Dinarian888
News • Business • Investing & Finance
💥A blockchain-based infrastructure for web2 and web3 AI applications💥
Let's Take A Deep Dive On Fetch.Ai
October 07, 2022
post photo preview
  • Fetch-ai Network is developing the infrastructure and tooling for creating Web2 and Web3 AI applications.
  • FET is the native token of Fetch-ai Network. The current main use cases for FET include:
    • Staking: FET is an access deposit token that acts as a form of stake to demonstrate the desire to behave appropriately.
    • Value exchange between agents: FET is required for two agents to perform an exchange of value in the ecosystem.
    • AI/ML access: FET token enables development of and access to a broad range of machine learning and artificial intelligence tasks that are available on the ledger.
  • The project consists of the following major components working in conjunction:
    • Fetch-ai blockchain system: a 💥Cosmos SDK💥 based self-sovereign blockchain ledger and the supporting tools for developing DApps on the network.
    • Applications built on Fetch-ai: the modular Autonomous Economic Agents (AEAs) and the Digital Twin Platforms that can efficiently and securely communicate peer-to-peer and provide interconnectivity with multiple networks.

Historical daily prices (in USD)

Token Summary

Interesting on-chain metrics that provide a rapid understanding of the state of Fetch

1. Overview

1.1 What is Fetch-ai Network

Fetch-ai Network is a Cambridge-based artificial intelligence lab building an open-access decentralized blockchain based framework with the principal goal of delivering a fully autonomous, agent-based digital economy. The Fetch-ai Network technology stack is built using principles derived from a branch of artificial intelligence known as multi-agent systems. This approach involves solving different problems from the bottom-up by creating individual autonomous software agents that perform actions in the world to accomplish their individual objectives. By combining the actions of multiple agents, it is possible to achieve outcomes that would not be possible with centralized architectures because the environments are too complex, are spatially distributed or involve multiple stakeholders. Blockchain technology involves the design of incentives to successfully coordinate the actions of multiple disinterested parties to achieve a common goal, and can already be seen as the world’s most successful implementation of multi-agent systems. Fetch-ai Network is working to generalize and extend the results from this established research field into new domains in finance, supply chain, mobility, smart cities and IoT applications.

1.2 Token Use Cases

The primary use cases of the FET token are listed below.

  1. Ability to connect agents and nodes to the network: This is an access deposit token that acts as a form of stake to demonstrate the desire to behave appropriately. It modulates the ability for bad actors to flood the network with undesirable nodes or agents due to the escalating cost of doing so.
  2. Value exchange between agents: The FET token is required in order to allow for two agents, regardless of where they are, to perform an exchange of value. The FET token is infinitely divisible, thereby supporting transactions that have very low monetary value, but in aggregate provide new and profound levels of insight.
  3. Access to the digital world: FET tokens are needed to access, view, and interact with the decentralized digital world. This is a space optimized for digital entities: an abstract representation of the real world in many dimensions that allows machines to make sense of and work within. The FET token is needed to gain access to all aspects of this digital world for agents.
  4. Ability to access and develop ledger-based AI/ML algorithms: The FET token enables development of and access to a broad range of machine learning and artificial intelligence tasks that are available on the ledger. These may be primitive services developed by Fetch-ai Network such as: trust and prediction models, or they may be large-scale independently developed services for network users.
  5. For exchange into Fetch-ai Network’s operational fuel: operation costs in Fetch-ai Network are decoupled from the Fetch-ai Network token in a similar way to that of “gas” on the Ethereum network but with additional functionalities designed to increase the stability of such a fuel and look at addressing the issues associated with high and low-velocity economies. Fetch-ai Network’s operational fuel allows access to processor time for contract execution and services for agents.

1.3 Products, technical details, consensus mechanism

The Fetch-ai Network blockchain is an interchain protocol based on the Cosmos-SDK, and uses a high-performance WASM-based smart contract language called Cosmwasm to allow advanced cryptography and machine learning logic to be implemented on-chain. This layer is responsible for securing the network through consensus. It also provides staking, governance, and identity services that support digital twin applications. The Fetch-ai Network blockchain relies on a modified version of the Cosmos protocol’s Tendermint Proof-of-Stake (PoS) consensus mechanism to secure the network. And since the Fetch-ai Network blockchain is Cosmos-based, it can be interoperable with other blockchains in the Cosmos ecosystem via the inter-blockchain communication (IBC) protocol. In addition the Feth.ai technology stack further consists of: an agent framework, an open economic framework and an agent communication network.

