Alison💡

AI Product Manager @ Bytedance 🔎📖 // ride the wave 🌊 // prev: PayPal, INSEAD, Tsinghua

[Alison💡 @0xAxC]

Why $pippin($135M mcap) will flip $ai16z($1B mcap)?

TLDR
1. Pippin’s creator, Yohei(@yoheinakajima), has a proven track record with BabyAGI(@babyAGI_ ), is highly respected in Web2, and is a guy who’s always ahead of trends(or to be bold, someone who creates trends🤔just go compare the timestamps of his GitHub work with Google search trends).

2. Pippin’s AI framework is designed for “self-building” agents, far beyond simple platform bots. While self-building isn’t fully functional yet due to challenges like third-party integration or memory issue, the framework lays the groundwork to make it possible in the near future.

3. Unlike most AI coins, Pippin will have utility. While Yohei has only shared brief hints so far, it’s clear that utility will be one of the focus as the project evolves.
--------------------
So who is Pippin? Pippin(@pippinlovesyou) is an AI influencer and digital being, brought to life through AI-generated SVGs, named by ChatGPT, and nurtured by its visionary creator, Yohei. For more on Pippin’s origins, do explore Pippin's genesis story here (https://t.co/MIYKSMZisG) In this thread, I’ll mainly focus on the recently open-sourced Pippin AI agent framework and potential $pippin utility.

1. Pippin Framework
Before diving into the Pippin framework, I’d like to first explain what a "self-building" agent is. Since the development of BabyAGI, Yohei has been iterating on several of its core features, including dynamic skills, self-reflection, and graph-based memory. His recent focus has been on creating “self-building” agents.

Yohei has shared his thinking on self-building agents, breaking them down into four levels:
- Level 0: No Self-Build. Predefined Skills
At this level, the agent operates with a predefined function library(e.g. skills) created by the developer. When triggered, the agent simply executes an action or produces an answer based on the functions available.
- Level 1: Request-Based Skill Generation
The agent can build its own skills when triggered. In the future, it will be able to use the functions it has built for itself.
- Level 2: Need-Based Skill Creation
When asked to perform a task, the agent evaluates whether it has the required skills. If not, it writes code to build the necessary function and executes it.
- Level 3: Anticipatory Skill Development
At this level, the agent anticipates the skills it will need based on its character or objectives. It proactively writes code, builds the functions, and executes them to fulfill its goals.

Currently, the Pippin framework enables new activity generation, but the self-building of skills is not yet functional. To understand this, consider an example: If you want an agent to be healthy, it needs to “eat” green vegetables instead of instant noodles. The first step is for the agent to understand how to “eat,” so it can choose what to eat based on the goal of being healthy. In this context, “eating” is a skill, and actions like “eating green vegetables” or “eating instant noodles” are activities the agent can generate, choose, and execute on its own.

For a virtual AI agent, skills might include posting on Twitter, speaking on Zoom, collaborating in Google Docs, etc. Based on these skills, the agent can begin running activities aligned with the objectives it’s given—such as generating content to promote the Pippin token(objective) through tweets(skill) or live streaming(skill) with LLM-generated content(activity).

The challenges of building self-building skills are clear—how do you establish vendor integration, build memory, and so on without human intervention? The Pippin framework is designed with these challenges in mind, with the goal of simplifying self-building and making it a reality in the near future.
Given the current framework, a key question arises: What skills does Pippin have if self-building is not yet an option? The Pippin framework integrates with Composio(@composio), offering 200+ skills and capabilities for AI agents to leverage. These range from social media tasks and collaboration tools to even e-commerce (e.g., Pippin buying me a pizza after I got rugged…). With this, your Pippin will be able to achieve its goals by tapping into a wide range of platforms and skills—isn't it exciting!

For more technical details, feel free to check out the GitHub page(https://t.co/qs1GJTy2Lc). I've also included a screenshot of the framework below for your reference.

