

What I'm looking for
Live AI
I’m interested in AI you can speak to while showing it what you’re doing. I want to point at something, say “this part,” move the camera, and ask a follow-up without having to explain the whole situation again.
For example, if I’m trying to fix something, I should be able to show what I’m seeing, try a suggestion, and have the AI follow what changed. A lot of the context comes from what we’re looking at together. Being able to refer to that directly makes the interaction much easier.
The details matter here. Can I interrupt it? Does it remember something I showed it a minute ago? Can it tell when I’m still explaining? Does its response arrive while it’s still useful? I’m interested in how people solve those problems and what they can build once speaking and showing things works well.
If you’re building something people interact with live, email me. I’d like to try it.
Invisible wearables
I really like WHOOP and Google Fit. I'm looking for an AI-first wearable that can do something useful with what it learns about you, without you having to ask every time. A chat window with your data attached doesn't really get there.
Working on glasses made me think a lot about how little of the underlying system the user needs to see. Voice interaction needs to work properly. What you show someone needs to be relevant and direct. Explaining every step the agent takes gets annoying very quickly.
By "invisible," I mean devices you can use without constantly thinking about operating them. I'm interested in what that takes, both in the hardware and in deciding when the AI should do something, ask, or leave you alone.
Building an AI-first wearable? Email me. I'd like to see what you're working on.
Internet infrastructure for AI agents
My agent should be able to book a table at a restaurant without someone having built an integration for that specific booking platform.
WebMCP is a really good example of where this should go. A website can expose the actions an agent can take, with enough information to actually use them. The restaurant tells the agent how to book a table, and the agent does it.
I think adoption is the bigger problem. Having a way to expose these actions only helps if the services people actually use implement it. That needs to be easy enough for an ordinary business to do, ideally through the tools they already use to build their website.
I've run into similar friction with google-tools-mcp. Finding a tool and using it still involves too much setup. I'd like to meet people making this easier on both sides: for businesses to offer, and for agents to discover and use.
Then there's scale. Discovery, authentication, permissions, and tool calls all have to keep working as more agents and services come online. I'm interested in the infrastructure that makes that possible without someone manually wiring up every new connection.
Building infrastructure for internet agents? Email me. I'd like to hear what you're working on.
For startups and founding teams
Karthik Thyagarajan is a founder-engineer studying Computer Science and Artificial Intelligence at Purdue. At Samsung Research America, he co-architected an always-on AI agent runtime for XR glasses. He is currently building Tavi, a voice teammate for engineering standups, and co-founded buildpurdue, a founder community at Purdue. His work spans agent infrastructure, on-device AI, product engineering, and evaluation.
For an early team building AI products, his strongest demonstrated fit is owning a broad technical problem from product decisions through implementation and evaluation. His current interests are live voice and video AI, AI-first wearables, and infrastructure that lets agents discover and use internet services.
Substantial ownership of agent systems
At Samsung Research America (May to August 2026), he co-architected an always-on agent orchestrator in Kotlin and owned half of a 25+ module runtime. His reported on-device router achieved 95% signal suppression and 90% intent-routing accuracy. His work also covered tool permissions, model routing, memory, and phone control, connecting model behavior to a usable device experience.
Builds teams and operating systems around a problem
As buildpurdue's co-founder and co-president, he helped turn an informal gathering into a founder community with a selective cohort, weekly co-working, funding, and founder-led problem-solving workshops. He built much of its web platform and integrated CRM, applications, events, and check-ins. His account describes data on around 500 students and a mailing list of about 1,000.
Makes technical claims testable
He built PARM, an open-source causal benchmark for when an agent should use personal memory: 157 frozen scenarios and 471 executable cases, with deterministic scoring and versioned replay artifacts. Its portfolio account explicitly distinguishes development results from held-out claims. He also built google-tools-mcp, a published MCP server exposing 169 Google Workspace tools through one OAuth flow.
Pursues difficult product problems
He is currently building Tavi, a voice teammate intended to answer codebase questions and handle small action items during engineering standups. The project tackles multi-person turn-taking, when to stay quiet, sandboxed coding, and synthetic meeting evaluations. This is ongoing work, rather than a claim of shipped adoption. His published interests explain the direction behind it.
Co-founder who ships and thinks about distribution
At Repple (November 2025 to June 2026), he co-founded a competitive fitness app released on the App Store and built the entire FastAPI, PostgreSQL, and Supabase backend, including synchronized workouts, ELO matchmaking, and weekly competition logic. His firsthand account also describes marketing before the UI existed, campus distribution, and why local community density mattered to the product.
This brief summarizes Karthik's public portfolio and firsthand accounts. Employment contributions and internal performance metrics are self-reported; linked code and artifacts provide additional evidence where available.