If AI is so powerful, why can't it be its own forward deploy engineer and figure out how it can be most useful to a company?
It's a damn good question, and the answer is because it's hard to make an agent that thoughtful.
But it's not stopping us @appy_ai from doing the hard work.
Today weβre 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. https://t.co/GnyjGFaLLA
@weird_ceo This is the way! 100% all in on this future, we operationalized how we do it, so for those that what a dedicated master agent to manage all this: https://t.co/rWudOstZ0d
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.
@illscience Wild to think through what this would mean as a consumer-investor; so many pockets of alpha to take advantage of.... I presume you're writing this around the time of funding something like this? If so sign me up for the beta ;)
@jessegenet@MiniMax_AI@openclaw@AnthropicAI Amazing, and not that you need it, but 100% forgiven for the frontier models setting up how to get cost leverage where it matters. I guess I'm not sure if "off the grid" is nearly as important as "can operate efficiently" when otherwise you're looking at $100-$300/day
I have 100% been tricked by a variation where they were making/selling little metal Warhammer sculptures.....
Fam, we're not just going to be cooked, we've been cooked and didn't even know it....
We're big lovers/users of @DevinAI - it turned everyone on our team (beyond just Eng, but design, product, research, etc) into contributors to our codebase/product.
Are you using any cross-org agent framework beyond claw? You might uniquely appreciate what an out of the box product provides.
Hermes will give you immense power if you're willing to dedicate yourself to becoming an AI agent builder.... but don't you already have a business you're running?
It's why we built @appy_ai - all the technical setup, jargon, and headache are removed.... and after a 60s signup, you've got a swarm of AI agents working alongside you.
Not next week, not tomorrow- right now.
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
At this point, if you're a public software co (or a large private) that doesn't understand "Everyone is going to become an agent platform".... I don't know what to tell you Β―\_(γ)_/Β―
Aaron is a smart guy, but I massively disagree w/ "1M AI Agent Operator jobs" as anything more than a short-term phenomenon.
You won't need to know integrations, MCP, etc etc. The AI knows that- it will ASK YOU and then implement.
You'll focus on quality and ambition, the rest is sorted.
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 hasnβt 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 agentsβwriting 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.
This is an amazing architecture from Cloudflare: https://t.co/DXK4X2Fea5
and we happen to be very aligned with it in @appy_ai's home grown stack.
There are a few key differences, most notably- I think the future of agent<->agent interactions, and _even the tool primitives themselves_ is made BETTER by being type-less.
Shocking to those that know me, I studied PL in college and worshipped at the church of SML '97.
But LLMs are a different thing entirely, and all that matters is intent communication and intent rails. The experience your LLM has is the one to optimize for, not the human's looking at the codebase or the execution logs.
This is exactly how every org can/should/will operate. This is what enables agent-first across all your operations
If you're not Zapier, and you just wanna get there today: @appy_ai has your back
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