Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Introducing the LorisTools API. Built for teams that need clean crypto derivatives data without maintaining brittle exchange integrations. • Real-time and historical funding rates • Open interest, volume, markets, and liquidation data • Cross-venue coverage across 43+ perp exchanges • Normalized endpoints for dashboards, models, and alerts • Simple...

LorisTools's profile picture

Loris Tools

3,912 subscribers

30,309 Aufrufe • vor 1 Monat •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

.01.xyz is now live on Loris Tools • Real-time funding rates from 01 alongside 25+ other venues • Open interest and volume tracking to spot positioning shifts as they happen • Liquidation data streaming live for 01's perpetual futures markets • Full historical funding charts: backtest any 01 pair's funding against every other exchange Check it out →
0:21

.01.xyz is now live on Loris Tools • Real-time funding rates from 01 alongside 25+ other venues • Open interest and volume tracking to spot positioning shifts as they happen • Liquidation data streaming live for 01's perpetual futures markets • Full historical funding charts: backtest any 01 pair's funding against every other exchange Check it out →

Loris Tools

15,298 Aufrufe • vor 4 Monaten

We're making comprehensive, real-time crypto market data accessible to AI agents 🤖 CoinGecko API now supports x402, the open payment protocol developed by Coinbase 🛡️ that lets AI agents (such as OpenClaw🦞 🦞) pay for crypto price and market data using USDC. Learn how it works 👇
0:28

We're making comprehensive, real-time crypto market data accessible to AI agents 🤖 CoinGecko API now supports x402, the open payment protocol developed by Coinbase 🛡️ that lets AI agents (such as OpenClaw🦞 🦞) pay for crypto price and market data using USDC. Learn how it works 👇

CoinGecko

145,218 Aufrufe • vor 5 Monaten

The Bloomberg Terminal of Prediction Markets. Private beta is live with Polymarket and Kalshi, delivering actionable insights for serious traders. - Real-time price tracking - Trading volume & liquidity data - Cross-market arbitrage - Recent trades & top holders - Edge discovery across markets - Graded Buy & Sell Signals Built by seasoned data and analytics veterans, to turn data into a trading advantage, now it’s your turn to test it. Private Beta is live. Like and reply for access.
0:35

The Bloomberg Terminal of Prediction Markets. Private beta is live with Polymarket and Kalshi, delivering actionable insights for serious traders. - Real-time price tracking - Trading volume & liquidity data - Cross-market arbitrage - Recent trades & top holders - Edge discovery across markets - Graded Buy & Sell Signals Built by seasoned data and analytics veterans, to turn data into a trading advantage, now it’s your turn to test it. Private Beta is live. Like and reply for access.

Predictefy

63,375 Aufrufe • vor 7 Monaten

OptimAI Search is now on Agentic(.)Market - and we’re bringing the stack to Base. Builders can plug into live, citation-backed context across web, social, and crypto data through MCP, API, and SDK - then pay seamlessly with x402. No brittle workflows. No manual stitching. Just agent-native access to real-time intelligence at scale. And with custom domain agents on the marketplace, specialized knowledge becomes reusable. Search is becoming infrastructure. Build with it: 💡 💡
1:06

OptimAI Search is now on Agentic(.)Market - and we’re bringing the stack to Base. Builders can plug into live, citation-backed context across web, social, and crypto data through MCP, API, and SDK - then pay seamlessly with x402. No brittle workflows. No manual stitching. Just agent-native access to real-time intelligence at scale. And with custom domain agents on the marketplace, specialized knowledge becomes reusable. Search is becoming infrastructure. Build with it: 💡 💡

OptimAI Network

62,432 Aufrufe • vor 2 Monaten

Today, we’re releasing Polymarket Journalism Tools, a free suite of tools for writers and researchers using prediction market data. Inside: Draft Analyzer: Paste your article and instantly find relevant polymarkets, ranked by volume and salience. Market Timeline: Find which news stories moved the odds, plotted directly on the price history. Suggest a Polymarket: Don't see the market you need? Send your idea directly to our markets creation team. PolyVideo: Sync Polymarket odds with any YouTube video and find the exact moments that the odds moved. API Explorer: Build your own Polymarket data pipelines the easy way. Read More 👇
1:21

