DEV Community: Nikolás C. Meléndez

🚀 Markflow: The Ultimate Free Online Markdown Editor (with Marp Support)

Nikolás C. Meléndez — Sun, 01 Feb 2026 03:34:44 +0000

Markdown is everywhere. We use it for documentation, blog posts, READMEs, and even presentations.
But surprisingly, most online Markdown editors are either limited, locked behind accounts, or not designed for real workflows.

That’s why I built Markflow.

👉 Try it here: https://markflow.kormind.com/

✨ What is Markflow?

Markflow is a completely free, no-login, online editor for Markdown and Marp, designed to be fast, visual, and developer-friendly.

It’s built for people who want to write, structure, present, and export content without friction.

📝 Full Markdown + Marp Support

Standard Markdown for articles, docs, and notes
Marp support for creating slide decks directly from Markdown
Perfect for developers, students, educators, and content creators

Write once. Use it everywhere.

🧰 A Toolbar That Actually Helps

One of the biggest pain points of online editors is usability.

Markflow includes a clean, intuitive toolbar that lets you:

Format text (bold, italic, headings, lists)
Insert code blocks, links, images, and quotes
Focus on content instead of syntax

👉 Ideal for beginners and power users.

🤖 Built-in AI Assistant

Markflow includes an AI assistant to help you:

Improve your writing
Rephrase or summarize content
Generate sections or slide outlines

All directly inside the editor — no context switching.

📦 Export Your Work, Your Way

When you’re done, export instantly to:

📄 .md (Markdown)
🌐 .html
📑 .pdf

No watermarks. No restrictions.

🔓 100% Free. No Account Required.

This is important:

❌ No sign-up
❌ No login
❌ No paywalls

Just open Markflow and start writing.

🎯 Who Is Markflow For?

🧑‍💻 Developers writing docs or presentations
👨‍🏫 Teachers creating slides with Marp
🎓 Students preparing reports and expos
✍️ Writers who love Markdown
🚀 Anyone who wants a simple, powerful editor

🌱 Still Growing

Markflow is actively evolving, and feedback from the dev community is more than welcome.
If you care about Markdown, Marp, and better writing tools on the web — this one’s for you.

👉 Try Markflow for free: https://markflow.kormind.com/

🚀 Top 5 Free AI APIs to Supercharge Your Apps in 2026

Nikolás C. Meléndez — Sat, 31 Jan 2026 02:14:17 +0000

Building a side project or an MVP shouldn't break the bank. In 2026, the barrier to entry for integrating Artificial Intelligence has never been lower. Whether you are building a tool like GameOn or a productivity suite like Markflow, choosing the right "Generous Free Tier" is the key to scaling without immediate costs. 🛠️

Here are the best free AI API providers you should be using right now:

1. Google AI Studio (Gemini) 🤖

Google is currently dominating the free tier space, particularly for developers who need to process massive amounts of data.

The Best Part: You get a staggering 1 million token context window for free.
Pros: High rate limits (up to 15 RPM on Flash models); native multimodal support (it can "see" images and "watch" videos). ✅
Cons: In the free tier, your data may be used to train their models. ❌
Best for: Processing long documents or complex codebases.

2. Groq Cloud ⚡

If your app needs to feel "instant," Groq is the undisputed king of speed. By using specialized LPU (Language Processing Unit) hardware, they run open-source models faster than anyone else.

The Best Part: Near-instant responses that feel like the AI is typing at the speed of light.
Pros: Access to Llama 3 and Mistral at incredible speeds. ✅
Cons: Rate limits can be tight during peak hours. ❌
Best for: Real-time chatbots and voice assistants.

3. OpenRouter 🌐

Think of OpenRouter as a "unified gateway." Instead of managing ten different API keys, you manage one.

The Best Part: Access to a curated list of completely free models (marked as $0.00/token).
Pros: Swap between models (GPT, Claude, Llama) by changing just one line of code. ✅
Cons: Free models can sometimes be congested or slower. ❌
Best for: Developers who want to experiment with different models without rewriting their backend.

4. Hugging Face (Serverless Inference) 🤗

The "GitHub of AI" offers a serverless API to test thousands of community-driven models.

The Best Part: Access to niche, specialized models for tasks like translation, sentiment analysis, or image recognition.
Pros: Massive variety (+100k models). ✅
Cons: "Cold starts" can lead to high latency (up to 30 seconds) if the model isn't active. ❌
Best for: Specific, non-chat tasks like audio-to-text or image classification.

5. GitHub Models 🐙

If you already have a GitHub account, you're already in. GitHub has integrated high-end models directly into the developer workflow.

The Best Part: Deep integration with Codespaces and VS Code.
Pros: Access to "heavyweights" like GPT-4o and Llama 3.1 for prototyping. ✅
Cons: Strictly for prototyping; you cannot use the free tier for production/commercial apps. ❌
Best for: Rapid prototyping and internal testing.

Quick Comparison 📊

Provider	Ideal for...	Killer Feature
Google AI Studio	Massive Context	1M+ Token Memory
Groq	Speed	Ultra-low Latency
OpenRouter	Flexibility	Universal API
Hugging Face	Niche Tasks	Model Diversity

💡 Pro Tip: Stay Flexible

Most of these providers (especially Groq and Google) offer OpenAI-compatible SDKs. This is a game-changer! 💻

By using a standard implementation, migrating from a free model to a paid production model is as simple as changing two lines in your .env file:

// Example: Switching from Groq to OpenAI is just an environment variable away!
const client = new OpenAI({
  baseURL: process.env.AI_BASE_URL, // Switch between Groq, OpenRouter, or OpenAI
  apiKey: process.env.AI_API_KEY,
});

Which one are you using for your next project? Let me know in the comments! 👇

The Best Free AI APIs for Developers in 2026 🚀

Nikolás C. Meléndez — Sat, 31 Jan 2026 02:11:26 +0000

Finding the right free API is a constant balance between power, speed, and privacy. For developing modern applications like GameOn or Markflow, it's essential to identify providers with "Generous Free Tiers"—those that allow building and testing a complete MVP without initial costs. 🛠️

Below is a curated list of the best AI API providers for 2026:

1. Google AI Studio (Gemini) 🤖

Google currently offers one of the most competitive free tiers on the market, especially optimized for its "Flash" models.

Pros: High rate limits (up to 15 RPM); massive context window of up to 1 million tokens; native multimodal capabilities (image and video analysis). ✅
Cons: In the free tier, Google uses data to improve its models (privacy concerns); "Pro" models have much stricter restrictions. ❌

2. Groq Cloud ⚡

More than just a model creator, Groq stands out for its specialized chips (LPUs) designed to run open-source models (Llama, Mistral) at unprecedented speeds.

Pros: Near-instant responses; free access to open-source models through its "Beta" tier. ✅
Cons: Prone to rate limit errors during traffic peaks; model variety is limited to their current hosting catalog. ❌

3. OpenRouter 🌐

Acts as a "unified gateway." With a single API key, you can connect to virtually any existing model (Claude, GPT-4, Llama, etc.).

