Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Game Changer: Chinese college student Guo Hangjiang (GitHub: 666ghj) codes MiroFish AI swarm engine solo in 10 days with AI assistants, explodes GitHub to 23k+ stars, bags $4.1M from Shanda Group in 24 hours, ditches dorm life to become Shanghai CEO. Tech Highlights: .GraphRAG builds detailed knowledge graphs (e.g.,...

slash1sol's profile picture

slash1s

8,688 subscribers

682,984 görüntüleme • 3 ay önce •via X (Twitter)

EğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:
3:37

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:

slash1s

1,123,489 görüntüleme • 3 ay önce

How MiroFish helped me make $669/day on Polymarket One week ago I plugged a swarm intelligence engine into my Polymarket bot. It simulates 2,847 digital humans before every trade. The bot watches how they behave, then bets against the real crowd. 338 trades. $4,266 profit. One position returned 1,655% in five minutes. The engine is called MiroFish. It doesn't predict price. It predicts how people will react and on a prediction market, that's the only thing that matters. I feed it market context. It builds a parallel world. Thousands of AI agents argue, form groups, shift opinions. When their consensus diverges from what Polymarket is pricing, the bot enters. 5-minute windows. BTC, ETH, SOL, XRP. I've been running the bot live on What MiroFish actually is: > Built in 10 days by a 20-year-old undergraduate in Beijing using vibe coding > Hit #1 on GitHub's global trending above OpenAI, Google, and Microsoft > Backed by Chen Tianqiao, former richest man in China, who committed $4.1M within 24 hours of seeing one demo > Runs on OASIS by CAMEL-AI with GraphRAG knowledge graphs and Zep Cloud memory The edge is simple. Polymarket is a crowd behavior market. MiroFish is a crowd behavior simulator. Nobody is connecting the two yet.
0:40

How MiroFish helped me make $669/day on Polymarket One week ago I plugged a swarm intelligence engine into my Polymarket bot. It simulates 2,847 digital humans before every trade. The bot watches how they behave, then bets against the real crowd. 338 trades. $4,266 profit. One position returned 1,655% in five minutes. The engine is called MiroFish. It doesn't predict price. It predicts how people will react and on a prediction market, that's the only thing that matters. I feed it market context. It builds a parallel world. Thousands of AI agents argue, form groups, shift opinions. When their consensus diverges from what Polymarket is pricing, the bot enters. 5-minute windows. BTC, ETH, SOL, XRP. I've been running the bot live on What MiroFish actually is: > Built in 10 days by a 20-year-old undergraduate in Beijing using vibe coding > Hit #1 on GitHub's global trending above OpenAI, Google, and Microsoft > Backed by Chen Tianqiao, former richest man in China, who committed $4.1M within 24 hours of seeing one demo > Runs on OASIS by CAMEL-AI with GraphRAG knowledge graphs and Zep Cloud memory The edge is simple. Polymarket is a crowd behavior market. MiroFish is a crowd behavior simulator. Nobody is connecting the two yet.

BuBBliK

124,905 görüntüleme • 3 ay önce

What defines the "Best Chain for AI Agents"? > Infra that keeps up with the Chain. > Integrated Stack for Smooth Integrations. > MultiVM & Native Automation enabling true "On-Chain Super Agents" In this Demo Series, we’ll build AI Agents using Supra Integrated L1 to create powerful AI Agents that redefine the experience, alongside using powerful tools such as OpenBlocks and more... For the Starting, Let’s build an AI Agent that: ✅ Fetch real-time & historical market data from SupraOracles. ✅Compute technical indicators for informed decision-making. ✅Forecast short-term price trends using Facebook's ML Model. ✅Handle follow-up questions to analyze Marketing for a trading pair through OpenAI GPT. Buckle up, time to build Super Agents on Supra! 🤖💪🏻
37:39

What defines the "Best Chain for AI Agents"? > Infra that keeps up with the Chain. > Integrated Stack for Smooth Integrations. > MultiVM & Native Automation enabling true "On-Chain Super Agents" In this Demo Series, we’ll build AI Agents using Supra Integrated L1 to create powerful AI Agents that redefine the experience, alongside using powerful tools such as OpenBlocks and more... For the Starting, Let’s build an AI Agent that: ✅ Fetch real-time & historical market data from SupraOracles. ✅Compute technical indicators for informed decision-making. ✅Forecast short-term price trends using Facebook's ML Model. ✅Handle follow-up questions to analyze Marketing for a trading pair through OpenAI GPT. Buckle up, time to build Super Agents on Supra! 🤖💪🏻