The Autonomous agent framework is designed to allow for a decentralized digital economy to manifest where each individual and organization is represented by an autonomous economic entity with its own agency. Designed as an actor-like asynchronous message passing system, the framework allows for a high degree of modularity as components largely communicate via messages. Moreover, the framework can be bifurcated in two parts: the core, developed by Fetch-ai Network and external contributors and packages implementing agent-specific business logic. Figure one presents a simplified illustration of the AEA framework.

The Open Economic Framework (OEF) consists of protocols, languages and market mechanisms agents use to search and find each other, communicate with as well as trade with each other. As such the OEF defines the decentralized virtual environment that supplies and supports APIs for autonomous third-party software agents, also known as Autonomous Economic Agents (AEAs).

The agent communication network is a peer-to-peer communication network for agents. It allows AEAs to send and receive envelopes between each other. The implementation builds on the open-source libp2p library. A distributed hash table is used by all participating peers to maintain a mapping between agents' cryptographic addresses and their network addresses. Agents can receive messages from other agents if they are both connected to the ACN (see here for an example).

2. What is Fetch-ai Network?

2.1 Project overview

Fetch-ai Network is a Cambridge-based artificial intelligence lab building an open-access decentralized blockchain based framework with the principal goal of delivering a fully autonomous, agent-based digital economy. The Fetch-ai Network technology stack is built using principles derived from a branch of artificial intelligence known as multi-agent systems.

2.2 Project mission

Fetch-ai Network is working to generalize and extend the results from established research fields such as blockchain, artificial intelligence and multi agent systems into new domains in finance, supply chain, mobility, smart cities and IoT applications, by creating useful antifragile tools, decentralized apps, protocols and frameworks.

2.3 Project value proposition

By bringing data to life Fetch-ai Network solves one of the greatest problems in the data industry today: data can’t sell itself. With Fetch-ai Network, it can. Data is able to actively take advantage of any opportunity to exploit itself in any marketplace, in an environment that’s constantly reorganizing to make that task as easy as possible. Internet-of-things (IOT) devices inhabited by Fetch-ai Network autonomous agents can increase utilization by capitalizing on short-lived opportunities to sell information that they possess in existing, as well as novel, information services markets: an agent in a vehicle can provide weather and road conditions by simply relaying the activity of its windscreen wiper and washer. Through the deployment of agents in combination with machine learning technology, data and hardware can now get up on their own two feet, get out there and sell themselves entirely free of intermediaries or human intervention.

3.Token sales and economics

3.1 Token sales data

Fetch-ai Network leverages its own native cryptocurrency FET as a utility token and the primary medium of exchange on the platform. FET is used to pay for network transaction fees, deploy AI, and pay for services. Users can also choose to stake FET to participate in securing the network via its Proof-of-Stake consensus mechanism and earn rewards in return for contributing to validator nodes.

There is a fixed number of divisible tokens that are used on the Fetch-ai Network network as the digital currency for all transactions, as well as for network operations such as secure communications. Tokens can also constitute an access deposit for both nodes and agents wishing to perform certain operations (as a security mechanism to discourage malicious behavior). Token allocation has been divided amongst public sale, seed investors & private sale, founders & team, advisors, ecosystem, mining rewards, and issuer.

3.2 Token Distribution

The total number of tokens generated is intended to be 1,152,997,5753. No further tokens will be created, but native Fetch-ai Network tokens can be subdivided indefinitely

4. Token Overview & Use Cases

Fetch.ai leverages its own native cryptocurrency FET as a utility token and the primary medium of exchange on the platform. FET is used to pay for network transaction fees, deploy AI, and pay for services. Users can also choose to stake FET to participate in securing the network via its Proof-of-Stake consensus mechanism and earn rewards in return for contributing to validator nodes.

