Superconductor
Building the autonomous software organization where AI agents work in parallel to solve complex engineering tasks without constant supervision.
At a Glance
- Software Engineers
- Engineering Teams
- AI-First Startups
- Open Source Contributors
AI Tools by Superconductor
(1)Superconductor
Parallel AI Coding Agent Manager
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Latest News
Superconductor launches native macOS app for agentic engineering
Superconductor listed as top AI orchestration tool for enterprise coding
Arjun Singh reveals VM-based agent orchestration for Rails
Superconductor startup demo at SF Ruby Conference
Products & Services
A native macOS application for running parallel AI coding agents locally with isolated git worktrees.
A cloud-based platform for spinning up multiple coding agents in virtual machines with live browser previews.
Market Position
Superconductor positions itself as a faster, more robust alternative to CLI-only or single-agent tools, emphasizing parallel execution in isolated environments (worktrees) and native macOS performance.
Leadership
Founders
Arjun Singh
Cofounder and CEO of Superconductor. Previously cofounder of Gradescope (acquired by Turnitin). UC Berkeley alum.
Sergey Karayev
Cofounder of Superconductor. Previously cofounder and CTO of Gradescope. PhD from UC Berkeley.
Executive Team
Arjun Singh
CEO & Cofounder
Ex-Gradescope cofounder.
Sergey Karayev
Cofounder
Ex-Gradescope CTO.
Board of Directors
Founding Story
Founded by the team behind Gradescope, Superconductor was started to solve the friction in managing multiple AI coding agents. The vision is to move from manual orchestration to an 'autonomous software organization' where agents operate in parallel isolated environments.
Business Model
Revenue Model
Subscription-based for cloud VM usage and enterprise features.
Pricing Tiers
Local-first macOS app usage.
Parallel agents in cloud VMs with live previews.
Target Markets
- Software Engineers
- Engineering Teams
- AI-First Startups
- Open Source Contributors
- Complex feature development using multiple LLMs in parallel
- Autonomous bug fixing and repo maintenance
- Parallelizing Rails application development
- Mobile/iOS agentic engineering
- Individual software engineers
- Early-stage engineering teams at AI startups