Google DeepMind x Cactus Compute Global Hackathon

This individual's profile lacks essential builder artifacts like GitHub/Replit links and project details. While they attended a hackathon, there's no evidence of hands-on building or shipped technical projects. Further information on coding proficiency and AI/ML experience is required.

no projects mentioned

This individual attended a Google DeepMind x Cactus Compute Hackathon in San Francisco. Further details regarding education, experience, and active building projects are not provided.

no projects mentioned

This candidate has minimal public code artifacts and community engagement. They attended one hackathon but lack demonstrable hands-on building experience. Further information on GitHub/Replit summaries and project descriptions is needed.

no projects mentioned

This individual has a Google affiliation but no specified role or years of experience. They attended one hackathon event. Their profile lacks public coding artifacts, project details, and educational background, limiting assessment of their technical building and coding abilities.

no projects mentioned

This individual has attended the Google DeepMind x Cactus Compute Global Hackathon. Further details about their technical skills, building artifacts, and experience are currently unavailable.

no projects mentioned

This individual holds a leadership role at a private venture. They attended a Google DeepMind x Cactus Compute Global Hackathon. Their profile lacks public code artifacts, AI project work, and detailed education.

no projects mentioned

Co-Founder of Daemo AI, a VC-backed AI infra/dev startup based in SF. B.S. in Mechanical Engineering from Yale. Before Daemo, I did a bit of work at Goldman Sachs and worked as an award-winning independent filmmaker. Passionate about building robust plumbing for the AI ecosystem—specifically turning probabilistic AI into deterministic, safe enterprise workflows.

highly interested in connecting with exceptional engineers and potential GTM/ops hires also looking to learn more about advancements in on-device/local inference (like FunctionGemma) & new approaches to AI safety

Building Daemo AI. I'm currently building the core deterministic runtime for Daemo AI. Specifically, I'm tinkering with C#/.NET SDKs and sandboxed V8 environments to create strict, verifiable execution guardrails for LLMs, preventing prompt injection and hallucinated side effects in enterprise legacy systems.

Founder, ex-quant trading.

Talk to me about your interests in quantum computing, agentic orchestration, and scaling solutions for multi-variant optimisation. Keen to meet database engineers, social media managers, or hear an interesting story.

Agentic solutions for finance and travel infra

Suman Debnath is a Technical Lead (ML) at Anyscale, where he focuses on distributed training, fine-tuning, and inference optimization at scale on the cloud. His work centers around building and optimizing end-to-end machine learning workflows powered by distributed computing engines like Ray, enabling scalable and efficient ML systems. Suman’s expertise spans Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Earlier in his career, he developed performance benchmarking and monitoring tools for distributed storage systems. Beyond engineering, Suman is an active community contributor, having spoken at over 100 global conferences and events, including PyCon, PyData, ODSC, AIE and numerous meetups worldwide.

Distributed Training and Inference

Nick Giometti is a Senior Principal at B Capital where he leads investments in AI & Infrastructure, and has partnered with companies like Perplexity, Poolside, and Figma. As a former quant and forever builder, Nick loves working with founders pushing the edge of human <> machine interactions. 

- AI Implementation - helping translate tribal knowledge into AI Engineering / systems engineering powered outcomes - Next Gen Process Automation - RPA & Process Mining - Vibe SDLC - I still haven't seen anyone Vibe Deploying - Implicit Human Feedback Capture - how do we "Show, Don't Tell" AI what good looks like - Agent Memory - How do we give agents long-horizon context without it becoming lossy - Agent <> UI - How do I maintain state between user, agent, and front ends?

- Due Diligence Agents - Combing over all my first-party research, supplementing with external knowledge, then catalyzing action - what's my thesis and how do I prove or disprove it with the information available - Systematic Sourcing - prescreening a 25M company universe into my top 250 companies that evolves as my theses adapt - Tool Building on top of our proprietary knowledge base

Writer, engineer, applied researcher interested in deploying AI compute at scale.

Machine learning Compound AI systems Novel applications of AI

Straude (Strava for Claude Code): https://straude.com/

Currently building warehouse robots as CTO of Remy AI (YC W26) Previously pursuing PhD in Machine Learning at Oxford specialised in Computer Vision - last paper recognised as one of the best in computer vision last year (Highlight CVPR 2025) Masters in Engineering from Oxford - top 10% cohort Patent pending in embedded systems from previous job at medical devices company

Computer vision Machine learning Robotics Engineering (mechanical, embedded systems, electronic)

Autonomous dexterous robots for warehouses

Apoorv Mehta is a Product Security Engineering Manager focused on building scalable application security programs and AI-assisted security workflows that integrate into real production environments. His background spans secure software development, system design, and embedding trust and identity into product engineering. As a judge, he looks for clear problem statements, strong technical design, and end-to-end execution with thoughtful security and real-world impact

AI in Product security

Member of Technical Staff @ATG | 1st place at YC Hackathon (Hack the Stackathon); 1st place at Microsoft Hackathon (AI Tinkerers); 1st place at Odyssey Hackathon

ATG (YC F25)

Karen Mossoyan is a Research Engineer at Cactus (YC S25). She has 2 years of experience and studied Computer Science at UCLA, Los Angeles City College, and International Baccalaureate.

Developing ML and NLP models as a Research Engineer at Cactus (YC S25). Previous work includes building NLP pipelines as a contract engineer at Blue Fever and conducting research in medical imaging and cytometry at City of Hope using Python, deep learning, and data analysis.

Alex Niehaus is the Co-Founder & CEO of Helicon, with 8 years of experience. He is in the United States and is looking for customers, with a preference for warm introductions.

Kavitha Rajendran is an AI/ML Specialist and Solutions Architect at Google, based in San Francisco, California. With over 12 years of experience, she holds an MS in Computer Science from The University of Texas at Dallas and a B.E. in Electrical and Electronics Engineering. Her tinkerer projects include post-training LLMs with Reinforcement Learning on TPUs and GPUs, alongside DIY Agentic workflows and architectures on GCP. Kavitha is looking for customers, design partners, speaking opportunities, and sponsorships, and prefers warm introductions via email for contact.

Post-training LLMs, Reinforcement Learning with TPUs and GPUs, DIY Agentic workflows, Agentic architectures on GCP, Generative AI, LLM fine-tuning, RAG pipelines, Scalable inference, vLLM, MLOps, ML platforms on GKE

Post training LLMs - Reinforcement Learning with TPUs and GPUs, DIY Agentic workflows and architectures on GCP.

Edmundo Sanz-Gadea López is the Co-founder and CTO at Helicon, a stealth company. He has 5 years of experience, with education from Delft University of Technology, ETH Zürich, and more, focusing on Robotics and AI.