Today, we’re releasing Polymarket Journalism Tools, a free suite of tools for writers and researchers using prediction market data. Inside: Draft Analyzer: Paste your article and instantly find relevant polymarkets, ranked by volume and salience. Market Timeline: Find which news stories moved the odds, plotted directly on the price history. Suggest a Polymarket: Don't see the market you need? Send your idea directly to our markets creation team. PolyVideo: Sync Polymarket odds with any YouTube video and find the exact moments that the odds moved. API Explorer: Build your own Polymarket data pipelines the easy way. Read More 👇

The Oracle

43,162 Aufrufe • vor 4 Monaten

Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.
0:56

Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's agent framework. These 17 agents work alongside SuperClaude's 14 base development agents, bringing the total available agent count to 31. The key difference: instead of generic AI responses to crypto queries, developers now have access to specialized agents trained for specific blockchain domains, from smart contract auditing to MEV research to DeFi strategy optimization. How the Agents Work After installation, the agents operate automatically based on query context. When you ask Claude Code to perform crypto-related tasks, the appropriate specialist agent is invoked: - "Audit this smart contract" → Crypto Security Researcher - "Find yield farming opportunities on Ethereum" → Crypto DeFi Strategist - "Analyze this wallet's transaction history" → Crypto Wallet Detective - "Identify arbitrage opportunities across DEXs" → Crypto DEX Arbitrageur No manual agent selection required. The system recognizes the task and routes it to the appropriate specialist. The 17 Specialized Agents Market & Trading Intelligence (4 agents) Crypto Quant: Mathematical models, algorithmic trading strategies, statistical arbitrage, and quantitative risk modeling. Crypto Market Researcher: Fundamental analysis, market trends, institutional adoption tracking, and regulatory landscape monitoring. Crypto Derivatives Trader: Futures and perpetuals analysis, options strategies, leverage management, and derivatives market intelligence. Crypto DEX Arbitrageur: Cross-exchange arbitrage identification, MEV strategy development, and automated profit extraction techniques. DeFi & Liquidity (4 agents) Crypto DeFi Strategist: Yield farming optimization, protocol analysis, liquidity provision strategies, and DeFi portfolio management. Crypto Liquidity Manager: Pool optimization, impermanent loss calculation and mitigation, market making strategies, and capital efficiency analysis. Crypto Governance Analyst: DAO structure evaluation, governance token analysis, proposal assessment, and voting mechanism research. Crypto Bridge Analyst: Cross-chain bridge security assessment, protocol comparison, interoperability solutions, and bridge risk evaluation. Security & Risk (3 agents) Crypto Security Researcher: Smart contract auditing, vulnerability detection, honeypot identification, and exploit pattern recognition. Crypto Security Engineer: Secure contract development practices, defensive programming patterns, and security implementation guidance. Crypto Risk Manager: Portfolio risk assessment, compliance monitoring, exposure analysis, and risk mitigation strategy development. On-Chain Analysis (3 agents) Crypto Wallet Detective: Blockchain forensics, wallet behavior analysis, transaction tracing, and entity identification across chains. Crypto On-chain Analyst: Transaction pattern analysis, wallet clustering, flow tracking, and on-chain metrics interpretation. Crypto MEV Researcher: MEV opportunity detection, flashloan arbitrage analysis, sandwich attack identification, and MEV protection strategies. Specialized Intelligence (3 agents) Crypto NFT Specialist: Collection valuation, rarity analysis, marketplace trends, and NFT ecosystem intelligence. Crypto Stablecoin Analyst: Peg stability monitoring, collateral analysis, depegging risk assessment, and stablecoin mechanism evaluation. Crypto Social Sentiment: Social media sentiment tracking, influencer monitoring, trending topic identification, and community analysis. Data Coverage: - 60+ blockchain networks - 2,000+ DeFi protocols - Real-time DEX data - CEX trading metrics - Social sentiment feeds - NFT marketplace data Compatibility: Works seamlessly with SuperClaude's existing agent framework. No configuration conflicts or manual routing needed. Practical Applications Smart Contract Development Security agents can audit contracts during development, identifying reentrancy risks, access control issues, and common vulnerabilities before deployment. DeFi Research Strategy agents query real-time pool data across networks, calculate yield-adjusted returns, and assess risks like impermanent loss or smart contract exposure. Trading Analysis Market agents access derivatives data, funding rates, liquidation levels, and order book depth across exchanges for informed trading decisions. Forensic Investigation On-chain agents trace fund flows, identify connected addresses, and analyze transaction patterns for security research or compliance work. Portfolio Management Risk agents evaluate protocol exposure, assess tail risks, and monitor positions across multiple chains and protocols. Why Specialized Agents Matter Generic AI models lack the domain-specific knowledge required for professional crypto work. A general-purpose AI might provide surface-level analysis of a smart contract, but a specialized security agent understands Solidity patterns, common exploits, and auditing methodologies. The agent framework solves this by routing tasks to specialists with deep domain knowledge: - A derivatives question goes to an agent trained on perpetuals, funding rates, and options greeks - A DeFi query reaches an agent that understands liquidity mathematics and protocol mechanics - A security audit is handled by an agent familiar with vulnerability patterns and exploit techniques This specialization produces more accurate, actionable insights than single-model approaches. Getting Started The agents are available now through npm. Requirements: - Node.js 16+ - Claude Code installed - No additional dependencies After installation, simply use Claude Code normally. When you ask crypto-related questions or request blockchain analysis, the appropriate agent is automatically invoked. The system handles routing, data retrieval, and response generation. Documentation covers individual agent capabilities, example queries, and integration patterns for different workflows. What This Enables With 17 specialized crypto agents, Claude Code becomes a comprehensive blockchain development and analysis environment: - Developers can audit contracts, optimize gas usage, and implement security patterns - Researchers can analyze protocols, compare yields, and assess risks - Traders can evaluate markets, identify opportunities, and manage positions - Security professionals can investigate exploits, trace funds, and assess vulnerabilities The agents provide access to blockchain data and specialized analysis that previously required multiple tools, APIs, and manual research. ghive.