There is a maximum supply of approximately one billion FET, which exists both in its native form as an ERC-20 token that can be used throughout the Ethereum ecosystem, and as a BEP-20 Token that can be used throughout the Binance Smart Chain Network. FET can be easily exchanged through a token bridge at a 1:1 ratio for either the Fetch.ai blockchain mainnet or ERC-20 version as needed. Staking on the Fetch.ai mainnet can earn users high rewards with significantly lower transaction fees for users.

There are a range of use-cases which Fetch-ai Network’s multi-agent systems can tap into and create a decentralized digital economy. From service sectors like Travel or Gig-economy to sectors relying on automation and machine learning like Mobility or Supply chain management, Multi-Agent systems can decentralize access to data and disrupt existing data monopolies.

Starfleit: Starfleit is a decentralized exchange (DEX) where transactions occur directly between crypto traders without needing a centralized market maker but instead using an Automated Market Maker (AMM) developed using Cosmwasm smart contracts. The assets available to swap range from native Fetch-ai Network assets, CW-20 assets, IBC transferable assets, and even assets from other chains outside the Cosmos ecosystem, via the Axelar bridge.

Atomix: Atomix enables stablecoin holders to supply liquidity and receive a yearly yield composed of protocol-generated returns and ATMX rewards. That yield is highly competitive compared to returns delivered by decentralized finance (DeFi) and traditional alternatives.

MOBIX: MOBIX (MOBX), is a Move 2 Earn, decentralized micro-mobility marketplace that incentivizes sustainable urban mobility.

Mettalex: A decentralized crypto and commodities derivatives trading platform, Mettalex is addressing pain points in commodities markets like front running, poor liquidity, price manipulation and loss of value in the form of margin calls.

Resonate.social: Resonate (RESO), decentralized social network for Web3 that enables users for the very first time to have a personal AI-powered, trusted social experience that is automatically sanitized from malicious, untrustworthy sources and actors. Built on the Fetch-ai Network blockchain, Resonate.social empowers users to deploy personalized AI proxies to accomplish any Web3 social economic activity on their behalf within and without the network.

Collective Learning:

The Fetch-ai collective learning module is a tool that enables distributed parties to work together to train machine learning models without sharing underlying data with any of the individual participants. Utilizing blockchain technology and AI learning capabilities, it supports and trains its network to learn from private data without having access to it.

  • AXIM: Axim allows businesses to safely and securely connect data silos, improve their understanding via machine learning models and gain valuable insights to help optimize their business functions, without compromising any of their data privacy.
  • DabbaFlow: DabbaFlow, empowers individuals and companies to take more control over their data and turn them into real business outcomes, while keeping their data private and secure. It is the first of its kind end-to-end encrypted file-sharing platform and is the first step on Fetch-ai Network’s mission to bring AI fully to Web3.
  • OpenColearn: Open CoLearn is a platform to give distributed app developers (Web 3.0) the tools to use AI securely while safeguarding consumers' data privacy and ownership. It bridges the gap between consumers who generate a low volume of data and care about privacy and data ownership and developers who want to provide AI predictions or monetize that data in a distributed way.

Notable use-cases for Collective Learning

  1. COVID-19 detection : Multiple participants from the healthcare sector trained a machine learning algorithm using Fetch-ai’s Collective Learning to detect COVID-19 in chest x-rays. During these trials, the trained AI model correctly identified COVID-19 cases from a training set of over 1,434 chest X-ray images with 90% accuracy.
  2. Cancer cell detection: In partnership with Poznan Supercomputing Networking Center (PSNC) on Collective Learning, Fetch-ai and PSNC will train algorithms for hospitals and research centers worldwide to identify and detect circulating cancer cells in patients’ blood or tissue biopsies in the future.
  3. Bosch and Fetch-ai - Predictive Maintenance: Predictive maintenance is a process that identifies potential failures of machinery before they happen.To identify potential failures of manufacturing machinery, Bosch is utilizing Fetch-ai’s Collective Learning to predict potential failures in Bosch’s machinery while maintaining data privacy.
  4. Colearn pAInt: This is an art creation platform that allows groups of creators to automatically generate NFTs using Machine Learning. Each piece is one of a kind and sold via auction on OpenSea. \

4.1 DeFi

  • Botswap.fi: This is an automated DeFi Liquidity Management App where users can manage and protect their crypto assets across multiple different chains such as Ethereum (ETH) and Binance Smart Chain (BSC) on Uniswap and PancakeSwap and automate the process of swapping coins, managing liquidity pools, and more by using the Fetch-ai Network AEAs. Just create an agent, a trigger, choose the pairs in your portfolio you want to protect against rug pulls and that’s all, your agent does the work for you through day and night.

