Hi everyone! Really loving the community and the overall philosophy embodied here. I posted the ICM Field Guide - ICM Field Guide I'm an AI educator in Austin teaching small-business owners and executives then helping them apply AI and automation in their business. Before that I spent 20+ years in enterprise technology. Looking forward to meeting more of you!

It seems like one of the big advantages of ICM is that it can be used with any model. How are you leveraging this? If you are using multi-models what systems are you using and how are you deciding what is being fed to which model? I'm asking because I'm very interested in Qwen and trying to figure out how to incorporate that as a local AI.

https://typesafe.ai Ummm...I'm going to have to digest this one for a hot second. Wanted to post it here so my fellow geeks get a good look at it ASAP. Here's a video post on LinkedIn: https://lnkd.in/p/gRZrtHjt

Saw a good article on voice skills this morning. Instead of adopting them I asked GPT Astra to run an audit. Btw, I already had a voice-editor skill but it still wasn't quite sounding like me. "Research how to get AI to write in your voice, including any specifics for Claude models or GPT models, if there are differences in the models. But I feel like the voice profile that I have currently, and the Voice Editor skill and the Voice Refine skill, aren't really nailing it. I have to frequently revise and tell it the same things and get it to not write like AI. Go find all the skills, plugins, connectors, do a last 30 days search of Reddit, X, and other platforms, and then come up with an action plan for how we can improve so that you can write in my voice and so Claude can write in my voice. So basically any agent with the right skills can write in my voice." And it came up with a solid plan (condensed version more sensible for posting here - the actual plan is longer, more detailed, and contains specifics about my environment). ---------------------------------------------------- The goal: drafts that need less work before I can send them, with the facts and intent intact. The plan ✍️ My writing + corrections ↓ Examples I review and approve ↓ Small shared voice guide + guidance for each situation ↓ Draft → Check → Small repairs ↓ 🧪 Blind comparison → My edits → Next review A few things I'm building into it: - Start with real evidence. Compare an AI draft with my final version where both exist. Keep track of who wrote what. - Match the situation. A quick email, technical explanation, and community post need different amounts of detail and polish. Pull a few relevant examples for the task. - Keep lessons conditional. Removing an extra question from one email doesn't mean every email should have exactly one question. The recipient and purpose matter. - Keep me in the loop. The system proposes examples and rules. I approve them before they change the voice guide. - Keep source material private. Test with fictional tasks. Share only examples I've approved for that purpose.

I'm struggling to get AI to nail my voice and the voice of my clients (getting AI to write in my and my clients' tone/style for IP/positioning work/website, content, etc.). I'm wondering if anyone has any tips. There's so much out there and I'm getting a bit overwhelmed trying to figure out where to start. Would love if anyone could point me in a specific direction. As context, I am an IP advisor and architect bodies of work for my client. I can do it intuitively on my own (not an official copywriter) , but it takes FOREVER, and I know AI can do it faster, but I just keep going in loops when I try to nail mine or client's voices. I also know that spending this much time on nailing someone's voice in their content is NOT the best use of my time. Would love any tips or leads.