The 16 hour AI challenge: Learn AI, build Products & Earn $10K/Month

ChatGPT 5 just dropped and guess what? 300 million jobs became obsolete overnight.

While companies are panic-firing entire departments, a small group of AI-skilled professionals are charging $10K/month as consultants to automate those same jobs.

The difference? They know the frameworks, workflows, and monetization strategies that 99% of people don't.

Join Outskill's 16-Hour AI Sprint this weekend (usually for $895)  and become the AI expert companies are desperately hiring – not firing. Register now for free

Date: Saturday and Sunday, 10 AM - 7 PM.

Rated 9.8/10 by trustpilot– an opportunity that makes you an AI Generalist that can build, solve & work on anything with AI.

In just 16 hours & 5 sessions, you will:

✅ Build AI Agents and custom bots that handle your repetitive work and free up 20+ hours weekly
✅ Learn how AI really works by learning 10+ AI tools,  LLM models and their practical use cases.
✅ Learn to build websites and ship products faster, in days instead of months
✅ Create professional images and videos for your business, social media, and marketing campaigns.
✅ Turn these AI skills into10$k income by consulting or starting your own AI services business.

Learn million $ insights used by biggest giants like google, amazon, microsoft from their practitioners 🚀🔥

Unlock bonuses worth $5100 in 2 days!
🔒day 1:3000+ Prompt Bible
🔒day 2: Roadmap to make $10K/month with AI
🎁Additional bonus: Your Personal AI Toolkit Builder

Join now for $0

I got my The Data Hero book in Vegas and read it on the flight home!!!! It was an easy read, but it hit hard. The core idea is simple. Most data teams are not held back by tools. They are held back by mindset. That landed for me because I see it in the field every week.

My friend, Malcolm Hawker keeps bringing the conversation back to business value and customers. Not in theory. In practice. Start small. Ship something useful. Measure outcomes the business actually cares about. Learn. Do it again. It sounds basic, but most teams still default to long roadmaps, heavy committees, and tool shopping. This book shows a cleaner path!!!!

What I liked is how direct it is about the traps. Protecting the status quo. Blaming process. Waiting for perfect data. Treating data as a back office service. You get a way out of each one. Short feedback loops with the business. Clear ownership. Product thinking for data. A bias to deliver value in weeks, not quarters. No fluff, just moves you can make

The timing matters. AI has raised the bar. If you keep doing what you did before, the gap between effort and impact will grow. The book makes that do or die point without drama. Change how you think and how you operate, or your work will get sidelined. I appreciated that it pushes leaders to look inward before buying anything new!

A few pages stuck with me on the plane. Treat data like a product with a real user and a real outcome. Define a minimum viable data strategy instead of a 60 page deck. Set one metric that ties to revenue, cost, risk, or customer experience and hold yourself to it.

Create space for controlled experiments so teams can try, learn, and adjust without fear. None of this requires a new platform. It requires a new posture

Who should read it. CDOs who need results now. CIOs who want their data spend to show up in outcomes. Data product managers, analysts, stewards, consultants who want to influence the roadmap. If you are anywhere near data leadership, you will find pages you can use the same week.

I finished the book as we touched down and wrote three notes for my next leadership call.

- One, tighten the loop with a business owner on a single use case

- Two, ship a small win in 30 days and measure it

- Three, clean up ownership so decisions move faster. That is the tone of the book. Clear, practical, and focused on impact

Well done, my friend, Malcolm. This is a useful playbook for anyone ready to move from activity to outcomes.

Here’s everything you need to know about Open Source Toolkit for building AI Agents. I have been exploring what it really takes to build practical AI agents, and the open-source ecosystem has come a long way.

There are now powerful tools for every layer of the stack:

• Browser automation to navigate and interact with the web

• Document processing with tools like DocOwl2

• Research frameworks such as GPT Researcher and Local Deep Research

• Vertical agents built for specific workflows

• Computer control with Open Interpreter and Self Operating Computer

• Voice interfaces powered by Parakeet v2 and ChatTTS

• Memory, evaluation and monitoring to refine performance

🔍 Stay Ahead in AI & Data! Join 137K+ Data & AI professionals who stay updated with the latest trends, insights, and innovations.

📢 Want to sponsor or support this newsletter? Reach out and let's collaborate! 🚀

Best,

Ravit Jain

Founder & Host of The Ravit Show