2025-02-05 19:39:32
Introduction
In modern React Native app development, UI plays a crucial role in user engagement. Instead of building components from scratch, developers often rely on UI libraries to speed up development, ensure consistency, and enhance user experience.
In this post, we’ll explore the top 10 React Native UI libraries based on features, ease of use, customization, and community support.
✅ Saves time and effort
✅ Ensures cross-platform consistency
✅ Provides better performance (optimized components)
✅ Comes with built-in accessibility
✅ Reduces design inconsistencies
Now, let’s dive into the best UI libraries for React Native developers in 2025! 🚀
📌 GitHub: React Native Paper
📌 Documentation: React Native Paper Docs
📌 Best for: Material Design Apps
npm install react-native-paper
import { Button } from 'react-native-paper';
<Button mode="contained" onPress={() => console.log('Pressed')}>
Press me
</Button>
✅ Use case: If you're building an Android-style app with a modern, sleek UI.
📌 GitHub: React Native Elements
📌 Documentation: React Native Elements Docs
📌 Best for: Versatile, ready-to-use components
npm install react-native-elements
import { Button } from 'react-native-elements';
<Button title="Click Me" onPress={() => console.log("Clicked")} />
✅ Use case: If you need a complete UI framework with minimal configuration.
📌 GitHub: NativeBase
📌 Documentation: NativeBase Docs
📌 Best for: Theming & Customization
npm install native-base
import { Button } from 'native-base';
<Button onPress={() => console.log("Clicked")}>Click Me</Button>
✅ Use case: If you want a flexible, customizable UI with a built-in design system.
📌 GitHub: React Native Gesture Handler
📌 Documentation: Docs
📌 Best for: Animations & Gestures
✅ Use case: If you need smooth gesture-based navigation like swipe, pinch, pull-to-refresh.
📌 GitHub: Vector Icons
📌 Best for: Adding icons easily
✅ Use case: If your app relies heavily on icons, e.g., buttons, navigation bars.
📌 GitHub: Snap Carousel
📌 Best for: Image & content sliders
✅ Use case: If you need a modern, smooth carousel for showcasing products, news, or galleries.
React Native Elements
React Native Paper
UI Kitten
Reanimated
Snap Carousel
🚀 Which React Native UI library is your favorite? Let me know in the comments!
2025-02-05 19:38:04
Trying to pick the best tech stack in 2025 is very hard, especially with all the new frameworks that are getting released.
It's more than just type safety. You also need good performance, scalability, developer experience (DX) and decent community so you don't get stuck.
Today, we will learn why Next.js (frontend) and Encore.ts (backend) might just be the best full-stack combination for modern developers.
Let's jump in.
In a nutshell, we are covering these topics in detail.
Note: Encore is an open source backend framework for TypeScript and Go. In this guide, when I say Encore, I'm talking about the TypeScript version (Encore.ts).
There are tons of awesome tech stacks out there and some of them are seriously impressive.
From what I’ve found, pairing Next.js for the frontend with Encore.ts for the backend is a winning combo.
In case you’re new to it, Encore is a backend framework and toolset that comes with a Rust runtime, API validation, integrated infrastructure, a developer dashboard and much more.
We will look at this stack through five main factors:
Whenever you are building a production-level application, it's always better to be type-safe (even if you can work without it).
Encore has a built-in type validation and it's all done in a way, that it's fully declarative which allows it to automatically parse and validate the incoming requests. It makes sure that it matches the schema with no boilerplate.
In short, it validates stuff before it even hits that javascript layer.
Both Next.js (with TypeScript) and Encore.ts enforce static type checking, reducing runtime errors and making code easier to refactor. Encore.ts schema-first approach makes sure that API contracts remain consistent.
Encore also makes your infrastructure type-aware and removes the need for connection strings or other boilerplate.
Next.js : With built-in features like server-side rendering (SSR), static site generation (SSG), automatic code splitting, image optimization, lazy loading, script optimization, cache, serverless functions, link optimization... (and much more), it's safe to say that performance is not a concerning factor while choosing Next.js as frontend.
There is a really nice blog on Optimizing Build Performance in Next.js by Turing.