2. Pippin Utility
While Pippin’s utility is still evolving, we can get a glimpse of its potential from Yohei’s demo together with the Pippin framework. Users will be able to stake Pippin to become active developers and participate in:
- Quests and competitions
- Submitting solutions to challenges
- Voting on other developers’ submissions
Subsequently, there will be three types of quests:
1. Challenge: Be the first to complete a challenge
2. Competition: Be the one to submit the best solution
3. Request: Work as an assigned developer for a specific task

In my opinion, this system is designed to attract and centralize top talent around projects and problem-solving, rather than acting as a traditional launchpad where developers are scattered and competing with each other. This approach is reminiscent of Kaggle, the Web2 machine learning platform that successfully draws the best talent to solve complex problems through collaborative challenges. I’ve shared a screenshot of the quest page below, and for more information, you can watch the Pippin demo video(https://t.co/LY643cAdhC).

It’s just the beginning. Check out the GitHub star history below comparing BabyAGI, Pippin, and ElizaOS(AI16Z)—are you sure you don’t want to jump on the train now? It’s about to take off! 🚀🚀

[Yohei @yoheinakajima]

https://t.co/gDEMDZREzb

[Alison💡 @0xAxC]

Feel this deeply. Sometimes I wonder if it's that we got better at managing feelings before they fully form, like a preemptive emotional immune system. The intensity comes back for me in moments of genuine surprise or when I let myself be truly bad at something new. The beginner's mind remembers how to feel.

[Alison💡 @0xAxC]

What if the next gold rush isn't AI building, but teaching AI your taste?

A Hollywood cinematographer sees 1000 things you don't - which shadow creates tension, why 24fps feels more cinematic than 60, when breaking the 180° rule works

We're moving from selling time → selling outputs → selling the pattern of how we see

Just like lawyers are teaching AI what "good" contracts look like for $500/hr, imagine cinematographers encoding their visual intuition, producers teaching story rhythm, sound designers transferring their feel for emotional frequencies

The most valuable asset isn't your work anymore, it's your taste, packaged as training data

Creative judgment that took decades to develop can now be captured, scaled, sold. Every expert should be asking: how do I bottle what makes my eye different?

The real disruption: human taste becomes a product, not a service

[Lenny Rachitsky @lennysan]

How a 22-year-old dyslexic dropout created the fastest revenue-growing business in history—$1M to $500M in just 17 months.

@BrendanFoody discovered that AI labs were facing a critical bottleneck: they needed human experts to create "evals"—tests that teach models what correct looks like. His company @mercor_ai began connecting labs with lawyers, doctors, engineers, and other specialists to create evals and training data for models (for $95-500/hour).

Today, @mercor_ai works with 6 of the Magnificent 7, all top 5 AI labs, has never had a customer churn, and has a net revenue retention of 1,600%.

In my conversation with Brendan, we discuss:
🔸 Why evals have become the primary bottleneck for AI progress
🔸 How exactly Mercor grew to $500M revenue in 17 months
🔸 Brendan’s meeting with xAI that changed his company’s trajectory
🔸 Which skills and jobs will be most valuable as AI continues to advance (hint: jobs with “elastic” demand)
🔸 Why Brendan believes AGI and superintelligence are not happening anytime soon
🔸 The three unique core values that drove Mercor’s success
🔸 How Harvard Lampoon writers are making Claude funnier

Listen now 👇
• YouTube: https://t.co/QyRSrh6cQt
• Spotify: https://t.co/uz5N9UNYoT
• Apple: https://t.co/xGATH3lEG3

Thank you to our wonderful sponsors for supporting the podcast:
🏆 @WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://t.co/XH3bKmeEkH
🏆 @Jira Product Discovery—Atlassian's new prioritization and roadmapping tool built for product teams: https://t.co/yK7ekWfAnV
🏆 @enterpret_ai—Transform customer feedback into product growth: https://t.co/kpk3hkjHxH

[Alison💡 @0xAxC]

@julianweisser This feels like a massive tell about enterprise software. We built tools so complex that we now need AI intermediaries to operate them. It's like hiring a translator for a language we invented ourselves

[Alison💡 @0xAxC]

@omooretweets Does the report include api calls(calls through third party applications) or purely from the ChatGPT UI?