Hive Intelligence

78,743 Aufrufe • vor 9 Monaten

I just built an AI agent that’s 10x smarter than anything using basic search APIs. Here’s what nobody’s telling you about AI development right now. Most developers are stuck using limited search APIs. They’re missing social media data, forums, live news, and answer engines. Their AI is effectively blind to 90% of the public web. The result: Stale data. Weak responses. And endless engineering overhead just to stitch everything together. What changed everything for me was Bright Data’s Web Discovery platform. Instead of juggling multiple APIs and unreliable sources, I got real-time access to every public data source through one unified API. Google. Bing. Twitter. Reddit. Instagram. TikTok. ChatGPT. Perplexity. Even historical web archives going back years. Here’s why this actually matters in practice: • One API instead of 10+ fragmented integrations • Real-time, constantly refreshed public web data • Coverage across search engines, social platforms, forums, and answer engines • Consistent data structure that just works • Way less time fighting data plumbing, way more time building intelligence I used it to build a real-time pricing monitor that tracks competitor pricing, social sentiment, and trending topics at the same time. Something that would’ve taken weeks of integration work happened in a single afternoon. The real breakthrough isn’t just access. It’s consistency. Reliability. And freedom. If you’re building search agents, RAG pipelines, or any AI-driven product, you’re handicapping yourself without comprehensive web data. Check the link in the comments to try it yourself. They’re offering trial credits, and the documentation is actually solid. This is the difference between AI products that work and AI products that dominate. Check it out here:
1:38

