The post Gaming Worlds Could Be The Answer To AI’s Data Problem appeared on BitcoinEthereumNews.com. Welcome back to The Prompt. Stanford PhDs Fan-Yun Sun and Sharon Lee cofounded AI research company Moonlake AI to help people “vibe code virtual worlds.” Moonlake AI Artificial intelligence is facing an existential crisis. Powerful AI models are gated by the quality of data they’re trained on. But even after scraping the internet for all its public data, paying some of the most intelligent humans to label and annotate data and generating troves of synthetic data, frontier labs are coming up short on data to improve model performance. And so the question remains— where will the data for the next major advancement in AI come from? Two former Stanford PhD students, Sharon Lee and Fan-Yun Sun, are trying to answer that question at their new startup, Moonlake AI. The pair are developing AI software that can quickly create visual simulation environments and “interactive 3D worlds” that might serve as the bedrock to create data for reasoning models— systems that carry out multiple steps to solve complex problems. The idea is that the tool will organically be used by people to generate 3D worlds for gaming, animation, filmmaking or even for education, which in turn would create data that can be used to train more advanced models, said cofounder Sun, who previously worked as a researcher at Nvidia, building virtual worlds to train and evaluate robots. “We know that data is very scarce right now,” Lee said. “And we believe these large scale interactive worlds are the next paradigm that allows you to scale the data infinitely.” Coming out of stealth today, Moonlake AI has raised $28 million in seed funding from AIX Ventures, Nvidia Ventures and Threshold Ventures. Lee said the program could also be used by AI researchers in fields like robotics to create digital simulations and verify if… The post Gaming Worlds Could Be The Answer To AI’s Data Problem appeared on BitcoinEthereumNews.com. Welcome back to The Prompt. Stanford PhDs Fan-Yun Sun and Sharon Lee cofounded AI research company Moonlake AI to help people “vibe code virtual worlds.” Moonlake AI Artificial intelligence is facing an existential crisis. Powerful AI models are gated by the quality of data they’re trained on. But even after scraping the internet for all its public data, paying some of the most intelligent humans to label and annotate data and generating troves of synthetic data, frontier labs are coming up short on data to improve model performance. And so the question remains— where will the data for the next major advancement in AI come from? Two former Stanford PhD students, Sharon Lee and Fan-Yun Sun, are trying to answer that question at their new startup, Moonlake AI. The pair are developing AI software that can quickly create visual simulation environments and “interactive 3D worlds” that might serve as the bedrock to create data for reasoning models— systems that carry out multiple steps to solve complex problems. The idea is that the tool will organically be used by people to generate 3D worlds for gaming, animation, filmmaking or even for education, which in turn would create data that can be used to train more advanced models, said cofounder Sun, who previously worked as a researcher at Nvidia, building virtual worlds to train and evaluate robots. “We know that data is very scarce right now,” Lee said. “And we believe these large scale interactive worlds are the next paradigm that allows you to scale the data infinitely.” Coming out of stealth today, Moonlake AI has raised $28 million in seed funding from AIX Ventures, Nvidia Ventures and Threshold Ventures. Lee said the program could also be used by AI researchers in fields like robotics to create digital simulations and verify if…

Gaming Worlds Could Be The Answer To AI’s Data Problem

6 min read

Welcome back to The Prompt.

Stanford PhDs Fan-Yun Sun and Sharon Lee cofounded AI research company Moonlake AI to help people “vibe code virtual worlds.”

Moonlake AI

Artificial intelligence is facing an existential crisis. Powerful AI models are gated by the quality of data they’re trained on. But even after scraping the internet for all its public data, paying some of the most intelligent humans to label and annotate data and generating troves of synthetic data, frontier labs are coming up short on data to improve model performance. And so the question remains— where will the data for the next major advancement in AI come from?

Two former Stanford PhD students, Sharon Lee and Fan-Yun Sun, are trying to answer that question at their new startup, Moonlake AI. The pair are developing AI software that can quickly create visual simulation environments and “interactive 3D worlds” that might serve as the bedrock to create data for reasoning models— systems that carry out multiple steps to solve complex problems. The idea is that the tool will organically be used by people to generate 3D worlds for gaming, animation, filmmaking or even for education, which in turn would create data that can be used to train more advanced models, said cofounder Sun, who previously worked as a researcher at Nvidia, building virtual worlds to train and evaluate robots.

“We know that data is very scarce right now,” Lee said. “And we believe these large scale interactive worlds are the next paradigm that allows you to scale the data infinitely.”

