Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-laun…

The AI Operating System for Companies @sdianahu The best AI-native companies have made their entire company queryable: every meeting, ticket, and customer interaction legible to an intelligence layer that learns from it. Building this today requires brutal integration work, and there's no product that connects all this context into a single layer that can reason across it.

Absolute cognitonuke This is the weakest that AI psyops will ever be, buckle up

how to set up hermes agent step by step. built-in memory, 40 tools, works on your phone, and what to think of hermes vs openclaw: 1. hermes is a personal AI agent that runs in your terminal. think of it like open claw but with built-in memory, 40 tools out of the box, and 90% cheaper token costs. you install it with one command. 2. the 3 problems with open claw that hermes solves: no memory (you keep repeating yourself), constant gateway restarts, and zero visibility into what you're spending on tokens. 3. hermes remembers everything. every completed task gets saved to memory. it searches through past logs to find solutions. over time it literally gets smarter at your specific workflows. 4. connect it to open router. you see exact costs per model per task. free models rotate weekly. one founder went from $130 every five days on open claw to $10 on hermes. same output. 5. it comes preloaded with skills. apple notes, imessage, find my, browser, web search, image generation, cron jobs. no hunting for plugins. 6. connect it to obsidian so it reads your entire vault. connect it to gstack for your dev environment. create custom skills for your specific workflows. 7. the biggest money saver: have it write code once for recurring tasks. then it runs without burning tokens every time. stop paying an LLM to do the same scrape or report daily. 8. run it on android via telegram. name your agents. talk to them like coworkers. in this episode imran shows you how to set this up. 9. you can run it bare metal, in docker, or serverless on modal. pick your risk level. i begged @imranye to come on @startupideaspod and walk through the full installation live. he made it impossibly clear. if you've heard of Hermes Agent and want the clearest explanation of how to get set up like a pro let me know what you want me to cover on the next ep this is the best personal agent setup video on the internet right now. watch

The amount of hype and BS going around about enterprise AI adoption is insane. Aaron @levie is the most AI forward-thinking CEO in public markets today. But even Aaron at $1BN in ARR is valued at $3.3BN and getting smashed by Wall St. I sat down with Aaron to understand WTF is happening, what is real and what is fake in enterprise, WTF to do with token budgets and wrote up my notes below. (Link to full episode in comments) 1. Why Dwarkash Was Wrong and Jensen Was Right on Upgrading Systems Upgrading software is a multi-year effort, not a "magical moment" where everything can be secured overnight. The reality of enterprise security is an ongoing, endless cycle of "leapfrogging" between defensive and offensive capabilities. Founders must realize that even with access to frontier models, the implementation cycle in the real world remains the primary bottleneck. 2. Why We Will Have More Lawyers in Five Years Not Less The industry is myopic about job elimination; AI makes it easy to generate content, but it hasn’t made it easier to get that content approved by a court or a patent office. As clients inundate lawyers with AI-generated contracts and memos, the "ultimate constraint" becomes the number of qualified humans available to review and approve the output. 3. What Role Does Not Exist Today That Will Be Incredibly Common in Five Years? We are about to see the creation of 500,000 to 1 million "Agent Operators". These technical-yet-business-savvy individuals will be responsible for "care and feeding" of agents—writing skills, understanding MD files, and redesigning workflows for agents rather than people. 4. Will Massive Software Providers Simply Be Turned Into a Database That Agents Crawl Over? While the user interface may shift to chat, the value is moving to the API layer and the "business logic" embedded above the database. Systems like ERPs are more than databases; they contain decades of complex logic for supply chains and accounting that agents must interact with, not replace. 5. What Everyone Thinks About Enterprise AI Adoption That They Get Wrong The assumption that the massive gains seen in AI coding will immediately translate to all other knowledge work is a "misread". Coding has specific idiosyncrasies that don't always exist in broader knowledge work, where human collaboration and regulatory loops are more complex. 6. Where Would You Be Investing if You Were a VC Today? Despite high valuations, Levie would still be "loading up" on frontier rounds. These companies have the potential to grow much larger because the ultimate market for AI is often larger than the industry currently realizes. 7. The Budget of Tokens Will Have to Move Out of IT Spend and Into Opex Enterprise AI shouldn't be treated as a tradeoff between software licenses. Instead, token budgets will move into regular operational expenditure (OPEX), where businesses trade off a marketing campaign for a more productive, automated marketing engine. This allows AI companies to tap into a massive pool of capital beyond the traditional, capped IT budget.

TBPN asked me how Zapier keeps AI from drowning in data. Short answer: we built it a brain. Three layers: 1. Company-level source of truth (strategy, values, ICP). Curated by me and a handful of senior leaders 2. Team-level context that cascades down 3. Individual context, private to each person. Meeting transcripts, Slack threads, project docs, etc. So when anyone at @Zapier talks to AI, they're not starting from scratch. They point the SDK at specific documents against the backdrop of that brain. Better inputs, better AI. Thanks to @TBPN for having me on

I fully reverse-engineered Ramp's internal AI operating system for marketing agencies. Their system — called Glass — is how they got 99% of their entire company using AI every single day. 350 reusable workflows. Every tool connected at first login. Memory that refreshes every 24 hours. Automations running while everyone sleeps. I partnered with my engineering team and we broke down every component inside it. Then we rebuilt the whole thing for marketing agencies. 76 pages. Every system. Every layer. Every step. Steal it. Comment "OS" and I'll send it directly. Must be a following to receive auto DM

Don't hire a finance team. Build an AI CFO instead. Watch Mike Dion build a fully functional AI CFO in N8n that: → Routes questions to the right specialist (FP&A, Accounting, Treasury) → Uses GPT-4.0 for reasoning, cheaper models for execution (saves 30-40%) → Answers in real time with proper context and recommendations → Takes 45 minutes to set up → Costs $5/month to run No coding required. AI writes your system prompts. Full setup tutorial below.

Last week @sebgoddijn shared Glass, Ramp's internal AI productivity tool, with the world. Nearly a million views later, everyone's asking the same thing: how did you actually build this? Read the full story here: x.com/buchan_sm/status/20445… The short of it: we built an app that builds itself A small team of us pretty much vibe coded the entire thing in about a month. The trick was teaching Glass how to improve its own codebase. Turns out if you discipline your AI agents well enough, they grow up to be high-functioning, self-sufficient adults in no time.