Hostraha Blog

Author: Stephen Ndegwa

  • Mastering Cursor: A Comprehensive Guide to Optimizing Your AI-Powered Coding Workflow

    In the fast-evolving world of AI-assisted development, Cursor stands out as a powerful code editor that integrates artificial intelligence directly into your workflow. Built on the foundation of Visual Studio Code, Cursor enhances productivity by offering features like AI autocomplete, chat-based coding assistance, and agent modes for automated task execution. Whether you’re a seasoned developer or a non-technical “vibe coder,” optimizing Cursor can transform chaotic coding sessions into efficient, error-free builds. This guide draws from expert insights, community tips, and real-world practices to help you get the most out of Cursor.

    What is Cursor? A Brief History and Overview

    Cursor launched in 2023 as an AI-enhanced fork of Visual Studio Code, designed specifically for programming with integrated AI capabilities. It quickly gained traction among developers for its ability to predict code edits, generate functions from natural language prompts, and handle complex tasks through AI agents. By February 2025, Cursor achieved a staggering $2.6 billion valuation amid rapid adoption. Some reports even pegged its valuation as high as $9 billion later in the year, reflecting the hype around AI coding tools.

    However, Cursor’s journey hasn’t been without turbulence. In March 2025, its AI began refusing certain code generation requests, redirecting users to learn programming fundamentals—a move that sparked debate about AI’s role in education versus automation. By July 2025, reports highlighted a sharp decline in user satisfaction, attributed to poor management decisions and community backlash over feature prioritization. Security issues also emerged, including a remote code execution (RCE) vulnerability disclosed in early August 2025, tracked as CVE-2025-54135, which allowed attackers to inject malicious code via Model Contest Protocol (MCP) servers. This flaw, dubbed “CurXecute” and “MCPoison,” was patched via an update, but it underscored the risks of AI-integrated tools.

    Despite these challenges, Cursor remains a go-to for many, with features like Copilot++ for intelligent edit predictions and support for models from Claude, Gemini, and GPT. As of August 2025, it’s valued for its potential in large-scale projects, though users emphasize the need for structured workflows to avoid “AI spaghetti”—messy, unmaintainable code generated without proper guidance. In 2025, Cursor has continued to evolve, with updates focusing on better integration of models like Claude 3.5 Sonnet and Gemini 2.5 Pro, making it even more versatile for tasks ranging from quick prototypes to full-scale applications.

    Core Optimization Strategies: The Planner-Executor Workflow

    One of the most effective frameworks for optimizing Cursor comes from a detailed thread by @0xDesigner, emphasizing a structured approach for non-technical users to achieve “vibe coding” with minimal errors. This workflow treats Cursor’s AI as a multi-agent system, dividing tasks into planning and execution phases to prevent distractions and bugs.

    1. Provide High-Level Goals, Not Specific Commands
      Instead of dictating exact steps, state the problem and desired outcomes. Ask questions like: “How would you make this? What do you need from me? What are your blind spots?” This dialogue reveals gaps in understanding and aligns the AI with your end-goal, allowing it to self-correct.
    2. Incorporate Custom Rules for Planner and Executor Roles
      Paste a set of custom rules into your .cursorrules file to define AI behavior. These rules create two modes:
      • Planner Mode: Acts as a project manager, breaking down tasks into small, verifiable steps with success criteria. Focus on high-level analysis without writing code.
      • Executor Mode: Handles implementation one task at a time, updating a scratchpad (.cursor/scratchpad.md) with progress, feedback, and lessons learned.
      Key elements of the rules include:
      • Adopting Test-Driven Development (TDD): Write tests before code.
      • Documenting lessons to avoid repeated errors.
      • Alternating modes for validation: Planner reviews Executor outputs.
      Community-curated rules from sources like cursor.directory can be customized further. Optimize by having the AI self-refine rules post-session. For example, include rules for including debug info, reading files before editing, and running npm audits for vulnerabilities.
    3. Start in Planner Mode
      Activate Planner at project outset to outline tasks. Use reasoning models (e.g., Claude 3.7) for thorough deliberation. Include verifiable metrics for each task to ensure progress tracking.
    4. Gather and Reference API Docs
      Use the @web tag to fetch current API documentation and store it in Markdown files. This prevents errors from outdated info and provides quick references.
    5. Switch to Executor Mode for Implementation
      Once planned, shift to Executor (using models like Claude Sonnet 3.5) to tackle tasks sequentially. Monitor via the scratchpad’s status board.
    6. Alternate Modes for Validation and Debugging
      After each task, revert to Planner to verify success. For bugs:
      • Direct the AI to “check the console” for exact error messages.
      • Revert to checkpoints rather than patching incrementally.
      • Rephrase prompts to uncover confidence gaps, e.g., “What do you need to feel 100% confident?”
    7. Restart if Stuck
      Narrow scope and restart with a simplified plan. Expect iterations—it’s part of refining the process.

