Category: blockchain

Everyone’s talking about OpenClaw. Some people love it. Some people think it’s a security disaster. Most of the takes are wrong. In this video we cut through the noise — what OpenClaw actually is, how it works, why the permissions model makes it both powerful and risky, and why the real skill isn’t installing it, it’s figuring out what you’d ask an AI assistant to do.

What if you could run large language models locally with just one command? Cedric Clyburn shows how Ollama, an open-source tool, simplifies deploying LLMs on your machine. Protect your data, save costs, and explore powerful AI solutions—all from your own system.

Excited to share the new OpenArt Video Upscale feature, powered by Topaz Labs! Now you can easily access and use the tool to enhance video quality. Try it out and see the improvements with ai upscaling!

Layoffs, consolidation, streaming losses, artificial intelligence and the rise of the creator economy are reshaping Hollywood, raising questions about whether the industry is just hitting a rough patch or in terminal decline.

In this video I introduce you to a feature on Forecaster that has completely changed the game for retail investors, traders, and wealth builders.

In this episode I compare the future of AI to the failure of the music industry in the early 2000’s.

In this tutorial, I show you step by step how to generate a full 9-panel ai storyboard from a single reference image. You’ll understand why generating all scenes as one image keeps everything consistent thanks to the self-attention mechanism, and how to turn each panel into animated clips Google VEO 3.1. The full process from idea to finished video takes under 10 minutes with zero regenerations.

I also share an advanced “bookend” technique where you define the first and last frame and let the AI fill in the middle panels. This gives you full creative control when you need a specific character arc or transformation for your how to make storyboard for video editing projects.

ChatGPT just got sued for inventing fake court cases. Claude just got banned by the US government. Hours after the ban, that same AI was used to select targets in a military operation, marking AI’s first conflict. And now ChatGPT, the one that hallucinates court cases, will replace Claude gaining access to classified military intelligence.

Copilot was supposed to be Microsoft’s next big leap. A PC you can talk to. A new category of computing. Instead, it’s everywhere and barely anyone wants it. Out of 450 million Microsoft 365 commercial seats, only about 3 percent are paying for Copilot. Even though Microsoft rebranded apps, pushed it into Windows 11, Edge, and Office, and spent tens of billions on AI infrastructure, adoption never matched the hype. Nadella compared it to the PC, the web, mobile, and cloud. That’s a wild claim for a tool most companies are still “piloting.” Forrester projected massive ROI. In reality, only a tiny fraction of organizations rolled it out company wide. Users complain it gives instructions instead of actually doing the task. Developers often prefer ChatGPT or Claude. Even Microsoft employees reportedly use other tools. So Microsoft pivoted. Copilot got bundled into higher priced personal plans. In some regions, customers were pushed into upgrades. Regulators noticed. Copilot might work in narrow coding use cases. But as the future of Windows and enterprise productivity? That dream looks a lot smaller than Microsoft expected.

Everyone thinks AI is responsible for the wave of tech layoffs. The reality is more structural: the era of cheap capital ended.

For almost a decade, near-zero interest rates pushed investors up the risk curve. Venture capital flooded the tech ecosystem, companies hired aggressively, and organizations added layer upon layer of management, coordination roles, and internal complexity. When money was essentially free, few companies had to ask the hardest question in business: who actually creates value here?

Now that capital is expensive again, that question has returned.

In this video, I tell the story of a former Amazon director I met on a ski lift at Mt. Bachelor who was recently laid off. At first glance, his story sounds like the narrative everyone is hearing right now: AI replacing workers. He had built an automation system using AI that could re-engage tens of thousands of small business accounts.

But when you look closer, the automation itself wasn’t new. Systems like this have existed for more than a decade in tools like Eloqua and Marketo. The real reason he lost his job wasn’t AI—it was a return-to-office policy.

His story reveals a deeper pattern inside large tech companies.

During the cheap money era, companies massively expanded headcount. Amazon doubled its workforce between 2019 and 2021, while revenue per employee dropped significantly. Organizations grew more political, more risk-averse, and slower as layers of internal coordination multiplied.

Now that capital is expensive again, those layers are being cut.

Economists call the outcome a K-shaped economy: after disruption, some people move upward while others fall behind. The dividing line is not intelligence or talent—it’s proximity to value creation.

People who own processes that generate revenue, protect margins, control distribution, or build systems move upward. People whose roles depend on organizational layers, titles, or presence inside a company become vulnerable.

The real shift happening in the economy is the collapse of abstraction—the distance between the person doing the work and the person paying for the value is shrinking.

AI is accelerating this trend, but it isn’t the root cause.

The real question every professional should ask is simple:
Do you own a process that directly produces revenue or protects margin?

Because the future belongs to the people who do.