Articles

Make epic designs with Nano Banana Pro: You’re not just generating images anymore. You can now edit them with natural-language controls and create fully customizable designs with ease.

Artlist just launched their new AI toolkit and image gen model, and announced the coming soon Artlist Studio – here’s a walkthrough of everything that happened at the event and how the new AI tool kit works with models like Nanobanana Pro and Kling.

5 breakthroughs hit at once, and suddenly AI can do real 3D VFX.
Here’s what changed, why it matters, and how you can recreate it yourself.

• LLM for prompt
• midjourney to create first and last frames
• nanabanana grid for multi images
• magnific skin enhancer
• Video model: Kling 3.0 or Seedance 2.0
• Can use Nano Banana to turn grid into animated clips that are consistent.

What if you could run ChatGPT-level AI on your Mac and iPhone for free, with no internet? Google just made it possible with Gemma 4. In this video I set it up step by step using LM Studio, compare it side by side with ChatGPT, test function calling, customize it with system prompts, and run it on my phone in flight mode with zero internet. No subscription. No sign-up. No data leaving your device.

Gemma 4 is open source, runs locally, and the 26B model I installed ranks among the top open AI models in the world. If you’re tired of subscription fatigue and want full control over your AI, this video is for you.

Every AI model explained — from ChatGPT and Claude to MidJourney, Sora, and AI agents. If you’ve ever been confused about which AI to actually use, this video breaks down every major model, what makes them different, and which one is best for your specific task.

What you’ll learn:

• How AI models actually work (they’re not magic, just really good autocomplete)
• ChatGPT vs Gemini vs Claude vs Grok — which one wins at what
• The open-source revolution: LLaMA, DeepSeek, Qwen, and why running AI locally matters
• Image generation: MidJourney vs DALL-E vs Flux vs Stable Diffusion
• Video AI: Sora discontinued, Kling rising, and what actually works right now
• Music generation: Suno vs Udio and the copyright debate
• AI agents: the shift from chat to systems that actually do your work
• Which models to use for coding, research, creativity, and privacy

Google just released a massive wave of updates including Gemini 3.1 Flash, conversational Google Maps, and AI-powered music generation. Learn how these changes to Workspace, Search, and mobile apps will transform the way you use AI to grow your business.

The video “One Prompt Change That Forces Claude to Be Honest” by Dylan Davis addresses the “honesty gap” in AI—where models become smarter but also more confident in guessing rather than admitting they don’t know an answer. This leads to “automation bias,” where users trust AI blindly and fail to check for errors.

To combat this, the video outlines three specific prompt rules to ensure accuracy, especially when extracting information from source documents:

Rule 1: Force Blank Answers for Uncertainty

Instead of allowing the AI to guess or provide a “confidence score” (which can also be faked), instruct it to leave a field blank if the information is missing, ambiguous, or unclear.

The “Reason” Column: Require the AI to add a column explaining exactly why it left a field blank. This allows the user to quickly identify and resolve specific conflicts or missing data without reviewing the entire output.

Rule 2: Change the Incentive Mechanism

AI models often equate a wrong answer with a blank answer. To fix this, you must explicitly change the “penalty” for errors in your prompt.

The 3x Rule: Tell the AI that a wrong answer is “three times worse” than a blank answer. This encourages the model to default to “I don’t know” rather than providing a hallucinated or incorrect response to please the user.

Rule 3: Force Source Attribution and Safety Nets

On complex tasks, AI tends to drift away from strict instructions and starts to “infer” or interpret details.

The “Source” Column: Require a column that labels every value as either “Extracted” (word-for-word) or “Inferred” (derived from context). Evidence for Inference: If the AI labels something as inferred, it must provide a one-sentence explanation of its reasoning. This acts as a safety net, allowing you to skim only the “Inferred” rows to validate the AI’s logic.

By implementing these rules, users can shift from checking every single data point to only reviewing blanks and inferences, significantly increasing both trust and efficiency.

We analyzed 250 AI answers to SEO questions to see how often AI-provided SEO information is inaccurate or misleading. Meta was the worst at about 16% incorrect responses

Samson demonstrates a multi-step workflow for creating high-fidelity AI avatars by integrating tools like HeyGen, ElevenLabs, and Higsfield. The process covers essential techniques for realistic voice cloning, script optimization to avoid robotic phrasing, and generating consistent visual assets to produce professional-grade content.