Encore.ts : It has a very high-performance Rust runtime, achieving up to 9x the request throughput compared to Express.js and 2x compared to Fastify. It does this by providing multi-threading in Rust, and handling many operations like request validation in Rust instead of JavaScript.
As per benchmark code on GitHub, Encore.ts in terms of cold startup times, is over 5x faster than Express and 17x faster than NestJS.
Next.js : It has a decent file system, built-in features like routing, automatic code splitting, hot module replacement and a rich plugin ecosystem, which makes DX a lot better.
Encore.ts : DX is one of the strong points in Encore, with built-in obserability (distributed tracing, metrics, logging) with a local dashboard, Automatic architecture diagrams (for a real-time overview), API Explorer (for testing your API endpoints), DevOps automation and reducing boilerplate.
Plus deploying your application is also much easier, with as simple as pushing to a git repository, removing the need for manual steps.
Next.js : It supports dynamic routing, and different rendering techniques like server-side rendering (SSR), static site generation (SSG), incremental static regeneration (ISR), making it useful for large, high-traffic web applications.
I was reading more about it and found an interesting article on How to Build Scalable Architecture for your Next.js Project.
Encore.ts : It improves the development of large-scale microservices applications by unifying your infrastructure with your application code and automating infrastructure provisioning and DevOps tasks.
Here's a code example that will clarify a lot of stuff.
import { api } from "encore.dev/api";
import { sql } from "encore.dev/storage";
// Define a database schema
const db = sql.open({
database: "mydatabase",
user: "dbuser",
password: "dbpassword",
});
// Define a service with an API endpoint
export const myService = api.service({
name: "my-service",
routes: {
hello: api.GET("/hello", async (req) => {
// Log a message
console.log("Received request for /hello");
// Query the database
const result = await db.query("SELECT 'Hello, world!' AS message");
// Return the result
return { message: result.rows[0].message };
}),
},
});
It also provides a visual tool known as Flow that gives you an always up-to-date view of your entire system, helping you reason about your microservices architecture and identify which services depend on each other and how they work together.
Encore.ts is cloud-agnostic by default, which means that it provides an abstraction layer over cloud provider APIs to prevent vendor lock-in. As your requirements evolve, you can adjust the provisioned infrastructure without changing your application code. That is insanely useful!
Next.js : The detailed docs and active community support is the reason so many developers prefer working with Next.js It also has a lot of plugins, tutorials and resources available. You can check Awesome Next.js which has 10k+ stars on GitHub.
Encore.ts : Encore takes care of cloud infrastructure and DevOps for you. This allows developers to create production-ready backends quickly, using tools like microservices, Postgres, and Pub/Sub, all without the usual complexity and DevOps hassle.
There are more amazing things you can do, so please check Next.js docs and Encore.ts docs.
If you are very new to Encore, I highly recommend watching this official tutorial.
In this section, we will be talking about how to get started with Next.js and Encore.ts.
According to the official docs, you can use a starter template to set things up quickly. I’ve followed the same template and you can check it out if you are interested.
To develop locally with Encore, you first need to install the Encore CLI. This is what provisions your local development environment and runs your Local Development Dashboard complete with logs, tracing and API documentation.
You can use any of the following commands (Linux, Windows, macOS in order).
curl -L https://encore.dev/install.sh | bash
iwr https://encore.dev/install.ps1 | iex
brew install encoredev/tap/encore
They also provide ts_llm_instructions.txt, a set of pre-made instructions to help LLM powered tools like Cursor and GitHub Copilot to understand how to use Encore. It made things a lot easier whenever I had doubts while using Cursor.
First, clone the template repository using the following command.
git clone --depth=1 https://github.com/encoredev/nextjs-starter.git
Then, navigate to the backend directory, install dependencies and create a new Encore application.
cd nextjs-starter/backend
npm install # Install dependencies
encore app init # Create a new Encore application.
During setup, you will be prompted to sign up for a free cloud account. It's completely free so I recommend doing that. You will also need to select a version, I'm choosing TypeScript for this guide.
Make sure you switch to the backend directory before running encore app init. I initially forgot so I had to do it again.
