Working on an interesting project right now and figured I'd share what I'm learning: helping a client automate their engineering document workflow using Claude Desktop + SharePoint/365 connectors. The challenge: They create ~600 "data packages" per year. Each one requires pulling structured data from their ERP, plus unstructured content (text, tables, images) from certification PDFs. Currently takes 30-60 min each, mostly manual copy-paste. The approach: Claude Desktop with MCP connectors to SharePoint + a SQL connector to their ERP. Goal is 50% time reduction. Early findings: - The 365 connector can pull text from PDFs but not images (yet) - Might need Claude Code for the image extraction step - Data normalization is the real bottleneck — some fields live in the ERP, some only in PDFs Anyone else working on similar enterprise doc automation? Curious what approaches have worked for you.

The headlines say OpenAI is under “Code Red.” $4.3B revenue in the first half of 2025… …but $7.8B in losses from research and running ChatGPT at global scale. Most people read that and panic. I read it and think: This is the clearest signal yet that the AI arms race is accelerating — and that builders inside this community are positioned perfectly. Google is pushing Gemini 3. Anthropic just dropped Claude Opus 4.5. OpenAI is preparing GPT-5.2, which Altman says is testing ahead of Google internally. All three companies are burning billions trying to win the same race: 👉 Build the engine that the rest of the world will automate on top of. And that’s where we come in. While the giants spend oceans of money competing… creators, consultants, freelancers, and automation builders in this group get access to tools that get: ⚡ Faster ⚡ Cheaper ⚡ More accurate ⚡ More capable ⚡ Easier to deploy into workflows Every few months, the ceiling rises — and our opportunity rises with it. The turbulence at the model level becomes leverage at the automation level. This is the best time in history to be building AI systems, agents, and automations. Not because things are stable… but because the instability at the top is creating massive upside for anyone who knows how to turn raw model power into real workflows. The AI companies burn billions. We build businesses. Let’s keep going. We’re early — and the advantage is widening