Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
5,018 documents
The document describes a shift from periodic customer reviews toward perpetual KYC, where customer risk profiles are updated continuously and reviews can be triggered by events such as unusual transactions or behavioral changes. It presents automation as a…
The article surveys proposed uses of blockchain, artificial intelligence, and smart contracts across finance, agriculture, and supply chains. It describes AI-enabled contracts as combining programmed transaction logic with changing data inputs, with examples…
The document presents AIXBT as a token for the Virtuals protocol, describing a proposed role in supporting AI agents that monitor blockchain events and carry out actions such as portfolio rebalancing, DeFi activity, and NFT trading. It also outlines claimed…
The document surveys seven possible uses of ChatGPT in crypto: trading education, market analysis, strategy drafting, price forecasting, risk guidance, portfolio monitoring, and news review. It also gives a high-level explanation of how a language model…
The document surveys several approaches to cryptocurrency price forecasting, focusing on Bitcoin. It describes power-law fitting to historical prices, quantile regression for estimating conditional price ranges, and compound annual growth rate as a…
The document introduces a planned centralized crypto trading service combining copy trading, trading bots, and signals in one marketplace. Its proposed process uses AI recommendations and trader statistics to suggest strategies or lead traders for users to…
The article describes Lightchain AI’s proposed Lighter Spot Testnet, focusing on two advertised design elements: Proof of Intelligence consensus and an AI Virtual Machine. Proof of Intelligence is presented as rewarding nodes for AI-related computation, such…
The document surveys Injective’s proposed role in on-chain finance through several ecosystem features: iBuild, an AI-assisted no-code tool for creating financial decentralized applications; a revenue fund intended to support ecosystem growth and token burns;…
The document describes Codatta’s proposed approach to data assetization: record contributions on-chain, combine human review with AI-assisted curation, and pay contributors ongoing royalties tied to data use. It presents the Crypto Account Annotation System…
The document describes security controls an exchange says it uses: external audits to identify vulnerabilities, defenses against distributed denial-of-service attacks, machine-learning analysis of user behavior and transactions, and blockchain monitoring for…
The document describes a partnership between Plume Network, an asset-tokenization ecosystem, and Allora Network, a decentralized AI network. The proposed use of collective intelligence is to support valuation, pricing, yield forecasting, and risk management…
The document surveys several approaches to generating yield in decentralized finance. It describes restaking and liquid restaking as ways to seek additional rewards while retaining access to tokenized staked assets. Other topics include…
The document presents DexCheck as a Web3 analytics platform that combines blockchain data with AI and machine-learning tools. It describes real-time on-chain information, social sentiment analysis, data integration for developers, and coverage of…
The document outlines Bittensor’s decentralized machine-learning network, where specialized subnets address tasks such as text generation, image recognition, and data analysis. It describes TAO as a means of paying for AI services and presents the network as…
The document describes Sahara AI as a blockchain platform for collaborative AI development and ownership. Contributors can retain rights to models and make them available for licensing, while the SAHARA token is described as a means to pay for model access,…
The article surveys cryptocurrency trends across emerging and advanced economies. It discusses stablecoins for payments and savings, AI tools for trading and portfolio analysis, meme coins seeking practical uses, and the growing links between digital assets…
The document explains Bittensor’s planned halving as a reduction in new TAO emissions and compares the mechanism with Bitcoin’s supply schedule. It describes subnets as separate marketplaces for AI services and says Dynamic TAO uses subnet tokens and…
The document describes proposed uses of artificial intelligence in decentralized finance trading platforms. It says trading engines may combine historical market data with social signals, blockchain activity, large-holder behavior, and software development…
This overview surveys NFT uses in gaming, metaverse environments, and financial applications. It distinguishes utility-focused assets, such as in-game items or access tokens, from collectibles whose value may depend more on speculative demand. It also…
The article surveys several financing approaches in crypto: venture-backed seed rounds, institutional participation, ecosystem partnerships, and accepting bitcoin as funding. It uses TradeTide, an AI-oriented crypto strategy platform, to illustrate a seed…
The document explains meme coins as cryptoassets whose demand often depends on internet culture, community participation, celebrity attention, and speculative trading rather than established utility. It uses Dogecoin as an early example and discusses how…
The document introduces FLock.io as a platform combining federated learning with blockchain mechanisms. In federated learning, participants train a shared model without sending their raw datasets to a central repository; blockchain is described as a ledger…
The article describes Solidus Ai Tech as a project combining artificial intelligence services, blockchain, and high-performance computing. It presents AITECH as the ecosystem’s utility and governance token, notes a stated maximum supply of two billion, and…
The document discusses the environmental cost of using generative AI, focusing on image generation with the AiMAGE model. It argues that image creation can require more computation than text tasks and that broad generative models may consume more energy than…