Product Marketing & GTM Leader · AI Product Builder

Marketer who ships, builder who positions.

§ 01 · About

My background & interests

I’m Elena Wang, a product marketing and GTM leader focused on driving adoption and revenue growth for technology platforms. I partner closely with Product and Engineering to shape positioning, launch strategy, and lifecycle programs that turn customer insight into measurable impact.

As AI reshapes how products are built and distributed, I’ve been building and shipping my own apps using AI-assisted development (“vibe coding”). This hands-on experience taught me what it actually takes to go from concept to production.

My goal is to help AI companies bridge technical capability with clear positioning, combining product sense with distribution strategy that drives sustained growth.

Outside of work, I’ve traveled to over 50 countries across all seven continents — an experience that shapes how I think about building and marketing for diverse users. I also enjoy staying active and challenging myself, including finishing the NYC Marathon.

§ 02 · Product

What I've built

3 shipped · 2026

Mobile networking tracker app for professionals to log career conversations and follow up

  • Designed conversation-centric UX with multi-select topic tagging, follow-up reminders, and networking insights
  • Launched on the App Store and Google Play

This project pushed me into mobile product development — navigating app build pipelines, native UX conventions, and the end-to-end process of shipping to real users on device.

Stack
ExpoReact NativeSupabasePostHogVercelGitHub

Impact Logger

AI-powered web app for professionals to track their impact and generate narratives for performance reviews, promotions, role changes, and resumes

  • Rebuilt from Lovable prototype using React, TypeScript, and Supabase for persistent data and auth
  • Integrated Claude API for improved output control and multi-step narrative generation
  • Key insight: single-pass LLM prompts fail without multi-step abstraction
  • Constraint discovered: inference cost makes naive LLM usage unsustainable at scale

This project deepened my thinking around AI product architecture, cost tradeoffs, and stateful system design.

Stack
Claude APIReactTypeScriptSupabasePostHogVercelGitHub

Return-to-running web app combining a PT strength routine with a structured walk/jog progression program

  • Built a mobile-friendly PWA using React, Vite, and localStorage — no backend, no auth, no data collection
  • Implemented a pain-check protocol before every run log, with automatic advancement blocks for serious pain

This project taught me how to scope and ship a focused, single-purpose tool as well as how much product thinking goes into something that feels simple on the surface. Built during recovery from a ski injury and released as MIT-licensed open source.

Stack
ReactViteCSS ModulesPWAVercelGitHub
§ 03 · Reflection

My thoughts on product & growth

Mobile & Networking
01 / 02

Networking utility is built on the follow-up, not the connection.

Mobile friction is the enemy of data quality and user habit.

Beta testing validates the “aha moment,” not just the code.

Successful products focus on retention loops over vanity metrics.

AI & Systems
02 / 02

AI features fail without structured input systems.

Inference cost is a product constraint, not an afterthought.

Stateful systems outperform one-shot prompts.

AI is most powerful when embedded in real workflows.

Ultimately, I believe product mechanics and distribution strategy must evolve together — technical capability only matters if the distribution model is built into the product’s DNA.

§ 04 · Writing

My articles & stories

§ 05 · Contact

Let's connect

I’m passionate about AI product marketing and product-led growth, and I’d love to connect with people building and marketing AI products.

Whether you want to exchange ideas, collaborate, or just say hello, please feel free to reach out.