Here’s the full AI Film masterclass breakdown — every tool, every technique, every mistake, and exactly what it cost.
This is a complete production walkthrough of “Dragon Blue,” my AI short film made with Seedance 2.0, Nano Banana Pro, Claude Cowork, and the Luma Agent board. I cover the entire pipeline from pre-production and reference image generation through video generation, post-production, and final delivery — including the multilingual prompting tricks, safety filter workarounds, and Omni model techniques that made this workflow possible.
Whether you’re exploring AI filmmaking for the first time or looking to level up your production pipeline, this video breaks down a real workflow you can replicate with the tools available right now.
Seedance 2.0 just went global — well, mostly. We break down exactly what the release means (and doesn’t mean), including API pricing, business account requirements, image rights contracts, and one platform where you can try it right now.
Plus: Tencent’s Wen 2.7 is about to drop with first/last frame generation, 9-grid image-to-video, voice referencing, and instruction-based editing. Could it take the #2 spot from Kling 3.0?
We also test Magnific’s new video upscaler and Topaz’s Astra model for AI video upscaling — side-by-side comparisons included.
And then things get weird: Pika just launched real-time video chat with AI agents through Pika Me. So naturally, I sat down for a live face-to-face interview with Flamethrower Girl. It’s janky. It’s fascinating. It’s the future.
Claude Cowork just added Projects, but most people are going to use them wrong — here’s how to actually set them up so you’re not wasting tokens or missing out on what they can do.
Seedance 2.0 has finally released — sort of. After months of delays, lawsuits, and rumors of cancellation, ByteDance has made the model available on CapCut and Dreamina… in seven countries. We break down what’s actually available, the new guardrails (including real face restrictions and C2PA watermarking), whether VPNs are worth the headache, and what this all means for a wider Western release.
Plus, Luma Labs dropped Uni One — a new thinking image model — and when you pair it with their boards and agents feature, the storyboarding and pre-production workflow gets genuinely powerful. We walk through a full pipeline from character reference to storyboard to video generation using Seedance 2.0 and Luma’s canvas tools.
Seedance 2.0 samples, prompting observations, the Omni model on CapCut, Luma Uni One, Luma Boards and Agents, thinking image models, AI video generation workflow, storyboard-to-video pipeline, region restrictions, C2PA content credentials, IP guardrails.
This video from SMB Capital explores how day traders can use the AI tool Claude to build “operational infrastructure” and gain a professional edge without needing to be a coder. The speaker emphasizes that the real advantage in modern trading isn’t just about the strategy itself, but the efficiency of a trader’s workflow.
The video outlines five specific practices to transition from a manual trader to a “Tier 3” trader who uses AI for institutional-level preparation and analysis:
1. Custom Price Alerts
Instead of relying on basic platform alerts (like simple price crosses), you can use Claude to write Pine Script (for TradingView) or ThinkScript (for Thinkorswim) code. This allows for “surgical precision” alerts based on complex criteria, such as a breakout of the 30-minute opening range combined with specific volume confirmation and VWAP positioning.
2. Pre-Market Game Plan Automation
You can create a reusable template in Claude to analyze your daily watchlist. By pasting in overnight news headlines and pre-market data, Claude can prioritize your stocks, identify key levels, and suggest potential setup types (e.g., VWAP bounce or momentum continuation). This reduces 60 minutes of manual research into a 5-minute automated process.
3. Custom Performance Analysis
The speaker demonstrates how to prompt Claude to write a Python script that analyzes your trade history (CSV files). This goes beyond basic P&L to find deep patterns, such as identifying which hours of the day or which specific setups are actually losing you money. This objective data helps remove “self-deception” and highlights blind spots that are hard to see manually.
4. Custom Order Entry and Exit Logic
Traders can use AI to build sophisticated exit strategies that aren’t standard on most platforms, such as a two-bar trailing stop that automatically moves up with market structure. This helps remove emotion from the selling process by making exits systematic and structural.
5. The AI Trade Autopsy
After the market closes, you can upload a screenshot of your trade and provide Claude with your entry/exit logic and emotional context. Claude acts as a “performance coach,” providing an objective report on whether you followed your rules, what mistakes were made, and what specific area you should focus on for improvement.
Key Principles for Using AI in Trading:
Specificity is Everything: Vague prompts yield poor results. You must provide detailed technical requirements and context.
Iterate and Refine: The first output is rarely perfect. You should expect to spend time “chatting” with the AI to tweak and test the code or analysis.
Verify, Don’t Blindly Trust: Always test AI-generated code in a paper trading environment or “break it” to understand the logic before using real capital.
Focus on Bottlenecks: The speaker suggests picking just one of these five practices—whichever solves your biggest current struggle—and focusing on it for two weeks to build a compounding advantage.
NVIDIA just committed $4 billion to two photonics companies that most people in tech have never heard of. That bet is worth understanding, because it is a signal that the entire AI hardware stack is about to change.
The part that surprised me when I went deep on this research: the real constraint holding back the next generation of AI is the wires. Not the chips. The copper cables carrying data between GPUs inside the world’s most powerful training clusters are hitting a hard physical ceiling. Heat, resistance, energy cost.
The physics of electricity is running out of room at exactly the moment AI needs more of it. What engineers are racing toward is photonics. Moving data as light.
No resistive heat, no signal degradation, and in some configurations, over 90% less energy for the same computation. MIT published that number and I had to reread the paragraph twice.This video breaks down how photonic computing actually works, why NVIDIA just put $4 billion behind it, and what the transition means for engineers building systems in this industry.
Nvidia’s GTC 2026 keynote just dropped some massive AI hardware reveals! From the insane Vera Rubin super platform to the game-changing DLSS 5 neural rendering and the localized NemoClaw AI agent, here is everything AI creatives and gamers actually need to know.
Jensen Huang took the stage to map out the next two years of AI infrastructure. While massive data centers and million-dollar racks like the NVL72 seem out of reach, this tech directly trickles down to reduce the speed and cost of your everyday AI video and image generation.
In this video, I cut through the noise of the 2.5-hour keynote to break down the actual specs of the Rubin GPU and Vera CPU, why gamers are debating the “GPT moment for graphics” with DLSS 5, and how Nvidia’s NemoClaw is stepping up to fix OpenClaw by bringing secure, local AI agents directly to your PC.
In a landmark lawsuit against tech giants Meta and YouTube, a jury found the companies negligent in causing harm to the plaintiff, a young woman, and failing to warn users about the dangers of their social media apps. Tech journalist Jacob Ward explains why this case opens a “can of worms” when it comes to suing technology companies for social media addiction.
I Gave Claude AI Full Access to Free TradingView Indicators… The Strategy It Built Was Insane AI coding tools are getting ridiculously powerful. Claude Code can now build strategies, backtest them, and optimize settings automatically. So I decided to test something crazy. I connected Claude AI directly to my TradingView environment using an MCP server and asked it to: • Choose the indicators • Build the Pine Script strategy • Backtest and optimize it • Find the best settings Then I verified everything on TradingView.
OpenAI announced that it was shutting down its video generation platform “Sora.” A deal that Disney struck with the company in December which resulted in a $1 billion investment will be dissolved. NBC News’ Brian Cheung reports on the latest developments and what it means for the tech giant.