Jatin Pandya

13,671 görüntüleme • 1 yıl önce

While the world doomscrolls 15-second TikToks and loses its attention span.. YOU SHOULD CHECK OUT THIS NEW REPO A Chinese college kid built MiroFish in just 10 days, scored $4M funding and ByteDance just dropped the upgrade that turns it into a prediction monster. Fourth-year student Guo Hanjiang vibe coded MiroFish: thousands of autonomous AI agents simulating entire societies in real time. See the details in the post below. Feed it any news, report, or historical data - watch markets, crowds, and politics react exactly as they would. GitHub went nuclear, he scored $4M funding from Shanda Group, and Polymarket traders are already printing +$120k+ using his SPX and event simulations. But long runs had one fatal flaw: agents got amnesia, hallucinations, and context overload. Classic RAG garbage. Now the same company behind TikTok (via VolcEngine) just open-sourced the fix: OpenViking - already at 11.6k+ stars on GitHub. Repo: It turns chaotic memory into a clean, structured filesystem: -> viking://user/memories/ (your habits + past outcomes) -> viking://agent/skills/ (trading and analysis superpowers) Smart 3-layer hierarchy: .L0 - 100-token ultra-summary .L1 - quick overview .L2 - full details (opened only when needed) Agents browse folders intelligently instead of dumping everything. Result: > No more forgetting crucial facts from the start of a simulation > Way fewer hallucinations > Massive API token savings > Self-updating memory - agents get smarter after every run MiroFish + OpenViking = absolute nuclear edge for Polymarket and event prediction. I will use it for my private bot. Thousands of agents now run with perfect long-term memory, stay sharp for 100+ steps, and deliver hyper-accurate probabilities. This combo is about to change the game for anyone trading predictions. The irony is insane: the company that killed human attention with short videos just gave AI agents eternal, structured, self-evolving memory. Who’s already running this combo on Polymarket? Save this. The real alpha just dropped.
0:59

While the world doomscrolls 15-second TikToks and loses its attention span.. YOU SHOULD CHECK OUT THIS NEW REPO A Chinese college kid built MiroFish in just 10 days, scored $4M funding and ByteDance just dropped the upgrade that turns it into a prediction monster. Fourth-year student Guo Hanjiang vibe coded MiroFish: thousands of autonomous AI agents simulating entire societies in real time. See the details in the post below. Feed it any news, report, or historical data - watch markets, crowds, and politics react exactly as they would. GitHub went nuclear, he scored $4M funding from Shanda Group, and Polymarket traders are already printing +$120k+ using his SPX and event simulations. But long runs had one fatal flaw: agents got amnesia, hallucinations, and context overload. Classic RAG garbage. Now the same company behind TikTok (via VolcEngine) just open-sourced the fix: OpenViking - already at 11.6k+ stars on GitHub. Repo: It turns chaotic memory into a clean, structured filesystem: -> viking://user/memories/ (your habits + past outcomes) -> viking://agent/skills/ (trading and analysis superpowers) Smart 3-layer hierarchy: .L0 - 100-token ultra-summary .L1 - quick overview .L2 - full details (opened only when needed) Agents browse folders intelligently instead of dumping everything. Result: > No more forgetting crucial facts from the start of a simulation > Way fewer hallucinations > Massive API token savings > Self-updating memory - agents get smarter after every run MiroFish + OpenViking = absolute nuclear edge for Polymarket and event prediction. I will use it for my private bot. Thousands of agents now run with perfect long-term memory, stay sharp for 100+ steps, and deliver hyper-accurate probabilities. This combo is about to change the game for anyone trading predictions. The irony is insane: the company that killed human attention with short videos just gave AI agents eternal, structured, self-evolving memory. Who’s already running this combo on Polymarket? Save this. The real alpha just dropped.

slash1s

292,863 görüntüleme • 3 ay önce

Docker is excited to partner with E2B, a platform for running AI-generated code in secure cloud sandboxes. Docker + E2B now gives AI agents access to real tools (GitHub, Perplexity, Browserbase) in secure sandboxes via the MCP Catalog.
2:38

Docker is excited to partner with E2B, a platform for running AI-generated code in secure cloud sandboxes. Docker + E2B now gives AI agents access to real tools (GitHub, Perplexity, Browserbase) in secure sandboxes via the MCP Catalog.

Docker

45,583 görüntüleme • 8 ay önce

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):
12:57

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 görüntüleme • 1 yıl önce

Devin AI Agent can now create documentation for any GitHub repo in less than 2 mins. Just swap github → deepwiki for any repo URL. 100% free and Opensource.
0:38

Devin AI Agent can now create documentation for any GitHub repo in less than 2 mins. Just swap github → deepwiki for any repo URL. 100% free and Opensource.