Once the setup is complete, you will get the Application ID and Cloud Dashboard URL. Just note these as you will need them later.
Now, you need to start your encore application inside the backend directory using the command encore run.
Go to frontend/package.json and replace {{ENCORE_APP_ID}} with your actual Encore application ID. You can also find this ID in encore.app.
"gen": "encore gen client {{ENCORE_APP_ID}} --output=./app/lib/client.ts --env=local"
Navigate to the frontend directory, open a new terminal window and generate a new request client using this command.
npm run gen # Inside the frontend directory
Running this command will generate the request client at (frontend/app/lib/client.ts) for your application. This enables communication between your frontend and backend.
Before proceeding, make sure the Encore app is running inside the backend directory. If not, you can restart it using encore run. Now, run the Next.js frontend as usual.
cd frontend
npm install
npm run dev
Once it's running, open http://localhost:3000 in your browser to see your application in action.
Similarly, you can access http://localhost:9400 to view Encore's local developer dashboard. Here you can see API Explorer, Service Catalog, Infra, Flow (visual tool), Snippets and more.
The local dashboard has small demos at the start so you won't get stuck for sure.
Make sure to keep the contract between the backend and frontend in sync by regenerating the request client whenever you make a change to an Encore endpoint.
It can be done by running npm run gen.
I'm assuming you have a GitHub repo with the code changes.
Open your app in the Encore Cloud Dashboard.
Go to your app settings and set the Root Directory to backend. It's because the encore.app file is in the backend directory.
In the integrations, connect your account to GitHub, which will open GitHub where you can grant access to the relevant repositories (s).
Once connected to GitHub, pushing code will trigger deployments automatically.
You can read more about deploying applications with Encore Cloud on the docs.
You can track the deployment progress in the Cloud Dashboard. Once complete, your app will be live in the cloud! 🎉
We will use Vercel for frontend deployment. Just create a new project on Vercel and point it to your GitHub repo.
In the project settings, set the root directory to frontend.
Once deployed, your frontend should be live!
Let's talk a little about CORS configuration too.
If you are running into CORS (Cross-Origin Resource Sharing) issues when calling your Encore API from your frontend then you may need to specify which origins are allowed to access your API (via browsers).
You do this by configuring the global_cors key in the encore.app file, which has the following structure:
global_cors: {
// allow_origins_without_credentials specifies the allowed origins for requests
// that don't include credentials. If nil it defaults to allowing all domains
// (equivalent to ["*"]).
"allow_origins_without_credentials": [
"<ORIGIN-GOES-HERE>"
],
// allow_origins_with_credentials specifies the allowed origins for requests
// that include credentials. If a request is made from an Origin in this list
// Encore responds with Access-Control-Allow-Origin: <Origin>.
//
// The URLs in this list may include wildcards (e.g. "https://*.example.com"
// or "https://*-myapp.example.com").
"allow_origins_with_credentials": [
"<DOMAIN-GOES-HERE>"
]
}
You can read the docs for more information on CORS configuration.
In the next section, we will be taking some examples of applications you can build using this tech stack.
We can build lots of innovative apps with Encore and Next.js, so let's explore a few that stand out. The first three have source code and the last two are just example ideas you can build.
You can build an uptime monitoring system that notifies you when your website goes down so you can fix it before your users notice.
The app will use an event-driven architecture and the final result will look something like this.
Here is the automatically generated diagram of the backend architecture, where white boxes are services and black boxes are Pub/Sub topics.
You can check the GitHub repository.
You can build a URL shortener with a REST API and PostgreSQL database. It will also help you learn how to create REST APIs with Encore and test your app locally.
In short, you need to implement the /url endpoint to shorten URLs using randomBytes for unique IDs. Set up a PostgreSQL database with a migration file to store the original and shortened URLs. Then you need to modify the API to insert data into the database upon shortening a URL.
Next, add an endpoint to retrieve the original URL by querying the database with the short ID. Test the API using the local development dashboard and include a /url listing endpoint to fetch all stored URLs.
You can check the GitHub repository.
Just to be clear, you don't need a frontend part for this, but it's a good way to improve your knowledge of Encore.
