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共 38170 篇Stripe is friendly to “friendly fraud”
Article URL: https://www.gingerlime.com/2026/stripe-seem-friendly-to-friendly-fraud/ Comments URL: https://news.ycombinator.com/item?id=48287982 Points: 310 # Comments: 220
Erin Brockovich made a map to track data centers around the country
Article URL: https://www.niemanlab.org/2026/05/erin-brockovich-made-a-map-to-track-data-centers-around-the-country/ Comments URL: https://news.ycombinator.com/item?id=48287952 Points: 266 # Comments: 296
Next.js 16 Broke My App in 4 Places and None of Them Threw an Error
The CI was green. Build passed. No TypeScript errors. No warnings. Everything looked clean. I...
Warp’s big bet on building open source with GPT-5.5
Warp uses GPT-5.5 and OpenAI models to coordinate coding agents across local, cloud, and open-source development workflows.
Election information and safeguards in 2026
Ahead of global elections, we’re helping people access information, supporting cyber defenders, and increasing AI transparency
Pope Leo Schooled the Tech Bros on Tolkien
The Holy Father referenced The Lord of the Rings in his encyclical about AI—an expert (if unintentional) troll of tech billionaires who keep misinterpreting the series.
Trouble exploring in ai/ml,idk where to being with [D]
So as the title says Context:I am a sophomore in computer science Have prior knowledge in maths(especially the relevant topics in ml) Good enough with numpy,pandas I don't really know where to start Ok internet every second guy is trying to make me earn 100k/year in 3 months while I just want to explore it for rn I want to approach it as a project based learning experience so what should be the way to start? submitted by /u/knowbodyknows22 [link] [留言]
Anthropic just published how they contain Claude agents, including two security incidents they got wrong
Anthropic dropped a solid engineering post this week about containment across claude.ai, Claude Code, and Cowork. One of the more transparent writeups from a major AI lab about what actually broke. The core insight: model-layer defenses are probabilistic and will always have a non-zero miss rate. So the real answer is hard environmental containment, not just safer models. Three patterns they use: -claude.ai: ephemeral gVisor containers, fully server-side -Claude Code: OS-level sandbox with human-in-the-loop approvals (93% get approved anyway, so approval fatigue is real) -Cowork: full local VM, credentials never enter the guest Two incidents they disclosed: A red team phished an employee into running a prompt that exfiltrated AWS credentials. Succeeded 24 out of 25 times. The model had nothing to catch because the user was the one typing it. Only egress controls would have stopped it. A third-party found that Cowork’s egress allowlist passes traffic to api.anthropic.com. An attacker embedded an API key in a file in the user’s workspace, Claude followed hidden instructions, and uploaded files to the attacker’s Anthropic account. Sandbox worked perfectly and still leaked data. Their lesson: an allowlist isn’t a destination filter, it’s a capability grant. Every function reachable through an allowed domain is an attack surface. The section on persistent memory poisoning and multi-agent trust escalation at the end is worth reading too if you’re building anything agentic. submitted by /u/Direct-Attention8597 [link] [留言]
Is Peter Thiel the target of Pope Leo's Gandalf quote? An investigation.
Parsing a papal proclamation.