I just built an AI agent that’s 10x smarter than anything using basic search APIs. Here’s what nobody’s telling you about AI development right now. Most developers are stuck using limited search APIs. They’re missing social media data, forums, live news, and answer engines. Their AI is effectively blind to 90% of the public web. The result: Stale data. Weak responses. And endless engineering overhead just to stitch everything together. What changed everything for me was Bright Data’s Web Discovery platform. Instead of juggling multiple APIs and unreliable sources, I got real-time access to every public data source through one unified API. Google. Bing. Twitter. Reddit. Instagram. TikTok. ChatGPT. Perplexity. Even historical web archives going back years. Here’s why this actually matters in practice: • One API instead of 10+ fragmented integrations • Real-time, constantly refreshed public web data • Coverage across search engines, social platforms, forums, and answer engines • Consistent data structure that just works • Way less time fighting data plumbing, way more time building intelligence I used it to build a real-time pricing monitor that tracks competitor pricing, social sentiment, and trending topics at the same time. Something that would’ve taken weeks of integration work happened in a single afternoon. The real breakthrough isn’t just access. It’s consistency. Reliability. And freedom. If you’re building search agents, RAG pipelines, or any AI-driven product, you’re handicapping yourself without comprehensive web data. Check the link in the comments to try it yourself. They’re offering trial credits, and the documentation is actually solid. This is the difference between AI products that work and AI products that dominate. Check it out here:

Hasan Toor

100,511 Aufrufe • vor 6 Monaten

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 Aufrufe • vor 10 Monaten

i got you - prediction markets need a dedicated trading terminal + analytics dashboard to attract more volume and provide greater clarity to traders on their performance. traders need data visualizations which are geared towards tracking prediction oriented metrics like calibration curve + brier score. after spending months talking to prediction market traders on twitter - i built out this prototype (mock data for now since Kalshi doesn't offer auth in their API). over the last few months i've become prediction market pilled - believing in a future where experts in topics can profit from their knowledge and a world where most people can make better decisions for their future based off of prediction market odds. a world where someone can see the odds of a jerome powell interest rate cut and decide whether or not to buy a house. a world where someone can directly see the odds of an election and have better data on whether they should move somewhere accordingly. (i want to work in prediction markets btw)
0:23

i got you - prediction markets need a dedicated trading terminal + analytics dashboard to attract more volume and provide greater clarity to traders on their performance. traders need data visualizations which are geared towards tracking prediction oriented metrics like calibration curve + brier score. after spending months talking to prediction market traders on twitter - i built out this prototype (mock data for now since Kalshi doesn't offer auth in their API). over the last few months i've become prediction market pilled - believing in a future where experts in topics can profit from their knowledge and a world where most people can make better decisions for their future based off of prediction market odds. a world where someone can see the odds of a jerome powell interest rate cut and decide whether or not to buy a house. a world where someone can directly see the odds of an election and have better data on whether they should move somewhere accordingly. (i want to work in prediction markets btw)

steve

17,525 Aufrufe • vor 8 Monaten

Architect’s traditional perpetuals exchange AX, one of the fastest-growing regulated derivatives exchanges in history, is opening trading to individuals and omnibus brokers. With $3B notional in <6 months in institutional trading, we’re excited to welcome a new investor class. We launched AX in February with the conviction that building the largest global regulated exchange for traditional asset perpetuals starts with addressing the needs of institutions and professional market participants: robust risk controls, institutional-grade infrastructure, responsible leverage, wash-trading and market manipulation prevention, no internal market maker, and a clear tier-1 regulatory framework. Our GTM strategy was a risk we took based on ambition, industry experience, intuition, and discipline. Our team is now ready to win the opportunities that risk has yielded, in expanding our markets internationally and building what’s needed here in US derivatives markets. AX offers individual traders the same benefits that have enabled our institutional customers to grow the exchange this large this quickly: a market-defining UX, infrastructure built by elite industry experts, superior book liquidity, low fees and open incentive programs, high-throughput and full-featured APIs, competitive margin, 24/7 one-on-one support, no auto-liquidation or ADL. Join us for the next stage of AX and Architect.
0:22