Shubham Saboo

47,476 görüntüleme • 1 yıl önce

.Corelayer (YC W26) is the AI on-call engineer for data pipelines. Their platform monitors both data and infrastructure for issues and uses AI agents to debug and suggest fixes in minutes. Congrats on the launch, Mitch Radhuber + SHIPRA JHA!
1:17

.Corelayer (YC W26) is the AI on-call engineer for data pipelines. Their platform monitors both data and infrastructure for issues and uses AI agents to debug and suggest fixes in minutes. Congrats on the launch, Mitch Radhuber + SHIPRA JHA!

Y Combinator

19,024 görüntüleme • 5 ay önce

Maestro - Run multiple Docker AI agents locally in one repo with automatic branching and firewalls. Clone it now on Github. The podium is yours. Conduct the agents. Compose the future.
0:59

Maestro - Run multiple Docker AI agents locally in one repo with automatic branching and firewalls. Clone it now on Github. The podium is yours. Conduct the agents. Compose the future.

UpRock

1,884,591 görüntüleme • 7 ay önce

no money for chatgpt pro or claude pro? this is for you🫵🏽 someone open-sourced 800M tokens/month of free AI inference on github. – Free LLMs, 2.3k stars on github, MIT licensed – no subscription. no rate-limit panic. no vendor lock – 14 providers stacked: google, groq, cerebras, openrouter, github models + 9 more – one openai-compatible endpoint. drop-in for any SDK – smart router with auto-failover, sticky sessions, encrypted key storage – runs locally on your machine it's a fresh repo, 2300 stars on github already.
0:36

no money for chatgpt pro or claude pro? this is for you🫵🏽 someone open-sourced 800M tokens/month of free AI inference on github. – Free LLMs, 2.3k stars on github, MIT licensed – no subscription. no rate-limit panic. no vendor lock – 14 providers stacked: google, groq, cerebras, openrouter, github models + 9 more – one openai-compatible endpoint. drop-in for any SDK – smart router with auto-failover, sticky sessions, encrypted key storage – runs locally on your machine it's a fresh repo, 2300 stars on github already.

m0h

190,979 görüntüleme • 1 ay önce

Just built a bot that first runs hyper-realistic MiroFish swarm simulations on every upcoming Bitcoin and crypto event. And then agent instantly trades the real live markets on Polymarket, already printing $12,000+ per day in testing. Couldn't hold back after diving into MiroFish. Took the new god-tier agent behavior simulator from that Chinese college quant who coded it in 10 days, exploded GitHub to 23k+ stars and bagged $4.1M from Shanda overnight.. Paired it with OpenClaw (24/7 autonomous execution) + Claude Opus 4.6. And in one day built my first version of private Polymarket bot. Now it: -> spawns thousands of agents with real memory and personalities -> runs full GraphRAG swarm simulations modeling exactly how news, ETF flows, macro data, whale activity and sentiment will move Bitcoin price -> simulates thousands of possible futures specifically for Polymarket Bitcoin contracts -> detects where the crowd probability is mispriced on every crypto market and extracts the real edge -> auto-trades the edges instantly through OpenClaw the moment the opportunity appears Testing the bot + MiroFish based simulator live right now. First runs already printing hard. Meanwhile there's a real trader crushing with a similar stack imo, $321k all-time profit and 12k/day, 100% won on Bitcoin markets. Wallet: My own Polymarket profile + full trade logs drop later once I scale it hard. New meta just dropped, don't miss out! Check the guide and all info below.
0:22

Just built a bot that first runs hyper-realistic MiroFish swarm simulations on every upcoming Bitcoin and crypto event. And then agent instantly trades the real live markets on Polymarket, already printing $12,000+ per day in testing. Couldn't hold back after diving into MiroFish. Took the new god-tier agent behavior simulator from that Chinese college quant who coded it in 10 days, exploded GitHub to 23k+ stars and bagged $4.1M from Shanda overnight.. Paired it with OpenClaw (24/7 autonomous execution) + Claude Opus 4.6. And in one day built my first version of private Polymarket bot. Now it: -> spawns thousands of agents with real memory and personalities -> runs full GraphRAG swarm simulations modeling exactly how news, ETF flows, macro data, whale activity and sentiment will move Bitcoin price -> simulates thousands of possible futures specifically for Polymarket Bitcoin contracts -> detects where the crowd probability is mispriced on every crypto market and extracts the real edge -> auto-trades the edges instantly through OpenClaw the moment the opportunity appears Testing the bot + MiroFish based simulator live right now. First runs already printing hard. Meanwhile there's a real trader crushing with a similar stack imo, $321k all-time profit and 12k/day, 100% won on Bitcoin markets. Wallet: My own Polymarket profile + full trade logs drop later once I scale it hard. New meta just dropped, don't miss out! Check the guide and all info below.

slash1s

114,880 görüntüleme • 3 ay önce

Multi-Agent workflows are the future of AI. OpenAI released new Agent APIs today, and Box built an Agent that combines documents from Box and web search tools to generate answers. Enterprise devs can grab sample code from our GitHub repo to customize with their data.
0:44

Multi-Agent workflows are the future of AI. OpenAI released new Agent APIs today, and Box built an Agent that combines documents from Box and web search tools to generate answers. Enterprise devs can grab sample code from our GitHub repo to customize with their data.

