent Model Center (MVP)**

* **Initial Version Deployment: 6%**
* * Launching the first version of the Model Center with pre-initialized Agent Tools provided by MATRIX AI, enabling users to assemble agents using these tools.
  * Defining functional requirements and architectural designs for Agent Model Center, covering model management, training, deployment, and monitoring.
  * Designing a flexible platform architecture to support foundational baseline models and AI Agent development.
* **Extension of Compatible Baseline Models: 6%**
* * Integrating DeepSeek V3 as one of the baseline models in the Agent Model Center.
  * Analyzing the architecture of DeepSeek V3 to assess compatibility with the existing framework.

4. AI **Agent MVP**

* Providing a Morpheus-based AI Agent MVP for user testing: 0%.

5. **Launch of Brainwave Distributed Database**

* Deployment of NeuraMATRIX’s first ecosystem application, initiating data collection and anonymization processes: 12%.
* * Optimizing SDK data management permissions to ensure Hypnus complies with App Store privacy requirements.

6. **Others**

{% embed url="<https://x.com/MatrixAINetwork/status/1876976194651853042https:/x.com/MatrixAINetwork/status/1876976194651853042>" %}

{% embed url="<https://matrixainetwork.medium.com/1st-report-of-january-2025-42ce05e35b0c>" %}

{% embed url="<https://coinmarketcap.com/community/articles/677e773d1348ae359d28c8e2>" %}

{% embed url="<https://coinmarketcap.com/community/post/348842467>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.matrix.io/bi-weekly-reports/1st-report-of-january-2025.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.