You can create a chat application that integrates Large Language Models (LLMs) like OpenAI's GPT and Anthropic's Claude with chat platforms such as Slack and Discord. It helps you build AI bots with unique personalities that can engage with users.
This is a microservices-based application, with each service handling a specific aspect of the chatbot ecosystem. The services use a combination of Encore APIs, pub/sub messaging and WebSocket communication to orchestrate the flow of messages between chat platforms and LLM providers.
You can check the GitHub repository.
A blog writing assistant that generates articles based on user input, allows editing and publishing as well.
Tech stack:
Next.js for frontend UI with an editor like Tiptap or Lexical.Encore.ts for backend API to handle text generation requests.OpenAI API (GPT-4) for generating article drafts.Resend for email notifications when articles are ready.Supabase for storing drafts and published articles.Example Flow:
A simple Notion-like app for real-time collaboration on notes and tasks.
Tech Stack
Example Flow
These should be enough to get you started.
If you're looking for a really nice crash course, you can refer to this video!
To be honest, no tech stack can be perfect but Encore and Next.js is definitely one of the best and recommended way to build full-stack applications.
Let me know if you have any other ideas.
Have a great day! Until next time :)
| You can check my work at anmolbaranwal.com. Thank you for reading! 🥰 |
  
|
|---|
2025-02-05 19:36:03
When starting out in software development, most people focus on one big question: Which programming language should I learn? But what if I told you that the language itself is one of the least important factors in becoming a great engineer?
I remember searching for a clear answer in university, only to get vague suggestions like "Learn Java!" or "Try .NET!"—without much reasoning behind them. But over time, I discovered something surprising: once you understand the fundamentals, switching between languages is easy. Today, AI can even generate code in any language for you.
So, what truly makes a great software engineer? It’s not just coding—it’s the hidden skills that never appear in job descriptions but make all the difference. The ability to understand problems deeply, communicate effectively, and adapt to change separates top developers from the rest.
Let’s dive into the skills that will shape your career.
I’ve often heard the phrase: "Don’t just love your product; love the problem you are solving." This resonates deeply because a product is meaningless unless it solves a real problem.
Many developers implement requests without fully understanding the context. Sometimes they fear asking questions or simply don’t have anyone to ask. This can be damaging, leading to issues during testing or actual use. To make solid technical decisions, you need to understand not only the problem the product solves but also the reasoning behind features or changes. Seeing the bigger picture helps you anticipate future changes and focus on what truly matters.
But solving a problem effectively requires more than just writing code—it demands a deep understanding of the business itself.
When developers ask questions to understand their work better, they gain valuable insights into the business. This knowledge enhances technical decisions and encourages active participation in product development. Instead of just coding, you can offer feedback, propose improvements, and contribute to shaping features.
By developing these non-technical skills, you become a more valuable and sought-after engineer. Companies today are looking for individuals who bring more to the table than just technical know-how.
However, understanding the business is just one piece of the puzzle. The best developers aren’t just experts in today’s needs—they’re ready to adapt to whatever comes next.
In today’s fast-paced world, adaptability is essential. Many companies start projects without clear definitions, making an agile mindset crucial. Learning to manage frustration—especially from factors beyond your control—is key to avoiding setbacks.
Key strategies for navigating ambiguity:
✅ Prioritize the real problem. Solve what truly matters, not just what’s asked.
✅ Validate early, iterate fast. Assumptions lead to failure—test and adapt continuously.
✅ Align on scope upfront. A shared vision prevents wasted effort.
✅ Spot roadblocks before they hit. Dependencies can derail progress—anticipate them.
✅ Keep everyone informed. Clear, frequent communication prevents misalignment.
✅ Set realistic deadlines. Don’t let time pressure force bad decisions.
✅ Negotiate scope, not quality. If time is tight, adjust deliverables—not standards.
In a fast-moving industry, waiting for instructions won’t get you far. The best engineers don’t just react to problems—they take action before issues arise.
While this skill is often associated with management roles, it is equally vital for developers. Identify and address obstacles that slow you down, assist teammates facing challenges, research new technologies, and be proactive when issues arise. Cultivating a proactive mindset can be a game changer.
