Show HN: PicoMQ – Durable Streams over HTTP, on object storage
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Formula 1 returned from its summer break with this weekend's Dutch Grand Prix.
Posted by Markus Vill, Software Engineer, Sean Keys, Security Engineer, and Istvan Nador, Software Engineer, Android Auto At Google, we believe our products should be secure by design, which is why we built the Android Automotive Operating System for Software Defined Vehicle (AAOS SDV) on existing, market-proven platforms, leveraging virtualization technologies like Cuttlefish. While our release announcements focused on the features, this blog post outlines some of the security concepts. Fo
The first site I ever failed to place in service passed every acceptance test I wrote for it. Cameras streamed. Switches held their uplinks under a simulated fiber cut. Audio was intelligible at every measurement point. The paperwork was clean. It still sat dark for three weeks, because the room where one of the redundant paths terminated belonged to a different crew on a different contract with a different completion date, and nobody had drawn that edge on any schedule. My validation was fine.
Technology leaders are under mounting pressure to modernize infrastructure, control multi-cloud operational spend, and build data foundations for generative AI. However, the discovery required for that level of transformation can entail weeks of manual spreadsheet analysis, mapping in-house infrastructure, and reconciling siloed, piecemeal cost estimates across disparate teams and sources. To help, we’re announcing AI-powered Quick Assessments in Migration Center, which delivers near-instant tot
AI agents are the ultimate insiders. We grant them permission to read emails, query databases, and trigger API calls. They don’t just retrieve information, they take action. Agents offer incredible potential for increased productivity and better customer experiences, but they also come with new security concerns. In our new State of AI infrastructure report, 79% of tech leaders cite security, governance, or operations as their most significant challenge to scaling inference. While there’s still
Metadata harmonization (standardizing labels, identifiers, and formats so datasets can work together) is still largely manual. This post shows how AI-powered metadata correction works in practice, covering two approaches, human-in-the-loop validation and autonomous agent-driven workflows, plus governance considerations for production deployment.
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