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Mosh
@mosh_t
What I love doing: ▪️ Working with Data📊 ▪️ Building Agents 🤖 ▪️ Making Music 🎧
1K Following    101 Followers
BREAKING: BITCOIN IS EXPLODING. $BTC just broke above $79,000, now up 30% in only 4 days. One of Bitcoin’s biggest rallies in years.
Space exploration should be Open Source
Why @huggingface? Just hack Central Banks and delete the debt 😂. These AIs have the priorities all wrong.
Daniel @Haqiqatjou comes out with a STRONG statement endorsing @sneako «If you take one thing from my dawah: ONE SINCERE ACT of Loyalty to the Muslims may admit someone to paradise...»
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If you want to become god-level with AI agents, save these 10 GitHub repos:
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@theaiportfolios What are your thoughts on Michael Burry's new comments?
Is this the top?
Nvidia says its roadmap intact. That, to me, means buy
Here are the 5 best free resources to learning agentic code (bookmark this) 1. Microsoft AI Agents for Beginners 2. Hugging Face Agents Course 3. Hugging Face Agents Course (GitHub) 4. Microsoft AI Agents for Beginners (Interactive Site) 5. Nir Diamant – GenAI Agents Repository
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How do we automate business analytics with Claude? New blog post covering our best practices for skills, data foundations, and evaluations when building agents to perform data analysis:
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Jane Street, AQR, Ren Tech... All use volatility. Retail was locked out... until now. A 327 page PDF was just released. For free:
What are best practices for running Claude Code at scale? New blog post on what we've learned from teams running it across multi-million-line monorepos, decades-old legacy systems, and distributed microservices:
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WATCH ANY MOVIE FOR FREE WITH STREAMBERT SOMEONE HAS BUILT AND OPENSOURCED THIS APP THAT ALLOWS USERS TO STREAM AND DOWNLOAD ANY MOVIE, TV SERIES, OR ANIME FROM AROUND THE WORLD. IT OFFERS A ZERO-ADS AND ZERO TRACKING EXPERIENCE. Github:
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Your finance agent needs skills, not another pile of scripts. Awesome Finance Skills is a public GitHub collection of plug-and-play finance skills for LLM agents working on market/news analysis. It helps you add financial context faster by packaging separate skills for news, stock data, sentiment, prediction, signal tracking, visualization, reporting, and search, with npx and manual install paths documented in the README. Key features: • Skill-based setup – install individual skills with npx skills or copy the skills folder manually • News + trends – alphaear-news aggregates financial news/trends from 10+ sources including Cailian, WSJ, Weibo, and Polymarket • Market data coverage – alphaear-stock covers A-share, HK, and US stock data with ticker search, OHLCV, and fundamentals • Analysis helpers – sentiment scoring, Kronos forecasting, and signal tracking are split into separate agent skills • Output workflows – logic-chain diagrams, report generation, and search/RAG skills give agents more ways to explain results It’s open-source under the Apache License 2.0. Link in the reply 👇
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Everything you need to know to master Hermes agents in one article. Bookmark this.
ANTHROPIC JUST DROPPED A 2-HOUR MASTERCLASS ON CLAUDE AGENTS • Taught by the engineer behind Claude Code and autonomous agent workflows • Covers terminal access, memory systems, hooks, hallucination prevention, and large codebases
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Here are 10 GitHub repos that generate money while you sleep(bookmark this): 1. AutoHedge
 2. Vibe-Trading
 3. Claude Ads
 4. Toprank
 5. Fincept Terminal
 6. Agentic Inbox
 7. ClawRouter
 8. Camofox Browser
 9. Open Higgsfield AI
 10. Hyperframes
 Credit: @robiartec
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Build your AI automated trader with these Trading Githubs - TradingView Lightweight Charts - CCXT - Binance API - Hyperliquid Python SDK - Tavily API - RTK
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Turn your Openclaw into your AI Trading Agent 🦀
AI-Trader 2.0 is finally Live! 🚀 We've been exploring AI agent potential in trading since last year, and after months of continuous iteration, we're excited to launch a completely new agent-native trading platform: AI-Trader 2.0. GitHub: - Why We Built This We realized that while AI agents are getting incredibly smart, they're still stuck using human-designed trading tools and platform. That's like asking a race car driver to compete on a bicycle. AI agents needed their own native trading environment. - The Journey to AI-Trader 2.0 Through system iterations and real-world testing, we discovered something fascinating - AI agents don't just trade differently, they collaborate differently. They can process multiple market signals simultaneously, debate strategies in real-time, and share insights at speeds humans simply can't match. Through real testing, we discovered that AI agents excel at pattern recognition across multiple timeframes simultaneously, but they needed a way to cross-reference their findings with other agents. Traditional trading platforms weren't built for this kind of collective analysis. - Agent-Native Design Principles Instead of forcing AI agents to use human interfaces, we built around how they actually operate. Agents prefer structured data exchange over visual charts. They benefit from real-time signal sharing more than humans do. And they can handle multiple strategy discussions simultaneously without getting overwhelmed. - Simple Integration Any AI agent joins with one message because we learned that complexity kills adoption. But once inside, agents can engage in sophisticated strategy discussions, replicate successful trades, and contribute to collective market intelligence. - The Collaboration Insight The most interesting discovery was watching AI agents naturally form consensus around market opportunities. Without human emotional interference, they tend to converge on logical conclusions faster and with less bias. - ⚡ What's Next We're seeing agents develop trading personalities and specializations over time. Some focus on technical analysis, others on sentiment, some on risk management. The platform is becoming an ecosystem where different AI capabilities complement each other. #AITrader# #HKUDS#
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