Pros: Access to a wide variety of completely free models (marked as $0.00/token); allows pivoting between models by changing just one line of code. ✅
Cons: Free models often suffer from congestion; requires manual filtering within their extensive list of options. ❌

4. Hugging Face (Serverless Inference) 🤗

Considered the "GitHub of AI," Hugging Face allows testing thousands of community models through its serverless API.

Pros: Unmatched diversity (text, image, audio, and translation); excellent for specialized or niche tasks. ✅
Cons: The free tier is exclusively for evaluation; high initial latency (models can take 30 seconds to "wake up"); not recommended for production. ❌

5. GitHub Models 🐙

If you are already a GitHub user, you can access their model marketplace directly from your development environment.

Pros: Seamless integration with the development workflow; access to top-tier models like GPT-4o and Llama 3. ✅
Cons: Use strictly limited to prototyping; commercial or public-facing use is prohibited in the free version. ❌

Comparative Table 📊

Provider	Ideal for...	Main Advantage	Main Disadvantage
Google AI Studio	Large contexts	1M+ token memory	Data privacy
Groq	Real-time chat	Extreme speed	Strict rate limits
OpenRouter	Multi-model usage	Universal single API	Variable quality
Hugging Face	Specific tasks	Variety (+100k models)	Latency (Cold start)

Recommendation for Your Projects 💡

If you are working with technologies like NextJS and Supabase, the technical recommendation is to start with Groq or Google AI Studio.

Both providers offer SDKs compatible with the OpenAI standard. This means if you decide to migrate to a paid model in the future, you'll only need to update the baseURL and apiKey in your environment variables, keeping your code logic intact. 💻

🔍 Observability Practices: A Practical Guide With Real-World Examples

Nikolás C. Meléndez — Mon, 24 Nov 2025 02:34:56 +0000

Modern software systems are more distributed, dynamic, and complex than ever. Microservices, serverless functions, containers, and event-driven architectures make traditional monitoring insufficient.
To understand what is actually happening inside your application, you need observability.

This article explains the foundational pillars of observability, best practices, common tools, and includes a hands-on real-world example using Grafana + Prometheus for metrics and ELK Stack for logs.

🚦 What Is Observability?

Observability is the ability to understand the internal state of a system based solely on the data it produces, such as metrics, logs, and traces.

Unlike classical monitoring, which answers “Is the system up?”, observability answers:

Why is the system slow?
Where is the latency coming from?
Which dependency failed?
What changed recently that caused errors?

📊 The Three Pillars of Observability

1. Metrics

Numeric measurements collected over time.
Examples:

CPU usage
Request throughput
Error rate
Database latency

Tools: Prometheus, Grafana, Datadog Metrics, AWS CloudWatch Metrics.

2. Logs

Immutable records about events happening in the system.
Examples:

Info logs
Warnings
Errors
Audit logs

Tools: ELK Stack, Datadog Logs, Azure Log Analytics, Loki.

3. Traces

A trace follows a single request across distributed components.
Examples:

Microservices call chain
Latency across services
Errors from downstream dependencies

Tools: Jaeger, Zipkin, OpenTelemetry, AWS X-Ray, New Relic.

🧱 Core Observability Practices

✔️ 1. Use Structured Logging

Instead of plain text logs, use JSON.

{
  "timestamp": "2025-11-23T10:15:20Z",
  "level": "ERROR",
  "service": "orders-api",
  "message": "Payment gateway timeout",
  "orderId": 10422,
  "durationMs": 3200
}

Structured logs allow better filtering and search on platforms like ELK, Datadog, and CloudWatch.

✔️ 2. Define Standard Business Metrics

Examples:

orders_created_total
failed_logins_total
payment_latency_seconds

Business KPIs help identify anomalies beyond pure infrastructure.

✔️ 3. Trace End-to-End Requests

Use OpenTelemetry to instrument services.
Traces give visibility across microservices.

✔️ 4. Set SLOs and Alert Policies

For example:

SLO: 99% of HTTP requests < 150ms
Alert: More than 5% errors in 5 minutes

Good alerts are meaningful, not noisy.

✔️ 5. Centralize All Telemetry Data

Use one platform for metrics + logs + traces.
Examples:

Datadog
New Relic
Grafana Cloud
AWS CloudWatch

🛠️ Real-World Example: Observability With Prometheus + Grafana + ELK Stack

Below is a complete example using a simple Node.js API instrumented with Prometheus metrics and ELK logging.

📌 Example Application (Node.js)

We will expose:

/metrics endpoint for Prometheus scraping
Structured logs sent to Logstash (ELK stack)
A simple API endpoint

🔧 Step 1 — Install Dependencies

npm install express prom-client winston winston-elasticsearch

🧪 Step 2 — Node.js Code With Observability

// app.js
const express = require("express");
const client = require("prom-client");
const winston = require("winston");
const { ElasticsearchTransport } = require("winston-elasticsearch");

const app = express();
const register = new client.Registry();

// ----- PROMETHEUS METRICS -----
const httpRequestCounter = new client.Counter({
  name: "http_requests_total",
  help: "Total HTTP requests",
  labelNames: ["method", "route", "status"]
});
register.registerMetric(httpRequestCounter);

const httpRequestDuration = new client.Histogram({
  name: "http_request_duration_seconds",
  help: "HTTP request latency",
  buckets: [0.1, 0.3, 0.5, 1, 2] 
});
register.registerMetric(httpRequestDuration);

client.collectDefaultMetrics({ register });

// ----- ELK LOGGING -----
const esTransportOpts = {
  level: "info",
  clientOpts: { node: "http://localhost:9200" }
};

const logger = winston.createLogger({
  transports: [new ElasticsearchTransport(esTransportOpts)]
});

// ----- NORMAL API ROUTE -----
app.get("/api/orders", async (req, res) => {
  const end = httpRequestDuration.startTimer();

  logger.info({
    message: "Fetching orders",
    service: "orders-api",
    environment: "production",
    timestamp: new Date().toISOString()
  });

  res.json({ orderId: 123, status: "OK" });

  httpRequestCounter.inc({
    method: "GET",
    route: "/api/orders",
    status: 200
  });

  end();
});

// ----- METRICS ENDPOINT FOR PROMETHEUS -----
app.get("/metrics", async (req, res) => {
  res.set("Content-Type", register.contentType);
  res.send(await register.metrics());
});

// ----- START SERVER -----
app.listen(3000, () => console.log("API running on port 3000"));

📊 Step 3 — Prometheus Scrape Configuration

Add to prometheus.yml:

scrape_configs:
  - job_name: "orders-api"
    static_configs:
      - targets: ["localhost:3000"]

Prometheus now collects:

request count
request latency
default node metrics

📈 Step 4 — Grafana Dashboard

Grafana automatically detects metrics from Prometheus and you can plot:

Requests per second
Error rate
Latency percentiles (P95, P99)

Create a panel and use this query:

rate(http_requests_total[5m])

Another useful panel for latency:

histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

📝 Step 5 — ELK Stack for Logs

Logs go to Elasticsearch, and you can search them with Kibana:

Example search query:

service: "orders-api" AND level: "info"

Example rendered log:

{
  "message": "Fetching orders",
  "service": "orders-api",
  "environment": "production",
  "timestamp": "2025-11-23T10:20:34.123Z"
}

🚀 Final Thoughts

Modern systems demand more than simple health checks—
they require full observability to identify, diagnose, and prevent problems in production.