Coming out of stealth today, Moonlake AI has raised $28 million in seed funding from AIX Ventures, Nvidia Ventures and Threshold Ventures. Lee said the program could also be used by AI researchers in fields like robotics to create digital simulations and verify if a task has been correctly completed. “For example, if a robot interacts with the blender in a kitchen in the environment we create, you can see if this solid becomes liquid and the fruit juice is blended and that would be a successful task,” she said.

Moonlake AI isn’t the only company using artificial intelligence to generate 3D worlds. World Labs, cofounded by esteemed Stanford faculty Fei Fei Li, who is known as the godmother of AI and was Lee’s mentor, is also working on spatial intelligence and creating interactive virtual worlds. In August, video generation startup Runway launched Game Worlds to let people create interactive games.

Let’s get into the headlines.

BIG PLAYS

OpenAI announced users will be able to buy products directly through ChatGPT from sellers on Etsy and Shopify. Some 700 million people already use the chatbot to search and compare products. OpenAI is reportedly planning to introduce ads within ChatGPT as it tries to monetize its star product. That’ll be important for the AI goliath, which has been spending billions buying compute from companies like Oracle and CoreWeave. The company booked $4.3 billion of revenue in the first half of 2025 on $2.3 billion in cash burn, The Information reported, but has committed to spend up to $500 billion on its 10 gigawatt AI infrastructure project called Stargate in the next four years. OpenAI projects $13 billion in revenue this year.

CHIPS + COMPUTE

AI cloud compute giant CoreWeave signed a $14.2 billion contract with Meta to provide the social media behemoth with computing power through 2031 to train new models. OpenAI also recently expanded its contract with CoreWeave and now plans to spend $22.4 billion on its infrastructure. The deal comes amid an industry-wide infrastructure arms race as leading AI companies splurge astronomical amounts of money on AI data centers and advanced silicon GPUs. Investors and tech companies would need to book some $2 trillion in annual revenue through 2030 to profitably fund its AI investments, according to a report by Bain and Company.

TALENT RESHUFFLE

Elon Musk’s businesses have experienced a significant churn among senior level leaders in recent months, the Financial Times reported. At xAI, Musk’s AI startup, both the chief financial officer and general counsel recently left, citing long hours of work as a key concern. The departures have been driven by burnout and because some staffers are fed up with Musk’s politics. AI talent has become a need-to-have for startups scrambling to win the AI race, which means there’s plenty of opportunity for employees to seek greener pastures.

AI DEALS OF THE WEEK

Axiom Math, an early stage startup that plans to train an AI model that can solve challenging mathematical problems, raised $64 million in seed funding at a $300 million valuation, I reported this week.

Up and coming legal AI startup Legora is in talks to raise more than $100 million at a $1.8 billion valuation, my colleague Iain Martin and I reported.

Periodic Labs announced it has raised $300 million in seed funding from Andreessen Horowitz and others. Cofounded by ChatGPT co-creator William Fedus, the startup plans to train artificial intelligence models for scientific discovery in areas like semiconductors, magnetism and superconductivity.

DEEP DIVE

When Nvidia, the $4.4 trillion juggernaut that makes the GPU chips undergirding the AI frenzy, needs to monitor its systems for threats and suspicious activity, it turns to a small startup named Grafana Labs to help it scour access logs and analyze vulnerabilities. And the chip maker isn’t alone. Uber, Anthropic and Adobe are among the other giants tapping Grafana’s tools — like a dashboard for operations across an entire business.

Now $6 billion-valued Grafana, known as an observability platform, on Tuesday said it hit $400 million in annualized revenue. The uptick in sales has been thanks in part to the growth of Grafana Cloud, which lets customers use its platform without setting up their own infrastructure like servers or storage, CEO Raj Dutt told Forbes.

The need for good observability services has ballooned in the AI era, especially as vibe coding from generative AI models has become more common. As people increasingly rely on AI to write code and build products, it’s become harder to keep track of what’s going on under the hood, when it’s being done and who’s doing it, Dutt said. “Software today is starting to look more like a living organism,” said Dutt. “It’s being built and shipped faster than ever — sometimes sloppily. That makes it critical to understand how your app is performing in production.”

Read more on Forbes.

MODEL BEHAVIOR

Tilly Norwood, an AI-generated character dubbed as an “AI actress,” received backlash from SAG-AFTRA, a union that represents over 160,000 (human) actors. The organization alleged the synthetic performer was generated using actors’ performances without permission or compensation. It also noted that the use of AI-generated figures could risk “jeopardizing performer livelihoods and devaluing human artistry.”

