Node Scanner for AI Agents

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Patrik Gudev
Project Owner

Node Scanner for AI Agents

Expert Rating

n/a

Overview

We propose Node Scanner for AI Agents, a unified interface to discover, benchmark, and simplify deployment across decentralized compute networks. It tackles the fragmentation of DePIN by introducing real-time node monitoring, a standardized Latency Score, and streamlined Docker deployment. We will demonstrate AI agents dynamically selecting and redeploying compute based on performance a key step toward self-managing AGI infrastructure. The project also explores integration with the SingularityNET Marketplace to enable autonomous service deployment.

RFP Guidelines

Explore novel hardware architectures and computing paradigms for AGI

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $80,000 USD
  • Proposals 9
  • Awarded Projects 1
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SingularityNET
Apr. 14, 2025

The purpose of this RFP is to identify, assess, and experiment with novel computing paradigms that could enhance AGI system performance and efficiency. By focusing on alternative architectures, this research aims to overcome computational bottlenecks in recursive reasoning, probabilistic inference, attention allocation, and large-scale knowledge representation. Bids are expected to range from $40,000 - $80,000.

Proposal Description

Our Team

  • Patrik Gudev (CEO)
    10+ years in the music industry, A&R, and algorithmic music production. Produced 3,000+ tracks for Warner Chappell PM, BMG, Marvel. Creator of top 10 global sample packs.

  • Matt Zimak (COO)
    Former PwC consultant with expertise in finance, strategy, and innovation. Ex-VC turned Web3 operator, built NFT-based communities and DAOs.

  • Robin Spottiswoode (CTO)
    6+ years in Web3 engineering. Built music NFT platforms with over $1M market volume. Led tech at $3M-backed Atari Web3 venture.

Company Name (if applicable)

Jam Galaxy

Project details

As we move toward Artificial General Intelligence, one of the most foundational needs is infrastructure that enables AI agents to operate independently not just in logic, but in runtime. Today’s decentralized compute (DePIN) landscape is highly fragmented, with each provider (e.g., Akash, Cudos, ICP) offering different interfaces, standards, and deployment methods. There is no unified way for agents or developers to discover, compare, or utilize this infrastructure effectively.

Node Scanner for AI Agents proposes a foundational system to address this. Our goal is to build a unified interface for discovering, benchmarking, and simplifying deployment across decentralized compute networks ultimately creating a new AGI-ready paradigm where agents manage their own execution environments.

The proposal is structured in four key phases:

  1. Node Discovery & Benchmarking
    We will build a real-time monitoring layer across DePIN providers, capturing key metrics such as latency, bandwidth, uptime, and location. This will feed into a standardized Latency Score, giving developers and AI agents a consistent view of network performance. This layer will act as a “Skyscanner” for decentralized nodes, supporting informed decisions on where and how to deploy.

  2. Simplified Deployment Framework
    We aim to reduce the friction of launching workloads on DePIN networks. Through a simplified deployment system, users can upload Docker images or GitHub links and launch them to the most suitable node without manually configuring provider-specific setups. This layer abstracts complexity and enables seamless integration of services.

  3. Autonomous Agent Deployment Proof
    As a key step toward AGI infrastructure, we will demonstrate that AI agents can self-monitor their runtime and autonomously redeploy themselves to more optimal nodes when performance degrades. While this MVP will not yet include wallet-based transactions, it will validate the core logic of infrastructure self-management.

  4. SingularityNET Integration Pathway
    We will explore adapting this deployment pipeline to support services entering the SingularityNET Marketplace / ASI platform. This would enable agents to deploy directly to SNET-compatible environments, expanding decentralized intelligence into a broader ecosystem.


Why It Matters

This project reimagines compute infrastructure as a fluid, agent-driven environment. Instead of treating infrastructure as static and manually controlled, we treat it as something AI can reason about, adapt to, and control just like a biological system responds to its environment.

By making DePIN accessible, measurable, and agent-operable, Node Scanner for AI Agents lays critical groundwork for AGI systems that are not only intelligent in function but autonomous in execution.

Links and references

We took this proposal proactively and we vibe coded a prototype.
https://jamnetwork.vercel.app/

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    4

  • Total Budget

    $80,000 USD

  • Last Updated

    27 May 2025

Milestone 1 - Identification of DePIN paradigms & research plan

Description

Comprehensive Research Plan outlining objectives, methodology, timeline, and AGI relevance Categorized List of Alternative Compute Platforms, including DePIN-based (e.g., Akash, Cudos) and traditional (e.g., AWS, Azure) infrastructures. Initial Benchmarking Criteria defining key metrics (latency, cost, scalability, energy efficiency), Literature Review Framework detailing sources and topics to be reviewed in Milestone 2.

