Builder, CTO, Founder
Most stacks are rented. Mine, I can rebuild from zero.
I buildthe layerunderneath
tunyt.com — the agentic OS for events
Veris — a camera that proves its photos are real
Agentic backends at Pokus, Bengaluru
Present tense
Now
Four things are true right now: a company I'm CTO of, a camera I'm shipping, a backend I'm paid to run, a degree I'm finishing. That's the surface. From here, the page goes down one layer at a time.
tunyt.com — the agentic OS for events
Veris — a camera that proves its photos are real
Agentic backends at Pokus, Bengaluru
B.Tech CSE (AI & ML), LPU — class of 2028
The surface
Products
The top layer — the part of the stack with real users and money attached. Shipped, paid for, and still running while I’m asleep.
Multi-agent orchestration across venue booking, ticketing, vendor coordination, and run-of-show — the whole operation an organiser would otherwise run out of a spreadsheet. Built from the ground up: agent workflows, real-time infrastructure, and the UI on top. I lead engineering direction, technical hiring, and system architecture. Tixin owned the ticketing primitive; tunyt owns everything that happens around it.
- TypeScript
- Agentic AI
- Multi-agent workflows
- PostgreSQL
- Redis
written across the full stack as CTO, agents through UI
Event-driven throughout: Kafka on the write path, Redis for read caching, CQRS to keep booking reads fast while writes stay consistent under burst. The whole thing auto-scales on a Kubernetes cluster I stood up by hand on two Raspberry Pi 5s, which is a deeply unreasonable place to serve a live on-sale from, and it held.
- TypeScript
- Kafka
- Kubernetes
- PostgreSQL
- MongoDB
- Redis
sustained peak through the Kafka pipeline
on a two-node Raspberry Pi 5 Kubernetes cluster
after PostgreSQL and MongoDB tuning on the booking read path
after the CI/CD automation rebuild
Microservices, distributed systems, and DevOps, taught to people one or two years behind me. The curriculum is the stack I actually run in production, built around hands-on labs for scaling systems, debugging live incidents, and real-world failure simulations rather than tidy exercises.
- Node.js
- Docker
- Kubernetes
- CI/CD
cumulative across backend and DevOps cohorts
The layer underneath
Primitives
When a primitive is load-bearing, I rebuild it rather than install it — a load balancer instead of Nginx, a queue instead of BullMQ, a key-value store instead of Redis. The library is rarely the worse choice. It’s that I’d rather not run something on the critical path I can only reason about from the outside.
Speaks RESP well enough that redis-cli connects and doesn't notice. Writing the protocol parser, the storage layer, and the command handling by hand — no shortcuts — is the fastest way I know to understand why Redis is shaped the way it is.
- Go
Built to understand what a load balancer actually does when it distributes a request. Dynamic auto-scaling for Bun backends, with active health checking, intelligent routing, and a real-time monitoring surface. Rebuilding it taught me more about connection handling than a year of configuring someone else's.
- Bun
- TypeScript
Command Query Responsibility Segregation with fully separated command and query paths, pulled out of the production system and made standalone so the pattern is legible on its own. This is the reference implementation behind Tixin's booking path.
- TypeScript
- Kafka
- PostgreSQL
Account, catalog, and order services talking over gRPC, fronted by a single GraphQL gateway, backed by PostgreSQL and Elasticsearch with real-time WebSocket wiring. A complete microservice topology small enough to hold in your head and real enough to break.
- Go
- gRPC
- GraphQL
- PostgreSQL
- Elasticsearch
Rooms, producers and consumers, and real-time media routing built on mediasoup. Multi-party video is where the naive mesh topology falls over, and an SFU is the thing you have to build to find out why.
- TypeScript
- WebRTC
- mediasoup
Down to the metal
Edge & Hardware
The software runs out, and there is still a layer: hardware, inference that never leaves the device, integrity that survives the trip off it. The combination is the point — a sensor that decides locally, and can still prove what it saw.
The camera derives an ed25519 identity from the device itself, signs every photo at the moment of capture, and a Solana Anchor program verifies that signature on-chain before anything is minted. Filecoin for storage, QR claim flow, proof of attendance. Built for a world where 'is this real' has stopped being answerable by looking — this kills deepfakes at the source rather than trying to detect them after.
- Rust
- Anchor
- Solana
- Filecoin
- Embedded
A custom multimodal emotion model fusing vision, audio, and context, running entirely locally — no cloud API in the loop. It remembers you across sessions and adapts to how it can actually help. Latency and privacy both improve when nothing leaves the device. This is where the infrastructure work and the softer thread meet.
- Raspberry Pi 5
- ESP32
- Python
- PyTorch
- Edge AI
A co-evolving multi-agent RL environment for cognitive security: the defender works a real intrusion while an adversary gaslights it with fabricated SIEM entries in real time. GRPO group sampling, adaptive curriculum, LLM-as-judge scoring. Built for the Meta × PyTorch × Hugging Face OpenEnv Hackathon India 2026.
- Python
- PyTorch
- MARL
- OpenEnv
Syncs with my tools, calendar, code, and agent setup, and has enough access to actually act rather than suggest. The point isn't a chatbot — it's that the cost of leaving flow to go find something is the tax I most wanted to stop paying.
- TypeScript
- LLM orchestration
- Agent workflows
A BERT-style bidirectional transformer that generates text by progressively unmasking tokens from a fully masked sequence, rather than left-to-right. Trained on TinyStories locally on Apple Silicon — small enough to actually finish, real enough to show why diffusion over discrete tokens is awkward and interesting.
- Python
- PyTorch
A microservice platform that reads GitHub, LinkedIn, and LeetCode to level a student's actual skills, generates a personalised roadmap, and runs live AI-agent mock interviews with facial emotion detection to score how they hold up under pressure.
- TypeScript
- Microservices
- AI agents
- Computer vision
The honest map
Stack
A thesis like that obligates an honest inventory. A badge wall tells you what I've installed, not what I'd stake an incident on — so this is graded instead: three rings, each with its honest qualifier attached, including the one that says I'm still learning.
Production
I ship this under real load, and I'm on call when it breaks.
- TypeScript
- Node.js
- Kafka
- Redis
- PostgreSQL
- MongoDB
- Docker
- Kubernetes
- CQRS
- Event-driven microservices
Working proficiency
Demonstrated and deployed, but less publicly load-tested.
- Go
- Python
- gRPC
- GraphQL
- WebSockets
- Next.js
- Terraform
- Ansible
- Jenkins
- GitOps
- Elasticsearch
- Event sourcing
- Saga pattern
Exploring
Real projects, real commits, not yet battle-tested. Listed here rather than claimed above.
- Rust
- Anchor / Solana
- C++
- Embedded (RPi 5, ESP32)
- Edge AI
- Multimodal emotion models
- Diffusion language models
- Multi-agent RL
Around the work
Community
Sovereignty isn't solitude. The same work, proven in rooms — competitions entered and won, classrooms taught, one hotel actually run.
Brand Wars at IIT Mandi. Innovation Deck. BHU. Undefeated.
11 entered, 11 won
Backend & DevOps at Learner's Arc
100+ students
Ran a hotel. Rebuilt how it sells.
+73% revenue in 3 months
GirlScript Summer of Code
recognised for impact
Hackathons across the LPU-adjacent circuit
4+ judged
Guest lectures on system design and backend engineering
Say hello back.
Let's build something that stays up: CTO and founding-team roles, hardware plus AI, research collaboration, or speaking. Also anything that has no business running on a Raspberry Pi. I read everything.

Aditya Dutt Pandey