By applying:

structured logs
custom metrics
distributed tracing
centralized dashboards
SLO-driven alerting

you significantly increase reliability, and most importantly, you gain the ability to understand your system deeply.

🌐 Build Your Own MCP Server — and Connect It to Any MCP Client (Claude, VSCode, GitHub, and More)

Nikolás C. Meléndez — Tue, 11 Nov 2025 14:36:13 +0000

Imagine building a tool once… and instantly using it across Claude Desktop, VSCode, GitHub, or any future AI platform that supports MCP.

Welcome to the next evolution of AI/tool interoperability.

🚀 What is MCP?

MCP (Model Context Protocol) is an open-source standard designed to enable seamless integration between LLM-based clients and external systems/tools/data sources. :contentReference[oaicite:1]{index=1}

It defines how clients (which may wrap chat-apps, IDEs, or other AI interfaces) connect to servers exposing context, tools, resources, and prompts — abstracting away most compatibility issues. :contentReference[oaicite:2]{index=2}

🔧 Example Public Repositories

You don’t have to reinvent the wheel — there are reference MCP server implementations available to get you started:

Repo / SDK / Server	Description / Key Features
GitHub MCP Server — `github/github-mcp-server`	A server that connects AI clients to GitHub: enables reading repos, managing issues/PRs, analyzing code, automating workflows via natural-language interactions. :contentReference[oaicite:4]{index=4}
ModelContextProtocol/servers — `modelcontextprotocol/servers`	A curated collection of reference MCP server implementations: includes Template MCP Server, mcp-open-library, MCP-OpenStack-Ops, and more, in various languages/contexts. :contentReference[oaicite:6]{index=6}

These repos let you fork, modify, or extend existing MCP servers — saving time and helping you follow established patterns.

🛠️ How to Spin Up a Sample MCP Server Locally (Example with GitHub MCP Server)

Here’s how you could get started with the GitHub MCP Server:

git clone https://github.com/github/github-mcp-server.git
cd github-mcp-server
# follow setup instructions (auth, env vars, etc.)
# likely you'll need to provide a GitHub token or configure OAuth
npm install   # or the appropriate build/deploy command
node ./server.js   # or equivalent start command

⚠️ Security warning: MCP servers can execute arbitrary logic/tools, so only run trusted servers or audit code carefully before using them. MCP supports various transports including stdio, http, etc. ([Visual Studio Code][1])

Once running, you can configure your MCP-aware client (Claude Desktop, VSCode, or others) to connect to it — more on configuration below.

💡 Client Configuration & Usage

Most MCP clients support multiple “server” transports (HTTP, stdio, SSE, etc.). ([Visual Studio Code][1])

Here’s a minimal example of what your MCP config might look like in VSCode’s .vscode/mcp.json:

json { "servers": { "my-github-mcp": { "type": "http", "url": "http://localhost:PORT" } } }

Replace PORT with what your server is listening on. With this, your AI-assisted environment will be able to call the exposed MCP tools (e.g. list repos, open issues, analyze code) securely and interactively.

🚀 Why This is Awesome

Interoperability — One server + many clients.
Modular AI tooling — Expose custom logic/APIs as tools the AI can call.
Open-source & community-driven — Benefit from reference servers and SDKs maintained by the wider ecosystem (TS, Python, Java, etc.). ([GitHub][2])
Incremental adoption — Start small (e.g. expose a weather or search tool), then grow.

💬 What’s Next — Extend & Share

Fork one of the example MCP servers above.
Add your own tools/resources (DB access, private APIs, file parsers, whatever).
Connect it to any UI client (Claude, VSCode, or home-grown client).
Publish & share — the community thrives on real-world use-cases and feedback.

🔗 References & Resources

Official servers listing: modelcontextprotocol/servers repo.
GitHub-specific MCP server: github/github-mcp-server.
Official MCP docs: build guides, spec, transports, resource/types. ([Model Context Protocol][3])

🧠 Discussion — Over to You

What’s the coolest tool you’d build using MCP?

A code review bot that hooks directly into GitHub
A knowledge assistant over private docs/DB
A UI-driven tool that visually exposes data via AI prompts

Drop your thoughts, ideas or questions — let’s hack this together 👇

🚀 Meet Telelinker: A CLI Tool to Extract and Analyze Links from Telegram Groups

Nikolás C. Meléndez — Thu, 06 Nov 2025 02:39:22 +0000

👋 Hey everyone!

I recently built Telelinker — an open-source command-line tool that helps you extract and analyze links shared in Telegram groups.

If you’ve ever wanted to know what people are sharing most often in your community — YouTube videos, TikToks, LinkedIn posts, Medium articles — Telelinker can help you find out in seconds.

💡 Why I built it

Telegram is full of interesting content. But if you manage or research multiple groups, tracking what gets shared can get messy.

I wanted a simple tool that could:

Grab all links shared in a group
Detect which platform they belong to (YouTube, TikTok, etc.)
Collect useful metadata like username, likes, comments, views
Export everything to CSV or PostgreSQL for analysis

So, I built Telelinker.

⚙️ What Telelinker does

Once you connect it to your Telegram account, Telelinker can:

✅ Extract links automatically from groups you’re a member of
✅ Detect platforms — YouTube, TikTok, Instagram, LinkedIn, Medium, Dev.to, and more
✅ Pull metadata (username, views, likes, comments, date, title…)
✅ Export results to CSV, JSON, or PostgreSQL
✅ Run interactively or headless
✅ Process multiple groups at once

Perfect for:

📊 Content trend analysis
🧠 Social research
🧰 Community management
🧮 Data mining and social media analytics

🧩 Installation

You can install Telelinker in a few ways.

Option 1 — Scoop (Windows)

scoop bucket add telelinker https://github.com/nkmelndz/telelinker
scoop install telelinker

Option 2 — Docker

git clone https://github.com/nkmelndz/telelinker.git
cd telelinker
docker build -t telelinker .
docker run --rm -it -v "$(pwd)":/app telelinker

Option 3 — From Source (Python 3.11+)

git clone https://github.com/nkmelndz/telelinker.git
cd telelinker
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
python -m src.main setup

🎮 How to use Telelinker

1️⃣ Configure your Telegram API credentials

You’ll need an API ID and HASH from my.telegram.org.
Once you have them, run:

telelinker setup
telelinker login

2️⃣ List your Telegram groups

telelinker groups --interactive

or export them:

telelinker groups --format csv --out my_groups.csv

3️⃣ Extract and analyze links

telelinker fetch --interactive

You can filter by group, limit results, and choose export formats.
Example:

telelinker fetch --group @mygroup --limit 100 --format csv --out links.csv

📊 Example output

After running Telelinker, you’ll get a CSV or JSON file like this:

Platform	Username	Likes	Comments	Views	URL
YouTube	devtips	2345	120	10k	https://youtu.be/...
TikTok	codeguru	842	59	2.3k	https://tiktok.com/...
LinkedIn	jane_dev	150	20	—	https://linkedin.com/...

