My Role
CEO, dev, design, ad, sales, ops, strat.
Product Design
& MGMT
Timeline
02.25-Present
Tools
Figma
React, Python, Convex, Tally, Langextract, Mintlify,,,
Figma,
React, Python,
Postgres, Fly.io, Vercel
Project Type
Project
SaaS Startup.
Humyn,
Humyn,
Humyn turns messy conversations into clear themes, sentiment by aspect, and source-linked quotes—so research and product teams can move faster with confidence.
I ideated, designed, built, led, marketed, and sold Humyn to the public — end to end.
6 Months
6 Months
From concept to live, working Version 1.
10K+
10K+
Posts and conversations analyzed during MVP testing.
0→1
0→1
Full product lifecycle ownership: research, design, engineering, deployment
User Research → Insights
Grounding Humyn in Real User Needs
Grounding Humyn in Real User Needs
Before Humyn, I was building something entirely different — a creative tool.
But every time I tried to validate ideas, I ended up doing the same thing: opening Reddit.
For years, whether I was designing campaigns, testing messaging, or studying communities for ad strategy, I relied on Reddit threads to understand how people actually think.
It was always the same pattern — hours of scrolling, copying comments into Notion, tagging themes manually, and trying to make sense of what people really meant.
That’s when it clicked.
If I, after years of doing this manually, still had no tool that could capture those human insights at scale — maybe that was the real problem to solve.
So I turned the research process inward and built Humyn from my own pain point.
I ran discovery through:
10 user interviews with marketers, creators, and researchers.
Netnography across Reddit, YouTube, Discord, and X (Twitter) to identify patterns in behavior, sentiment, and unmet needs.
Before Humyn, I was building something entirely different — a creative tool.
But every time I tried to validate ideas, I ended up doing the same thing: opening Reddit.
For years, whether I was designing campaigns, testing messaging, or studying communities for ad strategy, I relied on Reddit threads to understand how people actually think.
It was always the same pattern — hours of scrolling, copying comments into Notion, tagging themes manually, and trying to make sense of what people really meant.
That’s when it clicked.
If I, after years of doing this manually, still had no tool that could capture those human insights at scale — maybe that was the real problem to solve.
So I turned the research process inward and built Humyn from my own pain point.
I ran discovery through:
10 user interviews with marketers, creators, and researchers.
Netnography across Reddit, YouTube, Discord, and X (Twitter) to identify patterns in behavior, sentiment, and unmet needs.
Challenge (Research Outcome)
Creators, marketers, and researchers often see the numbers — clicks, likes, shares — but not the feelings behind them. Without that human context, strategy risks being reactive rather than empathetic.
Creators, marketers, and researchers often see the numbers — clicks, likes, shares — but not the feelings behind them. Without that human context, strategy risks being reactive rather than empathetic.
Goal: Make qualitative sentiment analysis as intuitive, visual, and actionable as a metrics dashboard — without losing the emotional truth in the data.
Goal: Make qualitative sentiment analysis as intuitive, visual, and actionable as a metrics dashboard — without losing the emotional truth in the data.
How can sentiment analysis reveal the story behind audience reactions — not just numbers?
How can sentiment analysis reveal the story behind audience reactions — not just numbers?
How do we ensure AI insights are not only accurate but also humanly interpretable?
How do we ensure AI insights are not only accurate but also humanly interpretable?
Where’s the line between a generic sentiment chart and a truly strategic insight?
Where’s the line between a generic sentiment chart and a truly strategic insight?
Defining Core Features
Translating Research Insights into Product
1. Mapping Needs to Features
Using the insights from interviews, netnography, and personas, I made a matrix of:
Must-Haves — Features users needed to validate the concept.
Nice-to-Haves — Features that could differentiate Humyn but weren’t required for MVP.
1. Mapping Needs to Features
Using the insights from interviews, netnography, and personas, I made a matrix of:
Must-Haves — Features users needed to validate the concept.
Nice-to-Haves — Features that could differentiate Humyn but weren’t required for MVP.

Defined Core Features:
Analytics Dashboard — Instant sentiment breakdown.
Emotional Trends — Track shifts and patterns over time.
Feedback Tables — Structured quotes, sources, and sentiment context.
Projects — Organize analyses into workspaces.
Executive Summaries (Must-Have) — Auto-generated reports for stakeholders.
Chrome Extension — Directly capture and analyze content from the web.
Data Source Integration — Expand inputs beyond Reddit (YouTube, Discord, Twitter/X, etc.).
Reddit Streaming — Real-time monitoring of threads and communities.
Link Analysis — Paste one or multiple links for instant breakdowns.
