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JFrog’s Package Traffic Controller (PTC) intercepts an organization’s public package traffic at the network layer and routes it through JFrog, so JFrog Curation policies and audit logging apply to every intercepted install without changing how developers work.

You don't need a complete compliance program on day one to make real progress on CRA. You need to close the most visible gaps first. This video covers where to start: None of this is CRA-specific busywork. It's the same foundation most mid-sized software companies need regardless of the framework.

Your competitive edge isn't the model; it's the data that only you have. Sonal Pardeshi, Head of AI Product at Splunk, explains why teams building on general-purpose models keep arriving at the same generic outcomes, and what changes when agents are grounded in your own machine data and your own governance. AI Toolkit and Agent Launchpad let teams build and run agents against your grounded data.

The cloud first default has flipped. But every workload decision now needs to be made more deliberately. Civo Product Director Russ Smith shares his key takeaway: understand your legal constraints, know your lock-in maths, and check whether the economics that made sense three, five, or ten years ago still hold up today. You don't need to fund a new platform. You need a managed sovereign place for your data that actually fits where you are now.

Learn more: speedscale.com.

ChatGPT appears to have dramatically changed how it searches the web — and Reddit is suddenly showing up far less in its citations. For years, Reddit was one of ChatGPT’s most frequently cited sources, at one point accounting for as much as 15% of citations. That also created an entire industry around influencing Reddit posts in hopes of getting brands surfaced inside AI-generated answers. Now, ChatGPT appears to be shifting toward more targeted searches of official websites, documentation, and help centers instead of broadly searching the open web and pulling in Reddit discussions.

On this episode of Masters of Data, we dig into what happens when AI actually delivers on its promise to eliminate busywork, and explore why removing the toil doesn't feel like the win everyone expected. We make the case that repetitive tasks build the intuition, pattern recognition, and muscle memory people need to do the harder work well. Security and engineering leaders rethinking how much triage and busywork to hand off to AI will find plenty to chew on here, especially anyone staring down a task list where every single item feels like the hardest one.

1,133 requests failed on one bad commit. The patch and the regression test are already written by the time anyone is asked to read the exact diff. This demo runs AURA as one step inside a Temporal workflow, alongside Codex. A GET request against a product catalog service goes from success to HTTP 500, and ClickHouse records the version, commit, trace ID, and exact error for every request. By the time AURA investigates, all 1,133 requests on that version have failed.

Open up an AI model and what's actually inside is a floating array of decimal points. No one can look at that and confirm it hasn't been tampered with, doesn't contain bias, or wasn't trained on poisoned data. This video covers why that changes how you need to think about trusting a model: If you can't unpick the model itself, you have to be able to trust its origin.