rdhyee

Joined 16 years ago

Karma
532
Submissions
94
Member since
Nov 22
[ my public key: https://keybase.io/rdhyee; my proof: https://keybase.io/rdhyee/sigs/nZh-ft9Mz9bzSL8uQVYowfc98TOmSFWsc8AhXpNpYfc ]
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Recent submissions

Comment · 21 days ago
I'm curious what you as an early-career math student would want the Simon Foundation to do differently from what it says it will do. I hope there is a way for students like to you to get a slice of the pie.
Comment · 9 months ago
Took this idea and ran with it using Fly's Sprites, inspired by Simon's https://simonwillison.net/2026/Feb/3/introducing-deno-sandbo.... Use case: Claude Code running in a sandboxed Sprite, making authenticated API calls via a Tokenizer proxy without credentials ever entering the sandbox.

Hit a snag: Sprites appear network-isolated from Fly's 6PN private mesh (fdf:: prefix inside the Sprite, not fdaa::; no .internal DNS). So a Tokenizer on a Fly Machine isn't directly reachable without public internet.

Asked on the Fly forum: https://community.fly.io/t/can-sprites-reach-internal-fly-se...

@tptacek's point upthread about controlling not just hosts but request structure is well taken - for AI agent sandboxing you'd want tight scoping on what the proxy will forward.

Comment · 1 year ago
The interview with Perplexity CEO Aravind Srinivas on DeepSeek is a good listen on a Friday afternoon as we ponder the implications of DeepSeek.
Comment · 2 years ago
Comment · 2 years ago
Full title of news blurb from UC Berkeley's I School:

> Award-winning Research Explores Risks of Using Synthetic Data to Train Facial Recognition Technology

Comment · 2 years ago
Lecture description:

Physical systems that can learn by themselves Andrea Liu

Brains learn and perform an enormous variety of tasks on their own, using relatively little energy. Brains are able to accomplish this without an external computer because their analog constituent parts (neurons) update their connections without knowing what all the other neurons are doing using local rules. We have developed an approach to learning that shares the property that analog constituent parts update their properties via a local rule, but does not otherwise emulate the brain. Instead, we exploit physics to learn in a far simpler way. Our collaborators have implemented this approach in the lab, developing physical systems that learn and perform machine learning tasks on their own with little energy cost. These systems should open up the opportunity to study how many more is different within a new paradigm for scalable learning.

Comment · 2 years ago
From the article:

    The fallout from the hack of a little-known but pivotal health-care company is inflicting pain on hospitals, doctor offices, pharmacies and millions of patients across the nation, with government and industry officials calling it one of the most serious attacks on the health-care system in U.S. history.
[....]

    Change Healthcare is a juggernaut in the health-care world, processing 15 billion claims totaling more than $1.5 trillion a year, the company says. It operates the largest electronic “clearinghouse” in the business, acting as a pipeline that connects health-care providers with insurance companies who pay for their services and determine what patients owe. It supported tens of thousands of physicians, dentists, pharmacies and hospitals, handling 50 percent of all medical claims in the United States, the Justice Department wrote in a 2022 lawsuit that unsuccessfully tried to block UnitedHealth from acquiring the company.
Comment · 2 years ago
From <https://www.nasonline.org/programs/distinctive-voices/upcomi...>:

Wednesday, January 31, 2024 The Future of Artificial Intelligence AI is all around us—recognizing our faces in photos, transcribing our speech, constructing our news feeds, navigating our driving routes, answering our search queries, and much more. But rapidly improving AI is poised to play a much bigger role in all of our lives. In this lecture, AI expert Melanie Mitchell will demystify how current-day AI works, how “intelligent” it really is, and what our expectations—and concerns—about its near-term and long-term prospects should be.

» Melanie Mitchell, Santa Fe Institute

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