Architect’s traditional perpetuals exchange AX, one of the fastest-growing regulated derivatives exchanges in history, is opening trading to individuals and omnibus brokers. With $3B notional in <6 months in institutional trading, we’re excited to welcome a new investor class. We launched AX in February with the conviction that building the largest global regulated exchange for traditional asset perpetuals starts with addressing the needs of institutions and professional market participants: robust risk controls, institutional-grade infrastructure, responsible leverage, wash-trading and market manipulation prevention, no internal market maker, and a clear tier-1 regulatory framework. Our GTM strategy was a risk we took based on ambition, industry experience, intuition, and discipline. Our team is now ready to win the opportunities that risk has yielded, in expanding our markets internationally and building what’s needed here in US derivatives markets. AX offers individual traders the same benefits that have enabled our institutional customers to grow the exchange this large this quickly: a market-defining UX, infrastructure built by elite industry experts, superior book liquidity, low fees and open incentive programs, high-throughput and full-featured APIs, competitive margin, 24/7 one-on-one support, no auto-liquidation or ADL. Join us for the next stage of AX and Architect.

Brett Harrison

23,943 Aufrufe • vor 15 Tagen

Your AI agent is useless once it needs to interact with the real world. Need email sending? Website hosting? Data scraping? That usually means: → API keys → subscriptions → integrations → hours of setup I gave my AI agent access to Zero, and it built + hosted personalized landing pages automatically without me configuring anything. Zero gives AI agents access to 14,000+ real-world services with no API keys or subscriptions. The coolest part? Everything runs on pay-per-use microtransactions, so you only pay for what you use. If you're building agents, this is worth trying. Get $5 free credit at 👉
1:12

Your AI agent is useless once it needs to interact with the real world. Need email sending? Website hosting? Data scraping? That usually means: → API keys → subscriptions → integrations → hours of setup I gave my AI agent access to Zero, and it built + hosted personalized landing pages automatically without me configuring anything. Zero gives AI agents access to 14,000+ real-world services with no API keys or subscriptions. The coolest part? Everything runs on pay-per-use microtransactions, so you only pay for what you use. If you're building agents, this is worth trying. Get $5 free credit at 👉

Toha Khan

86,826 Aufrufe • vor 1 Monat

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.
1:27

how you can use openAI codex & gpt 5.5 completely FREE (the full guide) 100% legit. no subscription, zero API cost. up to 1M+ token/day. you need just an openAI account and here's how to set it up in 5mins. openAI has a program that gives eligible developers free API usage every day in exchange for sharing API data that helps improve future models. it's not a one-time credit, your allowance refreshes daily. depending on your usage tier, you can get access to hundreds of thousands, or even millions, of free tokens every single day on supported models. here's how to activate it: 1️⃣open your API dashboard: 2️⃣go to settings → data controls 3️⃣enable data sharing for your organization or project 4️⃣make sure your account has a positive API balance 5️⃣save the settings if your account is eligible, you'll see a message confirming access to complimentary daily usage. before you turn it on, know the tradeoff: • prompts and outputs from shared projects can be used to improve openai's models • don't use it for confidential information, client work, or sensitive data • eligibility depends on your account type and settings for everyone else, it's an incredible deal. use it to: • learn AI development • build side projects • experiment with codex • test agents and automations • prototype ideas without worrying about API costs most developers burn money testing ideas. this lets you experiment at scale while spending little to nothing.

m0h

70,509 Aufrufe • vor 1 Monat

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,012 Aufrufe • vor 1 Jahr

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 Aufrufe • vor 8 Monaten

Bloomberg sells Wall Street a financial command center that costs $32,000 a year. So, a guy named Didier built a free alternative for ordinary investors like you and me. It is called OpenBB. Open it and you get a customizable financial workspace pulling market data, company fundamentals, filings, earnings transcripts, estimates, news, and economic indicators into one screen. > Stocks and ETFs > Options and futures > Forex and crypto > Financial statements > SEC filings > Global economic data Turn any dataset into interactive charts, tables, watchlists, and dashboards. Then ask its built-in AI Copilot to compare companies, explain price movements, summarize filings, or find patterns hidden inside the numbers. It reads the data already sitting on your dashboard and cites the exact sources behind its answers. You can also connect your own files, APIs, databases, research models, and custom AI agents. The underlying Open Data Platform is open source, has more than 70,000 GitHub stars, and installs with one command: pip install openbb The Community workspace is free for individual investors. Wall Street pays nearly $32,000 a year for a financial command center. OpenBB puts one in your browser.
0:33

