Found Google's Gemini CLI tool that writes documentation for you. 1. Install it: npm install -g @google/gemini-cli . 2. Go to your project folder: cd [path] . 3. Invoke agent by running: gemini . 4. Ask: "Create a comprehensive README with installation and usage examples".

ChatGPT Agent is live. It can:. • Control a virtual desktop. • Book travel autonomously . • Build presentations . We went from chatbot to digital employee in 2 years. What's the first task you'd delegate to an AI agent?.

My career progression in a nutshell:. Harvard taught me to analyze problems. FinTech taught me to solve them with code. Substack taught me to share the messy process.

Everyone wants to know "the secret" to breaking into data science. The secret:.- Build stuff. -Post it. - Repeat. It’s just about persistence.

3 years ago, none of the AI tools you use daily existed. ChatGPT, Claude, Cursor, GitHub Copilot, LangChain - all launched in the past 36 months. The entire landscape changed faster than a college degree takes to complete.

The AI tools everyone uses today didn't exist 2 years ago. Experience matters less than you think.

Every AI application builder was a beginner 18 months ago. That's how fast this space moves.

The truth about breaking into data science:. It’s 10% technical skills, 90% showing you can think through messy business problems.

Building AI is easy. Building AI that people actually want to use is hard.

Every AI demo works perfectly. AI implementations. not so much. What's been the biggest gap you've seen?.

AI courses teach you knowledge. Real projects teach you judgment.

Pattern recognition brain + urban architecture = weekend sketches

Building in public is vulnerable in a way that corporate jobs aren't. When you mess up at work, your team sees it. When you mess up online, the internet sees it. But the internet also gives you further reach.

Building in public taught me the difference between perfectionism and shipping. Perfectionism keeps your work private forever. Shipping lets you learn from real users.

I'm #84 in technology on Substack! . I started the AI Weekender ( to document my DS-to-AI transition six months ago with zero audience. I thought maybe 50 would care. This is the power of showing up consistently and sharing your learnings in public!.

Every company claims they're "AI-first" now. Most are still figuring out what that means. What’s ONE effective AI use case you’ve seen actually work?.

The AI job market isn't "learn Python and get hired.". It's "solve problems people care about and get hired.". Technical skills are table stakes. Business impact is the differentiator.

ML taught me to think in features and targets. AI taught me to think in examples and instructions.

Sometimes the answer is just a really good prompt and an API call. What’s a business problem you’ve recently solved with LLMs?.

That moment when you realize your 6 years of sklearn experience doesn’t prepare you for prompt engineering….