Source: https://www.forbes.com/sites/rashishrivastava/2025/10/01/gaming-worlds-could-be-the-answer-to-ais-data-problem/

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Photo by Pierre Borthiry - Peiobty on Unsplash Cryptocurrency APIs are essential tools for developers building apps (e.g. trading bots, portfolio trackers) and for analysts conducting market research. These APIs provide programmatic access to historical price data, real-time market quotes, and even on-chain metrics from blockchain networks. Choosing the right API means finding a balance between data coverage, update speed, reliability, and cost. In this article, we compare five of the most popular crypto data API providers — EODHD, CoinMarketCap, CoinGecko, CryptoCompare, and Glassnode — focusing on their features, data types (historical, real-time, on-chain), rate limits, documentation, and pricing plans. We also highlight where EODHD’s crypto API stands out in this competitive landscape. Overview of the Top 5 Crypto Data API Providers
  1. EODHD (End-of-Day Historical Data) — All-in-One Multi-Asset Data EODHD is a versatile financial data provider covering stocks, forex, and cryptocurrencies. It offers an unmatched data coverage with up to 30 years of historical data across the global For crypto, EODHD supports thousands of coins and trading pairs (2,600+ crypto pairs against USD) and provides multiple data types under one service. Key features include:
Historical Price Data: Daily OHLCV (open-high-low-close-volume) for crypto assets, with records for major coins going back to 2009 eodhd.com (essentially as far back as Bitcoin’s history). This extensive archive facilitates long-term backtesting. Real-Time Market Data: Live crypto price quotes via REST API and WebSocket. EODHD’s “Live” plan delivers real-time (typically streaming) updates with high rate limits (up to 1,000 requests/minute on paid plans) Developers can also use bulk API endpoints to On-Chain & Fundamental Data: While not an on-chain analytics platform per se, EODHD provides crypto fundamental metrics such as market cap (actual and diluted), circulating/total/max supply, all-time high/low, and links to each project’s whitepaper, block explorer These fundamentals give context beyond price, though advanced on-chain metrics (e.g. active addresses) are not included. Additional Features: EODHD stands out for its ease of use and support tools. API responses are clean JSON by default (with an option for CSV), and the service offers no-code solutions like Excel and Google Sheets add-ons to fetch crypto data without programming Comprehensive documentation and an “API Academy” with examples help users get started EODHD also provides 24/7 live customer support, reflecting its 7+ years of reliable service Pricing & Limits: EODHD’s pricing is very competitive for the value. It has a free plan (registration required) which allows 20 API calls per day for trying out basic Paid plans start at $19.99/month for end-of-day and live crypto data, allowing up to 100,000 calls per day— a generous limit that far exceeds most competitors at that price. The next tier ($29.99/mo) adds real-time WebSocket streaming, and the top All-in-One plan ($99.99/mo) unlocks everything (historical, intraday, real-time, fundamentals, news, etc.) All paid plans come with high throughput (up to 1,000 requests/min) Enterprise or commercial licenses are available for custom needs, and students can even get 50% discounts for educational Overall, EODHD offers an excellent price-to-performance ratio, giving developers extensive crypto (and cross-asset) data for a fraction of the cost of some single-purpose crypto APIs. 2. CoinMarketCap — Industry-Standard Market Data CoinMarketCap (CMC) is one of the most well-known cryptocurrency data aggregators. It provides information on over 10,000 digital assets and aggregates data from hundreds of CMC’s API is a go-to choice for current market prices, rankings, and exchange statistics. Key features include: Real-Time Quotes & Global Metrics: The API offers real-time price quotes, market capitalization, trading volume, and rankings for thousands of cryptocurrencies. It also provides global market metrics like total market cap, total volume, Bitcoin dominance, etc., updated (CMC’s data updates roughly every 1–2 minutes by default; true streaming is not yet available via their API.) Historical Data: Paid tiers unlock access to historical price data. CMC has data going back to 2013 for many assets, and enterprise plans provide all historical OHLCV data since 2013.The API endpoints include daily and even intraday historical quotes, but note that the free tier does not include historical price retrieval(free users get only latest data). Exchange and Market Endpoints: CoinMarketCap’s API covers exchange-level data (e.g. exchange listings, trading pair metadata, liquidity scores) and derivative market data (futures, options prices) on higher plans. This is useful for monitoring exchange performance and volumes across both centralized and decentralized exchanges. However, on-chain analytics are not CMC’s focus — the API doesn’t provide blockchain metrics like address counts or transaction rates. Developer Support: CMC provides comprehensive documentation and a straightforward RESTful JSON API . The endpoints are well-documented