Visa invests in Replit to power agentic payments for developers
TechCrunch AI5d agoBusinessVisa made an undisclosed investment in AI coding platform Replit and is exploring integrations with its payment products. The goal is to let developers and their AI agents accept customer payments directly inside Replit, potentially using Visa Intelligent Commerce and Trusted Agent Protocol. No joint product has been formally announced yet, while Replit is also expanding enterprise self-serve access with compliance and control features.
Robinhood Will Let Your AI Agent Trade Stocks and Make or Lose Money
The Verge AI6d agoBusinessRobinhood says traders can create a separate account for an AI agent and fund it with a chosen amount of money. The agent will then be able to buy and sell stocks across the market. The move pushes AI agents beyond advice or research into direct financial action, with real gains and losses possible.
Robinhood now lets your AI agents trade stocks
TechCrunch AI6d agoBusinessRobinhood will allow users to create a separate account with a pre-loaded balance that an AI agent can use to trade stocks. The limited description suggests a structure where agent activity is separated from the user’s main funds. The article does not specify supported agents, risk controls, launch timing, confirmation flows, or eligible assets.
The US is betting on AI to catch insider trading in prediction markets
Ars Technica AI17d agoBusiness隨著預測市場（如 Kalshi 和 Polymarket）的爆發性成長，美國商品期貨交易委員會（CFTC）正高度重視其中的合規問題。 監管機構計劃押注人工智慧（AI）技術，透過分析交易模式與異常數據，來捕捉預測市場中的內線交易。 此舉反映出美國政府對於新興金融預測平台加強監管的決心，並試圖利用科技手段維護市場公平性。
Introducing the Open FinLLM Leaderboard★ 75
Hugging Face Blog606d agoReleaseHugging Face 宣布推出「Open FinLLM Leaderboard」，這是一個專為金融領域大語言模型（FinLLMs）設計的開源評測排行榜。該排行榜旨在解決通用評測基準無法準確反映金融專業能力的問題，透過多維度的金融數據集（如情感分析、關係抽取、金融問答等）來評估模型。這為金融機構與研究人員在選擇、微調和部署金融 AI 模型時，提供了一個透明且標準化的參考依據。
Rocket Money x Hugging Face: Scaling Volatile ML Models in Production
Hugging Face Blog987d agoBusiness個人理財應用 Rocket Money 面臨交易分類模型流量劇烈波動的挑戰。透過導入 Hugging Face Inference Endpoints，他們實現了自動彈性伸縮（Auto-scaling），不僅大幅降低基礎設施成本，還簡化了部署流程，讓數據科學團隊無需依賴繁重的 DevOps 即可快速將模型推向生產環境，同時保持極低的延遲。