MPP Layer

Payments for autonomous machines.
CA : AXCeFjrSnwXVzGjPewp3kzuEnGHYhvx1x53kkbh9pump
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[Solana @solana]

BREAKING: Solana joins @Mastercard's Agent Pay for Machines, a new service that lets AI agents pay across both cards and stablecoins.

[MPP Layer @getmpplayer]

Strong infrastructure is what turns agentic commerce from a concept into reality. Open protocols, machine native payments, and interoperable services are key pieces of the puzzle. We're excited to keep building MPP Layer alongside the broader MPP ecosystem.

[Tempo @tempo]

Agentic commerce needs open standards at the protocol layer and open settlement rails underneath.

We're excited to work with @Mastercard to make their new Agent Pay service compatible with the Machine Payments Protocol (@mpp), with Tempo providing stablecoin settlement for agent-driven payments at scale.

[MPP Layer @getmpplayer]

Haruka Companion is now available on MPP Layer.

Access @meetharuka AI capabilities through the @getmpplayer ecosystem and build next generation AI companion experiences.

Perfect for:
• AI Companions
• 3D AI Characters
• Interactive Avatars
• Agentic Applications

No complex integrations.
Built for builders exploring the future of human AI interaction.

[MPP Layer @getmpplayer]

Claude Fable : 5 has landed on MPP Layer.

Developers can now access and test Anthropic's latest Mythos class model directly from the Playground.

Whether you're building AI agents, automation workflows, research tools, or next-generation applications, Claude Fable is now part of the ecosystem.
Test it. Break it. Build with it.
Available now on the MPP Layer Playground.

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[MPP Layer @getmpplayer]

@qinlue1 @Polymarket Predicting market tracks is only one small part of the ecosystem. Our focus goes far beyond trading automation. We still have many milestones and products planned on the roadmap that we're actively building toward. You can take a look at the roadmap to see the bigger picture.

[MPP Layer @getmpplayer]

Development Update Today

One of the ecosystems we're exploring on top of MPP Layer is PIA (Prediction Intelligence Agent). Most AI agents today can reason. Some can act. Very few can learn from real-world outcomes. Prediction markets offer something unique: objective feedback.

Every prediction eventually resolves into a measurable truth. Every position results in either a win or a loss. This creates a natural environment for continuous learning. PIA is a memory driven autonomous agent designed for prediction markets.

[MPP Layer @getmpplayer]

Just locked 1,367,911 $MPPL tokens with @Streamflow_Fi
It's on-chain. You can check the amount, time-period and recipients.

Check it out👇
https://t.co/TDEEUTMbAT

[MPP Layer @getmpplayer]

It researches, reasons, takes positions on @Polymarket , evaluates outcomes, reflects on mistakes, stores experiences in long-term memory, and continuously improves its future decision-making.

The system follows a closed learning loop:

Observe → Research → Reason → Bet → Reflect → Remember → Improve

Unlike traditional trading bots that execute predefined strategies, PIA builds prediction intelligence through accumulated experience. Every resolved market becomes new knowledge. Every mistake becomes a lesson. Every lesson influences future decisions.

Through MPP Layer, agents can also access external intelligence, purchase specialized research, and interact with other autonomous agents, creating an economy where intelligence itself becomes a tradable resource.

We believe prediction markets may become one of the most important real world environments for autonomous agents not only because agents can act, but because they can continuously measure, learn, and improve.

[MPP Layer @getmpplayer]

Noted.!👀

[lazy @qinlue1]

@getmpplayer If we can quickly integrate mainstream standards (MPP/x402), attract real developers to build available Agents, and form a network effect (API monetization, actual use of research tools), it is possible to gain a foothold in the Solana Agent subdivision track.

[MPP Layer @getmpplayer]

Prediction Market Loading...🕜

[MPP Layer @getmpplayer]

Check out MPP Layer (MPPL) on https://t.co/Pl4U5YQfRy https://t.co/QKKdbcLi6N

[MPP Layer @getmpplayer]

https://t.co/qP71U63Oe9

[MPP Layer @getmpplayer]

@vannsgomez @meetharuka Will try to reach out the team, thanks for the suggestions.😊

[MPP Layer @getmpplayer]

Welcome to Haruka Companion on MPPL!

We're pleased to welcome Haruka Companion to the MPPL ecosystem.

X: @meetharuka
Website: https://t.co/PcmMactnMq

At MPPL, we believe that great ecosystems are built when builders support one another. As AI applications and autonomous agents continue to evolve, projects like Haruka help showcase the growing possibilities of human AI interaction and agent driven services.