    This workflow minimizes the “infinite death loop” of errors, ensuring steady progress. In 2025, users have reported that combining this with MCPs (Model Contest Protocols) enhances automation for tasks like database setup.

    Advanced Tips and Best Practices

    Building on the core workflow, insights from the Cursor community and experts offer additional optimizations:

    • YOLO Mode for Prototyping: Enable quick iterations on prototypes without overthinking. Avoid using auto mode as it can lead to inefficient requests; stick to manual control for better results.
    • Test-Driven Development Emphasis: Draft tests first to specify behavior, then implement. This is especially useful in large codebases where AI might otherwise hallucinate.
    • Debug Statements and Type Hints: Add extensive debug logs, type hints, and docstrings for clarity. Always instruct the AI to read full file contents prior to modifications to prevent context loss.
    • Chat Mode for Context Confirmation: Start sessions in chat to ensure the AI grasps the project. For beginners, establish global AI rules and use @ to reference files or folders efficiently.
    • Prioritize Readability: Focus on clean code over premature optimizations. Use tools like Prompt Engineer inside Cursor to refine prompts, reducing retries by up to 50%.
    • Multi-Model Approach: Use Gemini Pro 2.5 for codebase scanning and error detection (due to its large context window), Claude Sonnet 3.5/3.7 for execution, and GPT o1 for complex debugging. Switch models based on task—e.g., free models for simple edits to save costs.
    • Documentation as Knowledge Base: Maintain detailed docs (PRD, tech stack, app flow) in .cursorrules or agents.md for consistent context. Write a Product Requirements Document (PRD) early to guide the AI.
    • 50-Step Implementation Plans: Create granular blueprints to guide the AI agent.
    • Version Control and Safety Nets: Use Git for checkpoints; avoid over-reliance on AI for critical tasks. Integrate open-source tools like Cipher MCP for memory layers across IDEs.
    • MCP Integration: Leverage MCPs for advanced automation, like database setup. Be cautious with untrusted MCPs to avoid security risks.
    • Performance for Large Projects: Let codebases index overnight; scope context with @file, @folder, @git. For large codebases, AI agents may struggle, so break tasks into smaller chats.
    • Iterative Reviews: Use AI for code reviews but focus human effort on strategy. Cursor is reliable for production-grade reviews with proper setup.

    For large-scale projects, organize code with SOLID principles and maintain separate chats per task to avoid context bloat. Community-shared cheat sheets can help kill hallucinations and speed up debugging.

    Recent Updates and Community Insights in 2025

    As of 2025, Cursor has introduced enhancements like better prompt engineering tools and integration with v0 for UI components. Users are exploring switches to alternatives like Claude Code for specific workflows, but many stick with Cursor for its IDE strengths.

    Community forums and Reddit threads provide goldmines of tips, such as avoiding auto mode to conserve requests and using agentic behavior cookbooks. For beginners, tutorials on YouTube offer step-by-step guidance. Check out this Cursor AI Tutorial for Beginners [2025 Edition] for hands-on setup.

    Tools like Prompt Engineer help convert vague prompts into structured ones, injecting database context to reduce hallucinations. Open-source projects reveal system prompts from Cursor and similar tools, emphasizing “best practices” in AI instructions.