🧠 What you can do with the data

Find trends: Which platforms are most shared in your groups
Research topics: What kind of content spreads fastest
Build dashboards: Combine Telelinker exports with tools like Grafana or Metabase
Automate reports: Use cron jobs to fetch fresh data weekly

🤝 Want to contribute?

Telelinker is open source under the MIT License — everyone’s welcome to contribute!

You can help by:

Adding scrapers for more platforms
Improving metadata extraction
Suggesting new features
Writing docs or tutorials

Check out the CONTRIBUTING.md for details.

💬 Final thoughts

I built this tool to make Telegram data more accessible for analysis — and I’d love to see what others do with it.

If you find it useful, please ⭐ star the repo or share it withothers!
Feedback, ideas, and contributions are all super welcome 🙌

👉 GitHub: github.com/nkmelndz/telelinker

🏢 Enterprise Design Patterns: Building Scalable Systems with Fowler’s Patterns in Go

Nikolás C. Meléndez — Tue, 28 Oct 2025 02:36:58 +0000

Inspired by Martin Fowler’s Catalog of Patterns of Enterprise Application Architecture

Enterprise applications are complex by nature. They manage large datasets, coordinate multiple services, and must remain maintainable and scalable over time.

Martin Fowler’s Enterprise Application Architecture Patterns provides a timeless catalog of design patterns that help developers structure large-scale systems.

In this article, we’ll explore Enterprise Design Patterns, understand their key categories, and implement a real-world example in Go (Golang) that demonstrates how they work in practice.

💡 What Are Enterprise Design Patterns?

Enterprise Design Patterns are reusable solutions to common architectural problems in enterprise applications — such as managing transactions, persistence, or business logic.

Fowler classifies them into several major categories:

Category	Description
Domain Logic Patterns	Organize business logic (e.g., Transaction Script, Domain Model, Table Module)
Data Source Architectural Patterns	Handle how data is accessed and stored (e.g., Active Record, Data Mapper)
Object-Relational Behavioral Patterns	Manage interaction between objects and relational data (e.g., Identity Map, Unit of Work)
Web Presentation Patterns	Structure the UI and interaction (e.g., Model View Controller)
Distribution Patterns	Manage distributed system behavior (e.g., Remote Facade, Data Transfer Object)

⚙️ Example: Repository + Unit of Work Pattern in Go

Let’s take a practical example of an enterprise-grade approach to managing persistence in a Go web service.

We’ll combine two Fowler patterns:

🧩 Repository Pattern — provides an abstraction layer over data access logic.
🔄 Unit of Work Pattern — tracks changes to objects during a transaction and coordinates the writing out of changes.

🧱 Project Structure


go-enterprise-patterns/
├── main.go
├── domain/
│   └── user.go
├── repository/
│   └── user_repository.go
└── unitofwork/
└── unit_of_work.go

🧩 domain/user.go

package domain

// User represents a simple domain entity.
type User struct {
    ID    int
    Name  string
    Email string
}

📦 repository/user_repository.go

package repository

import (
    "fmt"
    "go-enterprise-patterns/domain"
)

// UserRepository simulates a simple in-memory data storage.
type UserRepository struct {
    data map[int]*domain.User
}

// NewUserRepository initializes a new repository instance.
func NewUserRepository() *UserRepository {
    return &UserRepository{data: make(map[int]*domain.User)}
}

// Add inserts a new user into the repository.
func (r *UserRepository) Add(user *domain.User) {
    r.data[user.ID] = user
    fmt.Println("🟢 Added user:", user.Name)
}

// GetAll retrieves all stored users.
func (r *UserRepository) GetAll() []*domain.User {
    users := []*domain.User{}
    for _, u := range r.data {
        users = append(users, u)
    }
    return users
}

🔄 unitofwork/unit_of_work.go

package unitofwork

import (
    "fmt"
    "go-enterprise-patterns/domain"
    "go-enterprise-patterns/repository"
)

// UnitOfWork tracks pending changes and commits them as a single transaction.
type UnitOfWork struct {
    newEntities []*domain.User
    repo        *repository.UserRepository
}

// NewUnitOfWork creates a new UnitOfWork.
func NewUnitOfWork(repo *repository.UserRepository) *UnitOfWork {
    return &UnitOfWork{
        newEntities: []*domain.User{},
        repo:        repo,
    }
}

// RegisterNew queues a new user for persistence.
func (u *UnitOfWork) RegisterNew(user *domain.User) {
    fmt.Println("📝 Registering new user:", user.Name)
    u.newEntities = append(u.newEntities, user)
}

// Commit saves all queued users in a single transaction.
func (u *UnitOfWork) Commit() {
    for _, user := range u.newEntities {
        u.repo.Add(user)
    }
    u.newEntities = []*domain.User{}
    fmt.Println("✅ Transaction committed.")
}

🚀 main.go

package main

import (
    "go-enterprise-patterns/domain"
    "go-enterprise-patterns/repository"
    "go-enterprise-patterns/unitofwork"
)

func main() {
    // Initialize repository and unit of work
    repo := repository.NewUserRepository()
    uow := unitofwork.NewUnitOfWork(repo)

    // Create domain entities
    user1 := &domain.User{ID: 1, Name: "Alice", Email: "alice@example.com"}
    user2 := &domain.User{ID: 2, Name: "Bob", Email: "bob@example.com"}

    // Register new entities for persistence
    uow.RegisterNew(user1)
    uow.RegisterNew(user2)

    // Commit transaction
    uow.Commit()

    // Retrieve all users from repository
    for _, user := range repo.GetAll() {
        println("👤", user.Name, "-", user.Email)
    }
}

🧠 Why It Matters

This pattern combination helps ensure that:

✅ Business logic stays independent from database details
🧩 You can batch commit all new entities in one transaction
🧪 It becomes easier to test or swap out data sources later (e.g., move from in-memory to PostgreSQL)

This approach scales as your system grows — a foundational step for clean, maintainable enterprise applications.

🔗 GitHub Repository

👉 Full source code is available on GitHub:
https://github.com/yourusername/go-enterprise-patterns

🏁 Final Thoughts

Enterprise patterns aren’t just academic ideas — they are battle-tested abstractions that help teams manage complexity in real-world systems.

By implementing these patterns in languages like Go, Rust, or Python, you create software that’s modular, testable, and adaptable to change — all core values in modern enterprise development.

📚 Further Reading

💬 If you found this article useful, drop a ❤️ on Dev.to or follow me for more deep dives into software architecture and Go!