with examples, and categories include latest listings, historical quotes, metadata/info (project details), exchange stats, and The service is known for its reliability and is used by major companies (Yahoo Finance, for example, uses CoinMarketCap’s data feeds in its crypto Pricing & Limits: CoinMarketCap offers a free Basic plan with 10,000 credits per month (approximately 333 calls/day) and access to 11 core endpoint. The free tier is suitable for simple apps that only need current market data on a limited number of assets. To get historical data or higher frequency updates, you must upgrade. The Hobbyist plan starts at around $29/month (paid annually) and offers a higher monthly call allowance (e.g. ~50,000 calls/month) and more endpoints. Mid-tier plans like Startup ($79/mo) and Standard ($199/mo) increase the rate limits and data access — e.g., more historical data and additional endpoints like derivatives or exchange listings. For example, Standard and above allow intraday historical quotes and more frequent updates. Professional/Enterprise plans ($699/mo and up, or custom) provide the highest limits (up to millions of calls per month), full historical datasets, and SLA . Rate limits on CMC are enforced via a credit system; different endpoints consume different credits, and higher plans simply grant more credits per month. In summary, CoinMarketCap’s API is very robust but can become expensive for extensive data needs — it targets enterprise use cases with its upper tiers. Smaller developers often stick to the free or Hobbyist plan for basic data (while accepting the lack of historical data in those tiers) 3. CoinGecko — Broad Coverage & Community Focus CoinGecko is another hugely popular cryptocurrency data provider known for its broad coverage and developer-friendly approach. CoinGecko’s API is often praised for having a useful free offering and covering not just standard market data but also categories like DeFi, NFTs, and community metrics. Notable features: Wide Asset Coverage: CoinGecko tracks over 13,000 cryptocurrencies (including many small-cap and emerging tokens). It also includes data on NFT collections and decentralized finance (DeFi) tokens and protocols. This makes it one of the most comprehensive datasets for the crypto market. If an asset is trading on a major exchange or DEX, CoinGecko likely has it listed. Market Data and Beyond: The API provides real-time price data, market caps, volumes, and historical charts for all these assets. Historical data can be retrieved in the form of market charts (typically with daily or hourly granularity depending on the time range). Additionally, CoinGecko offers endpoints for exchange data, trading pairs, categories (sectors), indices, and even asset contract info (mapping contract addresses to CoinGecko listings). They also expose developer and social metrics for each coin — e.g. GitHub repo stats (forks, stars, commits) and social media stats (Twitter followers, Reddit subscribers) This is valuable for analysts who want to gauge community interest or development activity alongside price. No WebSockets — REST Only: CoinGecko’s API is purely REST-based; there is no built-in WebSocket streaming. Data updates for price endpoints are cached at intervals (typically every 1–5 minutes for free users, and up to every 30 seconds for Pro users). So while you can get near-real-time data by polling, ultra-low-latency needs (like high-frequency trading) are better served by other providers or exchange-specific APIs. Documentation & Use: The API is very straightforward to use — in fact, for the free tier no API key was required historically (though recently CoinGecko introduced an optional “Demo” key for better tracking). A simple GET request to an endpoint like /simple/price returns current prices. CoinGecko’s documentation is clear, and they even highlight popular endpoints and provide examples. Because of its simplicity and generous free limits, CoinGecko’s API has been integrated into countless projects and tutorials. Pricing & Limits: CoinGecko operates a freemium model. The free tier (now referred to as the “Demo” plan) allows about 10–30 calls per minute (the exact rate is dynamic based on system load) In practical terms, that’s roughly up to 1,800 calls/hour if usage is maxed out — very sufficient for small applications. 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Market Data & Exchange Coverage: CryptoCompare covers 5,700+ coins and 260,000+ trading pairs across a wide array of exchanges. It collects trade data from more than 170 exchanges (both centralized and some decentralized) to produce its aggregate indices (known as CCCAGG prices). The API provides real-time price quotes, order book snapshots, trade history, and OHLCV candlesticks at various intervals. For advanced users, CryptoCompare can supply tick-level trade data and order book data for deep analysis (these are available via their WebSocket or extended API endpoints). Historical Data: CryptoCompare is strong in historical coverage. It offers historical daily data for many coins and historical intraday (minute) data as well. By default, all subscription plans include at least 7 days of minute-level history and full daily history; enterprise clients can get up to 1 year of minute-by-minute historical data (and raw trade data) for