Proactive developers drive change. But ideas alone aren’t enough—you need to communicate them clearly to get buy-in and make an impact.
Communication is a common weakness in technical roles, leading to significant problems. Many developers struggle to explain their work to peers or fail to consider their audience, which can include non-technical individuals. Poor communication can result in wasted time, endless discussions, incorrect implementations, and even project failure.
You could be the best programmer in the world, but if you can't communicate effectively, your skills may become perceived as less relevant. Developing your communication abilities will set you apart from your colleagues and position you for future leadership roles.
Even the best engineers have blind spots. The only way to grow consistently is to seek and embrace feedback.
This practice applies to all professionals, not just those in tech. While many companies promote feedback through defined processes, not all prioritize career development. If you find yourself in such an environment, take the initiative to seek feedback.
While technical skills can be improved through documentation and practice, other skills often come from real-world experiences. Reach out to more senior colleagues and ask for both positive feedback and actionable advice. Observe how they handle various situations, such as team management and conflict resolution.
Beyond feedback, staying competitive means keeping up with industry trends. The best engineers never stop learning.
Keeping up with technological advancements is vital in the ever-evolving tech landscape. Staying updated will help you remain competitive. While you can’t learn everything, try to stay aware of emerging technologies and their applications.
As a software developer, you'll face various challenges every day. A good strategy is to research how others have solved similar problems, especially complex ones. Analyze different solutions to weigh their pros and cons, and identify ways to improve your own methods.
Technical skills are essential, but they’re only part of the equation. The most successful software engineers go beyond just writing code—they understand problems deeply, communicate effectively, adapt to change, and take initiative. These are the skills that truly define great engineers.
What skills have made the biggest difference in your career? Are there any underrated ones that more developers should focus on? Drop your thoughts in the comments!
Thank you very much for taking your time to read my article. Don’t forget to ❤️ if you liked it👏🏻
2025-02-05 19:31:12
At this juncture in economic history, AI has become a transformational force. It can predict possible bottlenecks, streamline processes, speed up operations with precise insights, and find inaccuracies within literal seconds.
The same stands true for Quality Assurance and software testing. Teams can no longer overlook AI’s advantages to their testing frameworks. While CFOs often hesitate at the initial setup and training costs, AI tools inevitably deliver higher ROI in the long run.
Additionally, ignoring AI will cause any team or company to drop in competitive value, as their peers leverage the advanced capabilities of AI engines. They will build better software, find bugs more efficiently, and release updates faster.
This article will expand on this point, diving into some of the hidden costs of ignoring AI testing in your QA strategy.
False economy describes a scenario in which an action/decision with apparent short-term financial savings leads to significant expenditure in the long run. Basically, it is the economic equivalent of “penny wise, pound foolish”.
Overlooking the pivotal role that AI will play in testing is a classic example of a false economy. While initial setup costs can be somewhat intimidating, the inclusion of AI and ML capabilities into test cycles has yielded overwhelmingly positive outcomes. On the other hand, the absence of AI often results in financial losses arising from low product quality, security gaps, and customer dissatisfaction.
The greatest advantage of introducing AI in QA (or any other industry) is efficiency. A Formstack and Mantis Research report found that many organizations are bleeding to $1.3 million yearly due to inefficient tasks slowing down employees. Many companies have recognized this, especially in their QA processes.
According to Forbes, the global artificial intelligence market is projected to expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. It is projected to reach $1,811.8 billion by 2030.
TestRail’s survey shows that 65% of respondents already leverage AI in their QA processes. The 35% who have yet to do so are missing out on a critical component in modern QA strategies.
Another survey found that 77.7% of organizations are using, or planning to use, AI tools in their workflows. They use AI for:
Faster test cycles
AI can execute test cases at 10x the speed and accuracy of human testers and current non-AI automation tools. It can continuously build and run tests, adjust test code to accommodate UI changes, find bugs, and suggest fixes — all in fractions of seconds. This reduces the time taken between deployments and enables faster software releases.
