Richard
Tandean
I architect AI-powered systems, build automation workflows, and create content at scale — bridging software engineering with artificial intelligence.
About me
AI-Driven Software Engineer bridging traditional engineering with artificial intelligence — building AI-powered systems, automating workflows, and creating content at scale.
Full bioLatest work
01
Web AppsSuperfreak Studio
Custom 3D Printing & Digital Manufacturing Platform
Co-founded Superfreak Studio in 2025 with Bintang Timurlangit, starting as a 3D-printed furniture brand. In 2026, expanded into a full custom 3D printing service platform where users upload and manufacture their own 3D models directly through the website — a digital manufacturing workflow system for on-demand 3D printing services.
Role
Co-Founder & Full-Stack Engineer
Responsibilities
- Co-founded the studio and architected the full-stack manufacturing platform from the ground up
- Built the Next.js frontend and NestJS backend with TypeScript across the entire stack
- Developed Superslice, an internal slicing system for production workflows
- Integrated Midtrans payment gateway and RajaOngkir shipping calculation for automated commerce
- Designed cloud-based file upload and storage infrastructure using Cloudflare R2 for 3D assets
- Designed the web-based ordering flow for custom fabrication services inspired by industrial manufacturing platforms like PCBWay
Key Features
- Manufacturing File Pipeline — Cloud-based file handling with Cloudflare R2 for 3D model uploads, asset storage, and manufacturing file management
- Custom Slicer (Superslice) — Internal slicing system built to support production workflows and prepare uploaded models for manufacturing
- Commerce Integration — Midtrans payment gateway and RajaOngkir shipping calculation to automate ordering and fulfillment for Indonesian customers
- Production Workflow — Users upload custom 3D files, configure print settings, calculate manufacturing costs, place orders, pay, and track submissions
Challenges
- 01Designing a web-based ordering flow for custom fabrication services that translates industrial manufacturing UX into 3D printing
- 02Managing upload and storage pipelines for large user-submitted 3D assets
- 03Building internal slicing infrastructure to convert uploaded models into production-ready print configurations
- 04Maintaining a unified TypeScript architecture across frontend and backend for rapid iteration
Key Learnings
- 01Translating industrial manufacturing workflows (PCBWay-inspired) into a consumer-facing 3D printing platform
- 02Building a custom slicer (Superslice) for automated production preparation
- 03Integrating payment and logistics infrastructure for a full commerce-enabled manufacturing service
- 04Architecting full-stack TypeScript applications with NestJS and Next.js for maintainability
02
AI & AutomationAI-Powered YouTube Shorts Automation
Autonomous AI Media Pipeline for End-to-End Video Generation & Publishing
A production-ready automation platform that transforms real-time news into fully rendered and published YouTube Shorts videos without human intervention. The system orchestrates news scraping, AI script generation, speech synthesis, subtitle synchronization, AI visual generation, FFMPEG rendering, and automated YouTube publishing through a configurable multi-channel automation architecture.
Role
Systems Engineer & Pipeline Architect
Responsibilities
- Architected end-to-end autonomous video generation pipeline from content ingestion to YouTube publishing
- Built configurable video profile system allowing dynamic customization of aspect ratio, resolution, subtitle positioning, AI visual behavior, scene composition rules, and rendering presets
- Migrated initial n8n prototype into a dedicated backend service architecture for scalability and maintainability
- Designed asynchronous queue workers to prevent concurrent FFMPEG jobs from saturating VPS resources
- Integrated ElevenLabs TTS, Whisper transcription, and Stable Diffusion into a unified multi-stage pipeline
- Implemented Google OAuth-based multi-channel YouTube publishing with dynamic profile routing
Key Features
- Content Acquisition — Scrapes real-time news/articles via Crawl4AI, extracts headlines and article content from trusted sources, triggers workflows via webhooks
- AI Content Generation — Converts news articles into short-form YouTube Shorts scripts, generates narration via ElevenLabs TTS, transcribes audio into word-level timestamps using Whisper
- Scene Orchestration — Splits scripts into scene-based segments, matches each segment to precise narration timestamps, generates AI visuals for every scene dynamically
- Video Rendering Pipeline — Uses FFMPEG to render scene clips based on exact segment durations, concatenates segments into final composition, burns dynamically positioned subtitles, synchronizes narration/subtitles/visuals automatically
- Publishing Infrastructure — Uploads videos automatically to YouTube, supports multiple YouTube channels through Google OAuth, routes automations dynamically based on selected video profiles
- Queue-Based Rendering — Designed asynchronous job queue workers to prevent multiple FFMPEG rendering jobs from running simultaneously on limited VPS infrastructure, preventing CPU/RAM exhaustion and pipeline slowdowns
- Fault-Tolerant Processing — Implemented stage-based processing and recovery mechanisms to prevent total pipeline failure when individual generation steps fail
Challenges
- 01Maintaining accurate alignment between AI narration, word-level subtitles, scene timing, and AI-generated visuals across the entire pipeline
- 02Optimizing long-running FFMPEG rendering jobs on constrained VPS infrastructure using queue-based concurrency control
- 03Integrating speech, language, transcription, rendering, and image generation systems into a coherent autonomous pipeline with recovery at each stage
Key Learnings
- 01Designing event-driven automation architectures that gracefully handle failures at any pipeline stage without losing progress
- 02Building resource-efficient rendering infrastructure with queue workers for production batch processing
- 03Engineering configurable, profile-driven systems that allow non-technical users to customize complex automation behavior
- 04Recognizing when to migrate from low-code prototypes (n8n) to dedicated backend services for scalability
03
Crypto Void
AI-Powered Crypto Media Automation
04
UPT JPD Chatbot
WhatsApp AI Chatbot for Education Services
05
EPICS In IEEE
An AIoT Based Smart Agricultural System for Pests Detection
06
BirdTec
Agile Technica
07
TemanHR
Agile Technica
Tech stack
AI & Automation
Frontend
Backend
Tools & DevOps
Traits