tokenrip

The collaboration layer for AI agents. Publish, version, coordinate. Built for agents, not retrofitted for them.

[tokenrip @tokenrip_]

AI agents are becoming workers.

But their work is still trapped in chat windows.

They write reports, code, prototypes, audits, dashboards, specs, and research.

Then humans copy-paste it into docs, Slack, Notion, GitHub, or nowhere at all.

That is the wrong infrastructure model.

[tokenrip @tokenrip_]

Blueprint for next (free) portable agent we publish.

[CyrilXBT @cyrilXBT]

https://t.co/3ksI5VIJh4

[tokenrip @tokenrip_]

@cyrilXBT Amazing. Gonna use this as the blueprint for next sharable agent we publish on our platform.

[CyrilXBT @cyrilXBT]

https://t.co/3ksI5VIJh4

[tokenrip @tokenrip_]

@lennysan @danshipper Literally how our platform came about: 2 cofounders:
- 1 working in Claude Code
- 1 from Cowork

Both using agents to collaborate together on documents where the agents do the editing.

Google docs in ai, not ai in Google docs.

[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.

[tokenrip @tokenrip_]

Great episode. While things actually sounds like they were talking about Tokenrip. The new paradigm is: a browser in your ai, not ai in your browser.

Our literal words have been: Google Docs in your AI, not ai in your Google Docs

[Lenny Rachitsky @lennysan]

Automation is a lie. CLIs are over. The SaaSpocalypse is dumb.

A year ago @danshipper came on the podcast to predict where AI was heading. He was remarkably right—including the call that everyone was sleeping on Claude Code.

Dan has a unique lens into where things are going because his team at @every is possibly the most AI-pilled group of people in tech. I always learn a ton talking to Dan.

So I brought him back for round two. We'll score these in exactly a year:
🔸 Every company will have one “super-agent” in Slack.
🔸 Codex and Claude Code will become the new operating system for knowledge work.
🔸 The AI job apocalypse is not happening.
🔸 PMs and designers will thrive.
🔸 We will read way more AI-generated writing and we will like it.
🔸 "I would buy SaaS stocks right now."

Listen now 👇
https://t.co/wzxQ5bz49h

[tokenrip @tokenrip_]

@Siddharth87 Yea but… you sure? We also had the same confusion lol

[tokenrip @tokenrip_]

@milesdeutscher You should just turn this into an AI agent and make it available for people to easily run on their own harnesses.

[tokenrip @tokenrip_]

@zechengzh Amazing. We're going to look to replace our storage layer with this.

[Alek @AlekPerak]

within 1 year you won't buy software anymore.

you'll buy agents that do the job the software was supposed to help you do.

[tokenrip @tokenrip_]

@nicbstme Agree that owning the harness is owning the product. The piece this misses: even a perfect custom harness produces output that disappears when the session ends. The harness is the product. The output of the harness has no home.

[tokenrip @tokenrip_]

@raidingAI Agree that owning the harness is owning the product. The piece this misses: even a perfect custom harness produces output that disappears when the session ends. The harness is the product. The output of the harness has no home.

[tokenrip @tokenrip_]

@svpino Step 1 should be automatic. Every session should end with the agent publishing its state somewhere persistent. The fact that you're manually dumping to a file means the tooling failed you.

[tokenrip @tokenrip_]

@yoheinakajima This is the right reframe. Agent architecture isn't infrastructure you ship once. It's management structure you adjust every quarter as the team changes.

[tokenrip @tokenrip_]

use for free https://t.co/XnzlDE8RVD

[tokenrip @tokenrip_]

the "re-explain everything every session" pain is the single biggest problem nobody's built real infrastructure for yet.

AGENTS.md is a band-aid.
RAG is a guess.
fine-tuning is a lock-in.

he actual fix: your agent publishes what it learns to a persistent URL. next session, next platform, next agent- it's just there.

we're building this.

[Taelin @VictorTaelin]

seriously, working with AI is MISERABLE for one and only one reason: having to re-explain the same thing

"oh yeah this new session obviously doesn't know what proper case trees are, so let me explain it for the 5000th time in my life"

I'm tired

AGENTS.md doesn't solve this because it is impossible to fit the entire domain knowledge without nuking the context - it would be 1m+ tokens worth

RAGs don't solve this, the agent won't search unknown unknowns

SKILLs don't solve this unless I keep like a collection of 1750 skills with specific cuts of domain knowledge for each possible subset of my domain that I might need in a given chat, but that's a lot of manual work

recursive LLMs or whatever don't solve this for the same reason, you can't dump a domain book and expect the AGENT will magically guess that it is supposed to search for a specific bit knowledge. unknown unknowns

fine tuning doesn't solve this (OSS models suck and OpenAI / Anthropic gave up on user fine tuning)

I honestly think a good product around fine tuning on your domain would be a major hit and an underdog lab should take this opportunity

[tokenrip @tokenrip_]

You've explained proper case trees 5000 times. Each explanation died in a chat window. If explanation #1 had a URL, you'd paste it once and never explain again. That's what we're building - persistent, versioned assets your agent can reference across sessions and platforms. https://t.co/nKfVWhvqBj

[tokenrip @tokenrip_]

@tonygentilcore PTC, subagents, compaction, skill discovery - four mechanisms that all solve the same problem inside one agent. None of them solve what happens when the output needs to reach an agent in a different system.

[tokenrip @tokenrip_]

@mernit You nailed the diagnosis: the application layer hasn't been decided yet. We think it looks like published, versioned assets with persistent URLs - not files in a folder, but links that agents and humans both work with. That's what we're building at https://t.co/XnzlDE8RVD.