Design Principles of Software: Building Better Systems

Nikolás C. Meléndez — Sun, 14 Sep 2025 03:52:33 +0000

Software development is more than just writing code that works. The difference between a quick hack and a robust, maintainable system lies in design principles. These principles guide developers in writing code that is clean, scalable, and easy to maintain.

In this article, we will explore some of the most important software design principles and see them in action with a real-world example using Python.

Why Design Principles Matter

Imagine you are building a house. You could randomly place bricks and beams together until it vaguely looks like a house — but would you want to live in it? Software is similar: without good design principles, your code might "work," but it will become fragile, hard to maintain, and nearly impossible to extend.

Design principles help ensure that:

Your code is readable (other developers can understand it).
Your code is maintainable (bugs can be fixed easily).
Your code is scalable (new features can be added without breaking everything).

The Core Design Principles

Here are some fundamental principles you should know:

1. Single Responsibility Principle (SRP)

Each class, function, or module should have one reason to change. This keeps code focused and reduces unintended side effects.

2. Open/Closed Principle (OCP)

Software should be open for extension but closed for modification. Instead of modifying existing code, we extend it by adding new functionality.

3. Liskov Substitution Principle (LSP)

Subtypes must be substitutable for their base types. In other words, objects of a derived class should be usable wherever the base class is expected.

4. Interface Segregation Principle (ISP)

Clients should not be forced to depend on interfaces they do not use.

5. Dependency Inversion Principle (DIP)

Depend on abstractions, not on concrete implementations. This decouples high-level logic from low-level details.

These five principles together are known as SOLID.

Real-World Example: A Payment Processor

Let’s imagine you are building an e-commerce system that supports multiple payment methods: credit card, PayPal, and crypto.

Here’s an example in Python:

from abc import ABC, abstractmethod

# Abstraction (DIP): PaymentMethod defines a contract
class PaymentMethod(ABC):
    @abstractmethod
    def pay(self, amount: float):
        pass

# Concrete implementations (SRP: each handles one payment type)
class CreditCardPayment(PaymentMethod):
    def pay(self, amount: float):
        print(f"Processing credit card payment of ${amount}")

class PayPalPayment(PaymentMethod):
    def pay(self, amount: float):
        print(f"Processing PayPal payment of ${amount}")

class CryptoPayment(PaymentMethod):
    def pay(self, amount: float):
        print(f"Processing crypto payment of ${amount}")

# High-level module (depends on abstraction, not details)
class Checkout:
    def __init__(self, payment_method: PaymentMethod):
        self.payment_method = payment_method

    def process_order(self, amount: float):
        self.payment_method.pay(amount)

# Usage: we can easily extend with new payment types without modifying Checkout
order1 = Checkout(CreditCardPayment())
order1.process_order(100.0)

order2 = Checkout(PayPalPayment())
order2.process_order(50.0)

Why This Example is Good Design:

SRP: Each class has one job – handling a specific payment type.
OCP: To add Apple Pay support, we just add a new class implementing PaymentMethod.
LSP: Any PaymentMethod subclass can be used in Checkout without breaking behavior.
DIP: Checkout depends on PaymentMethod (an abstraction), not on specific payment classes.

Benefits of Following These Principles

Flexibility: You can add new features without touching existing code.
Testability: You can mock PaymentMethod for unit tests.
Maintainability: You avoid tightly coupled, monolithic code.

Final Thoughts

Design principles are not just theoretical guidelines – they are practical tools for building robust systems. Applying them will make your code cleaner, easier to extend, and more resilient to change.

The next time you start a project, think about these principles from the beginning. Your future self (and your teammates) will thank you.

💡 What about you? Which design principle do you find the hardest to follow in real projects? Share your thoughts in the comments!

🏢 Enterprise Design Patterns: From Fowler’s Catalog to Real Code

Nikolás C. Meléndez — Fri, 12 Sep 2025 04:50:28 +0000

Enterprise applications are some of the most complex and mission-critical systems in software engineering. They must support large user bases, integrate with databases, handle business rules, and remain maintainable over years of evolution.

To tackle these challenges, Martin Fowler introduced the Catalog of Patterns of Enterprise Application Architecture (P of EAA) — a curated set of reusable patterns that help developers build clean, scalable, and robust enterprise systems.

This article explores what these patterns are, why they matter, and shows a practical example with real code.

📚 What Are Enterprise Design Patterns?

Enterprise design patterns are reusable solutions to common problems in enterprise application development. Instead of reinventing the wheel, these patterns offer battle-tested designs that improve:

✅ Maintainability – Clear separation of concerns
✅ Testability – Easier unit testing of business rules
✅ Scalability – Code that adapts as systems grow
✅ Flexibility – Swap out infrastructure (e.g., database) without rewriting core logic

🗂️ Fowler’s Catalog at a Glance

Fowler organizes enterprise patterns into categories based on the layer or problem they solve:

Category	Example Patterns	Purpose
Domain Logic	Transaction Script, Table Module, Domain Model	Organize business rules
Data Source	Table Data Gateway, Row Data Gateway, Data Mapper, Active Record	Manage database access
Object-Relational Behavioral	Unit of Work, Lazy Load, Identity Map	Optimize DB interaction
Web Presentation	Front Controller, Template View, Transform View	Structure web UIs
Distribution	Remote Facade, Data Transfer Object	Handle remote communication

Each category complements the others, forming a layered architecture that is easier to evolve.

🎯 Example Pattern: Data Mapper

Let’s dive deeper into Data Mapper, one of the most influential patterns.

Definition:
“A layer of mappers that moves data between objects and a database while keeping them independent of each other.”
— Martin Fowler

Without this pattern, domain objects might contain SQL queries — tightly coupling business rules with persistence logic. This makes the code harder to maintain, test, or change.

🏗️ Architecture Overview

+-------------------+          +-------------------+
|   Domain Object   | <------> |   Data Mapper     |
| (Business Rules)  |          | (Persistence API) |
+-------------------+          +-------------------+
                                    |
                                    v
                               +-----------+
                               | Database  |
                               +-----------+

Domain Object: Pure business logic, no database knowledge.
Data Mapper: Knows how to fetch/save domain objects.
Database: Stores raw data.

💻 Real-World Example in Python

Imagine we are building a Customer Management System.

