#python #automation #data #data_engineering #data_ops #data_science #infrastructure #ml_ops #observability #orchestration #pipeline #prefect #python #workflow #workflow_engine

Prefect is a tool that helps you automate and manage data workflows in Python. It makes it easy to turn your scripts into reliable and flexible workflows that can handle unexpected changes. With Prefect, you can schedule tasks, retry failed operations, and monitor your workflows. You can install it using `pip install -U prefect` and start creating workflows with just a few lines of code. This helps data teams work more efficiently, reduce errors, and save time. You can also use Prefect Cloud for more advanced features and support.

https://github.com/PrefectHQ/prefect

#go #metrics #monitoring #observability #open_telemetry #opentelemetry #telemetry

The OpenTelemetry Collector is a tool that helps you manage telemetry data easily. It can receive, process, and export data from various sources like Jaeger and Prometheus to different back-ends without needing multiple agents. Here are the key benefits It comes with reasonable default configurations and supports popular protocols, making it easy to use out of the box.
- **Performant** It is designed to be easily monitored.
- **Extensible** It supports traces, metrics, and logs in a single codebase.

This makes it simpler to manage your telemetry data in a unified and efficient way.

https://github.com/open-telemetry/opentelemetry-collector

#go #cncf #distributed_tracing #hacktoberfest #jaeger #observability #opentelemetry #tracing

Jaeger is a tool that helps you understand how different parts of your software work together. It's like a map that shows where data goes and how long it takes to get there. This helps you find and fix problems faster. Jaeger is free and open source, meaning anyone can use and improve it. It's supported by a big community and has clear guides on how to get started and contribute. Using Jaeger can make your software run more smoothly and efficiently.

https://github.com/jaegertracing/jaeger

#typescript #apm #application_monitoring #distributed_tracing #go #good_first_issue #jaeger #log #logs #metrics #monitoring #nextjs #observability #open_source #opentelemetry #prometheus #react #reactjs #self_hosted #tracing #typescript

SigNoz is a tool that helps you monitor and troubleshoot your applications easily. It combines logs, metrics, and traces in one place, allowing you to spot issues before they happen and fix problems quickly. It's cost-effective and open-source, similar to Datadog and New Relic but without the high costs. With SigNoz, you can monitor application performance, manage logs efficiently, track user requests across services, create customized dashboards, and set alerts for unusual activities. This makes it easier to identify and solve problems quickly, ensuring your application runs smoothly.

https://github.com/SigNoz/signoz

#c_lang #alerting #cncf #data_visualization #database #devops #docker #grafana #influxdb #kubernetes #linux #machine_learning #mongodb #monitoring #mysql #netdata #observability #postgresql #prometheus #raspberry_pi #statsd

Netdata is a powerful monitoring tool that helps you keep an eye on your servers, containers, and applications in real-time. Here’s what you need to know Netdata collects data every second, giving you immediate insights into your system's behavior.
- **Zero-Configuration** Netdata uses ML to detect anomalies and patterns in your metrics, helping you identify issues before they become critical.
- **Scalability** Netdata monitors everything from system resources to application logs, providing a complete view of your infrastructure.
- **Energy Efficiency**: Studies have shown that Netdata is the most energy-efficient monitoring tool, consuming fewer resources than other solutions.

Using Netdata benefits you by providing real-time, high-resolution monitoring, automated anomaly detection, and advanced visualization tools, all while being highly scalable and energy-efficient. This makes it easier to manage and troubleshoot your systems effectively.

https://github.com/netdata/netdata

#typescript #ai #analytics #datasets #dspy #evaluation #gpt #llm #llmops #low_code #observability #openai #prompt_engineering

LangWatch helps you monitor, test, and improve AI applications by tracking performance, comparing different setups, and optimizing prompts automatically. It works with any AI tool or framework, keeps your data secure, and lets you collaborate with experts to fix issues quickly, making your AI more reliable and efficient.

https://github.com/langwatch/langwatch

#typescript #cli #clustering #concurrency #dependency_injection #effect #error_handling #javascript #observability #opentelemetry #platform #schema #typescript #workflows

Effect is a powerful TypeScript framework that helps you build reliable and complex applications by managing side effects like logging, network calls, and database operations in a safe and organized way. It uses a core `Effect` type to describe workflows that are lazy, composable, and type-safe, allowing you to handle errors and dependencies explicitly. The framework is modular, with many packages for AI, CLI tools, distributed computing, SQL databases, and more, making it flexible for various needs. Using Effect improves code quality, concurrency handling, and maintainability, helping you write robust TypeScript apps efficiently[1][2][4][5].

https://github.com/Effect-TS/effect

#go #argocd #cloud_native #cncf #container_management #devops #ebpf #hacktoberfest #istio #jenkins #k8s #kubernetes #kubernetes_platform_solution #kubesphere #llm #multi_cluster #observability #servicemesh

KubeSphere is an easy-to-use, open-source platform that helps you manage Kubernetes clusters across clouds, data centers, and edge devices from one place. It offers a friendly web interface, supports multi-cluster and multi-tenant management, and automates DevOps tasks like CI/CD pipelines. You get built-in monitoring, logging, alerting, and security features such as role-based access control. It also includes an App Store for quick deployment of applications and supports various storage and networking options. This makes managing complex Kubernetes environments simpler, faster, and more secure, saving you time and reducing operational challenges.

https://github.com/kubesphere/kubesphere

#python #agents #ai #api_gateway #asyncio #authentication_middleware #devops #docker #fastapi #federation #gateway #generative_ai #jwt #kubernetes #llm_agents #mcp #model_context_protocol #observability #prompt_engineering #python #tools

The MCP Gateway is a powerful tool that unifies different AI service protocols like REST and MCP into one easy-to-use endpoint. It helps you manage multiple AI tools and services securely with features like authentication, retries, rate-limiting, and real-time monitoring through an admin UI. You can run it locally or in scalable cloud environments using Docker or Kubernetes. It supports various communication methods (HTTP, WebSocket, SSE, stdio) and offers observability with OpenTelemetry for tracking AI tool usage and performance. This gateway simplifies connecting AI clients to diverse services, making development and management more efficient and secure.

https://github.com/IBM/mcp-context-forge

#python #agents #gcp #gemini #genai_agents #generative_ai #llmops #mlops #observability

You can quickly create and deploy AI agents using the Agent Starter Pack, a Python package with ready-made templates and full infrastructure on Google Cloud. It handles everything except your agent’s logic, including deployment, monitoring, security, and CI/CD pipelines. You can start a project in just one minute, customize agents for tasks like document search or real-time chat, and extend them as needed. This saves you time and effort by providing production-ready tools and integration with Google Cloud services, letting you focus on building smart AI agents without worrying about backend setup or deployment details.

https://github.com/GoogleCloudPlatform/agent-starter-pack