Simon Pettibone

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

[Simon Pettibone @simbone_]

Is it me or does 80% of AI seem like it's for generating leads / GTM / outbound?

[Simon Pettibone @simbone_]

@garrytan The old map nobody's redrawn yet: our tools still assume humans store and retrieve.

The work now is agents generating the output. That's a factory job running on warehouse software.

[Simon Pettibone @simbone_]

@AnthropicAI changing their job postings: “Craving another hit of Fable? Come work for us… only way you’re gonna get it”

[ClaudeDevs @ClaudeDevs]

As a result of a US government directive, we are suspending access to Claude Fable 5 for all users. You can continue to use all other Claude models.

Here’s what this means for you:

Across Claude products, new sessions will run on your selected default model or Opus 4.8, and existing Fable 5 sessions will end with an error.

On the Claude Platform, requests to Fable 5 will also return an error. Please update your integrations to other Claude models.

We know this is a disruption to your workflows; we appreciate your patience and support.

[Simon Pettibone @simbone_]

Pretty easy to build a tool on @tokenrip_ specifically for skills that generalizes our solution for anyone. This is exactly how our portable agents work (centralized agent, mountable in Cowork, Code, Codex... ). Shoot me a DM and I'd be happy to put this together for you.

https://t.co/boGk5v5vy9

[Simon Pettibone @simbone_]

An ACTUAL solution, and the one we use: bootloader pattern:

1. Store the canonical versioned skill in a hosted location.
2. Add a thin bootloader sequence to the top of the skill: the sequence does a quick GET to just check the latest version against local version.
3. If the the version has been updated, then the skill downloads the full skill and replaces its local copy and tells the operator to re-run the skill (reboot)

This is what we use to ensure that our agent-building skill and cli are always up to date: @tokenrip_

[Simon Pettibone @simbone_]

Bootloader pattern solves this:

1. Store the canonical versioned skill in a hosted location.
2. Add a thin bootloader sequence to the top of the skill: the sequence does a quick GET to just check the latest version against local version.
3. If the the version has been updated, then the skill downloads the full skill and replaces its local copy and tells the operator to re-run the skill (reboot)

This is what we use at @tokenrip_ to ensure that our agent-building skill and cli are always up to date

[Hamel Husain @HamelHusain]

What’s the best way for non developers to

1. share skills with their team
2. automatically enforce that it’s always updated for everyone if changes are made
3 allow others to update it centrally

Github is not the best solution as it’s too clunky and doesn’t solve #2

Notion is a little better but can’t put code there

I’m tempted to create my own tools but someone surely has created this already??

[Simon Pettibone @simbone_]

An ACTUAL solution, and the one we use: bootloader pattern:

1. Store the canonical versioned skill in a hosted location.
2. Add a thin bootloader sequence to the top of the skill: the sequence does a quick GET to just check the latest version against local version.
3. If the the version has been updated, then the skill downloads the full skill and replaces its local copy and tells the operator to re-run the skill (reboot)

This is what we use to ensure that our agent-building skill and cli are always up to date: @tokenrip_

[Simon Pettibone @simbone_]

✅ Coding work

Knowledge work is next

[Aaron Levie @levie]

Lots of evidence of huge jumps in capability for Fable across coding (and related) tasks. It’s also a major jump in accuracy and success in complex knowledge work tasks.

In our Box AI Complex Work Eval, we tested the model against Opus 4.8 and saw huge boosts across almost every industry. For our eval we give the Box AI Agent, using Fable, a set of hard real world knowledge work problems that deal with enterprise documents. Then score how the agent performs the tasks.

The main differentiators for Fable vs Opus 4.8 is that it doesn't take shortcuts on complex reasoning, it gets multi-step calculations right, and it's significantly more consistent across runs. We saw the biggest leaps in Media & Entertainment (78% vs 61%), Technology (81% vs 73%), Financial Services (89% vs 83%), and Healthcare (66% vs 60%).

Here are some specific examples:

* Legal M&A due diligence: On a task reviewing NDA terms against a semiconductor company's contracting policy, Fable correctly identified that a joint-ownership clause violates exclusivity requirements while a liability cap is permitted under a Super Cap exception. Fable scored 100% vs Opus's 78%.

* Healthcare: On a clinical radiology error audit across 12 reports, Fable precisely categorized each error by severity grade and correctly concluded no Grade 3 errors existed. Opus prematurely escalated a case to "major error requiring immediate departmental review" when the evidence didn't support it — Fable 63% vs Opus 41%.

* Media & Entertainment: On a genre profitability projection task, Fable correctly recognized that a 20% Argentine tax deduction was already embedded in the source spreadsheet figures and didn't double-apply it. Opus applied it again on top — a compounding error across 4 genre calculations that took its score negative on the task vs Fable's 74%.

* Retail analytics: On a task analyzing high-growth product articles against an investment benchmark, Fable correctly computed each article's growth rate individually and identified that only 2 of 5 exceeded the threshold. Opus confused "high growth relative to average" with "above the benchmark" — scoring 61% vs Fable's 94%.

* Financial Services: On a 5-year debt facility projection, Fable correctly applied interest to opening balances and used the right capex figure. Opus applied interest to the total facility amount and computed tax from the wrong base — two compounding errors. Fable scored 83% vs Opus's 62%.

* Technology: On a SaaS feature valuation requiring computation of a Feature Value Index across multiple regions, Fable applied the formula correctly and got exact values for the markets. Opus got the arithmetic wrong on multiple criteria — Fable scored 100% vs Opus's 74%.

Overall, huge step change in complex analysis, work that requires analytical reasoning, and deep domain understanding. Fable will be available shortly in the Box AI Studio for customers to build agents with.

[Simon Pettibone @simbone_]

The drug dealer playbook:

1. First hit's free
2. Wait for full dependency
3. Jack the price
4. They'll pay. They always pay.

The Anthropic playbook:

1. Fable 5 drops, "included in your Max plan"
2. Two weeks of the best model ever made
3. June 22: usage-based pricing, on top of your subscription
4. They'll pay. They always pay.

Same playbook. Better margins. No legal exposure.

And shit, I'm not even mad. I'll be standing on the corner on the 22nd with cash in hand like everybody else.

Pure product moves itself.

Anthropic knows it. I know it. My credit card is about to know it.

[Simon Pettibone @simbone_]

@AnthropicAI runnin that drug dealer playbook. "Free" Fable samples, get em hooked, take their arms and legs.

[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 @simbone_]

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 @simbone_]

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

[Simon Pettibone @simbone_]

@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 @simbone_]

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 @simbone_]

@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 @simbone_]

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 @simbone_]

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

[Simon Pettibone @simbone_]

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 @simbone_]

@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