backtesting. This is valuable for quantitative researchers who require detailed price series. On-Chain Metrics and Other Data: In addition to market prices, CryptoCompare has expanded into on-chain metrics and alternative data. The API can provide certain blockchain statistics (they mention “blockchain metrics” and address data in their offerings)— for example, network transaction counts or wallet addresses for major chains. While it’s not as extensive as a dedicated on-chain provider, this allows blending on-chain indicators (like transaction volumes) with price data for analysis. CryptoCompare also integrates news feeds and social sentiment: the API has endpoints for the latest news articles and community sentiment analysis, which can help gauge market Reliability and Performance: CryptoCompare’s infrastructure is built for high performance. They claim support for up to 40,000 API calls per second bursts and hundreds of trades per second This makes it suitable for real-time applications and dashboards that need frequent updates. Their data is normalized through a proprietary algorithm to filter out bad data (e.g., outlier prices or exchange anomalies), aiming to deliver clean and consistent price indices (CCCAGG). The API itself is well-documented, and client libraries exist for languages like Python. Pricing & Limits: CryptoCompare historically offered a free public API (with IP-based limiting), but now uses an API key model with tiered plans. Personal/free use is still allowed — you can register for a free API key for non-commercial projects and get a decent allowance (exact call limits aren’t explicitly published, but users report free tiers on the order of a few thousand calls per day). For commercial or heavy use, their plans start around $80/month for a basic package and go up to ~$200/month for advanced packages. These plans might offer on the order of 100k to a few hundred thousand calls per month, plus higher data resolution. All plans grant access to ~60+ endpoints and features like full historical data download for daily/hourly (minute data beyond 7 days is enterprise-only). Enterprise solutions are available for customers needing custom data feeds, unlimited usage, white-label solutions, or bespoke datasets (pricing for these is via negotiation). In summary, CryptoCompare provides a very rich dataset and is priced in a mid-range: not as cheap as community resources, but more affordable than some institutional-grade providers. Its value is especially high if you need a mix of price, news, and basic on-chain data in one
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Extensive On-Chain Metrics: Glassnode offers over 800 on-chain metrics spanning multiple major blockchains (Bitcoin, Ethereum, Litecoin, and many others, as well as key ERC-20 tokens). This includes metrics like active addresses, transaction counts, transaction volumes, mining hash rates, exchange inflows/outflows, UTXO distributions, HODLer stats, realized cap, SOPR and much more. If you need to peer ino what’s happening inside a blockchain (not just its price on exchanges), Glassnode is the go-to source. For example, one can query the number of active Bitcoin addresses, the amount of BTC held by long-term holders vs. short-term, or Ethereum gas usage trends Market & Derivatives Data: In addition to pure on-chain data, Glassnode also incorporates off-chain market data for context. 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They have a Standard (Free) tier, an Advanced (Tier 2) paid tier, and a Professional (Tier 3) tier. The Free tier allows access to Basic metrics (Tier 1 metrics) at daily resolution, which covers a lot of fundamental data for major chains but not the more complex or derived metrics. The Advanced plan (around $29–$49 per month depending on promotions) unlocks Essential metrics (Tier 2) and provides up to hourly . The Professional plan (around $79 per month for individuals) gives access to all metrics (including Premium Tier 3 metrics) and finer resolution (10-min updates). However, there’s a catch: API access is only officially included for Professional/Enterprise users and may require a special add-on or enterprise . In practice, Glassnode does offer a free API but it is limited (e.g., you can query basic metrics via REST with a free API key, but many endpoints will return only if you have the right subscription). 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Why EODHD wins for most teams All-in-one: crypto + equities + FX under one API (consistent JSON/CSV). Great value: up to 100k calls/day from ~$19.99/mo — perfect for MVPs and production apps. Fast start: clean docs, code samples, Excel/Sheets add-ins, and bulk endpoints. Scale-ready: real-time REST & WebSocket, historical OHLCV, fundamentals, news. What you can ship this week Real-time crypto dashboards and alerts Backtests using years of OHLCV data Cross-asset analytics (BTC vs. S&P 500, ETH vs. USD) Spreadsheet models that refresh automatically 👉 Start for free with EODHD — grab your API key and make your first request in minutes.Try EODHD now (free tier available) and upgrade when you need more throughput. Top 5 Cryptocurrency Data APIs: Comprehensive Comparison (2025) was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
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