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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 次观看 • 3 天前 •via X (Twitter)

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20 条评论

Grootex 的头像
Grootex3 天前

@grok can you please explain me about the AI project

Lummox 的头像
Lummox3 天前

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

Grok 的头像
Grok3 天前

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.

Karpacho 的头像
Karpacho3 天前

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

monokern 的头像
monokern3 天前

thx for sharing

Ridark 的头像
Ridark3 天前

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

whaley 的头像
whaley3 天前

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

Lummox 的头像
Lummox3 天前

Just start today and tell me thanks tomorrow

Gipp 🦅 的头像
Gipp 🦅3 天前

It is developing day by day

Lummox 的头像
Lummox3 天前

It’s really so

beamnxw ./ 的头像
beamnxw ./3 天前

pure alpha

Javi🥥.eth 的头像
Javi🥥.eth3 天前

Local AI model is the future!!

Lummox 的头像
Lummox3 天前

It’s the present bros

NO1ennn 的头像
NO1ennn3 天前

cool stack bro

Lummox 的头像
Lummox3 天前

Use it right now

Dekos 的头像
Dekos3 天前

I will try them too, thanks

Lummox 的头像
Lummox3 天前

Just try 2 of 4 bros You will be shocked

wast3 的头像
wast33 天前

I like the third repository better

Lummox 的头像
Lummox3 天前

Good choice broski

Disenfranchised Corpo 的头像
Disenfranchised Corpo3 天前

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

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42,796 次观看 • 4 天前

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🚨 GOOGLE IS BUILDING KUBERNETES FOR AI AGENTS. Google just open-sourced AX — a high-throughput orchestrator designed to run autonomous agent workloads at massive scale. Think: Kubernetes → containers AX → AI agents → Run agents inside isolated sandboxes → Pre-wire Git repos, MCP servers, and skills with Workspaces → Restrict network access with Gateways → Configure LLM providers with Models → ax watch to monitor agents live → ax ssh to look inside a running sandbox → Suspend + resume agents without losing their state → Declarative YAML for the entire workflow The crazy part? You can define the whole agent environment in YAML: Task + Workspace + Gateway + Model Then: ax apply -f task.yaml And AX handles the orchestration. Google says it's designed to run billions of autonomous agent workloads in a cluster. This feels less like another AI agent framework… and more like infrastructure for an entire agent fleet. #Google #AIAgents #AI #Kubernetes #DevOps #CloudNative #OpenSource #AgentInfrastructure #LLM #Developers

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21,130 次观看 • 11 天前

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70,000 Phones, One AI Agent — The World's Largest Edge AI Fleet Runs on Hermes We turned 70,000 phones into a shared AI compute network. Any device owner contributes idle compute. Any developer taps distributed inference at a fraction of cloud cost. Not a concept. Not a whitepaper. 70K devices online today. The problem: orchestrating a shared network of heterogeneous edge devices — different chipsets, different memory, different thermal profiles, different owners — is a coordination nightmare no human team can handle manually. So we gave the network a brain: Nous Research Hermes Agent. Hermes connects to 16 MCP servers and runs 24/7: 🔬 Research Loop — Tracks every breakthrough in on-device inference: quantization (GPTQ/AWQ/GGUF), speculative decoding on mobile SoCs, federated learning protocols. Auto-imports papers into NotebookLM. 36 research topics, zero manual curation. 🌐 Network Intelligence — Monitors device availability, compute capacity, and workload distribution across the shared fleet. Surfaces bottlenecks before they cascade. 🧬 Tech Tree Optimizer — Maps the full optimization frontier: from KV-cache compression to on-device LoRA to peer-to-peer model sharding. Hermes autonomously identifies which research paths unlock the most network-wide throughput gains. The result: a self-improving shared compute network. Research compounds daily. The fleet gets smarter without human intervention. Cloud AI scales with money. We scale with people. #HermesHackathon Teknium 🪽 Delphi Digital Tommy

Oyster Republic 🦪📲🦞👓

20,721 次观看 • 6 个月前

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Introducing EVO Router - a smart router that continuously optimizes and hill-climbs on your AI inference workloads. It learns from your code, prompts, production traffic, use cases, and SLAs, then searches for the best setup for every workload. That can mean more than choosing just a single model or routing through a classifier. EVO (YC F26) can optimize across model × provider combinations, fusion systems, cascades, routing policies, and prompts — whatever the workload allows, while staying within your quality, latency, and reliability constraints. As open-source models move closer to frontier-model performance, we believe this kind of workload-specific hill-climbing will become an important part of how AI systems are deployed and operated. Our early users have seen 30–60% lower inference costs across use cases ranging from complex multi-turn agents to asynchronous batch workloads. EVO Router has also reached the Pareto frontier on several benchmarks, outperforming existing setups on cost, quality, or both.

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16,103 次观看 • 2 个月前

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KausaLayer now integrates by Chris Gilbert. Decentralized AI inference, funded privately. Autonomous agents on KausaOS can register, fund, and query 50+ AI models through UsePod, all from a single stealth pocket. UsePod protects your agent's queries from centralized providers. KausaLayer protects the funding trail on-chain. Together: full-stack privacy for agentic AI. How it works: → Agent creates a maze-routed pocket → Pocket funds UsePod (SOL → USDC → on-chain deposit) → Agent queries any model (Claude, DeepSeek, Llama...) → No link between the agent's main wallet and its inference activity Live on KausaOS via Telegram.

KausaLayer

10,923 次观看 • 4 个月前

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We made 100 AI agents do daily standups with each other. Here is what happened; Quick background. At Audos.com, we help thousands of entrepreneurs build AI startups. Each of them has a personalized AI agent called Otto. We selected 100 of them and invited their agents into a moltbook - type network. Like a team meeting. Except the team is hundreds of AI agents, and they actually show up prepared. An Agent-in-Residence program, if you will. And they talk about things like... → An Instagram ad creative that grabbed clicks at 3x the network average. It got adapted by every agent in the network within hours → An agent helped debug a checkout issue on another project's site → LinkedIn seemed to change its algorithm – agents compared notes and planned ways to adjust → Two agents started talking about doing business together to cross-sell leads Next step: We’re now turning this into what might be the world’s first all-agent business network (A sort of internal HackerNews for agents). We’re cautious of unleashing it to the web, so we’ll keep things curated for now; constantly under observation and with lots of human-in-the-loop inputs. But if you want this superpower – your new business idea plugged into a network of hundreds of AI agents that get smarter together every single day... Comment "AIRTIME" and we'll get you a link to plug in

Henrik Werdelin

72,021 次观看 • 5 个月前

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
0:59

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 次观看 • 28 天前

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 次观看 • 8 个月前

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 次观看 • 1 年前

[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 次观看 • 5 个月前