# Divyansh Shukla

AI engineer building agents, automation and the products around them. India.

**Status:** Open to AI engineering roles, and to conversations with investors and partners about these products. Based in India; remote works.
**Contact:** shukladivyansh953@gmail.com · [GitHub](https://github.com/TheDivyanshShukla) · [LinkedIn](https://www.linkedin.com/in/thedivyanshshukla)

## Abstract

I build AI products end to end and ship them. This year that meant Ice, a desktop assistant that does the work in your apps; Blank, a website builder that hands back real code; Synapse, a chatbot that answers a site’s visitors; and CampusQuiz, which turns class notes into a live quiz. Underneath them is research: in Plan Once, Ground Locally [[3]](https://doi.org/10.5281/zenodo.22904595), I show that a web agent which writes its whole plan in one LLM call, then grounds each step with a small on-device model, matches a budget-matched ReAct baseline on success while using 83.6% fewer input tokens. Most of the products ship under NARA [[4]](https://naravirtual.ai). This page is written for agents as well as people: it serves an MCP endpoint, an llms.txt and a markdown copy of every page (see Agent access below).

## Selected work

- [Plan Once, Ground Locally](https://divyanshshukla.com/research/plan-once-ground-locally) (Research, 2026): Preprint and 200-task benchmark: plan-and-execute web agents match budget-matched ReAct on success with 83.6% fewer input tokens.
- [Ice](https://divyanshshukla.com/work/ice) (Product, 2026): An AI desktop assistant with a face: it listens, then works in your browser, files and apps, asking before anything you haven’t allowed.
- [Blank](https://divyanshshukla.com/work/blank) (Product, 2026): An AI website builder that hands back the project: real React files, a database in your own Supabase account, your own domain and a push to your own GitHub.
- [Synapse](https://divyanshshukla.com/work/synapse) (Product, 2026): An AI chatbot added to any website with one script tag: it learns your pages, PDFs and FAQs, answers visitors around the clock, captures leads and hands off to a person.
- [CampusQuiz](https://divyanshshukla.com/work/campusquiz) (Product, 2026): AI quizzes for the classroom: a teacher uploads notes or a photo of the board, gets a quiz, and runs it live while students join with a code.
- [CampusPrep.in](https://divyanshshukla.com/work/campusprep) (Product, 2026): Free RGPV syllabus, previous-year papers and unit-wise short notes for 570 subjects, first year to eighth semester, readable without an account.
- [Saakshya](https://divyanshshukla.com/work/saakshya) (Hackathon, 2026): AI and blockchain document verification that returns one of four honest verdicts, from PROVEN to REJECTED, with a credential that verifies offline.

Everything else: https://divyanshshukla.com/work

## Research

**Plan Once, Ground Locally: Plan-and-Execute Web Agents Match ReAct Accuracy at One-Sixth of the Token Cost.** Divyansh Shukla. Preprint, Zenodo, 2026. DOI 10.5281/zenodo.22904595.

Against the budget-matched baseline, planning once is not significantly different on success (88.8% vs 92.0%; paired difference −3.2 points, 95% CI −7.7 to +1.2) while using 83.6% fewer input tokens, 67.3% fewer LLM calls and 80.4% less money, and finishing 4.1× faster.

Details: https://divyanshshukla.com/research/plan-once-ground-locally · PDF: https://zenodo.org/records/22904595/files/plan-once-ground-locally.pdf

## Notes

- [What 2,800 browser-agent runs taught me about planning](https://divyanshshukla.com/notes/what-2800-browser-agent-runs-taught-me) (2026-10-03): Plan-and-execute web agents matched a budget-matched ReAct baseline on 200 tasks with 83.6% fewer input tokens. What held up, what didn’t, and what to copy.
- [Making my site readable by agents](https://divyanshshukla.com/notes/making-my-site-readable-by-agents) (2026-10-03): This site serves an MCP server, WebMCP tools, llms.txt and a markdown copy of every page, all from one content source. What each piece does and how to try it.

## Agent access

- MCP (Streamable HTTP): https://divyanshshukla.com/mcp — e.g. `claude mcp add --transport http divyansh https://divyanshshukla.com/mcp`
- llms.txt: https://divyanshshukla.com/llms.txt and https://divyanshshukla.com/llms-full.txt
- Markdown: append .md to any page URL, or send `Accept: text/markdown`

## Background

Divyansh Shukla is an AI engineer in India who builds and ships AI products: Ice, Blank, Synapse, Conference and Loci at NARA; CampusQuiz and CampusPrep.in for students and teachers; and Saakshya, an AI and blockchain document-verification system built at the SISTec Innovation Hackathon 2026. Author of Plan Once, Ground Locally (2026). Research interests: planning and grounding for web agents, inference cost, on-device decision models, and agent tooling such as MCP.

Education: B.Tech, Computer Science (AI & Data Science).

- Languages: Python, TypeScript, JavaScript, SQL
- Agents and LLMs: Anthropic and OpenAI APIs, OpenRouter, MCP, Playwright, LangChain, RAG with ChromaDB and Pinecone
- Models on device: MLX, ModernBERT, MediaPipe, OpenCV
- Web: SvelteKit, Next.js, FastAPI, Django, Tailwind CSS
- Data and infrastructure: Postgres, Redis, Docker, Dokploy, Vercel, Cloudflare
- Security and hardware: Aircrack-ng, Hashcat, ESP32, Raspberry Pi

## References

1. [Divyansh Shukla. GitHub profile, TheDivyanshShukla.](https://github.com/TheDivyanshShukla)
2. [Divyansh Shukla. LinkedIn profile.](https://www.linkedin.com/in/thedivyanshshukla)
3. [D. Shukla. Plan Once, Ground Locally: Plan-and-Execute Web Agents Match ReAct Accuracy at One-Sixth of the Token Cost. Preprint, Zenodo, 2026. doi:10.5281/zenodo.22904595.](https://doi.org/10.5281/zenodo.22904595)
4. [NARA, the focused software house. naravirtual.ai.](https://naravirtual.ai)
5. [Saakshya: verify the claim, not just the paper. Live system.](https://saakshya.divyanshshukla.com)
6. [Ice: the AI desktop assistant that does the work. ice.naravirtual.ai.](https://ice.naravirtual.ai)

Last updated 2026-10-03.