James

I paired Google’s NotebookLM with Perplexity—and it feels like they were built for each other. ​Here’s the step-by-step workflow to completely level up your AI research: 👇

@Orion_Vers7x Great

If you’re building online, this list will save you money. 8 powerful FREE alternatives to expensive tools 🔥

@Eric_Smith08 Totally agree! It's amazing how quickly tech evolves. Can't wait to see how it helps boost productivity even more.

NotebookLM Is Becoming Way More Powerful Than Most People Realize Here’s How to Turn It Into Your Smartest AI Assistant 📷

@iam_elias1 Sounds like a classic case of tech running wild! Glad she got some clarity at the Genius Bar. Hope those settings help her battery last longer now!

Her Apple Watch battery dropped to 78% after just one year. She wore it daily. She charged it overnight. She used it like every other Apple Watch owner she knew. Yet her battery had degraded faster in 12 months than her iPhone had in 3 years. She took it to the Genius Bar, expecting them to confirm it was defective. The technician ran every diagnostic. "Your watch isn't broken. It's just been running 24 hours a day doing things it doesn't need to do. There are 4 default settings on every Apple Watch that hammer the battery overnight. Apple knows. They've known since the first Series 1 launched. They don't change the defaults." She asked why. He gave the same answer Apple Store employees have learned to give silence. Then he opened the Watch app on her iPhone and walked her through everything. Here's what he showed her. 🧵

@Viora_Tech_Ai That's amazing

After 3 years using Claude, I can say it’s the technology that has revolutionized my life. Here are 18 prompts I use daily that have transformed my day to day; they could do the same for you: (Save this 🔖)

@jackcoder0 One of the strongest arguments yet for why AI governance must include economic modeling, not just safety discussions.

Two economists just published a mathematical proof that AI will destroy the economy. Not might. Not could. Will — if nothing changes. The paper is called "The AI Layoff Trap." Published March 2, 2026. Wharton School, University of Pennsylvania. Boston University. Peer reviewed. Mathematically modeled. The conclusion is one sentence. "At the limit, firms automate their way to boundless productivity and zero demand." An economy that produces everything. And sells it to nobody. Here is how you get there. A company fires 500 workers and replaces them with AI. A competitor fires 700 to keep up. Another fires 1,000. Every company is behaving rationally. Every company is following the incentives correctly. And every company is building a trap for itself. Because the workers who were fired were also customers. When they lose their jobs faster than the economy can absorb them, they stop spending. Consumer demand falls. Companies respond by cutting costs — which means automating more workers — which means less spending — which means more falling demand — which means more automation. The loop has no natural exit. The researchers tested every proposed solution. Universal basic income. Capital income taxes. Worker equity participation. Upskilling programs. Corporate coordination agreements. Every single one failed in the model. The only intervention that worked: a Pigouvian automation tax — a per-task levy charged every time a company replaces a human with AI, forcing them to price in the demand they are destroying before they pull the trigger. No government has implemented this. No major economy is seriously discussing it. Meanwhile the numbers are already tracking the curve. 100,000 tech workers laid off in 2025. 92,000 more in the first months of 2026. Jack Dorsey fired half of Block's workforce and said publicly: "Within the next year, the majority of companies will reach the same conclusion." Nobody is doing anything wrong. Companies are following their incentives perfectly. That is exactly the problem. Rational behavior. At scale. Simultaneously. With no mechanism to stop it. Two economists built the math. The math leads to one place. Source: Falk & Tsoukalas · Wharton School + Boston University ·

Use Case C: The Data Table & Strategy Trick Once that raw competitor data is loaded into NotebookLM, it’s time to synthesize. ​Prompt it to format the unstructured research. It will instantly generate a clean Data Table comparing metrics (which you can export directly to Google Sheets with one click). ​Bonus: You can even ask NotebookLM to act as a consultant and generate a strategic plan of action based entirely on the successful competitor data you just fed it.

Use Case B: Autonomous Competitor Analysis Want to crush your niche? ​Use Perplexity's agent (via the Comet browser) to autonomously analyze the top competitors in your field over the last 30 days. It will literally browse the web, open channels, and compile the data for you hands-free. ​Once it's done, just copy the raw text analysis and paste it into NotebookLM as a "Copied Text" source.

I paired Google’s NotebookLM with Perplexity—and it feels like they were built for each other. ​Here’s the step-by-step workflow to completely level up your AI research: 👇

Google Gemini can now edit your videos!!! 10 prompts that turn Gemini into your personal video editor. Here’s the full system 👇🏽

GOODBYE DISNEY+ 👋 GOODBYE NETFLIX 👋 GOODBYE SPOTIFY 👋 No more monthly subscriptions. ChatGPT turned my laptop into a free entertainment hub better than all of them combined. Here are 9 prompts to rebuild the entire system for free. Save this.

🚨 BREAKING: Google Gemini can now analyze any stock like a Wall Street analyst (for free). Here are 10 insane Gemini prompts that replace $4,000/month Bloomberg terminals: (Save this 🔖 you’ll need it later)

One of the biggest design flaws on the internet: Everything is optimized to continue. Nothing is optimized to conclude. 🎯Feeds don’t end. 🎯Videos autoplay. 🎯Recommendations multiply endlessly. So your brain never gets a clean stopping signal. And without realizing it, your attention becomes fragmented across hundreds of micro-interactions every day. That’s why I think the next generation of recommendation systems needs a different philosophy. Not: “How do we keep users here longer?” But: “How do we help them leave with something valuable?” That shift changes everything. This feels like an early step toward that thinking:collective-info.com/login Good technology captures attention. Great technology respects it.