Gauri Gupta
@gauri__gupta
building http://parallel.ai | research @mit @mitmedialab, bachelors @iitdelhi
Excited to share what we've been building at Parallel Web Systems with @paraga - an evidence-driven response grounding system that sets new standards for agentic proof of work. [1/n]
You SHOULD have a thesis about the world when you’re building a company. Otherwise you’re trapped in an echo chamber of technical milestones that don’t mean anything. That’s the kind of mindset @btaylor has. And needs, when he's leading @OpenAI, @facebook, & @salesforce.
If you're a student wondering what you should study in the world of AI, it's still the same: math, physics, chemistry, biology, computer science, engineering. STEM teaches reasoning, objectivity and how to think. It makes you better at learning everything else. In STEM, ideas…
The harder your evals or data is, the better and generalisable your models/agents become..
Really interesting paper. Fits the theme that we should make our modeling problems harder, not easier, so that they are forced to learn more and generalize better.
Can't imagine being a Windsurf employee right now.. classic big-tech reorg vibes all over.
To put it mildly, the past week at Windsurf has been crazy. There have been a lot of different rumors and reports, so I want to share a transparent account of how it actually went down. Before I start, I just want to say that Varun and Douglas were great founders and this…
If building with AI excites you, this is your chance to get plugged into the MIT AI Hub of MCP servers. Join us tomorrow (Wednesday, April 9 at 5PM PT) for a hands-on MCP tutorial. Launch your own MCP client/server and start building. Tutorial: lu.ma/nanda
Hi everyone! Check out our latest work on robustness in invariant representations and its implications!
Check out our work on "Domain Generalization in Robust Invariant Representation" which got accepted in ICLR'23 workshop PML4DC! Link to open source code: github.com/GauriGupta19/D… Arxiv link: arxiv.org/abs/2304.03431 #MachineLearning #DomainGeneralization #InvariantRepresentation