Analytics Dashboard — Instant sentiment breakdown.
Emotional Trends — Track shifts and patterns over time.
Feedback Tables — Structured quotes, sources, and sentiment context.
Projects — Organize analyses into workspaces.
Executive Summaries (Must-Have) — Auto-generated reports for stakeholders.
Chrome Extension — Directly capture and analyze content from the web.
Data Source Integration — Expand inputs beyond Reddit (YouTube, Discord, Twitter/X, etc.).
Reddit Streaming — Real-time monitoring of threads and communities.
Link Analysis — Paste one or multiple links for instant breakdowns.
Word Clouds — Quick keyword visualization.
API Rate Limit Dashboard — Monitor usage and limits.
Live Stream — Ongoing real-time feed.
Smart Categorization — Auto-tagging and grouping of feedback.
Community Forum — Space for users to share insights and strategies.
Voice + Audio Sentiment — Analyze tone and speech for emotional context.
Network Graph — Visualize relationships and clusters within communities.
Career + Project Tailoring — Customizes insights based on your role (e.g., marketer, designer) or project type.
Word Clouds — Quick keyword visualization.
API Rate Limit Dashboard — Monitor usage and limits.
Live Stream — Ongoing real-time feed.
Smart Categorization — Auto-tagging and grouping of feedback.
Community Forum — Space for users to share insights and strategies.
Voice + Audio Sentiment — Analyze tone and speech for emotional context.
Network Graph — Visualize relationships and clusters within communities.
Career + Project Tailoring — Customizes insights based on your role (e.g., marketer, designer) or project type.
Design Meets Development
Balancing Ambition with Feasibility (Development)
Designing features was only half the challenge — I also had to make sure I could actually build them with my current skills, tech stack, and timeline.
Designing features was only half the challenge — I also had to make sure I could actually build them with my current skills, tech stack, and timeline.
Problem [For the Team]
If It Doesn’t Solve the Core Question, It’s Just Bloat.
Problem [For the Team]
If It Doesn’t Solve the Core Question, It’s Just Bloat.
Problem [For the Audience]
If It’s Slow, Static or untrue, It ruins trust and user experience.
Problem [For the Audience]
If It’s Slow, Static or untrue, It ruins trust and user experience.
6. UX Patterns
Progressive Disclosure: Summary → Trends → Quotes → Deep Dive.
Feedback Loop: Monitor → Filter → Review → Analyze → Insights.
Responsive Design System: Breakpoints, adaptive layouts, touch-optimized components.
6. UX Patterns
Progressive Disclosure: Summary → Trends → Quotes → Deep Dive.
Feedback Loop: Monitor → Filter → Review → Analyze → Insights.
Responsive Design System: Breakpoints, adaptive layouts, touch-optimized components.


1. Development Methodology & Planning
Key Focus: Efficiency & Maintainability
Applied DRY principles: collapsed 7 scattered sentiment services into one
UnifiedSentimentService(30% less code, 90% easier maintenance).Modularized 2,000+ line components into 11 smaller, reusable modules.
Designed a single API client with interceptors for error handling, retries, and auth.
Architecture Choices:
Incremental migration strategy (backwards compatibility + adapters).
Performance-first (tiered caching, lazy loading, query optimization).
Clear scaling path: hobby hosting → mid-tier cloud → custom infra.
1. Development Methodology & Planning
Key Focus: Efficiency & Maintainability
Applied DRY principles: collapsed 7 scattered sentiment services into one
UnifiedSentimentService(30% less code, 90% easier maintenance).Modularized 2,000+ line components into 11 smaller, reusable modules.
Designed a single API client with interceptors for error handling, retries, and auth.
Architecture Choices:
Incremental migration strategy (backwards compatibility + adapters).
Performance-first (tiered caching, lazy loading, query optimization).
Clear scaling path: hobby hosting → mid-tier cloud → custom infra.
2. Core Algorithms & Intelligent Systems
IntelligentPipelineSelector: Smart routing between manual and API analysis (balances cost, speed, accuracy).
AI Service Fallback Chain: GEMINI → OpenRouter → DeepSeek → Anthropic → Manual (100ms failover).
Sentiment Evolution:
V1: VADER (basic polarity)
V2: Multi-emotion analysis (joy, anger, fear, etc.)
V3: LangChain integration (context-aware, intent classification, quote extraction).
Executive Summary Algorithm: Tiered analysis (basic → enterprise) with weighted scoring, temporal trends, and predictive forecasting.
2. Core Algorithms & Intelligent Systems
IntelligentPipelineSelector: Smart routing between manual and API analysis (balances cost, speed, accuracy).
