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@laura_garcia shared a post, 1ย week, 3ย days ago
Software Developer, RELIANOID

๐— ๐—ช๐—– ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ: ๐—Ÿ๐—ฒ๐˜€๐˜€๐—ผ๐—ป๐˜€ ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ฒ๐—ฑ ๐—ณ๐—ผ๐—ฟ ๐—ฎ๐—ฝ๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฑ๐—ฒ๐—น๐—ถ๐˜ƒ๐—ฒ๐—ฟ๐˜† & ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜†

๐Ÿ”ต ๐— ๐—ช๐—– ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ: ๐—Ÿ๐—ฒ๐˜€๐˜€๐—ผ๐—ป๐˜€ ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ฒ๐—ฑ ๐—ณ๐—ผ๐—ฟ ๐—ฎ๐—ฝ๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฑ๐—ฒ๐—น๐—ถ๐˜ƒ๐—ฒ๐—ฟ๐˜† & ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† After an intense week in Barcelona, one thing is clear: ๐Ÿ‘‰ The future is not just about connectivityโ€ฆ itโ€™s about ๐—ต๐—ผ๐˜„ ๐—ฎ๐—ฝ๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ฑ๐—ฒ๐—น๐—ถ๐˜ƒ๐—ฒ๐—ฟ๐—ฒ๐—ฑ, ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ฒ๐—ฑ, ๐—ฎ๐—ป๐—ฑ ๐—ฐ๐—ผ๐—ป๐˜๐—ฟ๐—ผ๐—น๐—น๐—ฒ๐—ฑ ๐Ÿš€ Key takeaways ๐Ÿญ. ๐—”๐—œ ๐—ถ๐˜€ ๐—ป๐—ผ๐˜„ ๐—ถ๐—ป๐—ณ๐—ฟ๐—ฎ๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ AI-driven networks are h..

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@laura_garcia shared a post, 1ย week, 4ย days ago
Software Developer, RELIANOID

Retailers invest millions in digital experiencesโ€ฆ

but what happens when the checkout stops working? A recent outage affecting the online services of Tesco has once again sparked an important discussion: how resilient are modern retail platforms? Customers reported failures during some of the most critical moments of the shopping journey: - Checkout..

tesco outage
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@hamzmu shared a link, 1ย week, 5ย days ago
Fellow, Rootly

On-Call Burnout: What Incident Data Doesnโ€™t Show

Incident dashboards measure system health, but rarely show the workload and strain engineers face when responding to alerts. Incident load isnโ€™t only about the number of incidents, but the patterns surrounding them. In this article we explore these patterns and introduce On-Call Health, an open-source tool that analyzes engineering signals to surface early burnout trends, highlighting why incident volume alone isnโ€™t enough and why after-hours interruptions, workload stacking, and long-term trends matter.

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At its core, Argo CD treats Git as the single source of truth for application definitions. You declare the desired state of your Kubernetes applications in Git (manifests, Helm charts, Kustomize overlays), and Argo CD continuously compares that desired state with what is actually running in the cluster. When drift is detected, it can alert you or automatically reconcile the cluster back to the Git-defined state.

Argo CD runs inside Kubernetes and provides:

- Declarative application management
- Automated or manual sync from Git to cluster
- Continuous drift detection and health assessment
- Rollbacks by reverting Git commits
- Fine-grained RBAC and multi-cluster support

It integrates natively with common Kubernetes configuration formats:

- Plain YAML
- Helm
- Kustomize
- Jsonnet

Operationally, Argo CD exposes both a web UI and CLI, making it easy to visualize application state, deployment history, diffs, and sync status. It is commonly used in platform engineering and SRE teams to standardize deployments, reduce configuration drift, and enforce auditability.

Argo CD is part of the Argo Project, which is hosted by the Cloud Native Computing Foundation (CNCF), and is widely adopted in production Kubernetes environments ranging from startups to large enterprises.