[Alison💡 @0xAxC]

@julianweisser This feels like a massive tell about enterprise software. We built tools so complex that we now need AI intermediaries to operate them. It's like hiring a translator for a language we invented ourselves

[Alison💡 @0xAxC]

This EPS idea hits different. The real poison isn't just the volume, it's how the constant stream of engagement bait actively distorts reality. When every post needs to be rage/shock/awe to cut through the noise, we're training ourselves to see the world in increasingly extreme terms. A quality multiplier could literally make nuance profitable again. Imagine if thoughtful takes earned 10x what hot takes did.

[Alison💡 @0xAxC]

Tinkering with an idea that got me:
> 3.5k views in 24h
> 19% upvote ratio (so you can tell it's not pleasant content lol)
> 31 people writing long essays on why I'm wrong
> 10+ stealth website visitors

Lesson: Bad attention beats no attention every time. The real problem is being ignored.

Plot twist: Reddit haters wrote the most helpful 7-8 line feedback I've ever received.

Worth the roast.

[Alison💡 @0xAxC]

@lennysan It reminds of this post, naming is such an art
https://t.co/varQovQB3U

[signüll @signulll]

people think “branding” is just fonts & color palettes. elon’s whole career is littered with high leverage naming & narrative framing moves.

- tesla: evokes innovation, history, electricity in a single word

- spacex: futuristic but grounded in aerospace tradition

- boring company: self aware af

- neuralink: says exactly what it is, but with sci fi allure

& now of course open ai which only seems obvious in hindsight. what a ridiculous streak. once you’re good, twice you’re great but like 5+ times??

[Alison💡 @0xAxC]

@omooretweets What would the definition of companion? Are teacher, mentor also considered companion?

[Alison💡 @0xAxC]

Great thread. Running the ‘Home Screen Test’ for consumer apps (4x7 grid), almost none are AI-native today. Are pain points not sharp enough yet, or do users just need more time to adapt to new behavior?
Feels like B2B will lead in the near term, where workflows are clearer and ROI easier to prove.

[Alison💡 @0xAxC]

@MacroMate8 Go deep on underserved markets (prediction markets, gaming assets) rather than competing on BTC/ETH liquidity?

[Alison💡 @0xAxC]

@simonecanciello In Asian society, superstition does play a notable role in the day to day life as well as other kinds of services that let you “know more about yourself” like MBTI or personal color analysis

[Alison💡 @0xAxC]

@jimchang I think payments need more than speed, they need predictable cheap fees and other non-functional features. Also for consumer to merchant payment, it’s tough to pull people away from the entrenched card networks

[Alison💡 @0xAxC]

@_mattwelter I'm curious about the retention!

[Alison💡 @0xAxC]

@ns123abc Hard to discern the exact thesis behind this investment, but in Asian society, superstition does play a notable role in the day to day life

[Alison💡 @0xAxC]

@mert When the market is talking about it, the alpha has gone already

[Alison💡 @0xAxC]

@ahboyash @cluely Jarsy founder is also solid, a silicon valley veteran

[Alison💡 @0xAxC]

Inspired by @saranormous Sarah Guo's talk on State of Startups and AI 2025

[Alison💡 @0xAxC]

Most AI products fail because they're thin ChatGPT wrappers.

Been thinking about what actually works:
The 2x2 matrix of AI transformations:
> Unstructured → Structured (natural language → code)
> Structured → Unstructured (data → insights)
> Unstructured → Unstructured (summarization)
> Structured → Structured (format conversion)

Sweet spot? Unstructured → Structured with clear validation loops.

Why? You immediately know if it worked. Code runs or doesn't. No ambiguity.

But the real moat isn't the transformation -> it's the thickness of your wrapper.

Cursor and Harvey win because they understand entire workflows, not just single prompts. They accumulate context ChatGPT can't access.

The paradox: Start with one atomic job-to-be-done, but architect for workflow expansion from day one.

Pick something people already do 10+ times daily, suffering through copy-paste with ChatGPT.

The best products create compounding value. Each interaction makes the next one better. Tight feedback loops. Learning from corrections.

If you're building AI products: Stop thinking "what would be cool" and start thinking "what are people already jerry-rigging?"

[Alison💡 @0xAxC]

@iCoooookie Need to concat and edit through capcut!

[Alison💡 @0xAxC]

Fun fact: What if you could access Veo3 video generation through third-party platforms at a fraction of the direct subscription cost? 🤔