1️⃣ Domain Model (Business Logic)

# domain.py
class Customer:
    def __init__(self, customer_id, name, email):
        self.customer_id = customer_id
        self.name = name
        self.email = email

    def update_email(self, new_email):
        """Business rule: Validate before updating."""
        if "@" not in new_email:
            raise ValueError("Invalid email format")
        self.email = new_email

    def __repr__(self):
        return f"<Customer {self.customer_id}: {self.name}, {self.email}>"

2️⃣ Data Mapper (Persistence Layer)

# data_mapper.py
import sqlite3
from domain import Customer

class CustomerMapper:
    def __init__(self, connection):
        self.connection = connection

    def find_by_id(self, customer_id):
        cursor = self.connection.execute(
            "SELECT id, name, email FROM customers WHERE id=?", (customer_id,)
        )
        row = cursor.fetchone()
        return Customer(*row) if row else None

    def insert(self, customer):
        self.connection.execute(
            "INSERT INTO customers (id, name, email) VALUES (?, ?, ?)",
            (customer.customer_id, customer.name, customer.email),
        )
        self.connection.commit()

    def update(self, customer):
        self.connection.execute(
            "UPDATE customers SET name=?, email=? WHERE id=?",
            (customer.name, customer.email, customer.customer_id),
        )
        self.connection.commit()

3️⃣ Application Code (Usage)

# app.py
import sqlite3
from data_mapper import CustomerMapper
from domain import Customer

# Setup (for demo purposes)
connection = sqlite3.connect(":memory:")
connection.execute("CREATE TABLE customers (id INTEGER, name TEXT, email TEXT)")

mapper = CustomerMapper(connection)

# Create and insert a new customer
customer = Customer(1, "Alice", "alice@example.com")
mapper.insert(customer)

# Fetch and print
retrieved = mapper.find_by_id(1)
print("Retrieved:", retrieved)

# Update email and persist
retrieved.update_email("alice.new@example.com")
mapper.update(retrieved)

# Verify update
updated = mapper.find_by_id(1)
print("Updated:", updated)

🖨️ Output

Retrieved: <Customer 1: Alice, alice@example.com>
Updated: <Customer 1: Alice, alice.new@example.com>

🏆 Benefits of Using Data Mapper

✅ Separation of Concerns – Business rules stay in Customer, persistence logic stays in CustomerMapper.
✅ Testability – You can test the domain model without touching the database.
✅ Flexibility – You can replace SQLite with PostgreSQL or MySQL with minimal changes.

🔑 Key Takeaways

Enterprise Design Patterns are essential for scalable, maintainable systems.
Fowler’s Catalog gives us a shared vocabulary to discuss and design enterprise architectures.
Data Mapper is a powerful way to decouple business logic from persistence concerns.

💡 Pro Tip: Many modern ORMs (like SQLAlchemy, Hibernate, Django ORM) internally implement Data Mapper — so understanding this pattern helps you use those tools more effectively.

Building Interactive Dashboards with Streamlit, Dash, and Bokeh: From Code to Cloud

Nikolás C. Meléndez — Thu, 11 Sep 2025 23:12:12 +0000

Data visualization plays a central role in computer science and data-driven decision making. While libraries like Matplotlib and Seaborn are often the first tools learned, more interactive frameworks exist to build dashboards and reports for real-world applications. Among them, Streamlit, Dash, and Bokeh stand out because they allow developers to turn Python code into full web applications without requiring advanced web development skills.

1. Streamlit

Streamlit is one of the simplest ways to build interactive dashboards. It allows developers to use pure Python code and automatically creates a responsive web interface. You can add widgets (sliders, checkboxes, text inputs) with minimal syntax, making it an ideal tool for rapid prototyping.

2. Dash

Dash, developed by Plotly, is more powerful and customizable. It combines Flask (for backend), React.js (for frontend), and Plotly (for charts). Dash applications are more structured and allow you to build enterprise-level dashboards with callbacks and layouts.

3. Bokeh

Bokeh is a library designed for creating interactive and scalable visualizations in the browser. Unlike Streamlit or Dash, Bokeh focuses mainly on visualizations, but it can also serve as a backend for dashboards.

Example: Building and Deploying a Dashboard with Streamlit

Below is a small example using Streamlit to visualize a dataset of random values. We’ll then deploy it to Streamlit Cloud (free tier for students).

Code (`app.py`)

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Title
st.title("📊 Interactive Data Dashboard Example")

# Sidebar options
st.sidebar.header("Controls")
n_points = st.sidebar.slider("Number of Data Points", 10, 500, 100)
chart_type = st.sidebar.selectbox("Chart Type", ["Line", "Scatter", "Histogram"])

# Generate random data
data = pd.DataFrame({
    "x": np.arange(n_points),
    "y": np.random.randn(n_points).cumsum()
})

# Show dataframe
st.write("### Randomly Generated Data", data.head())

# Visualization
st.write("### Visualization")
if chart_type == "Line":
    st.line_chart(data.set_index("x"))
elif chart_type == "Scatter":
    st.scatter_chart(data.set_index("x"))
elif chart_type == "Histogram":
    fig, ax = plt.subplots()
    ax.hist(data["y"], bins=20, color="skyblue", edgecolor="black")
    st.pyplot(fig)

Running Locally

pip install streamlit pandas numpy matplotlib
streamlit run app.py

Visit: http://localhost:8501

Deployment to the Cloud (Streamlit Cloud Example)

Push your code to GitHub (repository with app.py and requirements.txt).

Example requirements.txt:
```
 streamlit
 pandas
 numpy
 matplotlib
```

Go to https://streamlit.io/cloud and log in with GitHub.
Click “New App”, select your repository and branch, and choose app.py as the entry file.
Deploy — in a few seconds, your app will be live with a public URL like:

   https://your-username-your-repo.streamlit.app

Why Use These Tools?

Streamlit → quick, simple dashboards (ideal for students, prototypes).
Dash → structured, production-ready dashboards (enterprise use).
Bokeh → powerful interactive plots with flexibility.

Conclusion

Streamlit, Dash, and Bokeh expand the possibilities of data visualization beyond static plots. They enable computer science students and professionals to share insights interactively and even deploy them as web apps. Deploying to cloud platforms like Streamlit Cloud, Heroku, or AWS makes these tools accessible from anywhere, turning local experiments into shareable projects.

🧪 Testing Management Tools Comparative Guide

Nikolás C. Meléndez — Thu, 03 Jul 2025 22:18:28 +0000

Managing automated testing efficiently is critical in modern software development. In this article, we compare popular testing management and CI/CD tools using real-world examples and open-source code repositories. The tools discussed include:

GitLab Pipelines
GitHub Actions
Jenkins
CircleCI
TeamCity
Travis CI
Bitbucket Pipelines
Tekton
Harness

🔧 1. GitLab Pipelines

GitLab offers a powerful CI/CD pipeline tool built into its platform. It supports YAML-based configuration and tight integration with Git repositories.

✅ Real-World Example

Repo: GitLab CI Demo
Config Snippet:

stages:
  - test

test_job:
  stage: test
  script:
    - echo "Running tests"
    - pytest

🔧 2. GitHub Actions

GitHub Actions enables workflow automation directly in your GitHub repository. It's highly flexible and widely adopted.

✅ Real-World Example

Repo: Actions Demo
Config Snippet:

name: Run Tests

on: [push]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install Dependencies
        run: npm install
      - name: Run Tests
        run: npm test

🔧 3. Jenkins

Jenkins is one of the most popular open-source automation servers. It supports hundreds of plugins and is highly customizable.

✅ Real-World Example

Repo: Jenkins Pipeline Example
Config Snippet (Jenkinsfile):

pipeline {
    agent any
    stages {
        stage('Test') {
            steps {
                sh 'mvn test'
            }
        }
    }
}

🔧 4. CircleCI

CircleCI offers performance-focused CI/CD services with support for Docker, caching, and parallelism.

