Do we really need a "mental reset"? So, since I've made the decision about four months ago to lock in on producing something of value by next year, I have found that any time I'm just sitting there not working towards my goal is wasted time to me. Even when I'm at the gym doing cardio, I have some sort of goal-related video playing on my phone, while others are listening to music. It's been like this for months for me. For instance, this weekend I went north to camp with some family. I really enjoyed it, but anytime we weren't actively doing something, I wanted to grab my laptop and get to work. I found myself reading articles instead of engaging in conversation, and trying to thumb through Skool instead of watching the football game. While others talked about old times, other people, etc, I was thinking about what I need to do next, how I get to this next point, what steps need to be taken, and in what order, etc... Does anyone else find themselves like this? Do you need a "mental reset"? If you do, is it just for a few hours or a long weekend? Jorvek Journal

Earlier this year I reorganized my daily routine after reading Buy Back Your Time by Dan Martell. It’s changed my entire day for the better. What I was still lacking was that same organization in learning more about AI automation/workflows. Kind of like a ‘sub buy back your time’? I blocked off my time to study, but when that time came I was all over the place with study material and videos. Along comes Alex Hormozi then Skool into my life. I’ve found that the classrooms within these groups is that missing piece to focus down even more. So if you ever feel like you’re also all over the place, find a good classroom and go through all the videos before moving on to the next group classroom. If you want to still check out the latest and greatest videos, set a time just for that. For instance I watch classroom videos from 5am to 6am. From 6am to 630am I scroll through YouTube to see what’s new. From 630am to 700am I practice what I’ve learned. Then I head off to work. It works and it’s awesome. I hope your journey is going well. If you have a focused routine that works for you post it below.

My first 'on my own' project was cleaning up my Gmail inbox. I created this flow using a Gmail trigger, which pulled all of the inbox into the AI Agent. The agent determined how the email would be labeled based on the instructions. The flow then moved onto removing the email from the inbox (remove label). Finally, the flow was routed to Telegram to send a predetermined message for the emails, based on where they were labeled. When the multiple nodes were funneled, I initially had them straight to the Telegram node, but this caused 12+ messages at a time since it was pinging for every one of them. To resolve this, I added an aggregate node to append them and then funneled through a code node to put them into one summary, which triggered the Telegram node only once, regardless of how many emails came in. I want to add more AI automation so it creates labels based on the sender. For instance, I get the newsletters/notifications from Nate Herk and others. Generating a node for each is no different than creating a rule in Gmail.

Google Cloud just dropped a new resource packed with 101 architectural blueprints designed to help developers and business leaders move from inspiration to implementation. Each blueprint outlines a real-world generative AI use case—like document automation, sales forecasting, fraud prevention, patient outcome improvement—paired with a complete tech stack built on Google Cloud services. The use cases span industries from retail to healthcare, finance, manufacturing, media, and more. Whether you're looking to unify online and in-store retail, power AI-driven product search, automate content generation, or streamline clinical workflows, this guide gives you a ready-made foundation to start building. https://cloud.google.com/blog/products/ai-machine-learning/real-world-gen-ai-use-cases-with-technical-blueprints?utm_source=chatgpt.com

Google recently published a hands-on guide to creating a low‑latency, bi‑directional, real‑time voice agent using its Gemini model and the Agent Development Kit (ADK). Here’s the core breakdown: - Start with a basic conversational agent — one with persona and trained knowledge, but no external tool access. - Make it more capable by integrating tools like Google Search and the Maps MCP Toolset, giving your agent real‑world data and dynamic capabilities. - Use RunConfig with bi-directional streaming (BIDI) to configure seamless voice input/output and allow interruptions — for natural, conversational feel. - Manage concurrency with Python's asyncio and TaskGroup, enabling your system to listen, think, and speak simultaneously. - Encode audio responses in Base64 for smooth transmission, and stream text transcripts in real-time to support rich interaction. Everything you need is in the blog—code samples, configuration tips, and architectural insights to help you get started faster and smoother. https://cloud.google.com/blog/products/ai-machine-learning/build-a-real-time-voice-agent-with-gemini-adk?utm_source=chatgpt.com