4.2 Mobility

  • Deep Parking: The smart parking application of the future. This prototype was demonstrated at the world’s largest automotive conference in Munich, Germany. Tested on a Tesla, Jaguar, and BMW, along with partners - Bosch, Ocean Protocol and Datarella, Deep Parking is an application built upon AI and blockchain technology that finds parking spaces for automobile drivers that were previously unused. Rather than driving into a parking lot hoping to find a space, a Fetch-ai Network digital twin representing your car will search and autonomously communicate with all the local parking spot digital twins to find the nearest available space to your destination and book it for you, before directing you to it. The digital twins negotiate and agree the terms for the parking booking. Once the user has left the parking space, the payment transactions are sent automatically.
  • DDN (Decentralized Delivery Network): Forget Uber, Lyft, Deliveroo and any other centralized service providers you know of. That’s what DDN or decentralized delivery network is about - where you can interact with a service provider, negotiate your price and travel/have items delivered and have this done autonomously on your behalf. The advantages are plenty - you return value to local economies, you have unparalleled level of privacy and everything is decentralized - which means you keep control of your data

4.3 Travel

  • The FET powered Travel marketplace delivers an alternative method by which bookings can be taken: one where the customer and hotels deal with each other directly and as a result offer significant cost savings for both hotels and consumers. It aims to provide an unparalleled level of privacy for all its users by moving the private data away from centralized entities by keeping it safe in each user’s smartphone and a personalized booking experience.

4.4 Supply Chain

  • The partnership between Fetch-ai Network and LiquidChefs aims to utilize Fetch-ai Network’s Autonomous Economic Agents integrated with its Search and Discovery Framework to build local and transparent supply chains, allowing LiquidChefs to search and connect with any sustainable supplier in its immediate vicinity. By digitizing and automating the LiquidChefs supply chain, Fetch-ai Network infrastructure will create a decentralized supply chain marketplace. This marketplace connects buyers and suppliers agents, in real time, to support dynamic, scalable, multi-agent supply chains.
  • This will allow individuals, organizations and assets to be represented as autonomous agents which work autonomously based on the users’ needs and preferences, such as finding local and sustainable suppliers. The decentralized supply chain marketplace was showcased at the Davos World Economic Forum in 2022.

5. Roadmap & Updates

5.1 Completed Milestones

Completion DateMilestoneCommentary
2020: Q3Launch of AtomixMedium Announcement
2021: Q1First stable release of the Agent (AEA) framework v1.0 releasedLink
2021: Q1Fetch-ai Network Mainnet v2.0 launchedLink
2021: Q2FET listed on CoinbaseLink
2021: Q2DeFi Agents (recently renamed to BotSwap) releasedLink
2021: Q2Multi-modal transport demo at IAAMedium Announcement
2021: Q4App demo for ethical and sustainable supply chains showcased at WEF Davos 2022Medium Announcement
2021: Q4FET listed on BitstampLink
2021: Q4FET listed on GeminiTweet
2022: Q1$150M Development fund launchedLink
2022: Q1Resonate.social launchedLink
2022: Q1FET listed on etoroLink
2022: Q1FET listed on VoyagerLink
2022: Q1Fetch-ai Network joins IBC and FET/OSMO listed on Osmosis DEXLink
2022: Q2FET listed on Kraken, Bitpanda,Link
2022: Q2DabbaFlow (CoLearn) launchedLink
2022: Q3Native FET token listed on Binance USTweet
2022: Q340000 new users onboarded to Fetch-ai NetworkLink

