Quick workflow I stumbled into that's been surprisingly useful. Instead of manually analyzing a platform or community, I have Claude Code do it: • Navigate to the target site • Screenshot key pages • Analyze content patterns • Report back structured findings Did this for this Skool community yesterday. In about 10 minutes I had: • Community size and activity levels • All the content categories • Which post formats get engagement • Platform-specific formatting quirks (like code blocks not rendering) • Tone patterns from high-performing posts The browser automation caught things I would have missed. Like discovering the platform doesn't render markdown properly — would have built the wrong thing without that insight. The prompt pattern is simple: "Navigate to [URL], analyze the content structure and engagement patterns, tell me what resonates with this audience." Works for competitor analysis, community research, content strategy — anywhere you need to understand what actually works vs what you assume works. Anyone else using Claude's browser tools for research like this?

Most of my work focuses on AI automation for clients, but at the end of the year I also created an AI-powered New Year’s greeting video for one of them. I’d like to add that I’m not a video or movie expert and I don’t have a background in this field. Still, with the help of AI tools, I was able to create the final video without any major issues: - VEO 3.1 – video clips - Nono Banana Pro – images - ElevenLabs – audio background and voice-over - CapCut – final editing and assembly If you like the video, feel free to give it a like 👍

I just built a complete AI video generation system—and I'm giving away the entire thing for free. What's cool about this system is it automates the whole process. You type in a topic, and it generates a full video with voiceover, visuals, and sound effects. All the code is yours to download and customize. 👉 Watch here: https://youtu.be/QQNQc7cIbu0 Currently, it's trained to generate awesome AI Pokemon nature documentaries for Bulbasaur, Charmeleon, and Haunter! Check out the videos below! You can watch the full breakdown below! Here's what you'll learn: ✅ The complete tech stack: Kling 2.5 for video, Gemini for images, ElevenLabs for voice/SFX, and Claude Code to orchestrate everything ✅ My "Prompt Driven Development" process: How I iterate on each prompt until the output is perfect before moving to the next step ✅ The 7-step pipeline: From research to story generation to image creation to video generation to voice to sound effects to final assembly ✅ How to customize it: This isn't just for Pokemon—I'll show you how to adapt this system for any video topic you want ✅ The full source code: Everything I built, all the prompts, and the setup guide so you can run it yourself The best part? Once you understand this pattern, you can build AI video systems for any niche. Download the complete source code here: 👉 https://github.com/bhancockio/pokemon-ai-video-generator Can't wait to see what you build with this! Cheers, Brandon Hancock P.S. Want to see how you can convert this AI workflow into a real-world production application? In December, I'm dropping a brand new ShipKit template showing exactly how to transform this local video generation system into a full SaaS platform that anyone can use. If you want access to that (plus all our other production-ready templates for AI chat, AI workflows, RAG, and agents), check out ShipKit.ai—close to 300 developers are already using it.

Hi everyone 👋, I am curious if anyone used OpenAI Function Calling / tools feature? Here is a quick 5 min videos that explains it well: https://www.youtube.com/watch?v=Qor2VZoBib0 Basically the functions are normal functions in your code, you pass the unstructured input/request and your functions definitions/descriptions to the LLM, and the LLM comes back with suggestions on what functions (if any) in your own code that should be called to process the request as well as the structured arguments (!) you would pass to the functions. While AI frameworks are great for many use cases, this approach feels leaner and more transparent. In some cases, introducing a full AI framework into an existing production environment just isn’t practical — maybe your team works primarily in C#, Node.js, or PHP, and you simply want to sprinkle in some AI-driven decision-making without adding major dependencies or new infrastructure. I am curious what others think about it. Has anyone else tried using the OpenAI Functions Calling (or a similar feature from another AI provider API) on a project in production? If so, how did it work out for you in terms of reliability or performance?

Google has significantly enhanced the capabilities of its Google AI Studio, which links to a lot of its new frameworks. This is looking great, and it allows you to upload your project to GitHub once it's done. It might be suitable for a quick proof of concept or if you are getting started and want to work through ideas to apps. https://www.youtube.com/watch?v=Kb83qnQ0hQ8