Holy cow. The Unicorn Project is on the Wall Street Journal bestseller lists!!!
#2 in Hardcover Business category!
And astonishingly, it’s also #8 across all Non-Fiction E-Books!!!
A DevOps book!! 🤯🤯🤯
🙏❤️🦄🌈
Paywall: https://t.co/2nEeC2HDMg
#UnicornProject
For software companies building AI apps and agents, GTM is part of the product work too.
Microsoft launched an AI Agent Go-to-Market Playbook that walks software teams through turning their agents into real products and publishing them where Microsoft customers are already looking.
The playbook covers:
✅validating the idea before overbuilding
✅designing the MVP with real cloud architecture ✅deciding how to package it
✅publishing through Microsoft Marketplace (with 6M+ monthly visitors)
If your team is trying to ship agentic systems that customers can actually use this, this is worth a look:
https://t.co/EU24hMxjuX
In collaboration with @msdev@Microsoft
I missed this brilliant piece from @unmeshjoshi on the nature of coding and the power of abstractions and vocabulary in the LLM age. https://t.co/FTDK9Gouxn
People that are building real things are all coming to this conclusion. You could argue that it’s because software engineers care about the code quality more than they should, but it’s really because if you don’t, you will get up with software that does not work well.
@crvvdev Hey Ricardo, have you seen the Airbus analysis. Of this topic ? I believe they were the first one to talk about it on a public talk.. but not sure. And sometime ago some stuff of warbird get leaked as well on reddit..
https://t.co/HUtVaGWsak
Did you literally know that Windows has something called Warbird that literally executes encrypted shellcode on your computer?
And that all of its functionality is not really known, we just know that exists and is actively running in everyones computers?
Just learned about the concept of a “telescope ranch” in Texas.
People pay to have their $10,000+ telescope rigs set up in the middle of TX to avoid light pollution.
Every night the roof rolls back off the warehouses.
Then you can remote in to your telescope and use it from anywhere in the world.
Another 9 open Erdos problems solved, this time by DeepMind team.
Interesting loop of LLM - Lean agents working autonomously, and only after it's verified formally, going through human review.
Claude Code is about to release a feature called /workflows that I think will be extremely significant.
Especially for Enterprise AI.
I talked about this in 2024 in a post called Companies Are Just Graphs of Algorithms.
Basically the idea is that all work is just an algorithm, i.e., a series of steps to accomplish a goal.
Skills and Cowork have been heading in this direction already, and we've seen what that's done to company valuations in various spaces.
Well this is closer to the final form.
It's turning the regular, expected work that's done in companies into pseudo-deterministic workflows that follow defined SOPs.
The human role will be determining what problems to solve (taste, expeirence, etc), building new products from that, and then optimizing these workflows from above.
But the work itself will be these workflows executed according to SOPs.
CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI.
So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have to happen to get sustainable results from agents.
“Look I made this awesome product prototype”. Yes but you didn’t have to review the code before it went into production and fix a bunch of issues.
“Look I generated a contract”. Yes but you didn’t verify all the terms before it goes out to the counterparty and didn’t have to wire up all the past contracts to work with.
The best thing you can do as a CEO is to use AI a *ton* to figure out the real implications of agents in the enterprise, and come out the other side with an appreciation for both the upside and the real work that goes into them.
Every internal “replace Splunk” project I’ve seen woefully underinvested in the front end experience. The back end scaling challenge appeals to the engineers more.
Between AI to create a better experience and AI _being_ the experience I bet those projects will be a lot more successful now.
Gene, I’ve now got both OpenClaw and Hermes installed and I’m enjoying them for different reasons. Hermes has stronger memory/context handling and is excellent at specific task execution. OpenClaw feels incredibly configurable and full of potential. So much to learn and play with these days!
PS: @steipete It was such a pleasure meeting Ben Miller in Munich. He's fantastic. Keep up all your amazing work — you are truly changing the world!
At the airport in Chicago, I ran into an engineering manager who shared how she was blown away by what happened when she put her OpenClaw in a WhatsApp group chat with her family/friends.
It was such an education and eye-opener for them, and changed how they viewed AI.
OMG, I love KiloClaw!
My heartiest thanks to Philipp Göllner for taking 2.5 hours to set up an OpenClaw for me two weeks ago — it was such a mind-expanding moment to see what it's capable of, and how much it can help on so many different fronts!
Like an idiot, I didn't accept the Mac mini that he offered me — because it meant I had to do the install/setup by myself. Hahaha.
But luckily, I had learned about KiloClaw, from the amazing folks at @KiloCode when they sponsored the Enterprise AI Summit!
I love KiloClaw!!! It's a hosted OpenClaw service, with superb documentation and walkthroughs. One example is their "five days of KiloClaw" posts here: https://t.co/uExNHnlKEu
If you know someone who's set up OpenClaw, it can be a multi-hour ordeal. Philipp and I did our install when the daily built was a bit rocky, and to get things working, we had to use the most recent beta build.
With KiloClaw, you're guaranteed a much faster start — there are times that I wish I could upgrade faster, but avoiding the boofaramas that you can run into with these types of very fast moving projects.
