One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.

Insane story. The TL;DR for those who don't know what happened: Leopold Aschenbrenner (former OpenAI researcher who wrote the famous "Situational Awareness" essay) launched a hedge fund built entirely around the AI thesis. Massive AI infrastructure buildout: chips, data centers, power - Leo bet big on AI being the future. His fund went from $225 million to $20 billion in under two years. Up +439% through June 2026 alone. Then July happened. A sharp AI stock selloff hit his concentrated long positions hard, and the fund has now had to unwind the entire public equity portfolio, reportedly selling a large chunk directly to Citadel. He's now going back to investors and lenders for fresh capital, and even offering some investors the option to buy portfolio assets directly. He's probably right about the AI bet long-term but just wayyy too overlevered. Every 3-6 months, stories like this pop up, and they're an excellent reminder to be EXTREMELY cautious with leverage...

We're entering the token-efficiency era of AI. GPT-5.6 Luna hits an intelligence score of ~51 at roughly $0.06 per task. Claude Opus 5 on Low effort scores about the same at over $0.30 per task. Same intelligence. ~5x the cost difference. Pure intelligence is no longer the strongest moat. Cost-efficiency needs to be baked in as every model is "capable enough" now.

Outside of our little 𝕏 bubble, the average person has no clue what's happening with AI. People still think "AI" means ChatGPT. If you've used an agent, connected an MCP, or better yet, run loops, you're literally in the top 0.1% of AI users. Unlimited opportunity.

Most people don't know this, but Anthropic regularly contributes to a GitHub repo of cybersecurity agentic skills. 700+ skills, 25+ security domains, 5+ framework mappings. I went through the whole thing and built a prompt that secures your codebases. It gives any AI agent the security skills of a senior analyst:

I used Claude to trade, and it made + $19,537 using my custom Market Research Desk. I just open-sourced the entire GitHub repo! Here's how to install, use, and run my Research Desk so you can make real money in the markets with AI: What it actually is: Think of this as a trading backtesting workspace. You can import real market data, define your rules, run trading simulations, and see exactly what happened (400+ strategies already pre-loaded). To get started: Step 1. Install it Terminal command git clone cd Backtesting-Engine npm install npm run dev Open the local URL printed in your terminal. Step 2. Run your first backtest → Open Backtesting from the sidebar → Keep Binance public market data selected (no API key needed), pick a symbol like BTCUSDT and a timeframe. 1 day is a good first run → Choose a strategy: moving-average crossover, RSI mean reversion - your choice → Set your starting portfolio cash and fee assumption → Hit Load data & run backtest Step 3. Read the result properly You'll see net P/L, max drawdown, win rate, average trade, profit factor, and time in market, all shown together. Step 4. Save and compare strategies Adjust a strategy's settings, then save it as a custom preset. It'll then show up under "My Saved Strategies", and you can revisit any past run under History to compare. Step 5. Extend it The codebase is small and readable on purpose. Feel free to add slippage, stop-losses, position sizing, shorting, or an entirely new strategy. I also recommend using Alpaca for stocks (optional; your API keys stay local). Took me weeks to build. Enjoy! →

I dug through OpenAI's "Pacing the Frontier" paper. Tbh, It's quite shocking. OpenAI is actually asking for the US government to slow AI progress for safety. My theory: It has nothing to do with safety and is rather competitive positioning. Models like Kimi K3/other open weights are catching up, and the only viable solution for Frontier Labs to stay ahead is to deliberately stall AI progress as a whole. Open-weights like Kimi are actually trained using outputs and techniques derived from frontier models. Meaning, every time a frontier lab ships something groundbreaking (like Fable), open-weight labs can catch up faster and cheaper by learning from it. Dario has also suggested the US start blackballing the sale of powerful AI chips to China. Hard to believe this entire narrative is purely "safety" based.

Less than 48 hours ago, Anthropic released a FULL guide on how to properly prompt Claude 5 models. After reading it, I discovered 99% of people are using Claude 5 the wrong way. It covers Opus, Fable & Sonnet 5 and how to get the best outputs in each. Use Claude the RIGHT way:

ChatGPT voice is actually insane. In today's video, I walk you through my full guide for using the new ChatGPT voice. It's mind-blowing! Watch now 👉

One of the most valuable videos on Claude + TradingView. You're missing out if you're not using AI to help you trade financial markets. How to use Claude to build a personal trading system (full video).👇

I backtested 400+ trading strategies with Claude, found a winner, and made + $19,537. In this video, I walk you through the entire process. Backtesting WINNING trading strategies with AI (cheat code):

Claude + TradingView is a complete cheat code. 99% of traders don't know this, but with this combo, anyone can find WINNING trading strategies. How to find strategies, convert them into Pine Script, import them into TradingView & more:

The most valuable AI system you're not running yet: an AI second brain. The concept goes back to Karpathy's knowledge wiki earlier this year, and it's still one of the highest-leverage workflows in AI. Connect your Hermes agent, and it becomes 10x more powerful. Here's how to build it (from scratch): Step 1. Download Obsidian Head to obsidian.md and download the desktop app. Once downloaded, go ahead and create a new Obsidian vault. Vaults are just where notes/text are stored locally. Start dumping everything in here: - Personal goals - Meetings - AI context/preferences The more you put in, the more powerful your second-brain becomes. Step 2. Connect Hermes to your Obsidian vault If you don't have Hermes yet, download the desktop app here: [ Then, paste this prompt: "I want you to connect to my new Obsidian vault and act as my second-brain personal assistant - do everything necessary to set up that connection now." This gives Hermes direct access to everything inside your notes vault. Step 3. Let it self-evolve Every time you add a new note, Hermes will automatically ingest it. Prompt Hermes: "Every day, scan my second brain database. Every time I add a new note, use it to create reusable skills and workflows." Pro tip: Once Obsidian is connected to your agent, you can just prompt it directly to add notes/context/data. This works really well if you're prompting Hermes on the go from Telegram/mobile. Save this and build your second brain system now.

This is the one GitHub repo that everyone needs to save. The CEO of Obsidian literally open-sourced his entire Claude Skills vault. It's his personal Obsidian vault template, and it's built to 10x your AI productivity. To get started: 1. Download the vault in the GitHub link below 2. Unzip the .zip file (Claude can help here) 3. Open Obsidian & create a new vault pointing to that folder →