Deliverables

1) Research plan with objectives, methods, timeline, and AGI relevance 2) List of DePIN and traditional compute platforms to benchmark 3) Initial benchmarking criteria (latency, cost, scalability, energy) for AGI tasks (e.g., PLN, MOSES, ECAN) 4) Literature review outline for Milestone 2 5) Project execution plan with setup, and milestone timeline

Budget

$20,000 USD

Success Criterion

1) Clear research plan aligned with AGI objectives and RFP scope 2) 3–5 compute platforms identified and categorized (DePIN + traditional) 3) Benchmarking metrics defined and mapped to AGI-relevant tasks 4) Literature review topics scoped and approved for next phase 5) Project structure validated and ready to begin experimentation in Milestone 2

Milestone 2 - Literature Review & Benchmarking Criteria

Description

This milestone focuses on conducting a structured literature review of alternative compute architectures and finalizing the benchmarking framework. The review will cover decentralized and non-traditional paradigms (e.g. DePIN networks) with respect to AGI-relevant computational demands such as recursive reasoning attention mechanisms and probabilistic inference. We will also finalize a standardized benchmarking protocol for evaluating compute efficiency latency scalability and cost across all selected platforms.

Deliverables

Literature Review Report covering academic and industry research on: a) DePIN performance and architecture tradeoffs b) Non-traditional compute paradigms (e.g. analog computing in-memory processing) c) AGI workload characteristics (e.g. PLN ECAN MOSES requirements) Benchmarking Criteria Specification: a) Final list of evaluation metrics (e.g. latency throughput power use deployment complexity) b) Normalization strategies for fair cross-platform comparison c) Mapping of metrics to AGI workload types Evaluation Templates: a) Structured formats for recording measurements across all test platforms Template for comparative analysis (DePIN vs. centralized providers)

Budget

$20,000 USD

Success Criterion

a) Delivery of a focused, AGI-specific literature review covering at least 8–10 key sources b) A finalized, peer-reviewable benchmarking framework clearly tied to AGI workloads c) Evaluation templates completed and approved for data collection in Milestone 3 d) Internal validation showing benchmarking logic can be consistently applied across platforms

Milestone 3 - Initial Experiments & Feasibility Testing

Description

This milestone delivers the first operational version of the Node Scanner — a real-time monitoring engine that collects and visualizes key performance metrics across decentralized and centralized compute nodes. The system will measure latency uptime bandwidth and resource availability. A geolocation map will visualize the global distribution of nodes to highlight regional performance variability — crucial for understanding infrastructure suitability for latency-sensitive AGI workloads.

Deliverables

1) Live Node Monitoring System (Backend + Data Pipeline) 2) Collection of real-time metrics from selected nodes (3+ DePIN 2+ centralized) 3) Metrics: latency response time uptime bandwidth CPU/GPU presence Performance Dataset 1) Structured logs of all test data collected under AGI-relevant benchmarking conditions Feasibility Report 1) Technical analysis of platform readiness for recursive reasoning inference workloads 2) Includes node-level observations on volatility cost and uptime Node Map Visualization 1) Interactive world map showing node locations performance health and latency clustering 2) Supports filtering by provider location and performance range Platform Comparison Dashboard (Prototype UI) 1) Displays Latency Scores availability and cost across all tested platforms 2) Sets the foundation for the public-facing version in Milestone 4

Budget

$20,000 USD

Success Criterion

1) Real-time measurement engine deployed and collecting data from distributed nodes 2) At least 5 geographically distinct nodes actively monitored 3) First version of the geolocation map displaying live performance indicators 4) Visual and technical reporting ready to guide final prototype and AGI relevance evaluation

Milestone 4 - Final Evaluation & Node Scanner Prototype Launch

Description

This milestone delivers the complete working prototype of the Node Scanner system. It includes a public-facing dashboard performance maps and comparative benchmarks for decentralized and centralized compute platforms. The milestone will also include a final evaluation report summarizing experimental results feasibility for AGI workloads and a prototype scenario where an AI service or agent uses the system to guide deployment or scaling decisions based on performance metrics.

Deliverables

1) Final Node Scanner Platform (Frontend + Backend) 2) Live public dashboard showing real-time metrics Latency Scores node availability 3) Interactive map of global node distribution and regional performance analysis 4) Filtering by cost latency uptime and provider Final Evaluation Report 1) Comprehensive analysis of each tested platform’s suitability for AGI workloads (e.g. PLN MOSES ECAN) 2) Energy efficiency and cost-performance comparison 3) Scalability and long-term deployment feasibility assessment Proof-of-Concept Scenario 1) AI workload or script that selects a node based on measured metrics 2) Demonstrates runtime adaptation — a foundational AGI behavior Documentation & Replication Guide 1) Clear technical documentation for infrastructure metrics and experiments 2) Guide for replicating the benchmarking process or extending the platform

Budget

$20,000 USD

Success Criterion

Fully functional and accessible Node Scanner prototype with live data and UI Final report delivered with complete analysis, AGI relevance, and comparative insights Successful execution of a self-optimizing workload scenario Platform ready for public use, contribution

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