    Common Pitfalls and How to Avoid Them

    • Over-Reliance on AI: Treat Cursor as a junior assistant—review outputs and fix manually when needed. For production code, combine AI reviews with human oversight.
    • Vague Prompts: Be specific with tech stacks and constraints to prevent hallucinations. Use detailed instructions in agent mode.
    • Security Oversights: Scan for vulnerabilities (e.g., npm audit) and avoid untrusted MCPs. Always update to patch known issues like the 2025 RCE vulnerability.
    • Context Overload: Start new chats and limit scope for snappy performance. For memory carryover, use scratchpads or external tools like Cipher.

    Conclusion

    Optimizing Cursor isn’t about letting AI take over—it’s about structuring your workflow to harness its strengths while maintaining control. By adopting the Planner-Executor model, leveraging multi-model approaches, and incorporating community best practices, you can build robust applications efficiently. As AI tools evolve in 2025, staying adaptable and security-conscious will keep you ahead. Experiment with these tips, refine your .cursorrules, and watch your productivity soar. If you’re new to Cursor, start small and scale up—happy coding!

    For more resources, explore the Cipher MCP repo for memory layers or community forums for ongoing discussions.

  • How to Create a WordPress Site

    📝 Tutorial: How to Create a WordPress Site

    WordPress makes it easy to build a blog, business website, or online store without coding.
    Follow this step-by-step guide to set up your site.

    Step 1. Access the WordPress Installer

    • Open your web browser.
    • Visit your domain (e.g. https://yoursite.com).
    • You’ll see the WordPress Installation Screen.

    Step 2. Choose Your Language

    • Select your preferred language (e.g. English).
    • Click Continue.

    Step 3. Enter Database Information

    On the database setup screen, fill in these fields (placeholders only):

    • Database Name: sample_db
    • Username: sample_user
    • Password: SamplePassword123!
    • Database Host: localhost
    • Table Prefix: wp_ (leave as default unless you want multiple sites in one DB)

    Click Submit, then Run the Installation.

    Step 4. Configure Your Site

    Now enter your site details:

    • Site Title: My Sample Blog
    • Username: adminuser
    • Password: DemoPass!2024
    • Your Email: admin@example.com
    • Search Engine Visibility: Leave unchecked (so search engines can index your site).

    Click Install WordPress.

    Step 5. Log Into WordPress

    • Once installation is complete, you’ll see:
      “Success! WordPress has been installed.”
    • Log in at:
      https://yoursite.com/wp-admin
    • Enter your Username and Password.

    Step 6. Explore the Dashboard

    The WordPress dashboard is your control center. From the left sidebar you can:

    • Posts → Add New → publish blog posts.
    • Pages → Add New → create static pages (About, Contact, Services).
    • Appearance → Themes → change your site’s look.
    • Plugins → Add New → install extra features (e.g. SEO tools, page builders).
    • Settings → General → update site title, tagline, timezone.

    Step 7. Publish Your First Post

    1. Go to Posts → Add New.
    2. Enter a title: “Welcome to My Blog”.
    3. Write some sample content.
    4. Click Publish.

    Your post is now live at https://yoursite.com.

    ✅ Congratulations! You’ve created a WordPress site 🎉

  • Deploying Kubernetes in Production: A Complete Guide

    Introduction

    Kubernetes has become the de facto standard for container orchestration in production environments. However, deploying Kubernetes in production requires careful planning, proper security measures, and robust monitoring solutions. This comprehensive guide will walk you through the essential steps and best practices for deploying Kubernetes clusters that are secure, scalable, and maintainable.

    Whether you’re migrating from a traditional infrastructure or starting fresh with cloud-native applications, this guide covers everything you need to know to successfully run Kubernetes in production.

