Persistent sandboxes & durable shared storage feel like the right primitives for autonomous engineering.
The magical part, at least for me, is when an agent can test its changes against real infrastructure. The tedious parts of our stack isn't the actual coding, its the devops loop around it. How does this k8s change impact sandbox deployment rollouts? Does adding a new protocol message type for our shared filesystem break existing filesystems? Does the new image actually boot correctly with nested-virt?
These are scenarios the codex/claude code/whatever can test without my input now. I attach the required creds to the sandbox, and just spin up codex (with --dangerously-bypass-approvals-and-sandbox of course) and let it get to work. I can do that by making sure the sandboxes the agents start up with have an appropriate gcp service account, letting claude/gpt create the resources it needs to test.
So now the question is how to scale that. Can development become deploying a fleet of agents on durable, credential-scoped computers that can build, deploy, observe, debug, and iterate against real infrastructure in parallel? I think it can, we just need the right primitives.
Ghostty is great. So why not try hooking it up to some ephemeral sandboxes?
Experimenting with compiling Ghostty to webassembly and connecting it to a pty inside the sandbox.
I'm curious if Anthropic (and other labs) watermarking text output could lead to less AI slop. If it's so easy to detect emails & webpages as AI-generated, then what happens when gmail and google starts auto-marking them as spam?
Today I woke up to some really unexpected good news. My latest blog post for Adapt, where I shared a bit about my AI workflow, made it to the front page of Hacker News. https://t.co/BMGVTZgvUu
So many amazing people on X. I wanted to analyze the 1.5k responses to Elon’s Grok 4.6 build retweet to connect with the very best. This is my CEO use case of using Adapt + X + Apollo with our company @adapt agent connected to 47 integrations. @MarioNawfal you were top of the list.
Real example of Adapt as our company agent in Slack today, shared by one of our engineers
“Adapt found the real issue. I came to the same conclusion as DS, but Adapt went further.”
We’re moving from asking, “What AI do you use?”
to “What AI does your company run on?”
to “What AI is running your company?”
Today, we’re launching your @ company agent in Adapt. Your company’s own AI, branded with your name and deployable within minutes.
Tag @ yourcompany in Slack and ask it to do the work.
As we go through this change, it is important that a company owns its AI.
Own the agent, @ yourcompany.
Own the knowledge it builds.
Own the model routing.
Own the experience.
The company agent is the durable layer. It gives your company access to the best frontier and open-weight models, choosing the right model for each task to balance quality, speed, and cost.
When you tag @ yourcompany, you’re talking directly to your business through one front door. It learns how your company works, chooses the right model and skill, delegates work to specialized agents, and becomes more valuable as your company’s knowledge grows.
A few things make this possible.
It has to connect to every system your employees rely on, with the right access and permissions to understand the business and take action.
It needs its own secure computer to write code, build apps, analyze data, operate software, and do the work.
It has to be everywhere your people work. Web, Slack, iMessage, GitHub, and more.
Vercel CEO Guillermo Rauch articulated this perfectly when describing @ v, the internal company agent they built.
“If agents become the foundation of modern companies, you being in complete control, from source to runtime to data to token, seems like a pretty big deal.”
Every company will have its own @ company agent.
Adapt is how you deploy yours.
What AI is running your company?
chief of staff agents managing your marketing agent and your infra agent etc is play. it's fun but centralizing and having a universally intelligent agent that can just use skills or different integrations as needed is better. There are too many cases where information is missing or left out unnecessarily, and the overhead becomes more cost than it provides
@Jumballaya It's also interesting seeing the different intelligence cocktails for different teams. Yesterday I was on an open weight cocktail, some prefer sol, other fable/opus, each team united in a single company agent, but working with the best intelligence for the work they do.
Excited to announce today our branded company agent @Adapt!
It's really encouraging and exciting to see some of the best teams in the world (Vercel, Ramp, Block, etc.) building these company agent products and then having the base-level product that any team can build their own in minutes.
When I think back on my time at Postman (a great team in its own right!) it was awesome to see us start to explore our AI journey, most of which was leveraging Slack bots that were built with a very specific purpose: like one for user research, one for competitive analysis, and so forth. We would brand these, give them their own unique handles, and then they received surprising adoption and oftentimes pretty meaningful insights to help us orient and make decisions.
I think trying something small and self-contained is the right way for teams to start their AI journey. But with the rising capabilities of frontier-level intelligence, we've come to believe that teams don't need multiple agents that fragment insights and leave information segmented and silo'd. They need one agent that does work, and does so in the open, in team-based collaboration surfaces like Slack. One universal agent, with the right context via integrations and the ability to do real work with an integrated sandbox and filesystem, replicates all of those sub-agents in a much more powerful and flexible way. And because everyone has access and can see what's possible, it raises the bar of AI-fluency across the whole company. The one agent becomes a rising tide that lifts all boats, not a select few.
It's still very early, so we have so much to do, so much to build, but I am very encouraged by the release today which gives a glimpse of what I genuinely believe will be the way in which the bulk of teams work in the future in an AI-native manner.
Check it out and perhaps just as importantly: let me know what you think and what you're building.
https://t.co/b8hRxe5Q3s
Reviewing AI-generated code is the hot topic, but my middle ground is different: build what I want, validate it, learn in the flow, then split it into small reviewable pieces where I can fix the code and architecture.
https://t.co/aLJhqJQ6Do