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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 +...

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Lummox

7,834 subscribers

22,028 просмотров • 3 дней назад •via X (Twitter)

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Комментарии: 20

Фото профиля Grootex
Grootex3 дней назад

@grok can you please explain me about the AI project

Фото профиля Lummox
Lummox3 дней назад

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

Фото профиля Grok
Grok3 дней назад

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.

Фото профиля Karpacho
Karpacho3 дней назад

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

Фото профиля monokern
monokern3 дней назад

thx for sharing

Фото профиля Ridark
Ridark3 дней назад

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

Фото профиля whaley
whaley3 дней назад

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

Фото профиля Lummox
Lummox3 дней назад

Just start today and tell me thanks tomorrow

Фото профиля Gipp 🦅
Gipp 🦅3 дней назад

It is developing day by day

Фото профиля Lummox
Lummox3 дней назад

It’s really so

Фото профиля beamnxw ./
beamnxw ./3 дней назад

pure alpha

Фото профиля Javi🥥.eth
Javi🥥.eth3 дней назад

Local AI model is the future!!

Фото профиля Lummox
Lummox3 дней назад

It’s the present bros

Фото профиля NO1ennn
NO1ennn3 дней назад

cool stack bro

Фото профиля Lummox
Lummox3 дней назад

Use it right now

Фото профиля Dekos
Dekos3 дней назад

I will try them too, thanks

Фото профиля Lummox
Lummox3 дней назад

Just try 2 of 4 bros You will be shocked

Фото профиля wast3
wast33 дней назад

I like the third repository better

Фото профиля Lummox
Lummox3 дней назад

Good choice broski

Фото профиля Disenfranchised Corpo
Disenfranchised Corpo3 дней назад

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

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72,021 просмотров • 5 месяцев назад

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THIS IS F**KING DANGEROUS SOMEONE JUST TURNED CLAUDE INTO AN AUTONOMOUS PENTESTER. HexStrike AI connects an LLM to 150+ professional cybersecurity tools through MCP. You get: → 12 specialized AI agents → 150+ security tools → Network & web security testing → Automated pentesting workflows → Local execution on Kali Linux The wild part? Claude autonomously generated a SQL injection payload, ran the test, and solved a PortSwigger lab. AI + real security tooling is getting serious. REPO BELOW

MAX

61,693 просмотров • 28 дней назад

LIVE NOW - AI on Ethereum: 8004, x402 and the Botconomy AI agents are moving onchain, so Ethereum needs new rails for identity, reputation, and payments. We had Davide Crapis and Austin Griffith to break down: ERC-8004: “AI passports” for agents (identity + reputation + validation) Why reputation + verification matters when bots become the main onchain users x402: pay-per-call payments for agent-to-agent services What “agent commerce” looks like when bots can discover → trust → pay The Clawdbot/OpenClaw moment, and why the next UX is talking to your wallet Watch now. -------------- TIMESTAMPS 0:00 “AI is the new UI,” and you’ll be talking to your wallet 4:09 Is there an arms race for AI activity across chains? 7:32 The Clawdbot/OpenClaw moment: agents get OS-level power 13:18 Why crypto is native to agents 19:41 Austin’s setup: Claude bot, heartbeat loops, and real autonomy 22:28 The token incident: how a bot “got a treasury” 28:46 Prompt injection meets wallets 38:20 ERC-8004 explained: identity, reputation, validation 40:35 x402 + 8004: discovery + payment rails for agent commerce 48:38 8004 “scans” and early adoption reality 1:05:32 What agents could build when they can hire other agents 1:12:38 Tooling for the new builder era: Speedrun → Wingman 1:25:23 “Worst time to be a junior dev…” 1:28:15 Predictions: clean English becomes the sign of AI
1:37:18

LIVE NOW - AI on Ethereum: 8004, x402 and the Botconomy AI agents are moving onchain, so Ethereum needs new rails for identity, reputation, and payments. We had Davide Crapis and Austin Griffith to break down: ERC-8004: “AI passports” for agents (identity + reputation + validation) Why reputation + verification matters when bots become the main onchain users x402: pay-per-call payments for agent-to-agent services What “agent commerce” looks like when bots can discover → trust → pay The Clawdbot/OpenClaw moment, and why the next UX is talking to your wallet Watch now. -------------- TIMESTAMPS 0:00 “AI is the new UI,” and you’ll be talking to your wallet 4:09 Is there an arms race for AI activity across chains? 7:32 The Clawdbot/OpenClaw moment: agents get OS-level power 13:18 Why crypto is native to agents 19:41 Austin’s setup: Claude bot, heartbeat loops, and real autonomy 22:28 The token incident: how a bot “got a treasury” 28:46 Prompt injection meets wallets 38:20 ERC-8004 explained: identity, reputation, validation 40:35 x402 + 8004: discovery + payment rails for agent commerce 48:38 8004 “scans” and early adoption reality 1:05:32 What agents could build when they can hire other agents 1:12:38 Tooling for the new builder era: Speedrun → Wingman 1:25:23 “Worst time to be a junior dev…” 1:28:15 Predictions: clean English becomes the sign of AI

Bankless

46,809 просмотров • 8 месяцев назад

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):
15:38

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 просмотров • 1 год назад

[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
1:40

[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 просмотров • 5 месяцев назад