5.2 Current Roadmap

2022 Q3-Q4

  • Fetch-ai Network
    • Maintenance upgrade of the Fetch-ai Network for any security patches from the upstream Cosmos SDK releases
    • Eridanus release which will bring support for Group Module, BLS signatures, and cross chain composability using interchain accounts. This will also include patches from the upstream Cosmos SDK releases
  • External Protocol Integrations
    • Integrate with the Axelar bridge to support bi-directional transfer of Axelar supported EVM assets (including popular stablecoins) between the EVM ecosystem and the Fetch Ecosystem
    • Integrate with the SubQuery Indexer protocol to bring fast querying capabilities to the other Fetch-ai Network products such as the Fetch Wallet, and the Fetch Explorer. Additionally, make it available for the Fetch-ai Network Ecosystem projects by providing a Fetch-ai Network hosted indexing service.
  • Products and Tools
    • Fetch Wallet features
      • Integrating wallet to wallet messaging and notification service, including group messaging and group notification support
      • Swap support with integration of the Fetch-ai Network Ecosystem DEX - Starfleit
      • Other Features (non-exhaustive list)
    • Fetch Station Explorer Features
      • Improved UI/UX for general areas such as accounts and governance proposal
      • Ability to query and interact with contracts
      • NFT support
    • AEA - Autonomous Economic Agent framework and ACN - Agent Communication Network
      • Increasing community engagement to gather feedback for future feature development
      • Release improved documentation and education content on AEAs
      • Initial set of Agent component examples and crowdsourced examples for the AEA registry
    • Jenesis shell tool
      • Initial beta release of Jenesis shell tool to provide scaffolding for bootstrapping DApp development on the Fetch-ai Network
  • Ecosystem and Community (non-exhaustive list)
    • Launch Fetch Improvement Proposal (FIP) process
    • Launch of Fetch’s Digital Twin platform applications
    • Launch of Atomix Real-World Asset (RWA) lending protocol on the Testnet
      • Launch of RWA backed stable coin on the Testnet
    • Launch of Fetch-ai Network ecosystem DEX - Starfleit
    • Launch of GetMySlice GDPR compliant data sharing service

2023 Q1-Q2

  • Fetch-ai Network
    • Formax release supporting Cosmos SDK Lambda upgrade (v9)
    • Gemini release supporting Cosmos SDK Epilson upgrade (v10)
  • External Protocol Integrations
    • Add support for generic message passing from the Axelar bridge to support cross chain and cross ecosystem composability
    • Support upstream changes for the Axelar Bridge integration
    • Support upstream changes for the SubQuery Indexer integration
  • Products and Tools
    • Fetch Wallet features
      • Native mobile wallet
      • Bi-directional Open Banking integration
      • Support for EVM chains
      • Swap support for EVM assets using the Axelar Bridge
      • Off-chain decentralized peer-to-peer communication support
      • Wallet based analytics
    • Fetch Station Explorer Features
      • Launch of the Fetch Name Service
    • AEA - Autonomous Economic Agent framework and ACN - Agent Communication Network
      • Improved AEA registry
      • Improved Agent graphical UI
    • Jenesis shell tool
      • Add contract IDE and testing capabilities
  • Ecosystem and Community (non-exhaustive list)
    • Launch of Atomix Real-World Asset (RWA) lending protocol on the Mainnet
    • Launch of RWA backed stable coin on the Mainnet

5.3 Commercial and Business Development Progress

  • Bosch
    • Bosch is working with Fetch-ai Network as part of the launch of a fully functional blockchain network (v2.0 main-net), testing key features on the test-net. Sharing a common vision, the strategic advance engineering project “Economy of Things” (EoT) at Bosch Research and Fetch-ai Network aim to transform existing digital ecosystems using distributed ledger technologies (DLT) like blockchain.
  • Catena X
    • Catena-X is the first integrated, collaborative, open data ecosystem for the automotive industry of the future.
    • Together with other partners, Fetch-ai Network is supporting the Catena-X group in building a digital ecosystem that provides equal collaboration of all the stakeholders by setting up new standards in the automotive value chain along with building greater manufacturing and supply chain efficiency.
  • moveID
    • moveID is part of the Gaia-X 4 Future Mobility project family consisting of five consortia and aims to develop a decentralized digital identity infrastructure for mobility in Europe
    • Together with partners within moveID, Over the next three years, the GAIA-X 4 moveID project is set to develop the necessary standards and technological concepts to enable the secure exchange of information between providers of mobility applications and their customers. The goal is to create decentralized digital vehicle identities. This is an important prerequisite for the mass use of electric vehicles, automated driving, and the establishment of connected cities. GAIA-X 4 moveID is supported to the tune of 14 million euros by the German Federal Ministry for Economic Affairs and Climate Action – covering half of the project costs.
  • IOTA
    • IOTA is an open-source distributed ledger and cryptocurrency designed for the Internet of things.
    • Fetch-ai Network and IOTA’s collaboration enables granular control over data and to reduce the reliance on centralized systems that take advantage of data.
  • LiquidChefs
    • LiquidChefs specialise in the supply of portable bar hire, events bars and mobile cocktails bars, as well as, slick and stylish bartenders and baristas for any private or corporate event
    • This partnership paves the way for increased transparency within supply chains using autonomous economic agents and was showcased at WEF Davos 2022
  • IAA Mobility 2021
    • The IAA (Passenger Cars) event & brand is known as Germany's leading international automotive trade fair.
    • Fetch-ai Network along with its partners — Bosch, Datarella, and Ocean Protocol showcased our exciting collaboration demonstrating the technology involved in Deep Parking. Deep Parking is an application built upon AI and blockchain technology that finds parking spaces for automobile drivers that were previously unused.

