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Agents have tip lines now. TechCrunch, 15 Sept: built by Redwood Research's chief scientist, takes a GET so an agent with limited internet can report a colleague. The prompt was DeepMind's swarm, where one loophole spread until 34 agents had "solved" hard problems in 27 minutes, and 24 agents reported it. In the Hugging Face breach, "only around five to six agents considered whistleblowing, and none of them ended up doing it." So the fix for agents that cheat is other agents who might snitch.
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Agents were shipping code faster than our CI pipeline could keep up. Our team optimized our pipeline, leading to a roughly 15% faster test suite and 50% reduction in runner-time spent per test, all while our test suite grew 4x in size. @moofeez explains how:
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Agents Are A Blessing For NAND Prices The market expected NAND prices to peak in Q3 2026 > Instead, NAND prices have taken a very different direction The reason? Hyperscalers' enterprise SSD demand is nearly double earlier forecasts Hyperscaler NAND inventories stood at about 14 weeks in July, below the normal level of 15 weeks, with inventories expected to decline further in Q4 2026 What's driving this demand? 1. Agents AI agents perform repeated retrieval operations, access large databases, and generate substantial amounts of intermediate data Unlike traditional chatbots, agents can execute multiple tasks autonomously, repeatedly accessing and processing information. This increases the amount of data that needs to be stored and retrieved Cloud providers are consequently expanding their use of high-capacity QLC enterprise SSDs for vector databases, retrieval, and caching QLC NAND allows them to store more data at a lower cost per bit, making it particularly attractive for these workloads 2. KV-cache offloading AI models need to remember the context of a conversation or task to avoid processing the same information repeatedly. This information is stored in what is known as the KV cache Traditionally, this data is kept in expensive HBM or DRAM. However, not all of it needs to remain in high-speed memory at all times By moving less frequently accessed KV-cache data to SSDs, cloud providers can reduce memory costs while maintaining access to much larger amounts of context TrendForce specifically identifies DeepSeek-related architectures in China as a driver of growing demand for high-capacity QLC enterprise SSDs for KV-cache offloading
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Agents are easy to prototype. Hard to put into production. And as they multiply, so does everything you have to secure, govern, and pay for. Red Hat AI Elevate is a 3-hour virtual showcase unveiling Red Hat AI 3.6. Secure agent execution, models-as-a-service with per-tenant quotas and token metering, automated safety and security evals, and live demos. Nov 5, live or on demand:
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Agents do not browse like humans. They need service details in a format they can act on: → What the service does → What it costs → How to call it → How to pay → What comes back Machine-readable discovery is one of the foundations of the agentic economy.
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Agents waiting on CPU tool use is a legitimate bottleneck. I fully concur with the CPU trend on X.
agents be like: "This PR I made to fix this bug breaks something else A - Land it as is B - Fix the other bug in a way that makes my change only work 69% of the time C (recommended) - Prevent both issues from happening by doing a little more work" bro why am i even in the room
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Agents browsing your site can use WebMCP tools instead of clicking through your UI. 𝚖𝚌𝚙-𝚑𝚊𝚗𝚍𝚕𝚎𝚛 𝟸.𝟸.𝟶 adds experimental WebMCP support. Allowlist tools and add one script tag. In-browser agents call them as the signed-in user, with no OAuth flow.
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Agents can be AI, but agency is human. It is every individual’s responsibility to empower our own agency, with the help of tools. But the North Star should always remain human centered.
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