Introducing (L)Earn AI agent on NEAR AI

Introduction

You probably heard that now you can call ChatGPT over the phone. This seems unreal. It is cool but do you have an idea whom you’re gonna be talking to? The best you can do is to rely on service provider reputation – OpenAI OpCo, LLC.

Issues with Current AI

• Centralized Models: Most widely-used models like GPT-4 or Claude are closed-source and centralized.
• Dependence on Centralized Infrastructure: Even “open-source” models often run on centralized platforms like Groq or Together AI.
• Opaque Operations: It is impossible to verify how agents operate—including prompts, limitations, and stop lists.
• Data Privacy Concerns: Users have little visibility into how their data is handled or stored.

These challenges emphasize the need for responsible AI use and caution when relying on AI agents for critical decisions.

AI and Learning

Lucky you if you graduated from school before getting spoiled by AI.

But what if you are not able to verify stuff just because you have not developed learning skills, critical thinking, reasoning skills or just because you are too young or you got addicted to “helpful and efficient” AI agents? This could be a serious issue. Calculators began as costly, limited tools, giving schools years to adapt. Now, AI is transforming education instantly, impacting every subject. Students will cheat with AI but also integrate it into their work, challenging educators and questioning the relevance of traditional assignments.
Common understanding is that unless assignments are completed in class, it’s impossible to reliably determine if the work is human-made.

Verifiable AI can change this.

NEAR AI Cloud

NEAR.AI is the answer.
While in Alpha, some exciting features are already available to try. Nothing compares to learning by trying so let’s do it.

Go to the NEAR AI Cloud

You may sign in with your  GitHub or Google account. The Dashboard is simple and clear – you you need is to explore docs and available models (many more to come).

At LNC we decided to choose this one.

How it is different from models provided by OpenAI, Anthropic and others? This particular inference is running OS model deepseek-chat-v3-0324 hosted in Trusted Execution Environment. This allows us to be sure that we indeed send requests to the known untweaked model and those requests CAN NOT be shared with 3rd parties!

🕺(L)Earn AI

The decentralized NEAR AI Cloud infrastructure represents a significant step forward in enabling user-owned AI. By offering transparency, control, and flexibility, NEAR AI Cloud empowers users to interact with AI models in a way that ensures data privacy and fosters trust.

LNC, as a NEAR pioneer, is at the forefront of leveraging this infrastructure for practical applications, including the live Learn AI agent, the Lean NEAR Tutor (in development), and the planned WooCommerce Shopping Agent.

Veritable AI in 

Let’s see how  it works at real. We encourage you to try it yourself to better understand the concept and think how you personally may benefit from using verifiable AI VS private one, eg. ChatGPT, Anthropic, Grok and so on.

Start chat with (L)Earn AI

Let 🕺 refer to LNC private knowledge base and think a bit

DYOR, review and reflex the answer. Explore the reference source, take quizzes  – it helps you to memorize new concept efficiently

Now let’s see whom you are actually talking too here. See that beautiful green checkmark?

Here you can see that:

1. You are talking to Deep-chat-v3-0324 model hosted at NEAR AI cloud in TEE
2. You’re calling the model with this prompt and these parameters.
   
3. Additional context provided by LNC RAG (knowledge base)
4. Most exciting feature – nStamp! What is that? Please explore how digital finger prints work. nStamp is basically data hash written on NEAR blockchain. So this particular nStamp 7JjvLR5yfZotgebHiNw74gXckCezAxKDyih8C4AwxW5M contains hash of the (L)Earn AI. Anyone can verify the origin account, timestamp and the hash at NEAR explorer of their choice. So you don’t need to blindly trust website/chat/model admins – the rules are defined and known, and they’re written in stone (well, on the NEAR blockchain, which is even better). If any actor – user, agent or model tries to tweak the data for some reason – verification fails.
   

❓Ask

As a learner, you’re invited to actively participate in this innovative (L)Earn ecosystem. Ask the (L)Earn AI relevant questions, pay with nLEARNs, gain knowledge, and earn nLEARNs by contributing to the AI’s improvement.
Together, this approach brings a deeper and richer meaning to the concept of (L)Earn, blending learning with earning in a positive sum game with AI.

Ask (L)Earn AI also available at Telegram

Check out the most recent (L)Earn AI🕺  agents:

📝Summarize

LNC Summarize🕺 Agent quickly breaks down complex guides into easy-to-understand summaries . It pulls out the most important points and explains them clearly, helping students grasp key concepts without wading through lengthy materials. This time-saving tool makes learning more efficient while keeping the essential information intact. If you’re not a native speaker, you can ask the agent to summarize it in your language!

✏Comment

In AI era it is safe to assume AI first commenting. 
(L)Earn Comment Agent 🕺analyzes the topic of (L)Earners choice and drafts a comment.
The (l)Earner then reviews this draft and posts it with any necessary edits so the comment does reflect the user’s point.
Once comment posted, the nLEARNs reward is to be distributed according AI and user participation! 🤖and🕺 work and learn together!

✅Quiz

(L)Earn AI Quiz is the first step to introducing the future of learning – personalized, on demand verifiable AI assisted learning. Choose the difficulty and (L)Earn AI will create a quiz to help you memorize the major concepts!
As (L)Earn AI intelligence evolve most likely it will be integrated with other NEAR projects – for example as learning assistant chat bot at other Dapps and Telegram chats, or (L)Earn AI can also cooperate with other NEAR AI agents like NEAR Docs agent and help users to get more technical development related help.

🚩AI Feedback

If you like the answer, tap “Like it? Tip Author”—the authors gets 2 nL and the AI learns what works.
If you see a mistake, tap “Not quite right? Flag”—you earn 4 nL and help the AI improve.
Either action teaches the AI what works and what needs fixing while sharing incentives with you.

📚Happy (L)Earning!🕺

Updated: October 14, 2025