Great to see the lives are in the calendar - bummed I missed the first one as I would have made it had I know. Great to see something in the calendar

Hey guys, It's been a crazy few weeks! Let me get through today's meetings then I'll tell you what's been going down :) How you guys been though?

OK - Yesterday I just looked around. Today I actually went to the classroom. I have a bit of an idea of what I want to do. Obviously thanks to AI - I have a bit of a plan. Here it is folks as I am here to make myself more accountable This is my theory/plan of what I might attempt. It is what I am aiming for right now. Thanks to the LETS BEING ETC I have used that to sort out what I am going to work on Let’s begin Phase 0 + Phase 1 together so you move from “plan” to “action.” Phase 0 was first 🧭 Step 1 – Confirm Your Focus Arena Problem Zero → “Tax Depreciation Workflow Assistant” whose aim is to cut time lost chasing property-manager and owner details before a job is ready to inspect. ✅ Later I can then focus with another focus (admin cleanup, photo management, etc.). 🗂 Step 2 – Build Your AI Builder Cockpit You can set this up today in less than 30 minutes: 1️⃣ Innovation Log/Wish List (Google Sheet / Notion / Excel) Date Problem Observed Who It Affects Pain (1-5) Current Workaround AI Hypothesis Next Test Result / Learning 2️⃣ AI Asset Folder (on Drive or PC) /AI_Projects /01_Problems /02_Experiments /03_Outputs 3️⃣ Time Blocks 1 × 90 min “Deep Build” session each week 1 × 15 min “Review & Reflect” slot (e.g., Friday afternoon) 🔍 Step 3 – Start the Observation Pass Take one live or recent job (e.g., M5464 or M5467). Write down the full path from Job Open → Paid Invoice and note friction points. Typical examples: Stage Friction Why it Hurts Collect Info Missing PM details / build date / tenant access notes Adds 2-3 emails per job Photos → Summary Manual naming & sorting photos 20 min/job Draft TDS → Invoice Re-entering data manually into template Risk of typos + slow 🧠 Step 4 – Apply the 5 Whys to One Pain Point Pick the worst one (“missing info from PMs”) and ask “why?” five times. We’ll capture that text in your log. ✍️ Step 5 – Draft the Problem Statement “Property managers and owners experience delays in Tax Depreciation Schedules when onboarding new jobs because key property information is collected manually via emails and calls, causing 2-5 day delays and preventing same-week report turnaround and cash flow.”

Hey builders :) I noticed a lot of people are trying to access our community. But they see the price tag attached and walk away.. So lets address that. We have some new flexible pricing tiers now for our skool! Free Premium And VIP For everyone that has paid to be here. Thank you 🙏 your membership has automatically been upgraded to Premium at no extra cost or charge :) For everyone that got free access, you still have it :) and can now choose to upgrade whenever you want to! You also now have no excuse for inviting your friends and family that have an interest in AI, Robotics and Automation :) USE YOUR INVITE LINKS!! 😁 This also means we'll have A LOT of interesting people coming into the community now there's a free access option, more people equals more builders!! The AI space is all about the people. It's here to help us do our jobs and live our lives better. We simply can't do that without people having a vested interest. Just like all of us :) Appreciate you - Hamish

Hey builders 👋 I’ve been watching the paid‑AI hype for a while, and every time someone talks about “the next big thing” from a giant, I’m reminded of why we love the open‑source movement. 1️⃣ You own it. With a paid model you’re locked into the vendor’s pricing, limits and data policy. With an open‑source LLM you can run it on your own GPU, tweak the weights, and keep 100 % of the data you feed it. Ownership = freedom. 2️⃣ No price hikes. Open‑source doesn’t have a quarterly “upgrade” bill. If you want more compute, buy hardware or move to the cloud – you decide the cost. Paid AI can suddenly bump your token price and hit your budget like a surprise tax. 3️⃣ Community‑driven innovation. Every bug fix, new feature or model release is a community effort. That means faster iteration and more diverse use‑cases than any single company can ship on its own. 4️⃣ Security & privacy. Running locally means no data leaves your network unless you choose to share it. That’s a game‑changer for regulated industries and privacy‑concerned founders. 5️⃣ Learning & growth. Open source lets you dive into the code, experiment with architectures and build a portfolio that shows real engineering skill. Paid APIs are black boxes – great for quick prototypes, but not a path to deep expertise. Bottom line: Open‑source AI gives you control, cost‑efficiency and a community that’s building the future together. Paid AI is fine for one‑off projects or when you need instant scaling, but if you’re serious about building something that lasts, the open‑source route is the smart move. Drop a 👇 if you’re already on the open‑source side or if you’ve got questions about setting up a local LLM. Let’s keep the conversation going!