CFO roundtable: AI growth, pricing, and forecasting (June 2025 Fintech Newsletter) - Andreessen Horowitz
<a href="https://news.google.com/rss/articles/CBMiaEFVX3lxTE1ncGpTMWlJSnNtV1FjYjRQOXZhYU93bmdCTkFxNHNoM2lPbXA5YXdESmN0ckI2ZmQzRXBSZjVlWGdlZmw0ZUc5anJDYWVGUEdla3hMSmpYQ01ISGptRVdmanVoTXVOU0FR?oc=5" target="_blank">CFO roundtable: AI growth, pricing, and forecasting (June 2025 Fintech Newsletter)</a> <font color="#6f6f6f">Andreessen Horowitz</font>
Could not retrieve the full article text.
Read on GNews AI finance →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
forecast![AI-enabled Medical Devices Market Size, Share | Forecast [2034] - Fortune Business Insights](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-robot-hand-JvPW6jsLFTCtkgtb97Kys5.webp)
I Built an MCP Server So Claude Can Answer Questions About Its Own Usage
Here's something that didn't exist until recently: you can ask Claude how much Claude Code you've been using , and get a real answer backed by your actual data. You: "How much have I used Claude Code this month, and is my streak going to survive?" Claude: "You've logged 47.3h interactive + 83.1h AI sub-agent work in March, for 130.4h total. You're on a 36-day streak with 22 Ghost Days. Based on your last 14 days, your streak is likely to survive — you've been active 100% of days this month." That's cc-mcp . An MCP server that gives Claude real-time access to your Claude Code usage stats. The problem with analytics tools I've built 26 other Claude Code analytics tools. You run them, they print stats, you close the terminal. The knowledge doesn't go anywhere useful. What I wanted was for Cla
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
J-CHAT: Japanese Large-scale Spoken Dialogue Corpus for Spoken Dialogue Language Modeling
arXiv:2407.15828v2 Announce Type: replace-cross Abstract: Spoken dialogue is essential for human-AI interactions, providing expressive capabilities beyond text. Developing effective spoken dialogue systems (SDSs) requires large-scale, high-quality, and diverse spoken dialogue corpora. However, existing datasets are often limited in size, spontaneity, or linguistic coherence. To address these limitations, we introduce J-CHAT, a 76,000-hour open-source Japanese spoken dialogue corpus. Constructed using an automated, language-independent methodology, J-CHAT ensures acoustic cleanliness, diversity, and natural spontaneity. The corpus is built from YouTube and podcast data, with extensive filtering and denoising to enhance quality. Experimental results with generative spoken dialogue language m
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!