yetone
Featured
kill-ai-slop
Kill AI Slop is a multilingual field guide and agent skill that catalogues 33 common AI-generated design tells with interactive before-after demos, and provides a tool to scan and fix web projects.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It converts passive video content into an interactive, evidence-grounded AI coach that preserves uncertainty, separates claims from facts, and provides four modes (Learn, Practice, Apply, Reference) for active learning.
Who it is for
- Startup founders
- AI agent developers
- Startup advisors and educators
- Entrepreneurship students
Use cases
- Stress-test a startup thesis against five hard questions (leverage, timing, conviction, momentum, power)
- Learn startup principles through adaptive lessons and quizzes
- Practice founder exercises with scoring and progressive hints
- Reference timestamped decision rules and caveats from the interview
Strengths
- Every consequential claim is timestamped and mapped to evidence in provenance.json
- Explicitly separates speaker's claims from verified facts and inferences
- Four interactive modes that adapt to user goals (Learn, Practice, Apply, Reference)
- Preserves uncertainty and avoids treating rhetorical numbers as benchmarks
Considerations
- Derived from a single public interview, not a comprehensive startup resource
- Not affiliated with Sam Altman, OpenAI, or Y Combinator; independent educational project
- Instructional materials are currently written in Chinese (though the skill interacts in the user's session language)
README quick start
Ambitious AI Startup Playbook
Turn a 39-minute founder interview into an AI coach that can teach, challenge, and pressure-test your startup decisions.
Sam Altman: "Never a Better Time to Do a Startup" — Watch on YouTube →
Most founder content gives you ideas to agree with.
This repository gives your AI agent a system to work through those ideas with you.
It transforms Sam Altman's 2026 Startup School interview, Never a Better Time to Do a Startup, into a reusable, evidence-grounded Agent Skill. Instead of summarizing the video, the skill can teach the principles, quiz you, stress-test a real startup thesis, and help you turn an ambitious vision into the next data-generating move.
What can it do?
The skill automatically works in four modes:
| Mode | What your agent does |
|---|---|
| Learn | Teaches one principle at a time, checks understanding, and adapts the next lesson |
| Practice | Gives a founder-thesis exercise, scores the attempt, and reveals hints progressively |
| Apply | Reviews a real startup idea using observable evidence, falsifiable assumptions, and concrete next steps |
| Reference | Finds the exact decision rule, caveat, source claim, and video timestamp |
It helps founders reason about five hard questions:
- Leverage: If AI makes implementation radically cheaper, are you merely shipping the old idea faster—or attempting something newly possible?
- Timing: What changed in technology, cost, iteration speed, or incumbent advantage to make this startup possible now?
- Conviction: Is your non-consensus belief getting stronger because of evidence, or only because people disagree with you?
- Momentum: What imperfect next step will generate information even if it fails?
- Power: Can the product preserve a meaningful safety floor without concentrating control or reducing human agency?
Try asking
Use ambitious-ai-startup-playbook to stress-test my startup thesis.
Do not flatter me. Find the assumption most likely to break.
Teach me how to distinguish non-consensus conviction from founder delusion.
Quiz me before showing the answer.
Review my AI product for safety, concentration of power, and user agency.
Label every conclusion as source claim,
Description
An evidence-grounded AI founder coach generated from Sam Altman's Startup School interview.
Related repositories
Similar projects matched by category, topics, and programming language.
gnipbao
Featured
story-to-handdrawn-video
A Remotion-based tool that converts Chinese story text or ordered hand-drawn images into vertical hand-drawn diary-comic animation with handwritten captions, left-to-right reveals, optional page-curl transitions, and silent H.264 output for post-production dubbing.
MoonshotAI
Featured
Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.