Bloomberg sells Wall Street a financial command center that costs $32,000 a year. So, a guy named Didier built a free alternative for ordinary investors like you and me. It is called OpenBB. Open it and you get a customizable financial workspace pulling market data, company fundamentals, filings, earnings transcripts, estimates, news, and economic indicators into one screen. > Stocks and ETFs > Options and futures > Forex and crypto > Financial statements > SEC filings > Global economic data Turn any dataset into interactive charts, tables, watchlists, and dashboards. Then ask its built-in AI Copilot to compare companies, explain price movements, summarize filings, or find patterns hidden inside the numbers. It reads the data already sitting on your dashboard and cites the exact sources behind its answers. You can also connect your own files, APIs, databases, research models, and custom AI agents. The underlying Open Data Platform is open source, has more than 70,000 GitHub stars, and installs with one command: pip install openbb The Community workspace is free for individual investors. Wall Street pays nearly $32,000 a year for a financial command center. OpenBB puts one in your browser.

Ihtesham Ali

14,546 Aufrufe • vor 13 Tagen

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition
56:10

Here's a short preview of my interview with Rowan 🛡️ from Sapien, a really unique project which blends AI, Crypto, and drawing value from human contributors cc Trevor Koverko for visibility. Summary In this episode, Rowan Stone,co-founder and CEO of Sapien, shares his path from entrepreneur to building a mission-driven company at the intersection of AI and crypto. He discusses how Sapien is tackling one of the biggest challenges in AI: sourcing high-quality, human-generated data at scale. Rowan emphasizes the importance of aligning incentives from the start, building trust with contributors, and creating a system where real people help train more useful, nuanced AI models. The conversation touches on strategic partnerships, market demand, and how onboarding and education will define the future of the data economy. Takeaways — Rowan previously sold a company to Coinbase before launching Sapien. — Sapien’s goal is to monetize human understanding for AI training. — Real-world data from real people is essential for effective AI. — The need for labeled, high-quality data is growing exponentially. — Incentives and quality control are deeply integrated in Sapien’s model. — Onboarding and contributor education are critical for scale. — Sapien sees collaboration—not just competition—as a strength. — Upskilling contributors increases data quality and platform value. — Crypto-native incentives enable transparent, scalable coordination. Timeline (00:00) Introduction to Rowan Stone and His Background (02:55) The Vision Behind Sapien (06:06) Understanding AI and Data Annotation (09:01) The Role of Humans in AI Development (12:14) Sapien’s Unique Approach to Data Annotation (14:50) Partnerships and Customer Base (18:11) Quality Control and Community Involvement (21:13) On-Chain Coordination and Incentives (23:56) Demand for AI Data and Market Insights (29:51) The Future of Data Demand (30:44) Collaboration Over Competition (32:49) Revenue Generation in Crypto (35:31) The Two-Sided Market of Sapien (39:08) Customer Success Stories (44:35) The Role of Skills in Data Contribution (47:59) The Importance of Education and Onboarding (49:20) Inspiration and Influences (50:11) Overrated Trends in AI and Crypto (51:11) Distribution Channels for Onboarding (54:19) The Impact of TikTok on User Acquisition

papiofficial ᛤ

29,967 Aufrufe • vor 1 Jahr

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.
0:51

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.

𝕋𝕎𝔼𝔼𝕋

20,357 Aufrufe • vor 1 Jahr

Introducing Einstein Sales Agent on Salesforce's new Agentforce Platform! Scale your sales force easily with autonomous sales agents! Sales agents can turn deals over to humans directly in Salesforce Sales Cloud! Humans with AI drive customer success, together. 🔹 24/7 automated pipeline nurturing 🔹 Personalized engagement with inbound leads using trusted CRM data 🔹 Seamless transitions and human handoffs across all channels 🔹 No need for complex conversational scripts 🔹 Operates across sales, service, marketing, and commerce 🔹 Reaches leads on their preferred channel, in their preferred language 🔹 Configurable with sales strategies and knowledge, just like training a seller 🔹 Transparent governance dashboards for full oversight 🔹 Extensible low-code tools 🔹 MuleSoft for cross-system actions 🔹 Real-time, harmonized data with Data Cloud See Einstein Sales Agents in action at Dreamforce 2024! Now everyone is an Einstein. ❤️
3:11

