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How AI helps scientists design the next generation of medicines

Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…

2026-07-23 原文 →
开发者

Achieving Compliance as a Platform Engineering Team by Helping Developers

When a new platform team set out on implementing their roadmap through forced workflows with poor documentation, developer experience declined. Success came from simplifying governance, prioritizing what matters, and rolling out compliance incrementally through prevention, detection, and communication. Empathy, focus, and shared purpose drove successful adoption. By Ben Linders

2026-07-23 原文 →
AI 资讯

Ask HN: If OpenAI hacked HuggingFace, why aren't OpenAI prosecuted?

Hugging Face put up a blogpost saying that they reported the hacking to the authorities: https://huggingface.co/blog/security-incident-july-2026 It's not clear whether they mean authorities for their US HQ or the French authorities. But I can't find any announcement of the follow-up. Is there a case open against OpenAI the company? Is their management interviewed by the authorities? Anyone detained yet? I guess the people who left the model running unattended risk at least a suspended sentence h

2026-07-23 原文 →
AI 资讯

End-to-End Encryption and “Going Dark”

New paper: “ Encryption and Globalization 15 Years Later: End-to-End Encryption and the Third Round of the ‘Going Dark’ Debate “: Abstract : This Article updates and expands on 2012 research on encryption and globalization, analyzing what the authors call “Round 3” of the Going Dark Debate: the current controversies over end-to-end encryption (E2EE). Governments around the world have proposed, and in some cases enacted, laws limiting E2EE for law enforcement and national security purposes. This Article explains the underlying technologies and market developments for a law and policy audience to assess those proposals critically. The Article proceeds in three parts tracking three rounds of the Going Dark Debate. Round 1 covers the Crypto Wars of the 1990s, when U.S. export controls on strong encryption ultimately fell in 1999. Round 2 covers the period roughly 2010 to 2015, when encryption-in-transit became widespread but lawful access remained available through cloud providers, giving rise to what the authors called a “golden age of surveillance” rather than a period of going dark. Round 3 addresses the current debate over E2EE, where no entity between sender and recipient can read the plaintext...

2026-07-23 原文 →
AI 资讯

Debugging Live Audio at Scale: QUIC and MoQ relays

A few weeks ago, a back-of-envelope calculation told us that scaling Tula Spaces to 100,000 concurrent listeners would need roughly 100 small VPS boxes. That number was scary enough to derail a roadmap conversation — and, as it turned out, almost entirely wrong. This is the story of how we got from that panic number down to a real, measured, production-ready plan: five boxes, not a hundred, with every claim backed by a test rather than a guess. If you're building anything real-time at scale — live audio, video, multiplayer, anything running over QUIC — the failure modes we hit are probably waiting for you too. The panic number was built on an assumption, not a measurement The original math went something like: X% of a CPU core per listener, therefore Y cores needed, therefore Z boxes. It felt rigorous because it had numbers in it. But every number in that chain was inherited from an untuned, default configuration nobody had actually interrogated. The first rule we relearned the hard way: a scary capacity number is a hypothesis, not a fact, until you've tried to break it on purpose. Myth #1: "It's too much crypto for one core" Our relay runs one process per box, encrypting media for every connected listener over QUIC. At ~0.4% of a CPU core per listener at a modest bitrate, the instinctive explanation was cryptographic overhead — AES doesn't usually struggle at these data rates, so something had to be off. It wasn't crypto. It was packet count . Our encoder was emitting one CMAF fragment every 21 milliseconds — about 47 fragments a second, each one a separate send over the wire, per listener. That's a syscall and framing tax, not a cipher tax. Crypto didn't even show up in a profiler trace once we went looking. The instinct to reach for "batch the fragments" didn't work, though — that's the first place the story took a turn. Myth #2: Batching the container doesn't touch the wire We tried extending the CMAF fragment duration from 21ms to 200ms, expecting a straightfor

2026-07-23 原文 →