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India’s Fastest Growing AI Startup

| 17 products mentioned
Y Combinator Y Combinator host
Mukund Jha guest
Madhav Jha guest
Watch on YouTube ai agents no-code development software engineering automation production deployment non-technical users multi-agent systems startup growth strategy

Emergent founders Makund and Madav Jha discuss how their AI coding agent platform has reached 7 million apps built in just 8 months, making it one of Y Combinator's fastest-growing companies. The episode explores Emergent's strategy of building production-ready software for non-technical users by starting from first principles with a powerful engineering foundation, rather than trying to add complexity to simpler prototyping tools. The founders share how they're democratizing software development, allowing domain experts without coding knowledge to build real, revenue-generating applications.

Key takeaways
  • • Emergent achieved hypergrowth by focusing on shipping production-ready apps rather than just prototypes, differentiating from competitors like Lovable and Bolt that excel at front-end mockups.
  • • The platform uses multi-agent architecture with specialized sub-agents for testing, design search, and API integration to manage context efficiently and handle increasingly complex applications.
  • • Building custom infrastructure, including their own Kubernetes-based container stack, allows agents to receive real-time feedback during deployment and prevents environment mismatches between development and production.
  • • 80% of Emergent users are non-technical despite the platform having enterprise-grade capabilities, unlocking a massive market of small business owners and domain experts previously blocked by technology barriers.
  • • The founders deliberately adopted an influencer marketing strategy via TikTok and Instagram early on to rapidly scale distribution while competing as a second-mover against established players.
  • • Emergent implements continual learning where agents automatically generate and refine skills based on previous trajectories, enabling the platform to improve its performance on recurring tasks over time.

Recommendations (7)

SWE-bench uses

"We looked at this benchmark called SWE-bench and we took on this challenge of becoming number one on that benchmark."

Mukund Jha · ▶ 3:08

Claude Opus
Claude Opus uses

"Opus is a different class of model right now it's going to enable extremely long horizon task."

Mukund Jha · ▶ 6:28

TikTok
TikTok uses

"We used TikTok, Instagram and a bunch of influencers to really spread the word out."

Mukund Jha · ▶ 8:03

Kubernetes
Kubernetes uses

"We built our own Kubernetes tech stack from ground up, the container tech stack."

Mukund Jha · ▶ 10:07

Python
Python uses

"We have a Python backend server."

Mukund Jha · ▶ 10:50

React
React uses

"We have a React front-end server."

Mukund Jha · ▶ 10:51

GPT-4
GPT-4 uses

"When we started, GPT-4 was the first model that we sort of started looking at."

Mukund Jha · ▶ 6:08

Mentioned (10)

Lovable
Lovable "We saw like Lovable was growing like crazy, Bolt was growing like crazy." ▶ 4:40
Bolt
Bolt "We saw like Lovable was growing like crazy, Bolt was growing like crazy." ▶ 4:40
VS Code
VS Code "Even though we have this VS Code editor we kind of hide it because nontechnical users get panicke..." ▶ 13:30
Cursor
Cursor "Our coding agent is so powerful that we basically internally use it as a replacement for Cursor a..." ▶ 13:19
Devon
Devon "I think Devin had just come out, so really really early." ▶ 3:03
Asana
Asana "This is an actual Asana clone that our team built. We're saving around $3,000-$4,000 a month in s..." ▶ 19:41
Jira
Jira "His first prompt was clone Jira." ▶ 22:04
Vercel
Vercel "Our deployment which almost mirrors what Vercel would look like is done by two people." ▶ 25:36
Equine Mind "She just launched her app like a couple weeks back. It's called Equine Mind on app store." ▶ 35:27
OpenAI API
OpenAI API "Nontechnical users probably don't know the concept of API key. How do I get an OpenAI API key?" ▶ 20:27

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