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Developing generative AI applications is very different from developing traditional machine learning applications. These are the steps.
Grafana creator Torkel Ödegaard traces the open-source project’s journey to help developers visualize what’s going on inside distributed cloud-native infrastructure.
AI can’t replace bad developers because it only works for good developers. Recognizing when code from an LLM will fail requires skill and experience.
Lightspeed will apply generative AI toward automating the operation of OpenShift clusters and Red Hat Enterprise Linux environments.
GitHub Artifact Attestations, based on Sigstore, signs and verifies the integrity of software artifacts in GitHub Actions workflows.
New tools for filtering malicious prompts, detecting ungrounded outputs, and evaluating the safety of models will make generative AI safer to use.
Python developers still prefer Django but are exploring alternative frameworks to draw on specific features or adapt to changing project demands.
The Podman Desktop extension features sample apps and a developer playground for exploring LLM use cases.
Here's what you need to know about Istio, Google's open source service mesh platform for managing data sharing between microservices in a network.