Learn system design

DevOps Learning Path for Beginners 👇 For individuals starting out, having proficiency in the following areas is recommended: 1. Version Control: - Git: Focus on mastering basic commands, branching and merging, collaboration, conflict resolution, version tagging 2. Linux Administration: - Understand system architecture, command line basics, file management, user administration, permissions, and shell scripting 3. Programming: - Python and Go are recommended; beginners should focus on mastering the language syntax, data structures, control flow, functions, libraries 4. Databases: - Learn SQL/NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB) and master data modeling, querying, indexing, transactions, and database management for efficient data storage and retrieval 5. Networking: - Grasp essential concepts like IP addressing, subnetting, firewalls, routing, TCP/IP protocols, network topologies, Load balancers, VPNs and security to manage and troubleshoot network infrastructure 6. CI/CD: - Learn automating the build and deployment pipelines, version control integration, testing automation, containerization, and monitoring 7. Containerization: - Docker/conatinerd: Containerization for portable app packaging - Kubernetes: Container orchestration for scaling apps - Helm: Kubernetes package manager for streamlined deployments 8. Cloud Platforms: - Get Familiar with AWS, Azure, GCP, and their services 9. IaC: - Terraform: Learn Terraform's HCL for efficient, automated cloud infrastructure provisioning 10. Software Configuration Management: - Ansible: Focus on writing YAML playbooks, understanding modules and roles, and automating server and configuration management efficiently 11. Monitoring & Logging: Learn defining metrics, data scraping, alert rule setup, and data visualization for monitoring and troubleshooting 56K+ read my DevOps and Cloud newsletter: techopsexamples.com/subscrib… What do we cover: DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁 Consider a repost if this is helpful.

Alguien en Reddit acaba de publicar el prompt perfecto para ChatGPT para obtener respuestas claras, precisas y directas:

Linux is free. - Docker is free. - Kubernetes is free. - Git and Github are free. - GitHub Actions is free. - Python is free. - AWS, GCP, Azure are free (limited use). - Terraform is free. - ArgoCD and Flux are free. - Prometheus and Grafana are free. Your laptop and internet connection: That’s all you need to start. No expensive bootcamps. No fancy certifications. Just you, your determination, and these free tools. While others are complaining about job markets and layoffs, DevOps engineers are getting multiple offers. Companies are desperate for people who can automate, deploy, and manage infrastructure. The same skills that took me from X to 3X salary, All learned using free tools. Every successful DevOps engineer started exactly where you are right now. Zero experience. Zero connections. Just curiosity. They didn’t wait for the “perfect” time. They didn’t make excuses about not having money for courses. They downloaded Docker, broke things, fixed them, and repeated. Start today. Your future self will thank you.

Este repositorio es una joya. Te da todos los pasos e instrucciones para proteger y asegurar tu servidor Linux. Perfecto por si tienes un servidor propio o VPS: github.com/imthenachoman/How…

Acabo de encontrar la Wikipedia del prompt. Y probablemente sea el mejor sitio de IA que he visto en los últimos dos años. ¿El concepto? Es una biblioteca gigante de prompts compartidos por los usuarios. Puedes filtrarlo todo: → la IA (ChatGPT, Claude, Gemini, etc.) → el idioma → el tono (profesional, divertido, conciso, técnico…) Cada prompt está valorado, probado y se puede mejorar. Incluso puedes añadir los tuyos. Es una base viva, una verdadera inteligencia colectiva. No una vitrina comercial para vender prompts. Y ahí entendí algo: 👉 El futuro del prompting no vendrá de los expertos, sino de las comunidades. La gente comparte sus mejores prompts, otros los prueban, los corrigen, los mejoran. Resultado: un sistema que aprende más rápido que cualquier formador. Sin correos electrónicos que dar. Sin trampas. Sin tonterías. Solo una herramienta que hace exactamente lo que esperamos de la IA: acelerar la transmisión del conocimiento. Para conseguirlo:👇

Sam Altman reveló literalmente cómo convertirse en multimillonario

This guy literally dropped the best career advice you’ll ever hear

Este repositorio recopila todo lo que necesitas para trabajar con IA y LLM en tus proyectos. +120 bibliotecas organizadas para cada fase del desarrollo: → Entrenar, ajustar y evaluar modelos → Desplegar aplicaciones con LLMs y RAG → Ejecutar modelos de forma rápida y escalable → Extracción de datos, crawlers y scraper → Crear agentes autónomos basados en LLMs → Optimización de prompts y seguridad github.com/KalyanKS-NLP/llm-…

AWS Zero to Hero in 30 Days with projects, presentations, interview questions and real world DevOps examples.

Andrej Karpathy summarizes how neural networks function, including the process of using a loss function in training and applying backpropagation for optimizing the network parameters through gradient descent.

I saw this whole playlist last month. If you’re serious about backend engineering, don’t sleep on it. This will be worth your time.

probably the cleanest backend playlist to exist

System Design isn’t one big concept. It’s a staircase. Most engineers try to skip to “distributed systems.” Then, wonder why everything collapses under load. You can’t copy-paste scale. You earn it, step by step. Step 1: Foundations - Learn how data moves. - Networking. Databases. Caching. APIs. - You can’t scale what you don’t understand. Step 2: Mechanics - Learn how systems behave under stress. - Queues. Consistency. Observability. Failures. - This is where good engineers become reliable ones. Step 3: Architecture - Learn how to design for change. - Trade-offs. Evolution. Resilience. - You stop asking “how do I build this?” and start asking “how will this break?” Most people collect patterns. Few develop principles. The difference? Depth. The deeper you go, the less you rely on luck, and the more you rely on design.

10 GitHub repositories that will make you a 10x software engineer:

If you build a React front end app and connect it to a Django/Node JS backend and link it to a Postgres database all inside a docker container and then manage to deploy it to the cloud with your own domain name. You’ve cleared 80% of this list.

As a backend dev in 2025 , learn these 11 skills to keep you relevant in this Job market : 1. API Design - REST/GraphQL/gRPC 2. Authentication & Authorization - OAuth2, JWT, OpenID Connect, Passkeys 3. Databases - SQL, NoSQL, sharding, indexing, query tuning 4. Caching - Redis, CDN, edge caching strategies 5. Event-Driven Systems - Kafka, Pulsar, streaming pipelines 6. Concurrency & Async - reactive programming, structured concurrency 7. Distributed Systems - microservices, service mesh, eventual consistency 8. Security - HTTPS, encryption, zero trust, OWASP top 10 9. Observability - logging, tracing, metrics, OpenTelemetry 10. Cloud & Deployment - Docker, Kubernetes, serverless, GitOps 11. AI Integration - LLM APIs, vector databases, retrieval-augmented systems Stop jumping from one language to another.

When you're building an application, different parts need to communicate - both within the app and with the outside world. This is made possible by APIs - and REST has become a popular way to architect your APIs. Here, @KraneOliver teaches you key REST API principles by walking you through building an Express movie app. freecodecamp.org/news/learn-…

I love discussing : "Design a system for 1 million users." Most jump to tech: "I'll use Kafka, Kubernetes, blah blah!" Game over 🥵 I can tell you this is a red flag. The best engineers start with a question, not a technology. A question that reveals if they truly understand scale...🧵

These days I watch system design videos by @piyushgarg_dev then read book (System design by alex xu) to build intuition about systems architecture