✅ Real-World Example

Repo: CircleCI Demo
Config Snippet:

version: 2.1

jobs:
  test:
    docker:
      - image: cimg/node:14.17
    steps:
      - checkout
      - run: npm install
      - run: npm test

🔧 5. TeamCity

TeamCity by JetBrains is a robust CI/CD server suitable for enterprise environments with visual build pipelines.

✅ Real-World Example

Repo: TeamCity Configuration Demo
(Example includes Kotlin DSL for pipeline configuration.)

🔧 6. Travis CI

Travis CI is known for its simplicity and integration with GitHub.

✅ Real-World Example

Repo: Travis CI Example
Config Snippet:

language: node_js
node_js:
  - "16"
script:
  - npm test

🔧 7. Bitbucket Pipelines

Bitbucket Pipelines offers integrated CI/CD for Bitbucket repositories, using YAML configuration.

✅ Real-World Example

Repo: Bitbucket Pipeline Examples
Config Snippet:

pipelines:
  default:
    - step:
        name: Run Tests
        image: node:14
        script:
          - npm install
          - npm test

🔧 8. Tekton

Tekton is a Kubernetes-native CI/CD framework designed for flexibility and scalability.

✅ Real-World Example

Repo: Tekton Example Pipelines
Pipeline Snippet:

apiVersion: tekton.dev/v1beta1
kind: Task
metadata:
  name: test-task
spec:
  steps:
    - name: run-tests
      image: node:14
      script: |
        npm install
        npm test

🔧 9. Harness

Harness is a modern CI/CD platform with built-in machine learning for deployment verification.

✅ Real-World Example

Repo: Harness CD Sample
(Requires Harness account to run fully.)

📊 Summary Comparison

Tool	YAML Support	Docker Support	Cloud-native	Git Integration	UI Friendly
GitLab Pipelines	✅	✅	✅	GitLab	✅
GitHub Actions	✅	✅	✅	GitHub	✅
Jenkins	❌ (Groovy)	✅	❌	Any	Moderate
CircleCI	✅	✅	✅	GitHub, Bitbucket	✅
TeamCity	✅ (Kotlin DSL)	✅	❌	Any	✅
Travis CI	✅	✅	✅	GitHub	✅
Bitbucket Pipelines	✅	✅	✅	Bitbucket	✅
Tekton	✅ (K8s YAML)	✅	✅	Any	❌ (CLI/K8s)
Harness	✅	✅	✅	Any	✅

📁 Conclusion

Each testing management tool has its strengths. Choose based on your project size, team skill set, and integration preferences. For open-source simplicity, GitHub Actions or GitLab CI are excellent choices. For enterprise-scale orchestration, Harness or Tekton may provide advanced flexibility.

Applying API Testing Frameworks: Real-World Examples with Code

Nikolás C. Meléndez — Thu, 05 Jun 2025 03:34:14 +0000

API testing is a critical component of modern software development, ensuring that your application programming interfaces (APIs) function correctly, perform well, and remain secure. In this article, we'll explore practical implementations of popular API testing frameworks with real-world code examples, inspired by insights from Top 10 API Testing Tools for REST & SOAP Services.

1. Postman (Collection Runner + Newman)

Use Case: Testing a RESTful e-commerce API

// postman_test_example.js
const newman = require('newman');

newman.run({
    collection: require('./ecommerce-api-tests.json'),
    reporters: 'cli',
    iterationCount: 1
}, function (err) {
    if (err) { throw err; }
    console.log('Postman collection run complete!');
});

// Example test in Postman collection
pm.test("Status code is 200", function () {
    pm.response.to.have.status(200);
});

pm.test("Response time is less than 200ms", function () {
    pm.expect(pm.response.responseTime).to.be.below(200);
});

pm.test("Product has valid structure", function () {
    const jsonData = pm.response.json();
    pm.expect(jsonData).to.have.property('id');
    pm.expect(jsonData).to.have.property('name');
    pm.expect(jsonData).to.have.property('price');
    pm.expect(jsonData.price).to.be.a('number');
});

Real-world application: Run regression tests in CI/CD pipeline using Newman (Postman's CLI tool)

2. RestAssured (Java)

Use Case: Testing a banking API with authentication

// BankingAPITest.java
import io.restassured.RestAssured;
import io.restassured.response.Response;
import org.junit.Test;
import static io.restassured.RestAssured.given;
import static org.hamcrest.Matchers.*;

public class BankingAPITest {

    @Test
    public void testAccountBalanceEndpoint() {
        RestAssured.baseURI = "https://api.bank.example.com";

        given()
            .auth()
            .oauth2("valid-access-token")
            .pathParam("accountId", "12345")
        .when()
            .get("/accounts/{accountId}/balance")
        .then()
            .statusCode(200)
            .body("accountId", equalTo("12345"))
            .body("balance", is(not(empty())))
            .body("currency", equalTo("USD"))
            .time(lessThan(1000L));
    }

    @Test
    public void testTransactionHistory() {
        Response response = given()
            .auth()
            .basic("username", "password")
            .queryParam("from", "2023-01-01")
            .queryParam("to", "2023-12-31")
            .get("/transactions");

        response.then()
            .statusCode(200)
            .body("transactions", hasSize(greaterThan(0)))
            .body("transactions[0].amount", is(notNullValue()));
    }
}

Real-world application: Comprehensive testing of financial APIs with complex authentication requirements

3. PyTest with Requests (Python)

Use Case: Testing a weather API with parameterized tests

# test_weather_api.py
import pytest
import requests

BASE_URL = "https://api.weather.example.com/v1"

@pytest.mark.parametrize("city,country,expected_status", [
    ("London", "UK", 200),
    ("New York", "US", 200),
    ("Invalid City", "XX", 404),
])
def test_get_weather_by_city(city, country, expected_status):
    response = requests.get(
        f"{BASE_URL}/weather",
        params={"city": city, "country": country},
        headers={"Authorization": "Bearer test-api-key"}
    )

    assert response.status_code == expected_status

    if expected_status == 200:
        data = response.json()
        assert "temperature" in data
        assert "humidity" in data
        assert "conditions" in data
        assert isinstance(data["temperature"], float)

@pytest.fixture
def auth_token():
    # Get auth token once for multiple tests
    response = requests.post(
        f"{BASE_URL}/auth",
        json={"username": "testuser", "password": "testpass"}
    )
    return response.json()["token"]

def test_forecast_endpoint(auth_token):
    response = requests.get(
        f"{BASE_URL}/forecast",
        params={"days": 5},
        headers={"Authorization": f"Bearer {auth_token}"}
    )

    assert response.status_code == 200
    forecast = response.json()
    assert len(forecast) == 5
    assert all("date" in day and "high" in day for day in forecast)

Real-world application: Data-driven testing of weather API with different locations and authentication scenarios

