FORMulator
FORMulator allows users to perfect any type of body movement on their own, by leveraging computer vision technology to provide instant feedback. The app uses a combination of pose estimation and machine learning to analyze the user’s movements and provide real time feedback on their form, helping them to improve their performance.
Origin and hackathon context
FORMulator was the first hackathon I did at Purdue, and I was super excited. The idea started as Just Dance but using a camera that tracked your pose and compared it in real time. Midway through, we had a realization: the same approach could generalize to anything where form matters — golf, basketball, ballet, playing the cello. We ran with that. We didn't make as much progress as I would have liked, since it was our first college level hackathon, but we won a prize and learned a lot about PoseNET and real time video streaming.
Core thesis
The thing about form is that, given a reference, you can grade a person's form in comparison to it. There's enough reference content on the internet to cover nearly anything from beginner to advanced. Where computer vision steps in is for the large differences, the nuanced comparisons where the gap between your form and the reference is visible but hard to articulate in writing. That's what CV handles that written instruction alone can't.
What I'd build now
If I picked FORMulator back up today, I'd think first about the use case, analyze the form of target users, and design from first principles. That would mean being more deterministic: picking apart key distance and angle metrics for hand and foot movement, along with timing and speed. I'd focus on one type of form before trying to generalize, perhaps ballet or golf.
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.