#typescript #boilerplate #examples #go #graphql #nodejs #prisma

https://github.com/prisma/prisma-examples

#python #amazon #ansible #aws #book #devops #docker #examples #jeff_geerling #kindle #kubernetes #leanpub #playbook #vagrant

https://github.com/geerlingguy/ansible-for-devops

#javascript #demo #examples #federated #federation #federation_dashboard #module #module_federation #nextjs #stream_federated

https://github.com/module-federation/module-federation-examples

#css #design #examples #textbook #tutorials

https://github.com/adamschwartz/magic-of-css

#javascript #automated_tests #cypress #cypress_example #e2e_tests #examples #recipes #testing #unit_testing

https://github.com/cypress-io/cypress-example-recipes

#rust #example #examples #exercise #exercises #practice

https://github.com/sunface/rust-by-practice

#rust #actix #actix_web #examples

https://github.com/actix/examples

#python #bigquery #examples #gke #google_cloud_compute #google_cloud_dataflow #google_cloud_ml #google_cloud_platform #solutions #tools

https://github.com/GoogleCloudPlatform/professional-services

#c_lang #bpf #ebpf #examples #libbpf #tutorial #xdp

This tutorial helps you learn eBPF (Extended Berkeley Packet Filter) step by step with practical examples. It covers basic concepts, code examples in languages like C, Go, and Rust, and real-world applications in areas such as observability, networking, and security. The tutorial is designed to be easy to follow, starting with simple "Hello World" programs and progressing to more advanced topics. It also provides pre-configured GitHub templates to quickly set up and run eBPF projects, making it easier for developers to focus on coding without worrying about the setup. This makes learning and using eBPF much simpler and faster.

https://github.com/eunomia-bpf/bpf-developer-tutorial

#jupyter_notebook #aws #data_science #deep_learning #examples #inference #jupyter_notebook #machine_learning #mlops #reinforcement_learning #sagemaker #training

SageMaker-Core is a new Python SDK for Amazon SageMaker that makes it easier to work with machine learning resources. It provides an object-oriented interface, which means you can manage resources like training jobs, models, and endpoints more intuitively. The SDK simplifies code by allowing resource chaining, eliminating the need to manually specify parameters. It also includes features like auto code completion, comprehensive documentation, and type hints, making it faster and less error-prone to write code. This helps developers customize their ML workloads more efficiently and streamline their development process.

https://github.com/aws/amazon-sagemaker-examples