$10M ARR in 60 days with context engineering

Xiankun Wu built Kuse to $10M ARR in 60 days with zero VC funding and zero advertising. He reveals the context engineering framework that 99% of AI builders miss, the Threads growth hack (intern army + hundreds of accounts), and why MVO (Minimal Viable Output) beats MVP for AI products. Full Writeup: https://www.news.aakashg.com/p/xiankun-wu-podcast Transcript: https://www.aakashg.com/context-engineering-is-the-secret-how-kuse-hit-10m-arr-in-60-days-without-vc-funding/ ---- Timestamps: 0:00 - Intro 1:19 - Why Prompts Fail 5:23 - $10M ARR in 60 Days 7:23 - Hidden Story: Design Agent Pivot 9:07 - Threads Growth Strategy 11:28 - Ad Start 12:20 - Threads Accounts Demo 17:06 - Visual Context Engineering 20:10 - The Mom Analogy 22:12 - RAG vs Fine-Tuning vs Prompt Engineering 26:26 - MVO Before MVP 31:43 - Ad Start 32:48 - Demo: Creating PRD in Kuse 44:43 - Advice for AI Founders 56:12 - Outro ---- 🏆 Thanks to our sponsor: Reforge: http://reforge.com/aakash ---- Key Takeaways: 1. Context engineering beats prompting - One prompt won't work. Like hiring someone who knows nothing about your company—impossible to get results in 5 seconds. Accumulate context, build knowledge base, let AI know you over time. Combines system prompts, user prompts, memory, and RAG. 2. The Mom analogy - Your mom knows your preferences, goals (grow taller for basketball), what makes you happy. She doesn't need detailed instructions. That's context engineering. AI that knows you creates better results and positive loops. 3. Threads growth hack - Created hundreds of accounts posting use cases daily. Zero ad spend. Why it works: Threads gives traffic generously, less crowded than X, no creator hierarchy. Result: 3M impressions/month, hundreds of daily visits. Targeted Taiwan/Hong Kong markets. 4. MVO before MVP - Traditional: Feature → PRD → Design → Ship. Xiankun's way: Get model output right FIRST. Use RAG, prompting, fine-tuning for Minimal Viable Output. Then productize. "If no desired outputs, don't spend time productizing." 5. Visual context engineering - Use spatial tools: draw squares, graphs, sketches. AI understands spatial relationships. Unlike ChatGPT where files disappear, Kuse gives 2D space to store/reuse. Graphic operating system for AI that compounds. 6. The pivot story - Started as design agent. Users uploaded documents instead. Knowledge base usage far exceeded design. Pivoted to horizontal knowledge-based AI. Listen to your users. 7. Why X sucks for growth - Structured creator hierarchy. Can't farm traffic without famous connections. Good for VC fundraising, terrible for user acquisition. Threads and Instagram are underserved with real users. 8. Compounding context power - Regular chatbots: one-off, context disappears. Kuse: processes files when you're away, pre-prepares everything. Like having ingredients ready vs ordering each time. Each interaction improves. 9. Trading company origin - Co-founded YC company, created trading company, made money, funded Kuse with profits. Built without VC pressure. "Entrepreneurship is a game of focus." Building without chasing VC gives fresh perspective. 10. Future vision: productivity playground - "Not building productivity tool, building playground." When AI takes jobs (2030-2040), people need fulfillment. Kuse is amusement park where people pretend to work, feel satisfaction. Going to pure pleasure, not efficiency. ---- 👨‍💻 Where to find Xiankun Wu: LinkedIn: https://www.linkedin.com/in/xiankunwu/?originalSubdomain=hk Threads: https://www.threads.com/@kusehq?hl=en Company: https://www.kuse.ai/ 👨‍💻 Where to find Aakash: Twitter: https://www.x.com/aakashg0 LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com #contextengineering #aipm #kuse #startupgrowth #productmanagement ---- 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 200K+ listeners. 🔔 Subscribe and turn on notifications to get more videos like this.

Aakash GuptahostXiankun Wuguest

Nov 21, 202557mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Context engineering powers Kuse’s viral growth and rapid $10M ARR

Prompts often fail because users expect perfect results without giving sufficient project background, so repeated tweaking is an expectation-and-context problem as much as a model-capability problem.
Kuse’s core thesis is “context engineering”: accumulating documents, state, and intent over time so AI behaves like a long-tenured colleague rather than a one-off chatbot session.
The company’s rapid revenue narrative was enabled by long “silent” building plus a distribution wedge in Taiwan/Hong Kong via Threads, where an “intern army” scaled many use-case accounts into organic traffic.
“Visual context engineering” is positioned as both a UI and workflow: a 2D canvas/whiteboard that makes it easier to organize, reuse, and combine files and AI outputs, reducing reliance on sophisticated prompting.
For AI product development, X.K. emphasizes validating “minimal viable outputs” (MVO) before investing in full “minimal viable product” (MVP) builds, and staying focused on users rather than fear of platform shifts or competitors.

IDEAS WORTH REMEMBERING

5 ideas

Prompting breaks when context is missing, not when users lack “magic words.”

X.K. compares prompting to hiring a new employee: with no background on goals, constraints, and progress, perfect execution from a short request is unrealistic, so users end up iterating endlessly.

Design products to accumulate context so results improve with usage.

Kuse pushes users to store materials in one place and reuse them, creating a compounding loop where the system knows more about the project over time and needs less prompting.

Use multiple intent channels (visual + selection + structure), not just text prompts.

“Visual context engineering” frames the canvas as a way to express spatial relationships among docs/objects and to select/recombine inputs, making intent clearer than pure conversational chat.

RAG is the workhorse for doc-centric products; fine-tuning is optional and heavy.

Kuse relies heavily on RAG plus strong file/OCR/document processing; X.K. downplays fine-tuning as resource-intensive relative to the product’s primary needs.

Preprocess files asynchronously to make downstream AI interactions faster and smoother.

Instead of doing all retrieval processing at query-time like many chatbots, Kuse processes folders/documents ahead of time so future tasks feel like “ingredients already on the table.”

WORDS WORTH SAVING

5 quotes

People expect AI can deliver exactly as people wish within such a short description… is basically impossible.

— Xiankun Wu

Context engineering is like your mom knows you very much… so she can cook something that caters to your purpose.

— Xiankun Wu

Before you have the minimal viable product, you should have… minimal viable output first.

— Xiankun Wu

If it is the useful solution, don’t pretend to be… creating a very complicated… solution here.

— Xiankun Wu

Entrepreneurship is a game of focus.

— Xiankun Wu

Why prompts fail (expectations vs missing context)Context engineering as accumulated project knowledgeVisual context engineering and 2D canvas workflowsRAG vs fine-tuning vs prompt engineering in practiceAsync document/file preprocessing for faster reuseThreads growth strategy in Taiwan/Hong KongMVO (minimal viable outputs) before MVPCompounding context vs one-shot AI toolsFounder focus: users over competitors/VC pressureKuse positioning: Miro vs Dropbox/Box comparisons

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