
PRAXIS
Persistent local memory for AI-assisted dev — every session opens knowing your architecture, decisions and history. Zero bytes leave the machine.
AI Product Engineer — first idea to shipped product.
Currently building LLM applications
Computer Science graduate building AI-powered products, developer tooling and full-stack apps with AI-native workflows. I care about products that ship, memory that persists, and models that behave — currently deep in agentic AI, RAG and local-first AI tooling.
Two competitive AI internships, a published research paper, and six builds across developer tools, scientific computing, voice AI and data-transparency systems. Open to Founding Engineer / AI Product Engineer / AI Engineer roles.
AI developer tools, scientific computing, voice AI and data-transparency systems — each opened to solve a real problem. Scroll: the work moves sideways.

Persistent local memory for AI-assisted dev — every session opens knowing your architecture, decisions and history. Zero bytes leave the machine.

Local-first inspector that shows which exact words — yours or a benchmark's — sit inside an AI model's training data. Side-by-side evidence, never a score.

The terminal built for agentic coding — several Claude Code sessions side by side, with their transcripts read live: who's waiting, what was decided, which files changed.

“Hey Nimbus.” A wake-word assistant that runs entirely on your machine at $0 — 26 tools, multi-step chaining, persistent memory, measured safety.

Computation that refuses false precision — posterior samples show which conclusions the data actually supports, and a verdict gate declines what it can't defend. 172 tests, CI-proven.

Local-first safety layer for AI coding — tracks AI-generated changes and enables instant rollback of the unwanted.
The core languages, tools and frameworks I use to build robust software and train intelligent models.
Two competitive AI internships and a CS degree — with a published paper and an ongoing Agentic AI program along the way.
Generated human-aligned training signals through strict instruction-following evaluation, correctness checks and quality assessment across text, image and video. Ranking, comparison, prompt writing and defensible decisions to improve model behavior.
Completion certificate ↗Built an end-to-end personality-prediction system with NLP pipelines and multiple ML models, incl. BERT / DistilBERT embeddings — evaluated with standard classification metrics for real-world trade-offs.
“Reinventing Image Generation” — FrameGenX, ijraset
Open to Founding Engineer, AI Product Engineer, and AI Engineer roles — plus research and startup collaboration.
vaishak.v.nair.dev@gmail.com →