And my heartiest thanks to Evan Jacobson for his superb help.
If you've been OpenClaw-curious like me (I've been lurking since December!), but afraid to try, give KiloClaw a try!!!
I'm finally understanding why my friends love it so much. And I was shocked by how much my wife, @notoriousMVK, loves Marvin, our OpenClaw, too!
cc @jasonacox@brainscott@jgallimore
Tired of the pain yet? Come to GitLab and take back control of your destiny.
I’ll even throw in the first year free for anyone switching from GitHub who signs a new three year agreement. DM me
Stripe’s observability team wrote an agent to help respond to incidents.
“Some early quick wins made us think it would be easy. It wasn’t”
- Rob Miles, Mike Cowgill #o11ycon
Brendan Hopper, Matt Beane and I have a thesis, one that I've been sharing around lately, and we want CEOs and boards to hear it.
Before I get to the thesis, let's revisit Clayton Christensen's Innovator's Dilemma (ID), the theory he developed at HBS to explain why big companies often get eaten by upstarts during technology shifts.
In short, the ID says incumbents serve their best customers so well, and tune themselves so ruthlessly for doing exactly what they do today, that they can't chase the disruptor tech coming up from below until it's too late.
The classic solution to the Innovator's Dilemma is to create a "bubble" in your company. You carve out an innovation team with a budget and mandate, as unfettered as practical by the parent organization. This is to combat the 2-level trap presented by the dilemma.
The economic trap is Christensen's original point: a disruptive technology can't justify itself under your existing P&L, because it serves smaller or weirder customers at margins your real business would never accept.
The governance trap is what gets piled on top once you're big: SOC2, FedRAMP, etc. mean every new idea has to clear a lot of process before it can move. The bubble is intended to escape both at once, with its own economics and permission slips.
The standard innovation "bubble" solution famously doesn't work very well. You may solve the problem inside your bubble, but you often can't roll it out to the rest of your company for the original reasons. Everyone is focused on doing their current stuff, and nobody has time for a major change.
Our thesis is that there is an entirely different way out of the dilemma this time around. No bubble needed, as long as you follow a simple rule. That rule is, let your people play. Give them back any time they earn from automating their jobs with AI. Then incentivize them to use that time to improve the company's processes.
When you see an engineering team announce a 40% productivity boost from adopting AI — a number that's been showing up in plenty of LinkedIn posts lately — your first reaction as a CEO or manager is probably to say, that's awesome, we can do more work now! Or you might simply expect to see 40% more output from the team.
Either way, you have just asked them to spend their extra time building faster horses (your current business) instead of letting them go figure out what a car would look like for your company. They gained some productivity from AI, which could have been your ticket out of the Dilemma, and you immediately slurped it back for your existing business.
This will get your company killed in the medium to long haul, because your company tomorrow will look almost nothing like it does today. Conway's Law says your software and your org chart mirror each other; as AI rewrites how you build software, the org has to shift to match. But if you're stealing the hours back saved by your employees, then you're not letting your org pivot naturally in the direction it needs to shift.
@RealGeneKim and I saw this in person at @arkanalabs a few weeks back. As long as your people know they'll be recognized and rewarded if they improve the company's processes — public credit for cross-team workflow wins, promotion criteria that actually count process improvements, managers who treat freed-up hours as a feature rather than a budget line — then they will use their "play time" to seek out other teams, and start pivoting you to becoming AI-native. This way it can unfold in whatever bespoke way is most natural to your company, rather than in some ivory-tower research bubble. For every company, the way it unfolds will be a bit different.
I think of this approach, of giving the time back to the humans who automate parts of their jobs with AI, as the new solution to the Innovator's Dilemma. The old bubble solution was to separate a bunch of people from their regular jobs, and try to give them the freedom to solve the problem in isolation.
In contrast, by giving your regular employees their hours back, the innovation bubble is still there, but it's now dispersed across the company, as lots of very tiny bubbles: one bubble per person who has liberated some hours.
If you've ever read Slack by DeMarco and Lister, a great book from back in the 90s, then our thesis should resonate. What companies need is to empower their own employees, the ones who actually work together (even across departments)--the ones who know how the business works--to shift the company in the new directions together. Gradually, but with intentionality.
You still have the frankly awful problem of token budgets. For every employee you upskill into baseline AI literacy (which I'd define loosely as using coding agents throughout the workday), you've added a non-trivial opex spend — for the heaviest agentic users it can run into five figures a year. I won't sugar-coat it; you need to find that money somehow. I don't have a magic solution, but I'm very happy that other models are catching up to Claude, because they're becoming good enough for real work now.
But token budgets alone aren't enough. To live through the Innovator's Dilemma this time around, your employees need a time budget, too. Give it to the ones who earn it using AI, then incentivize them properly, and I think you're headed in roughly the right direction.
Thank you for coming to my TED tweet.
I've kept my agent swarm busy with the @codescene MCP for a few days now. I call success! Settling for 9.97, because there is a userscript that just makes more sense as a long file than split up.