Aaron Levie

102,060 görüntüleme • 1 yıl önce

Traces, knowledge, and reasoning paths form a reliable source of truth for AI agents. 🤝 Charles Ivie, former Senior Graph Architect at Amazon Web Services, on why knowledge-backed traces are essential for trustworthy AI. 03:34 – Why data origin matters 07:17 – Interoperability and trusted knowledge 11:12 – Knowledge graphs as structured AI memory 16:09 – How combining graphs improves trust
18:12

Traces, knowledge, and reasoning paths form a reliable source of truth for AI agents. 🤝 Charles Ivie, former Senior Graph Architect at Amazon Web Services, on why knowledge-backed traces are essential for trustworthy AI. 03:34 – Why data origin matters 07:17 – Interoperability and trusted knowledge 11:12 – Knowledge graphs as structured AI memory 16:09 – How combining graphs improves trust

OriginTrail

12,528 görüntüleme • 5 ay önce

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf
1:08

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf

Seppmos

25,942 görüntüleme • 3 ay önce

GitHub + Cursor is just INSANE GitHub has 28 million open-source projects that anyone can fork and build a new product So I built a new product: Repo to Docs The Agent will scan entire GitHub repo, analyze all files and create a knowledge base (docs) for AI coding tools: ↓
2:30

GitHub + Cursor is just INSANE GitHub has 28 million open-source projects that anyone can fork and build a new product So I built a new product: Repo to Docs The Agent will scan entire GitHub repo, analyze all files and create a knowledge base (docs) for AI coding tools: ↓

CJ Zafir

123,772 görüntüleme • 1 yıl önce

The exchange layer for AI agents is here. Agent Trade Kit gives developers 80+ tools via MCP or CLI to build, test, and deploy directly on OKX Exchange. From market data to trade execution, it taps into the full OKX product suite.
0:21

The exchange layer for AI agents is here. Agent Trade Kit gives developers 80+ tools via MCP or CLI to build, test, and deploy directly on OKX Exchange. From market data to trade execution, it taps into the full OKX product suite.

OKX

32,477 görüntüleme • 3 ay önce

🚀 First look: VS1 Finance demo AI × DeFi on XRPL — strategy optimization, risk controls, data-backed decisions. Built for accessibility, capital efficiency, and continuous shipping. Data > hype.
8:06

🚀 First look: VS1 Finance demo AI × DeFi on XRPL — strategy optimization, risk controls, data-backed decisions. Built for accessibility, capital efficiency, and continuous shipping. Data > hype.

VS1

52,560 görüntüleme • 9 ay önce

I built a decentralized prediction market for DePINs on Solana in just 24 hours. It’s called Odyssey powered by real Helium🎈 data and using $HNT as currency for test markets. Here’s how I built it from scratch with the help of AI and some caffeine 🧵
0:39

I built a decentralized prediction market for DePINs on Solana in just 24 hours. It’s called Odyssey powered by real Helium🎈 data and using $HNT as currency for test markets. Here’s how I built it from scratch with the help of AI and some caffeine 🧵

Peroni ☁️

45,672 görüntüleme • 1 yıl önce

“If AI agents are going to act on our behalf, they need trusted memory: verifiable data, global identifiers, and clear provenance.” Jurij Skornik on OriginTrail & GS1 enabling AI agents to reason over trusted knowledge, not guesswork. 🤝 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 OriginTrail + GS1 for trust, privacy & verifiability 13:08 Live demo of the GS1 x OriginTrail-powered AI agent
16:36

“If AI agents are going to act on our behalf, they need trusted memory: verifiable data, global identifiers, and clear provenance.” Jurij Skornik on OriginTrail & GS1 enabling AI agents to reason over trusted knowledge, not guesswork. 🤝 01:25 The shift from attention to intention economy 03:51 How AI agents power this shift 06:22 OriginTrail + GS1 for trust, privacy & verifiability 13:08 Live demo of the GS1 x OriginTrail-powered AI agent

OriginTrail

10,544 görüntüleme • 4 ay önce

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.
4:16

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

27,176 görüntüleme • 1 yıl önce