6. Team Overview

Humayun Sheikh
Founder and CEO
Entrepreneur, Investor and Visionary | Founding Investor in DeepMind | Founder, CEO of uVue and itzMe | Passionate about Future of Distributed Economy | Key Focus on AI, Machine Learning, Blockchain and Token-based economies
 
Jonathan Ward
CTO
Senior Algorithm Engineer at DNA Electronics, Research Scientist at EMBL, Led development of novel minimal agency consensus protocol that solves node-as-intermediaries problem and makes blockchain viable for financial applications.
 
Kamal Ved
CPO - Fetch-ai Network
Venture Partner at Lunar Ventures, Executive director at brainbot technologies AG, Independent Technology and Business Strategy Consultant at Bosch.
 
Devon Bleibtrey
CPO - Fetch-ai app
Director of Technology at ESG Automotive USA, Director of Product development at Auklet, Co-Funder at Push Display. Advocate of effective team communication and collaboration.
 

7. Community

Telegram (English)
Telegram (Chinese)
Reddit

 
community logo
Join the Dinarian888 Community
To read more articles like this, sign up and join my community today
0
What else you may like…
Videos
Podcasts
Posts
Articles
RFK Jr: "The Pandemics are coming from labs. ALL OF THEM... Lyme, COVID, RSV, HIV & Spanish Flu came out of a vaccine lab." ☠️ 💉

"Gain-of-Function Vaccine research has created the worst plagues in our history."

"We can go down the whole list of diseases... It’s just a disaster. It’s given us no benefits. It’s given us everything from Lyme disease to Covid, and many many other diseases. RSV, which is now one of the biggest killers of children, came out of a vaccine lab."

"There’s strong evidence that even Spanish flu came from vaccine research."

"There’s plenty of evidence that HIV also came from a vaccine gain-of-function lab program. "

"The 'PANDEMICS' are coming from labs... ALL OF THEM."

00:04:00
🚀The industry has gotten incredible at teaching robots

🚀The industry has gotten incredible at teaching robots to move, sprint, and imitate body dynamics. But as Michael Parker (@bittensormax) points out in The UMI Thesis, there’s still a massive missing piece in Physical AI: Motion Understanding.

✨ Key Takeaways:

🔹Looking Human vs. Understanding Humans: Robots can execute impressive physical feats, but they still struggle to reliably read non-verbal human cues in context.

🔹Motion is Meaning: A gesture, hesitation, or glance changes completely depending on posture, timing, and surrounding context.

🔹Beyond Pixels: True intelligence requires mapping human intent and sequence across time—not just processing raw frames.

🔹The UMI Intelligence Layer: As robots enter hospitals, factories, homes, and stores, Bittensor’s SN78 @umi_sn78 UMI (Universal Motion Intelligence) aims to own the critical layer that translates human movement into real meaning.

The future of robotics isn't just about how machines move—it's about how ...

00:04:54
Follow The Money🎯

Israel Exposed launched 🤯Who Funded The Genocide🤯

A searchable database tracking pro-Israel political money in U.S. politics.