For example, TestGrid’s CoTester comes pre-trained with advanced software testing fundamentals including comprehension of various tools, architectures, languages, and testing frameworks such as Selenium, Appium, Cypress, Robot, Cucumber, and Webdriver. That means it’s easy for your team to build tests in every language and framework without spending time training or picking up new tools.
The time eliminated at the test-building stage is significant enough to accelerate project releases by days, even weeks.
Wider Test Coverage
As AI can create sophisticated tests faster, it contributes directly to wider test coverage. The AI element can analyze massive datasets to create comprehensive test cases covering thousands of scenarios, including edge cases. With the right ML (machine learning) algorithm in place, these test cases can be designed to pick up on obscure bugs and push out the best possible product.
For example, Jenkins can integrate with AI testing setups to automatically initiate tests for each code commit.
Consider, as another example, an e-commerce platform under test. AI solutions like CoTester can analyze user behavior data, craft test cases for user scenarios matching different shopping patterns, and verify that the app works for users in all these scenarios. It can identify edge cases that the human mind is likely to miss and boost software quality — all at half the time required by humans/current automation solutions.
Faster & better feedback loops
While automation tools have sped up the rate of feedback reception in CI/CD pipelines, AI models can further speed up the process while also expanding on the number of insights.
For instance, AI models (like CoTester) can be specifically trained on data about an organization’s team members, team structure, tech stack, and code repo. Naturally, insights offered by such a tool will be more nuanced, comprehensive, and specific than more generalized insights from code-based automation tools.
By providing instant and better feedback on builds, AI capabilities unlock better test results and insights, which assist devs and QAs with finding and fixing bugs as early as possible in the SDLC.
Improved decision making
Unlike any other tool, AI engines can analyze large datasets of historical data to derive insights — a task too cumbersome for humans to accomplish. ML models, inherent in most AI protocols, can extract patterns and trends from previous bug reports. They can predict likely failure points, and create extra test cases to cover these areas of operation. It can also notify the team about common customer complaints and failure points, based on personal data.
In other words, AI models can assist testers with making better decisions during every stage of testing — planning, creation, execution, and analysis. By doing so within seconds (rather than days, as humans would), AI testing automatically brings greater quality to your QA strategy, while also cutting down on time between deployments.
Intelligent analytics and test prioritization
The mechanics of AI are capable of studying historical data and identifying likely issues and the components they will impact. IBM’s Watson is especially well known for providing insights into software modules that have historically faced bug-related failures.
This helps QA teams prioritize their testing efforts. It can also help rank tests based on preset criteria — code changes bug history, customer preferences, etc.
Improved test quality
One of AI’s core abilities in testing projects is being able to create self-healing tests. In other words, tests are automatically adjusted/updated to align with changes in the UI and source code. Consequently, testers don’t have to spend time updating individual tests with every change.
By taking human intervention out of the picture, AI engines don’t just speed up the process, but also keep tests consistent across the project lifecycle. All tests are automatically updated, which eliminates human oversight or inaccuracy — resulting in better test quality while reducing total time and effort.
Enhanced bug resolution
As bugs are captured via tests, AI engines can analyze logs, app behavior, customer preferences, and even predetermined requirements to identify root causes. Advanced root cause analysis is conducted automatically, and suggestions for bug fixes are presented to testers. Once again, this entire process takes minutes and supplements human testers’ evaluation of errors and their causes.
Dynatrace, a software observability tool, leverages AI to find the source of application performance issues automatically. It suggests possible underlying causes that minimize company costs arising from downtime.
Cost-Effectiveness
There is undoubtedly an upfront expense associated with implementing AI testing into your QA strategy. However, this expense pays for itself in the long run.
The many benefits of AI testing — increased automation efficiency, faster time to market, minimal defect-related costs, reduced test maintenance, improved test coverage, better resource optimization, and decision-making — all translate into higher software quality with less time and effort.
AI can be trained quickly on new technologies and protocols as compared to human testers. It makes fewer mistakes and works without rest. Depending on the tool, testers can follow a no-code or low-code approach to build fully capable tests, which reduces the need to hire many highly skilled QA professionals.