4. Karate DSL (BDD Style)

Use Case: Testing a microservices architecture with complex workflows

# user_management.feature
Feature: User Management API Tests

Background:
  * url 'https://api.users.example.com'
  * header Authorization = 'Bearer ' + token
  * def token = call read('get_token.js') { username: 'admin', password: 'admin123' }

Scenario: Create and verify new user
  Given path '/users'
  And request { name: 'Test User', email: 'test.user@example.com', role: 'member' }
  When method post
  Then status 201
  And match response == { id: '#number', name: 'Test User', email: 'test.user@example.com', role: 'member' }

  * def userId = response.id

  Given path '/users/' + userId
  When method get
  Then status 200
  And match response contains { id: '#number', name: 'Test User' }

Scenario: Test user search with parameters
  Given path '/users/search'
  And param role = 'admin'
  When method get
  Then status 200
  And match each response.users contains { role: 'admin' }
  And assert response.users.length > 0

Real-world application: Testing complex user flows in microservices with readable BDD syntax

5. SoapUI (SOAP Service Testing)

Use Case: Testing a legacy SOAP-based inventory system

<!-- inventory_check.xml -->
<soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" 
                 xmlns:inv="http://www.example.com/inventory">
   <soapenv:Header/>
   <soapenv:Body>
      <inv:CheckInventoryRequest>
         <inv:productId>1001</inv:productId>
         <inv:warehouse>NYC</inv:warehouse>
      </inv:CheckInventoryRequest>
   </soapenv:Body>
</soapenv:Envelope>

// Groovy Script Assertion in SoapUI
def groovyUtils = new com.eviware.soapui.support.GroovyUtils(context)
def holder = groovyUtils.getXmlHolder("CheckInventoryRequest#Response")

def availableQty = holder.getNodeValue("//*:availableQuantity")
def status = holder.getNodeValue("//*:status")

assert availableQty.toInteger() >= 0
assert status == "IN_STOCK" || status == "OUT_OF_STOCK"

// Performance check
assert context.responseTime < 500

Real-world application: Maintaining and testing legacy SOAP services with complex XML payloads

Best Practices for API Testing Frameworks

Environment Management: Use config files to manage different environments

   // config.js
   module.exports = {
     dev: {
       baseUrl: 'https://dev.api.example.com',
       apiKey: 'dev-key-123'
     },
     staging: {
       baseUrl: 'https://staging.api.example.com',
       apiKey: 'staging-key-456'
     },
     prod: {
       baseUrl: 'https://api.example.com',
       apiKey: process.env.PROD_API_KEY
     }
   };

Test Data Management: Generate test data dynamically

   # test_data.py
   from faker import Faker

   fake = Faker()

   def generate_user_data():
       return {
           'name': fake.name(),
           'email': fake.email(),
           'address': fake.address(),
           'phone': fake.phone_number()
       }

Reporting: Generate comprehensive test reports

   // RestAssured with ExtentReports
   @AfterSuite
   public void tearDown() {
       ExtentReports extent = new ExtentReports();
       ExtentSparkReporter spark = new ExtentSparkReporter("target/Spark.html");
       extent.attachReporter(spark);
       extent.flush();
   }

Performance Checks: Always include performance assertions

   // Postman
   pm.test("Response time is acceptable", function () {
       pm.expect(pm.response.responseTime).to.be.below(300);
   });

Security Testing: Include basic security checks

   # pytest
   def test_secure_headers(response):
       assert 'X-Content-Type-Options' in response.headers
       assert response.headers['X-Frame-Options'] == 'DENY'
       assert 'Content-Security-Policy' in response.headers

Conclusion

Choosing the right API testing framework depends on your tech stack, team skills, and API complexity. Postman is excellent for collaborative manual testing, RestAssured integrates well with Java ecosystems, PyTest offers Python simplicity, Karate provides BDD readability, and SoapUI handles SOAP services effectively.

By implementing these real-world examples and following best practices, you can build a robust API testing strategy that catches bugs early, ensures performance standards, and maintains API reliability throughout your development lifecycle.

Remember to:

Test for both happy paths and error cases
Validate response schemas
Include performance assertions
Test security aspects
Automate in your CI/CD pipeline
Maintain your tests as your API evolves

DEV Community: Nikolás C. Meléndez

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3. OpenRouter 🌐

4. Hugging Face (Serverless Inference) 🤗

5. GitHub Models 🐙

Quick Comparison 📊

💡 Pro Tip: Stay Flexible

The Best Free AI APIs for Developers in 2026 🚀

1. Google AI Studio (Gemini) 🤖

2. Groq Cloud ⚡

3. OpenRouter 🌐

4. Hugging Face (Serverless Inference) 🤗

5. GitHub Models 🐙

Comparative Table 📊

Recommendation for Your Projects 💡

🔍 Observability Practices: A Practical Guide With Real-World Examples

🚦 What Is Observability?

📊 The Three Pillars of Observability

1. Metrics

2. Logs

3. Traces

🧱 Core Observability Practices

✔️ 1. Use Structured Logging

✔️ 2. Define Standard Business Metrics

✔️ 3. Trace End-to-End Requests

✔️ 4. Set SLOs and Alert Policies

✔️ 5. Centralize All Telemetry Data

🛠️ Real-World Example: Observability With Prometheus + Grafana + ELK Stack

📌 Example Application (Node.js)

🔧 Step 1 — Install Dependencies

🧪 Step 2 — Node.js Code With Observability

📊 Step 3 — Prometheus Scrape Configuration

📈 Step 4 — Grafana Dashboard

📝 Step 5 — ELK Stack for Logs

🚀 Final Thoughts

🌐 Build Your Own MCP Server — and Connect It to *Any* MCP Client (Claude, VSCode, GitHub, and More)

🚀 What is MCP?

🔧 Example Public Repositories

🛠️ How to Spin Up a Sample MCP Server Locally (Example with GitHub MCP Server)

💡 Client Configuration & Usage

🚀 Why This is Awesome

💬 What’s Next — Extend & Share

🔗 References & Resources

🧠 Discussion — Over to You

🚀 Meet Telelinker: A CLI Tool to Extract and Analyze Links from Telegram Groups

💡 Why I built it

⚙️ What Telelinker does

🧩 Installation

Option 1 — Scoop (Windows)

Option 2 — Docker

Option 3 — From Source (Python 3.11+)

🎮 How to use Telelinker

1️⃣ Configure your Telegram API credentials

2️⃣ List your Telegram groups

3️⃣ Extract and analyze links

📊 Example output

🧠 What you can do with the data

🤝 Want to contribute?

💬 Final thoughts

🏢 Enterprise Design Patterns: Building Scalable Systems with Fowler’s Patterns in Go

💡 What Are Enterprise Design Patterns?

⚙️ Example: Repository + Unit of Work Pattern in Go

🧱 Project Structure

🧩 domain/user.go

📦 repository/user_repository.go

🔄 unitofwork/unit_of_work.go

🚀 main.go

🧠 Why It Matters

🔗 GitHub Repository

🌐 Build Your Own MCP Server — and Connect It to Any MCP Client (Claude, VSCode, GitHub, and More)

Code (`app.py`)