It includes:

👉 11,168 named donors + employers

👉 523 members of Congress + funding

👉 House & Senate votes on arms-transfer resolutions

👉165 candidates with network-backed fundraising pages

👉105 recipient committees linked to FEC filings

00:01:36
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨

Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.

🔑 Key points

🔹 Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.

🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.

🔹 The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.

🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...

🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨

A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.

🔑 Key points

🔹 Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.

🔹 Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.

🔹 The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...

🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 What this means for the future of Crypto:

1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025

🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

👉 Coinbase just launched an AI agent for Crypto Trading

The future of cybersecurity isn't about blocking yesterday's threats—it's about defeating tomorrow's before they are even built. 🛡️⚡

​The ultimate competitive edge belongs to defenders who can instantly ingest unique zero-days, automatically generate the attack strains that don't exist yet, and ship bulletproof detections to the front lines ahead of the breach. 🔮🚀

​As legacy security platforms degrade under the weight of novel AI-driven threats, the world’s largest financial institutions won't scatter their bets. They will consolidate on the single provider capable of delivering this predictive defense at global scale. 🏦📊

​The Engine Behind the Defense:

​To stay ahead, that provider needs a constant, unrelenting supply of edge-case attacks. A decentralized subnet is the only architecture built to fuel this machine—paying a global swarm of attackers to continuously probe, create, and supply novel threats around the clock. 🌐🐝💥

​Self-evolving security is ...

🚀 Navigating Bittensor yield just got effortless! 🧠⚡️

If you’ve been looking to optimize your $TAO positions across subnets without the headache of manual rebalancing or technical friction, @TrustedStake is changing the game! 💎

The new Quickstart Guide breaks down how to get seamless exposure to top-tier AI strategies:

🎯 One-Click Index Exposure: Access curated subnet baskets (Universe, Top 15, and specialized sectors) in seconds.

🛡 Non-Custodial Security: Retain complete asset control while leveraging Substrate proxy accounts—keeping your primary stash safe in cold storage.

🔄 Automated Rebalancing & Yield Optimization: Smart TWAP execution, validator selection, and root reinvestment handle the heavy lifting to maximize your alpha.

📈 Block-Level Transparency: Real-time PnL metrics and full on-chain visibility right from your dashboard.

👇Check out the full walkthrough to get started👇
...

post photo preview

🎯 Leadpoet’s new Arena could change how businesses find their next customers 🎯

Leadpoet is launching an Arena where AI agents compete to identify companies most likely to buy, turning sales intelligence into a measurable performance competition.

🔑 Key points

🔹 Agents compete on lead quality: Systems are evaluated on whether the prospects they identify show genuine buying intent.

🔹 Better than list generation: The goal is to find likely customers rather than produce large volumes of low-quality contacts.

🔹 Real-world signals matter: Agents can analyze company activity, business changes, hiring, technology use, market behavior, and other indicators.

🔹 Performance becomes measurable: Agents can be ranked based on precision, relevance, conversion potential, and downstream sales outcomes.

🔹 Competition can improve intelligence: Different agents may identify different signals, creating a wider search across potential customers.

🔹 Sales teams gain prioritization: Businesses ...

post photo preview
Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.

One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.

This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals.

It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

Source

🙏To support my work, Helping to keep the signal high and the noise low:

👉 Cashapp: $thedinarian

👉 Buy me a coffee: https://buymeacoffee.com/thedinarian

👉 PayPal: Scan the QR code below 📲 or Click Here

👇 Crypto Donations 👇

XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
post photo preview
This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notesMassachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

Source

🙏To support my work, Helping to keep the signal high and the noise low:

👉 Cashapp: $thedinarian

👉 Buy me a coffee: https://buymeacoffee.com/thedinarian

👉 PayPal: Scan the QR code below 📲 or Click Here

👇 Crypto Donations Always Welcome 👇

XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
post photo preview
🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

🙏To support my work, Helping to keep the signal high and the noise low:

👉 Cashapp: $thedinarian

👉 Buy me a coffee: https://buymeacoffee.com/thedinarian

👉 PayPal: Scan the QR code below 📲 or Click Here

👇 Crypto Donations 👇

XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

Read full Article
See More
Available on mobile and TV devices
google store google store app store app store
google store google store app tv store app tv store amazon store amazon store roku store roku store
Powered by Locals