Aman Khan

Overview

Aman Khan is a recurring figure in the AI product management learning ecosystem, appearing in practical, community-oriented sessions focused on AI fluency, hands-on tooling, and product sense. Based on newsletter mentions, he has participated in a high-attendance Zoom session on evaluating AI fluency in PM interviews, collaborated on a reproducible Claude Code workspace setup for PMs, and joined live OpenClaw and MCP builds aimed at teaching stronger AI product judgment.

For AI Product Managers, Aman Khan matters less as a standalone public profile and more as a signal of where practitioner attention is going: interview design for AI-era PM roles, operational workflows for coding assistants, and live demonstrations of AI product-building patterns. His appearances place him alongside builders and educators such as Ben Erez, Tal Raviv, Carl Vellotti, and Marily Nika in conversations that blend PM hiring, tool usage, and applied AI product sense.

Key Developments

• 2026-02-05: Aman Khan joined Ben Erez and Tal Raviv in a Zoom session about frameworks for evaluating AI fluency in PM interviews. The event reportedly drew 2,300 sign-ups and nearly 500 live attendees, indicating strong market interest in AI-specific PM assessment.
• 2026-02-11: Carl Vellotti and Aman Khan shared a complete Claude Code workspace setup for PMs, including configuration files and exact prompts designed to help others replicate the environment quickly.
• 2026-03-17: Aman Khan was featured with Tal Raviv in Marily Nika's live OpenClaw and MCP builds, positioned as a way to teach “true AI Product Sense” through practical demos rather than theory alone.

Relevance to AI PMs

1. AI-fluency hiring and interview design: Aman Khan's participation in the Ben Erez session ties him to one of the most important AI PM problems: how to evaluate whether candidates can reason about AI products beyond surface-level terminology. AI PMs can use this as a cue to improve interview loops around prompting, evaluation, failure modes, and product judgment.

2. Hands-on PM tooling workflows: The Claude Code setup shared with Carl Vellotti suggests a practical orientation toward reproducible AI-assisted workspaces. For PMs, this is directly useful when building personal systems for prototyping, spec writing, research synthesis, and collaborating with engineering using AI coding tools.

3. Applied AI product sense through live builds: The OpenClaw and MCP sessions imply a focus on learning by building and observing system behavior. AI PMs can take this as encouragement to move beyond slideware and develop intuition around orchestration, tool use, guardrails, and real-world AI failure patterns.

Related

• Ben Erez: Connected through the AI-fluency PM interview framework and Zoom session where Aman Khan appeared as a speaker or participant.
• Tal Raviv: A frequent co-mention with Aman Khan in both the interview-focused Zoom session and the OpenClaw/MCP live builds.
• Carl Vellotti: Collaborated with Aman Khan on sharing a full Claude Code setup for PM workflows.
• Marily Nika: Featured Aman Khan in a newsletter issue centered on AI product failures, guardrails, and live builds for stronger product sense.
• Claude Code: A key tool context tied to Aman Khan via the shared PM workspace setup.
• OpenClaw: Referenced as part of live builds involving Aman Khan to teach practical AI product sense.
• MCP: Mentioned alongside OpenClaw in the same live-build context, suggesting relevance to agent/tool integration workflows.