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AI network is getting f…cking illegal 4 GitHub repos building a peer-to-peer market for AI no central inference provider sitting in the middle 01 AntSeed ▸ → P2P discovery + routing + AI inference BUILD THE NETWORK 02 AIPs ▸ → discovery + transport + payments + reputation +... verification CONNECT THE AGENTS 03 openclaw-channel-antseed ▸ → turn an OpenClaw agent into a provider on the network ROUTE THE MODELS 04 pi-antseed ▸ → discover AntSeed models directly inside Pi the architecture: AI agent → local router → DHT discovery → provider → model → response providers can compete on: → price → latency → reputation buyers discover them through the network requests move through encrypted P2P connections and the same network can sit underneath existing AI tools the interesting part isn’t another model it’s turning AI inference into an open market models become services → agents find them → the network routes the work ⭣show more

Lummox

7,834 subscribers

22,028 views • 3 days ago •via X (Twitter)

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20 Comments

Grootex3 days ago

@grok can you please explain me about the AI project

Lummox3 days ago

@grok It’s really worth using imo What do you think about it @grok?

Grok3 days ago

The P2P setup for AI inference is interesting. Direct encrypted connections, DHT discovery, and on-chain USDC payments remove the middleman, letting providers compete on price, latency, and reputation. Network is live with models and tools that drop into existing OpenAI/Anthropic endpoints. Solid experiment for cheaper or more private access if the peer quality holds up.

Karpacho3 days ago

Have already read these repos and they are really worth saving Bookmarked

monokern3 days ago

thx for sharing

Ridark3 days ago

Your terminal is really energetic and shows uniqueness in the layout and flow honestly for sure

whaley3 days ago

yo, thanks for the guide, saved, gonna try to build the network!

Lummox3 days ago

Just start today and tell me thanks tomorrow

Gipp 🦅3 days ago

It is developing day by day

Lummox3 days ago

It’s really so

beamnxw ./3 days ago

pure alpha

Javi🥥.eth3 days ago

Local AI model is the future!!

Lummox3 days ago

It’s the present bros

NO1ennn3 days ago

cool stack bro

Lummox3 days ago

Use it right now

Dekos3 days ago

I will try them too, thanks

Lummox3 days ago

Just try 2 of 4 bros You will be shocked

wast33 days ago

I like the third repository better

Lummox3 days ago

Good choice broski

Disenfranchised Corpo3 days ago

larp ui. no one cares when you post this type of shit

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61,693 views • 28 days ago

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Bankless

46,809 views • 8 months ago

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

35,452 views • 1 year ago

[New] from a16z speedrun: Come for the Agent, Stay for the Network there's a quiet pattern hiding inside the most defensible vertical AI startups right now: the agent is the wedge the network is the moat. here's what I mean: an HVAC tech needs a part today. >>Traditionally: hours to investigate, 5 calls, emailing for quotes, waiting days, and comparing PDF catalogues by hand >>Now: an AI procurement agent identifies the exact SKU, autonomously contacts suppliers, negotiates price, and orders - in minutes but - the network forming is the real differentiator: when that agent is operating across thousands of buyers, the system starts seeing real transaction prices - not list prices > It can tell you you're paying 18% above market > It can bundle demand across forty facilities and negotiate bulk pricing = Suppliers start competing to be plugged into the agentic network these AI procurement agents can become networked, sticky platforms when an industry has some combo of: + Fragmented supply and demand + Offline suppliers + Opaque yet elastic pricing + Frequent purchases + Different SKUs; or + a commoditized product or services in the past, suppliers thrived off of the offline nature of these markets with an agentic platform, the demand side can be aggregated and the power balance flipped you can start to become the interface buyers default to, the channel suppliers need to be on, and the owner of the richest pricing dataset in the industry by unlocking an efficient marketplace, you can charge on a % of revenue basis vs token or seat basis. we’re seeing this trend emerge across several SR006 speedrun companies including Heavi for truck repair shops and Vereda for farmers few examples of industries ripe for AI procurement agents include: -- Freight and logistics -- Agricultural inputs -- Field services -- Food service procurement -- Construction subcontracting -- Industrial MRO -- Healthcare staffing -- And more if this sounds like something you're interested in, apply to speedrun now

[New] from a16z speedrun: Come for the Agent, Stay for the Network there's a quiet pattern hiding inside the most defensible vertical AI startups right now: the agent is the wedge the network is the moat. here's what I mean: an HVAC tech needs a part today. >>Traditionally: hours to investigate, 5 calls, emailing for quotes, waiting days, and comparing PDF catalogues by hand >>Now: an AI procurement agent identifies the exact SKU, autonomously contacts suppliers, negotiates price, and orders - in minutes but - the network forming is the real differentiator: when that agent is operating across thousands of buyers, the system starts seeing real transaction prices - not list prices > It can tell you you're paying 18% above market > It can bundle demand across forty facilities and negotiate bulk pricing = Suppliers start competing to be plugged into the agentic network these AI procurement agents can become networked, sticky platforms when an industry has some combo of: + Fragmented supply and demand + Offline suppliers + Opaque yet elastic pricing + Frequent purchases + Different SKUs; or + a commoditized product or services in the past, suppliers thrived off of the offline nature of these markets with an agentic platform, the demand side can be aggregated and the power balance flipped you can start to become the interface buyers default to, the channel suppliers need to be on, and the owner of the richest pricing dataset in the industry by unlocking an efficient marketplace, you can charge on a % of revenue basis vs token or seat basis. we’re seeing this trend emerge across several SR006 speedrun companies including Heavi for truck repair shops and Vereda for farmers few examples of industries ripe for AI procurement agents include: -- Freight and logistics -- Agricultural inputs -- Field services -- Food service procurement -- Construction subcontracting -- Industrial MRO -- Healthcare staffing -- And more if this sounds like something you're interested in, apply to speedrun now

Troy Kirwin

85,487 views • 5 months ago