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A practical DevOps roadmap: 1. Learn a scripting or programming language such as Python, Go, Bash, or JavaScript. 2. Understand operating systems basics, including Linux, Unix, server management, processes, and permissions. 3. Learn networking and security concepts, including DNS, TCP/IP, SSH, HTTPS, firewalls, and encryption. 4. Get comfortable with version control systems like Git, and platforms such as GitHub and GitLab, including branching, pull requests, and release workflows. 5. Study Infrastructure as Code tools like Terraform, Pulumi, Docker, Kubernetes, and Ansible. 6. Add monitoring and logging with Prometheus, Grafana, Loki, and Elasticsearch. 7. Build CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI, or CircleCI. 8. Understand cloud platforms such as AWS, Azure, Google Cloud, and DigitalOcean.

APIs and MCP both connect systems, but they solve different problems. Traditional API flow: App → HTTP request → API gateway → service → database/tool It works well when your application knows exactly which endpoint to call and what response to expect. MCP works differently. MCP gives AI apps a standard way to connect with: - tools - databases - files - APIs - external resources Instead of building custom integrations for each AI assistant, MCP serves as a shared protocol layer between the model and the tools it needs. Simple way to think about it: API = app talks to a service MCP = AI agent talks to many tools through a standard interface APIs are still the foundation. MCP is the layer that makes those tools easier for AI systems to use.

Pushing to production...

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Git commands you should know

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Laravel Tip 💡 The pipe() method lets you pass the collection to a callback and continue working with the transformed result.

Unpopular opinion: The best AI prompts are usually written after you’ve utried to solve the problem yourself.

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