NN`` MM (@libertarian_meme) on FAUN.dev()

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@squadcast shared a post, 1 year, 6 months ago

Opsgenie vs. Pagerduty: A Detailed Comparison

#pagerdu... #inciden... #opsgeni... #inciden...

This blog post compares two incident alerting and response platforms: Opsgenie and Pagerduty. It helps readers choose between the two based on their needs and budget.

Here's a quick breakdown:

On-Call Scheduling: Opsgenie is easier to use, Pagerduty is more powerful but complex.

Alerting: Pagerduty offers more sophisticated alerting with AI-powered noise reduction. Opsgenie provides the basics but lacks advanced features without extra cost.

Incident Response: Pagerduty excels with features like automated actions and deep ITSM integrations. Opsgenie offers basic functionalities.

Integrations: Pagerduty offers more integrations (including Atlassian ecosystem) while Opsgenie has a respectable library of essential connections.

Pricing: Opsgenie starts at $11/month/user, Pagerduty starts at $25/month/user (with additional costs for advanced features).

Overall, Opsgenie is ideal for those who prioritize user-friendliness and affordability. Pagerduty is better suited for those who need advanced features, strong integrations, and robust incident response capabilities, but are willing to pay a premium.

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@squadcast shared a post, 1 year, 6 months ago

Efficient On-Call Management and Incident Response with Microsoft Teams | Squadcast

#on call... #inciden...

This blog post discusses how Squadcast's Microsoft Teams application can improveon-call incident response workflows. It highlights the key features of the integration, including real-time incident notifications, actionable messaging, and clear on-call visibility. The post also details the benefits of using Squadcast, such as improved collaboration, reduced downtime, and enhanced situational awareness. It concludes by explaining the simple three-step integration process and mentions additional features of Squadcast.

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@faun shared a link, 1 year, 6 months ago

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Automation and the Jevons paradox

Tim Paul watched a talk about sustainable AI at Services Week 2024 by software developer Ishmael Burdeau, who mentioned the Jevons paradox. The paradox explains how energy efficiency gains can result in more energy consumption rather than less, seen in various scenarios such as improving road networ.. read more

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@faun shared a link, 1 year, 6 months ago

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Canonical releases Ubuntu 24.04 LTS Noble Numbat

"Canonical’s 10th Long Term Supported release sets a new standard in performance engineering, enterprise security and developer experience.".. read more

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@faun shared a link, 1 year, 6 months ago

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Build and deploy a 1 TB/s file system in under an hour

High throughput shared file systems are crucial for HPC and AI environments, providing the storage needed for training large models or conducting research. With Amazon FSx for Lustre, organizations can leverage AWS compute, network, and storage resources on-demand, reducing time and cost. By utilizi.. read more

Build and deploy a 1 TB/s file system in under an hour

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@faun shared a link, 1 year, 6 months ago

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Lessons from building an automated SDK pipeline

Cloudflare now offers software development kits (SDKs) for Typescript, Go, and Python. To get started, install the packages using the following commands: npm install cloudflare for Typescript, go get -u github.com/cloudflare/cloudflare-go/v2 for Go, and pip install --pre cloudflare for Python. Autom.. read more

Lessons from building an automated SDK pipeline

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@faun shared a link, 1 year, 6 months ago

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Implementing vertical autoscaling for Aurora databases using Lambda functions in AWS

AWS provides horizontal scaling, but vertical scaling is not available. By using Amazon CloudWatch Alarms, Amazon RDS Events, Simple Notification Service, and Lambda Functions, the goal of vertical autoscaling was achieved... read more

Implementing vertical autoscaling for Aurora databases using Lambda functions in AWS

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@faun shared a link, 1 year, 6 months ago

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Best practices for monitoring ML models in production

There are several key issues that can affect ML models' functional performance in production, including training-serving skew, data and concept drift, and data processing pipeline issues. Monitoring model performance in production requires setting up a system that can ingest data and prediction logs.. read more

Best practices for monitoring ML models in production

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@faun shared a link, 1 year, 6 months ago

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Ubuntu 24.04, Fedora 40, EndeavourOS, and TrueNAS 24.04 here

Last week saw the release of EndeavourOS Gemini, Fedora 40, and Ubuntu 24.04, all featuring the latest Linux kernel 6.8, GNOME 46 desktop, and some with KDE Plasma 6. Fedora has multiple editions including Workstation, Spins, Atomic Desktops, and variants for Server, IoT, Cloud, and CoreOS. Ubuntu 2.. read more

Ubuntu 24.04, Fedora 40, EndeavourOS, and TrueNAS 24.04 here

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@faun shared a link, 1 year, 6 months ago

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Lessons Learned When Building My DNS Resolver

The Implement DNS in a weekend project by Julia Evans is a guide for building your own DNS resolver from scratch, with explanations on the DNS message format and compression techniques. The Header section in the DNS message has a fixed length of 64 bits and contains essential flags for processing qu.. read more

Lessons Learned When Building My DNS Resolver