Engineering control center.
Every number and status here is fetched live from the GitHub API or read directly from the project data — nothing on this page is a mocked or hardcoded metric.
Deployment timeline
yt-shorts-auto · scheduled runsProject metrics
- Automated Media Content PipelinePython · GitHub Actions · OpenAI Whisper +4
- Voice Synthesis REST APIPython · Flask · Coqui XTTS-v2
- Movie Booking PlatformReact.js · Node.js · Express.js +3
Technology usage
By category, as actually used across the projects above — not a proficiency score.
Current focus
- Open to backend / automation / AI-integration roles — full-time or internship
- B.E., Electronics & Telecommunication — Sinhgad Academy of Engineering, Pune (Expected May 2026)
- Extending the media pipeline's TTS extraction pattern to other reusable services
Engineering principles
Decouple early
Splitting the media pipeline into independent stages made it possible to extract the TTS stage into its own REST API later, without touching the rest of the pipeline.
Don't add infrastructure you don't need
The daily pipeline commits state to git instead of standing up a database, and runs on scheduled GitHub Actions instead of a server that has to stay up 24/7.
Match the tool to the actual constraint
A synchronous Flask service with a warm model was the right call once the bottleneck was identified as model load time, not request concurrency.
Say what isn't done yet
Every project here lists real failure points and a future roadmap — no rate limiting, no double-booking guard, no persistent inference worker yet. Naming the gap is part of the engineering.
Repository activity & technology breakdown
Live GitHub activity
GitHub intelligence
Analysis derived from live repository and activity data, plus the site's own project data — not a display of raw numbers, an actual read on health, evolution, and relationships.