EDIT: WE HAD OVER 100 PEOPLE SIGN UP IN TEN MINUTES....GOING TO BE ROLLING OUT ACCESS ON SLOWER BASIS NOW SO I DONT RUIN MY BANK ACCOUNT WITH TOKEN SPEND FOR YALL (im covering ai costs for everyone) but keep applying we are keeping track and will be letting more people in and reaching out about getting you all access one by one especially VIP. Our Platform is here, the thing a lot of you have been asking for and I have been teasing Take your ICM folders and second brains, put them in the cloud, and work on them together in real time. You upload your folders and files. One strong model with a good harness reads the map and becomes the agent you need. No new agents to build, no zip files to pass around. Each workspace runs in its own container. It renders your markdown, edits your files, installs packages, runs Python and Playwright, and multiple people can work in the same space at once. No Mac Mini needed to keep your data separate either. We're opening it to VIP and alpha testers first, and I'm covering the AI cost while we test so cant do a full role out yet. Free AI use which is great for those users, not great for my bank account haha. ✍️ Sign up for access: https://docs.google.com/forms/d/e/1FAIpQLSdSnToxclxt8EgoB2GizrEvHP7gzOedVcRXdCkpi1SZwR8ZfA/viewform 🔗 Questions, or want to work heavier with this? Email info@eduba.io

I'm one of those people who constantly creeps on real estate. Zillow, LandWatch, county listings, doesn't matter, I'll scroll through them for fun. But I'm extremely ignorant on how land sales actually work, what a home costs to buy versus to build, and how to not take a complete bath. So I built a workspace that finds land for me. The workspace has a learn mode that teaches me the whole system from the same references. Everything in the files comes from the Acrewell Land Company channel on YouTube (https://www.youtube.com/@acrewell) or a public source I checked. The research runs on free data, the USDA soil maps, USGS ground data, FEMA flood maps, USFWS wetlands, and the county map sites. The workspace is set to Illinois right now, that's where I've been looking. Another state is one file with the same sections, and every source in that file gets checked the same way. Before anything moves forward, the workspace checks each candidate: how you get to it, what the soil is like, how steep it is, whether it floods, what it looks like from the street, and what it looks like from the air. Everything gets written down, and anything that's a problem gets flagged for me to look at. If something fails hard enough, it's out. And for the value, the workspace finds two recent sales, one better and one worse than the land, and uses the two prices to get the real price. From what I've watched, land sells 30 to 70% below the asking price, and a listing that's been up past 6 months could mean the market already rejected the price. So the workspace never uses asking price for value, and the price for one acre is never used either. The workspace runs three modes. BUILD works out what a barndominium costs, starting from what finished homes sell for in that county. INVEST finds land with a problem at a discount. RECREATION checks land for off-grid living. The offers to sellers are copy-paste, I send them myself. The workspace tracks every step to close.

Hey everyone! Just go a message from my client saying that he closed a deal (he organizes events) for a major client of his, using a proposal that was 100% AI generated, using an ICM system I've created. How the system works (TL;DR) - see the images with the input and the ouptut. It's not a prompt, it's a workspace the client's team operates every day. Messages and a voice note go in; an on-brand HTML proposal comes out, and a PDF when it closes. Two human gates: one on the content, one on the rendered PDF. The AI assists, the human decides. Three rules do most of the work: 1. The AI never invents. Every sentence traces back to a message or to the operator's dictation. Missing something → it asks. Data that arrived with nowhere to go → it also asks, never decides silently. 2. The AI never sets a price. Values come from the operator, always. 3. Nothing reaches the end client without explicit human approval. And the part I like most: when a proposal closes, the system asks about every correction the operator made: "was that just this one, or is it a rule from now on?" If it's a rule, it proposes the edit to the rules file. The next proposal is born better. --- The longer version, for whoever wants the mechanics The shape is a pipeline that empties into a record library. Each proposal is one run; each finished run becomes a record the next run can learn from. Inputs. The operator drops in everything that actually arrived: forwarded client messages, supplier quotes, a voice note with their own instructions. No inputs, no assembly. First question before building anything: is there an existing proposal to use as a base, or should I search the archive for similar events? Assembly. The AI loads only what this step needs — proposal anatomy, brand voice, design rules, hard constraints and builds the HTML in the company's identity. Photos come from a catalog with provenance, never from a search; real photos of the company's own events come first, and a reference image is always labeled as one. If a photo's commercial attribute doesn't match the item being sold, it doesn't go in. What it hands back isn't just a draft, it's a draft plus a list of what's missing, so the operator knows which supplier to chase.

Has anyone here been using Buzz yet? The idea of having humans and AI agents working together in shared channels (instead of jumping between Slack, GitHub, and separate chat windows) seems really interesting. I’m curious about real-world experience though: - Is it actually useful? - What workflows are you using? - Any surprises, limitations, or things you wish you knew before trying it? Would love to hear from anyone who’s spent some time with it.

Looks like Brian is coming on board: https://x.com/BrianRoemmele/status/2079946271281090944