    Prerequisites

    Before diving into the deployment process, ensure you have:

    • Basic understanding of containerization and Docker
    • Familiarity with Kubernetes concepts (pods, services, deployments)
    • Access to a cloud provider or bare metal servers
    • kubectl CLI tool installed
    • Helm package manager (recommended)

    Cluster Setup

    Choosing Your Deployment Method

    There are several ways to deploy Kubernetes in production:

    1. Managed Kubernetes Services

    • Amazon EKS – Fully managed Kubernetes service on AWS
    • Google GKE – Google’s managed Kubernetes platform
    • Azure AKS – Microsoft’s managed Kubernetes service

    2. Self-Managed Solutions

    • kubeadm – Official Kubernetes cluster bootstrapping tool
    • kops – Production-grade Kubernetes operations
    • Kubespray – Ansible-based Kubernetes deployment

    Example: Setting up a cluster with kubeadm

    # Initialize the master node
sudo kubeadm init --pod-network-cidr=10.244.0.0/16

# Configure kubectl for the current user
mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo chown $(id -u):$(id -g) $HOME/.kube/config

# Install a pod network (Flannel example)
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml

    Security Configuration

    Security should be a top priority when deploying Kubernetes in production:

    1. RBAC (Role-Based Access Control)

    Implement proper RBAC policies to control access to cluster resources:

    apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: production
  name: pod-reader
rules:
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["get", "watch", "list"]

    2. Network Policies

    Control traffic flow between pods using network policies:

    apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: deny-all
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress

    3. Pod Security Standards

    • Use non-root containers
    • Implement resource limits
    • Enable security contexts
    • Use read-only root filesystems

    Monitoring & Logging

    Comprehensive monitoring and logging are crucial for production Kubernetes clusters:

    Monitoring Stack

    • Prometheus – Metrics collection and alerting
    • Grafana – Visualization and dashboards
    • AlertManager – Alert routing and management

    Logging Stack

    • Fluentd/Fluent Bit – Log collection and forwarding
    • Elasticsearch – Log storage and indexing
    • Kibana – Log visualization and analysis

    Installing Prometheus with Helm

    # Add Prometheus Helm repository
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

# Install Prometheus
helm install prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --create-namespace

    Networking

    Proper networking configuration ensures reliable communication between services:

    CNI Plugins

    • Calico – Network policy and security
    • Flannel – Simple overlay network
    • Weave Net – Easy setup and encryption
    • Cilium – eBPF-based networking and security

    Ingress Controllers

    Choose an appropriate ingress controller for external traffic:

    • NGINX Ingress – Most popular choice
    • Traefik – Modern reverse proxy
    • HAProxy Ingress – High performance
    • Istio Gateway – Service mesh integration

    Storage Management

    Persistent storage is critical for stateful applications:

    Storage Classes

    apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast-ssd
provisioner: kubernetes.io/aws-ebs
parameters:
  type: gp3
  iops: "3000"
  throughput: "125"
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer

    Backup Strategies

    • Use Velero for cluster and persistent volume backups
    • Implement regular etcd backups
    • Test restore procedures regularly

    Deployment Strategies

    Choose the right deployment strategy for your applications:

    Rolling Updates

    Default strategy for zero-downtime deployments:

    spec:
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 25%
      maxSurge: 25%

    Blue-Green Deployments

    Minimize risk by maintaining two identical production environments.

    Canary Deployments

    Gradually roll out changes to a subset of users using tools like Flagger or Argo Rollouts.

    Troubleshooting

    Common issues and their solutions:

    Pod Issues

    # Check pod status
kubectl get pods -o wide

# Describe pod for detailed information
kubectl describe pod <pod-name>

# Check pod logs
kubectl logs <pod-name> -f

    Network Issues

    • Verify DNS resolution
    • Check network policies
    • Test service connectivity
    • Validate ingress configuration

    Resource Issues

    • Monitor resource usage with kubectl top
    • Check resource quotas and limits
    • Review cluster autoscaler logs

    Conclusion

    Deploying Kubernetes in production requires careful planning and attention to detail. By following the best practices outlined in this guide, you’ll be well-equipped to build and maintain a robust, secure, and scalable Kubernetes infrastructure.

    Remember that Kubernetes is a rapidly evolving ecosystem. Stay updated with the latest releases, security patches, and community best practices to ensure your production environment remains secure and efficient.