Simon Pettibone

building @tokenrip_ : publish an agent with memory, let anyone run it

[Lenny Rachitsky @lennysan]

My biggest takeaways from @danshipper:

1. The future of work will happen inside Codex or Claude Code. Instead of putting AI into your SaaS tool, you’ll use your SaaS tools inside your favorite AI agents' in-app browser. Dan spends all his time in Codex now—writing documents, managing email, doing research, everything. He's using Google Docs, PostHog, and everything he needs within the agent's in-app browser. The agent can see what he’s doing, and has all of his context, so he and his agent collaborate quickly and super effectively.

2. Automation is a lie—every automation needs a human. Dan's company doubled in size this year despite being incredibly AI-forward. Why? Because in order to make automation work well, you need humans making sure everything keeps working. This is why benchmarks are misleading—they measure AI on problems we’ve already framed and can score, but there’s always a higher frame.

3. PMs will win the AI era. Marcus, a former PM who previously ran Axios’s writing product, joined Every after getting super AI-pilled. Now he runs their product Spiral, and ships faster than anyone on the team. He pairs technical knowledge with spiky product sense, deep user empathy, and an eye for what matters. Dan thinks any PM who gets really AI-native will be incredibly dangerous because the building is done for you—what matters is figuring out what to build and if it’s great.

4. Full-stack designers are becoming superheroes. Designers used to make beautiful interactions that engineers didn’t want to build or couldn’t execute properly. Now designers don’t need to hand things off; they can build it themselves. Designers are naturally creative people, and AI is the perfect tool for them because it lets them bring their vision to life without the traditional bottlenecks.

5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume.

6. Every company will have one “super-agent” inside their Slack that every employee will use. Dan initially thought every employee would have their personal work agent, like a shadow AI org chart, but he’s completely flipped his view. He realized agents need humans who care about them. When someone gets tired of maintaining their personal agent, it becomes useless. The winning model is one forward-deployed engineer or AI-savvy person who maintains a company-wide agent (like Shopify’s River or Viktor), and then it trickles down to more specialized team agents as models improve and become less fiddly.

7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks.

8. We will read way more AI-generated writing, and we will like it. Human writing is incredibly important for things that matter, but for internal docs, planning, and email, AI-generated is often better because most people are bad at writing strategy documents.

9. Build software for humans and agents to use together. The current model is building a CLI that an agent uses independently. Instead, you and your agent should be using the app together. This creates new design challenges—agents can make a billion requests in three seconds, so you need approval flows, inboxes that summarize what happened, logs, and easy rollback.

10. Forward-deployed engineers are the new most essential role. The big model companies have teams of people managing their internal agents, and those teams aren’t going away. It’s different from traditional software building, and certain engineers love it. As models get better, this role will evolve—you’ll be managing more agents doing more things.

[Simon Pettibone @silostack]

RT @tokenrip_: Great episode and exactly the direction tokenrip is building in. The new paradigm is: a browser in your ai, not ai in your b…

[Simon Pettibone @silostack]

Man why does opus 4.7 love the term “load bearing” so much?

[Simon Pettibone @silostack]

@garrytan Nice! We have a different approach with @tokenrip_ : skills live in 1 place, referenced by everybody, and the actual local skill file is a thin bootloader that dynamically loads the skill on demand: No version drift.

[Simon Pettibone @silostack]

The gap now is actually collaboration vs coordination.

Coordination is what agent swarms handle: partitioned tasks orchestrated to accomplish a common goal.

Collaboration is unstructured and crosses team/org boundaries: independent parties working together through shared surfaces.

That's the gap Tokenrip attacks: https://t.co/Gz682nDD9T

[Simon Pettibone @silostack]

@trq212 Banger. Even with extremely thorough planning using brainstorming and the writing plans skills, there’s still bad architectural decisions that fall through the cracks. Great output for the review and eval phase . Gonna give it a shot. Cheers!

[Simon Pettibone @silostack]

Once you've got a workflow in place, easiest way to turn it into an agent USING YOUR EXISTING TOOLING (aka, without buying in to a new platform), is using Tokenrip: Moa is our agent-building agent, works in your existing platform, walks you through the whole process: https://t.co/Gz682nDD9T

[Simon Pettibone @silostack]

@AnthropicAI How do I get one of those government accounts?

[Simon Pettibone @silostack]

Hey Azamat,

We're building a platform for employees to build portable AI agents: build it once, whole team can run it from the tools they're already using.

So it's an inverted model: we house the brains and soul, inference runs on local harnesses/platforms.

Can send demo/deck if interested.

https://t.co/jp9gn9m3Nd

[Simon Pettibone @silostack]

@galdayan1895 @speedrun Building @tokenrip_ : Where employee-built AI agents become team infrastructure.

Anyone can build and publish an agent with an agent-building agent, and the whole team can run it, inside the AI tools they already use (Claude, ChatGPT, Cursor...): https://t.co/JwVg6kQNr2

[Simon Pettibone @silostack]

@CollinnHickey “Founders strengthened by struggle” - love the tagline

[Simon Pettibone @silostack]

@milesdeutscher Would be good to set up an agent with this stack built in

[Simon Pettibone @silostack]

@sjsandeep_jain that's the difference between bolted on ai and native ai. google docs puts ai in your docs. an ai agent puts your docs in ai.

[Simon Pettibone @silostack]

@karpathy And with @tokenrip_ just ask claude (or any harness) to publish the artifact and it'll give you a link to easily view and collaborate on the html

[Simon Pettibone @silostack]

Here's the thing: @tokenrip_ separates cognition from execution:
- versioned soul
- pinned behavior
- portable memory.

The agent survives the vendor. That's the floor, not the ceiling.

[Simon Pettibone @silostack]

Three questions to ask your AI vendor. The answers tell you which ceiling you're looking at.

[Simon Pettibone @silostack]

3. If you shut this down, what do I keep?"

Tests portability. Nothing portable = renting a session at relationship prices, on the vendor's terms.

[Simon Pettibone @silostack]

@hthieblot The deployment and memory layer for portable agents - publish an agent once, run from any harness with fully shared cross-session memory