I wanted to share a quick perspective on something that has drastically shifted my income over the last few months. Like a lot of people here, I’m always keeping an eye out for tech adjacent side income, but I stumbled into the AI data annotation space and the numbers have been honestly eye opening. I’ve been actively working across a few platforms specifically DataAnnotation, Snorkel, and Handshake. Coming into it, I was skeptical. We’ve all seen the low tier micro task sites that pay pennies. But because these specific platforms are training LLMs on complex reasoning, coding, and high level writing, the hourly rates for US-based freelancers are surprisingly substantial. The best part isn't just the money, though, it’s the absolute autonomy. I log on whenever I feel like it, work straight from my laptop, and pick the projects that actually interest me. Seeing how much corporations are pouring into AI development right now, it makes complete sense why they are paying a premium for quality human evaluation. It takes a lot of focus and sharp critical thinking to pass the initial assessments and keep getting high paying tasks, but the financial payoff has made it completely worth it. It’s wild to me that more people in the tech community aren't leveraging this right now. Curious if anyone else here is actively doing LLM training or prompt engineering evaluation? I'd love to compare notes on project availability, or fill in anyone who is trying to figure out how to break into the higher tier platforms.

Hey everyone! We’ve all seen AI generate cats, and we’ve all seen AI generate phones. But I wanted to push the boundaries of temporal consistency, multi-subject interaction, and complex emotional expressions—so I made this. The Challenge: Getting a single AI model to cleanly execute a sequential chain of logic (Subject A looks at phone -> Subject A reacts -> Subject A initiates physical interaction with Subject B -> Subject B reacts -> Both trigger a synchronized, high-energy physical motion like throwing their heads back in laughter) without the limbs melting into spaghetti or the faces distorting into horror movie territory. How I pulled it off: Instead of relying on a simple raw text-to-video generation, I had to utilize a custom workflow involving precise regional prompting, tailored motion brushes for the head-tilt trajectories, and a multi-pass upscale to keep the fur textures locked in during the rapid movement. I’m really happy with how natural the uproarious laughing animation turned out—it’s incredibly tough to get AI to capture that genuine, full-body humor dynamic without losing control of the character consistency. Curious to know—what tools or generation pipelines are you guys currently running to lock down synchronization between two active subjects? Drop your thoughts below, or feel free to hit me up if you want to swap notes on the workflow/seed settings I used for this render!

I've shared some of my LoRA Pilot work here. This is my other project, Forma, for the days you want to generate an image or animate a still without setting up a workflow. You choose a model, write a prompt and see the price before generating. Text to image, image editing and image to video are in the same studio. No subscription; you top up a balance when you need it. Your library is private by default, with no public feed. For a short ad, I'd start with a few stills, choose the composition, then animate that one. Saves paying for video attempts while you're still deciding what the shot should look like. I built it with occasional creators in mind. It's a generation tool, so you'll still want an editor for cuts, titles and sound. https://forma.cam

The first three Making R01 tutorials are live. We’re using our orange robot to work through LoRA Pilot (www.lorapilot.com), from the first session to preparing a dataset. 1. One robot, five worlds A first session in the LoRA Pilot workspace. https://youtu.be/_Z85_IgteLQ 2. Directing the scene Camera prompts in ComfyUI and a look at how closely the results follow them. https://youtu.be/Q3NH6ZSfbOM 3. Preparing the dataset Upload an archive, review the images and edit captions in TagPilot. This episode covers preparation; we don’t run training yet. https://youtu.be/07dfuzvv_Ik

► Start here: https://www.skool.com/digiaistudio/classroom/6eb858f0?md=b9ed495808a147d5b7f5917c16352d2c Phone photos of an empty room in. A staged listing pack out, in about an hour. Built for estate agents, property managers, stagers and interior designers. Also for anyone who wants a paid service to sell. You leave with: - Three staged photos in one matching style - A before and after for each room - An 8 second reveal clip One staging prompt. A Room Lock that protects what is sold with the house. GPT Image 2 for the photos, Seedance 2.5 for the clip. Every prompt is on the page. Pro members: it is in the classroom now.