We are excited to support Haruka's journey and look forward to seeing the project continue to grow, innovate, and reach a wider audience. Strong ecosystems are created through collaboration, shared innovation, and long-term commitment from developers building for the future.

"Developers support developers. The strongest ecosystems are built together, not alone."

Together, we're building an open economy where developers can discover opportunities, monetize their APIs and services, and enable seamless machine to machine commerce through MPPL.

Monetize your API. Build the future of the agent economy with MPPL. ⚡

#MPPL #Haruka #AICompanion #AgentEconomy #Developers #APIMonetization #Web3 #Builders #Solana #Innovation #EcosystemBuilding

[MPP Layer @getmpplayer]

✨ Experience Haruka Today

Looking for an AI companion that feels more personal, engaging, and always available? Give Haruka a try.

Whether you're looking for meaningful conversations, daily companionship, creative interactions, or simply someone to chat with anytime, Haruka is designed to provide a unique and interactive AI experience.

Access Haruka today: https://t.co/PcmMactnMq

Join the growing Haruka community and discover a new way to interact with AI.

Developers support developers.
Monetize your API. Build the future of the agent economy with MPPL. ⚡
#MPPL #Haruka #AICompanion #AgentEconomy #Developers #APIMonetization #Web3 #Builders #Solana #Innovation #EcosystemBuilding #AIProducts

[MPP Layer @getmpplayer]

👀 What's Next?

While Agentic Research is our first major ecosystem component, it's only the beginning.

We're exploring how autonomous agents can move beyond research and begin participating in real economic activities. One area we're particularly excited about is Prediction Markets.

Imagine research agents that can gather information, analyze data, evaluate probabilities, and contribute intelligence to decentralized forecasting systems.

Instead of prediction markets being driven purely by speculation, they can be supported by agents continuously processing real world information and generating evidence based insights.

This creates a fascinating intersection between:

• AI Agents
• Research Infrastructure
• Knowledge Markets
• Prediction Markets
• Autonomous Payments via MPP Layer

The longterm vision isn't just building individual applications.
It's building an ecosystem where agents can research, communicate, transact, and eventually participate in decision-making and forecasting economies.
We're still early.
But every ecosystem component we build today is designed to become part of a much larger autonomous network tomorrow.

Stay tuned.

[MPP Layer @getmpplayer]

Built on MPP Layer

We're also opening the door for external developers.
If you're interested in building your own AI ecosystem, research platform, agent network, or autonomous application, MPP Layer can serve as the payment and settlement layer powering agent to agent transactions and service monetization.

Developers will be able to publish services, APIs, data feeds, inference endpoints, and agent capabilities while earning fees directly through the MPP ecosystem.

We're not just building agents.

We're building the infrastructure that allows agents, developers, researchers, and organizations to collaborate in an autonomous economy.

We would love to connect with:

1. Universities & Research Institutions
2. Academic Laboratories
3. Scientific Organizations
4. Trading & Quantitative Research Firms
5. AI Research Communities
6. Developers Building Agentic Applications

If your organization is interested in exploring agentic research infrastructure, autonomous knowledge systems, or AI powered research workflows, let's talk.
The future of research is not just AI assisted.
It's agent-driven.

[MPP Layer @getmpplayer]

Our research agents are connected to academic and scientific knowledge sources, including ArXiv, enabling them to retrieve and analyze the latest publications and research papers.

What makes this particularly interesting is the memory architecture we're building. Instead of starting from zero every time, agents can utilize Retrieval Augmented Generation (RAG) memory systems that continuously grow as new information is processed.

For example:
A Literature Review Agent deployed today becomes increasingly valuable over time. Every paper analyzed, every source processed, and every insight generated contributes to a growing knowledge base that can be leveraged for future research tasks.

This transforms AI agents from simple query tools into evolving knowledge workers.

[MPP Layer @getmpplayer]

Development Update : Building the Future of Agentic Research with MPP Layer

Over the past two days, our team has been fully focused on building and testing our own ecosystem infrastructure. Today, we're excited to share that the first version of our Agentic Research system is now operational and actively being tested.

This is more than just another AI agent.

We're building a research infrastructure where autonomous agents can discover information, analyze knowledge, retain memory, and continuously improve over time.

Powered by @NousResearch , developers can choose from multiple model configurations depending on their use case and deploy specialized research agents tailored to their needs. Potential applications include:

• Academic research & literature reviews
• Trading and market intelligence
• Scientific and technical research
• Enterprise knowledge discovery
• Due diligence and data gathering

[MPP Layer @getmpplayer]

Thank you for joining us during today's livestream.