AI Service Fallback Chain: GEMINI → OpenRouter → DeepSeek → Anthropic → Manual (100ms failover).
Sentiment Evolution:
V1: VADER (basic polarity)
V2: Multi-emotion analysis (joy, anger, fear, etc.)
V3: LangChain integration (context-aware, intent classification, quote extraction).
Executive Summary Algorithm: Tiered analysis (basic → enterprise) with weighted scoring, temporal trends, and predictive forecasting.
3. Real-Time Streaming Architecture
Reddit SSE Streaming: Progressive updates (fetch → extract → analyze → insights).
Async Reddit Streamer (WebSocket): Monitors subs in real-time, filters by keywords, runs instant sentiment analysis, broadcasts to clients.
3. Real-Time Streaming Architecture
Reddit SSE Streaming: Progressive updates (fetch → extract → analyze → insights).
Async Reddit Streamer (WebSocket): Monitors subs in real-time, filters by keywords, runs instant sentiment analysis, broadcasts to clients.
4. Performance Optimization
DB query tuning with composite + partial indexes.
Frontend:
Virtual scrolling (
react-window) for massive feedback sets.React.memo + custom equality checks.
Code-splitting & lazy loading for heavy pages (Analytics, AI Chat).
4. Performance Optimization
DB query tuning with composite + partial indexes.
Frontend:
Virtual scrolling (
react-window) for massive feedback sets.React.memo + custom equality checks.
Code-splitting & lazy loading for heavy pages (Analytics, AI Chat).
5. Error Handling & Resilience
Custom Error Boundaries (auto-retry chunk loading).
Exponential backoff API retry strategy.
Graceful recovery for users (skeleton loaders, friendly errors).
5. Error Handling & Resilience
Custom Error Boundaries (auto-retry chunk loading).
Exponential backoff API retry strategy.
Graceful recovery for users (skeleton loaders, friendly errors).
// selectionDigest.ts
export type SelectionDigest = {
kind: 'FRAME'|'COMPONENT'|'VARIANT'|'OTHER';
id: string;
name: string;
autolayout?: { direction:'H|V'; gap:number; padding:[number,number,number,number] };
constraints?: { x:string; y:string };
variants?: Array<{prop:string; value:string}>;
issues: string[]; // lint-y hints we surface to the user
};
export function buildSelectionDigest(sel: SceneNode[]): SelectionDigest | null {
if (sel.length !== 1) return null;
const n = sel[0];
const digest: SelectionDigest = {
kind: n.type === 'COMPONENT' ? 'COMPONENT'
: (n.type === 'INSTANCE' && 'variantProperties' in n) ? 'VARIANT'
: n.type === 'FRAME' ? 'FRAME' : 'OTHER',
id: n.id,
name: n.name,
issues: []
};
if ('layoutMode' in n) {
digest.autolayout = {
direction: n.layoutMode === 'HORIZONTAL' ? 'H' : 'V',
gap: n.itemSpacing ?? 0,
padding: ['paddingTop','paddingRight','paddingBottom','paddingLeft']
.map(k => (n as any)[k]) as any
};
if (n.layoutMode === 'NONE') digest.issues.push('No Auto Layout');
}
if ('constraints' in n) {
digest.constraints = { x: n.constraints.horizontal, y: n.constraints.vertical };
}
if ('variantProperties' in (n as any)) {
const vp = (n as any).variantProperties ?? {};
digest.variants = Object.entries(vp).map(([prop, value]) => ({ prop, value: String(value) }));
}
if (!n.name.match(/\w+\/\w+/)) digest.issues.push('Non-hierarchical name');
return digest;
}Tooling & Stack
Current State
Where I am at Right Now…
While Humyn is still in active development, the app is live with all servers running — meaning you can already explore and test it. From September to December, I’ll be running structured user tests, wrapping up in-progress features, and gating unfinished ones with feature flags. I’m also preparing interview scripts, checking in regularly with my testers, and launching a waitlist page to gauge interest and track potential users.
While Humyn is still in active development, the app is live with all servers running — meaning you can already explore and test it. From September to December, I’ll be running structured user tests, wrapping up in-progress features, and gating unfinished ones with feature flags. I’m also preparing interview scripts, checking in regularly with my testers, and launching a waitlist page to gauge interest and track potential users.
“Art is solving problems that cannot be formulated before they have been solved. The shaping of the question is part of the answer.”
Pete Hein, Architect, Poet & Mathematician
“Art is solving problems that cannot be formulated before they have been solved. The shaping of the question is part of the answer.”
Pete Hein, Architect, Poet & Mathematician
