Introducing Einstein Sales Agent on Salesforce's new Agentforce Platform! Scale your sales force easily with autonomous sales agents! Sales agents can turn deals over to humans directly in Salesforce Sales Cloud! Humans with AI drive customer success, together. 🔹 24/7 automated pipeline nurturing 🔹 Personalized engagement with inbound leads using trusted CRM data 🔹 Seamless transitions and human handoffs across all channels 🔹 No need for complex conversational scripts 🔹 Operates across sales, service, marketing, and commerce 🔹 Reaches leads on their preferred channel, in their preferred language 🔹 Configurable with sales strategies and knowledge, just like training a seller 🔹 Transparent governance dashboards for full oversight 🔹 Extensible low-code tools 🔹 MuleSoft for cross-system actions 🔹 Real-time, harmonized data with Data Cloud See Einstein Sales Agents in action at Dreamforce 2024! Now everyone is an Einstein. ❤️

Marc Benioff

56,695 Aufrufe • vor 2 Jahren

Inside the Quant AI Ecosystem Where Intelligence Meets Markets The crypto and financial landscape is evolving fast, but one problem has remained constant: fragmentation. Traders and users are forced to jump between tools, charts, news feeds, analytics dashboards, sentiment trackers, just to answer one simple question: What’s actually happening in the market right now? This is exactly the gap the Quant AI ecosystem is stepping into. A unified intelligence layer for markets Quant AI is building an ecosystem where market interaction becomes simple, conversational, and accessible. Instead of navigating multiple platforms, users can interact directly with markets using chat and voice. That means: ➠ Asking for real-time market insights in plain language ➠ Tracking smart money movements across assets ➠ Understanding trends in crypto, stocks, and commodities in one place ➠ Acting faster with clearer, AI-driven context It’s not just another analytics tool, it’s an attempt to reshape how information flows in trading environments. Why the ecosystem approach matters Most platforms solve isolated problems: One for charts. One for signals. One for news. But Quant AI is moving toward something broader, an ecosystem where intelligence is centralized, and decision-making becomes faster and more intuitive. The goal is simple: Reduce complexity. Increase clarity. Improve execution. And in markets where speed and information matter, that shift is significant. Start here:👇 Quant AI #QuantAIPioneers
0:20

Inside the Quant AI Ecosystem Where Intelligence Meets Markets The crypto and financial landscape is evolving fast, but one problem has remained constant: fragmentation. Traders and users are forced to jump between tools, charts, news feeds, analytics dashboards, sentiment trackers, just to answer one simple question: What’s actually happening in the market right now? This is exactly the gap the Quant AI ecosystem is stepping into. A unified intelligence layer for markets Quant AI is building an ecosystem where market interaction becomes simple, conversational, and accessible. Instead of navigating multiple platforms, users can interact directly with markets using chat and voice. That means: ➠ Asking for real-time market insights in plain language ➠ Tracking smart money movements across assets ➠ Understanding trends in crypto, stocks, and commodities in one place ➠ Acting faster with clearer, AI-driven context It’s not just another analytics tool, it’s an attempt to reshape how information flows in trading environments. Why the ecosystem approach matters Most platforms solve isolated problems: One for charts. One for signals. One for news. But Quant AI is moving toward something broader, an ecosystem where intelligence is centralized, and decision-making becomes faster and more intuitive. The goal is simple: Reduce complexity. Increase clarity. Improve execution. And in markets where speed and information matter, that shift is significant. Start here:👇 Quant AI #QuantAIPioneers

The Agentic Alpha

16,825 Aufrufe • vor 1 Monat

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!
0:47

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

The Graph

425,373 Aufrufe • vor 2 Jahren