AI-driven testing is no longer optional; it is a necessity for modern QA strategies. The benefits — faster test cycles, wider test coverage, improved feedback loops, and intelligent analytics — far outweigh the initial investment. Companies that adopt AI will see enhanced software quality, reduced costs, and a competitive edge in the market. Ignoring AI in testing is a false economy, leading to higher long-term expenses due to inefficiencies, security risks, and lower product quality.
Source: This blog was originally published at testgrid.io
2025-02-05 19:29:07
The California Consumer Privacy Act (CCPA) has reshaped the way we approach software development and testing, putting a strong focus on data privacy and user control. At Opkey, security, transparency, and compliance have always been at the heart of what we do. That’s why staying aligned with CCPA isn’t just about meeting legal requirements, it’s about reinforcing trust with our customers and ensuring top-tier data protection.
We’re thrilled to share that Opkey’s test automation platform is CCPA-certified! This milestone reflects our ongoing commitment to safeguarding personal data and staying ahead in the ever-evolving landscape of privacy and compliance.
What is the CCPA?
The California Consumer Privacy Act (CCPA) is a privacy law that regulates how businesses all over the world are allowed to handle the personal information (PI) of users. The law which came into effect on January 1, 2020, is the first of its kind law in the United States.
Established to protect consumer rights to Privacy, the legislation expects companies to be transparent about the use of consumer data and provide consumers full control over the use of their data.
The world refers to CCPA as a gentler version of GDPR. However, it is not gentler on demands or penalties.
Opkey’s Journey to CCPA Certification
We went through a rigorous process of evaluating and improving our data privacy practices to become CCPA certified. Here’s how we did it:
Comprehensive data mapping – We performed thorough inventory of all personal information collected, processed, and stored. Create data flow maps to visualize data movement.
Privacy policies enhancements– We updated our privacy policies to meet all CCPA requirements and designed them to be fully transparent.
Consumer rights implementation- We established robust procedures for handling:
Right to know- Provided access to personal information collected.
Right to delete- Enabled consumers to request the deletion of their personal information.
Right to opt-out- Provided clear and easy-to-access mechanisms for consumers to opt out of the sale of their personal information.
Data security measures- We also implemented strong security measures like encryption, access controls, and regular security audits to protect against breaches and hacks.
Employee training- Our team underwent extensive training to ensure compliance with CCPA principles and best practices.
Regular audits and assessments- We regularly conduct internal and external audits to assess compliance and identify areas for improvement.
Data minimization- Collect only the necessary personal information and retain it for the shortest time possible.
International data transfers- Always ensure compliance with CCPA requirements when transferring data internationally.
Expert guidance – We regularly consult with CCPA compliance experts to validate our policy and its adherence to the CCPA standards.
What This Means for Opkey’s Customers
For businesses leveraging Opkey’s test automation platform, our CCPA certification reinforces our commitment to data security and compliance. Whether you operate in finance, healthcare, retail, or technology, you can trust that your data is safeguarded with the highest privacy standards.
With Opkey, you can seamlessly meet regulatory requirements without added complexity, ensuring compliance with ease. Plus, our privacy-first approach guarantees that your automated testing processes remain uninterrupted, so you can focus on innovation with complete peace of mind.
Our Commitment to Ongoing Data Protection
At Opkey, we believe data privacy isn’t just about meeting regulations; it’s about earning and keeping your trust. Our CCPA certification is a reflection of that commitment, but we’re not stopping there. As privacy laws like GDPR and SOC 2 evolve, we stay ahead of the curve, continuously strengthening our security measures and compliance strategies. From proactive risk assessments to cutting-edge encryption, we ensure your data is always protected. With Opkey, you’re not just using a top-tier test automation platform you’re partnering with a company that prioritizes security, transparency, and your peace of mind.
Opkey's CCPA certification is not just a symbol, it reflects our relentless commitment towards safeguarding consumer privacy and data security. As data privacy becomes increasingly vital, we’re proud to be at the forefront of compliance and transparency. Such an achievement serves as a reinforcement of our commitment to keeping your information secured while providing high-quality test automation solutions. As we continue to innovate, you can trust that privacy and security will always be at the core of what we do.
2025-02-05 19:27:04
By Mica Cerone
Introduction
As the Hedera ecosystem continues to grow, operators of the Hedera JSON-RPC Relay face new challenges, particularly in managing the costs associated with user transactions—especially in public environments. Imagine this: you have allocated a daily, monthly, or yearly HBAR budget and want to ensure it isn’t exceeded. Everything is running smoothly until certain users start submitting abnormally high-cost transactions. These transactions quickly deplete your HBAR resources, leaving less for other users and leading to an unfair distribution of the budget.
To address this challenge, we have re-engineered our previous HBAR rate limiter. The earlier version simply tracked a single total limit over a specified period. In contrast, the new design introduces a customizable Tiered HBAR Rate Limiter, integrated into the relay, allowing for more granular control and flexibility in managing rate limits. This new feature empowers operators to take control of their spending by categorizing users into tiers with defined spending limits.
In this blog post, we’ll explore how this tiered approach works, why it matters, and how operators can use it to protect their financial resources while supporting the Hedera ecosystem.
Understanding the Economic Model
Before diving into the functionality of the HBAR rate limiter, let’s establish some context regarding the relay’s economic model and how it compares to similar systems, such as Ethereum.
The Role of the Hedera JSON-RPC Relay
The Hedera JSON-RPC Relay serves as a critical bridge, connecting EVM-based applications to the Hedera network. By translating Ethereum Virtual Machine (EVM) JSON-RPC calls into Hedera-compatible requests, the relay enables dApp developers to leverage Hedera’s benefits seamlessly without modifying their existing codebases. These codebases are often built using third-party tools developed for the Ethereum network, such as Ethers.js and Web3.js.
EVM Equivalence and Gas Costs
To maintain EVM equivalence, gas costs charged to users reflect the computational work required, similar to Ethereum. However, in addition to executing transactions within the EVM, certain operations require additional Hedera File Service (HFS) transactions to process large requests.
Who Pays for These HFS Transaction Fees?
Currently, the relay operator bears the costs of these additional HFS transactions. In most cases, these costs are negligible. However, some users may repeatedly deploy large contracts (requiring multiple file transactions on Hedera) or submit high transaction volumes. Without a proper rate-limiting mechanism, these activities can lead to costs that exceed an operator’s initial estimates, quickly becoming unsustainable.
Introducing the Tiered HBAR Rate Limiter
To address these cost management challenges, we developed the Tiered HBAR Rate Limiter. By assigning different spending limits to users based on their tier, we enable sustainable and predictable cost management while still supporting the network’s growth.
How Does the Tiered System Work?
The tiered system assigns users (or groups of users) to specific HBAR spending plans tailored to their behavior and contribution to the network. Think of it like Netflix subscriptions—spending plans can be assigned individually or to groups, similar to personal and family subscription categories.
Basic Tier: General Users
By default, every new user who sends a request to our relay is automatically assigned a unique spending plan under the BASIC tier (Tier 3).
Extended Tier: Supported Projects
Users who exceed the usual activity levels but contribute significantly to the network may qualify for an EXTENDED tier (Tier 2) spending plan.
Privileged Tier: Trusted Partners
The PRIVILEGED tier (Tier 1) is reserved for strategic or critical partnerships, providing users with a significantly higher spending limit.
Configuring the Rate Limiter
Operators can fine-tune the rate limiter to match their specific needs. The relay’s documentation provides a comprehensive guide to configuration options.
Explore the detailed configuration guide in this GitHub repository.
Benefits of the Enhanced HBAR Rate Limiter
Conclusion
The newly redesigned HBAR rate limiter is a powerful tool for JSON-RPC Relay operators to control costs and manage user activity effectively. By assigning tiered spending limits to different user groups, operators can support the growth of the Hedera ecosystem while safeguarding their financial resources and ensuring fair allocation.
Call to Action
If you're a relay operator, we encourage you to explore the new rate limiter features and consider how they can benefit your operations. For users, understanding these tiers can help align your activity with operator policies and optimize your interaction with the Hedera network.
Check out this GitHub repository for a detailed configuration guide.