#API Error Handling

[daintilyultimateslayer]

Best LHDN- services in Malaysia

What Is Middleware in the Context of E-Invoicing?

Middleware acts as the bridge between your internal ERP or accounting system and the MyInvois API. It handles data transformation, validation, error handling, authentication, and communication with the electronic invoicing system.

Instead of trying to retrofit complex LHDN requirements into your legacy ERP, middleware offers a decoupled and flexible layer that can:

Validate and map invoice data to the official e invoice format

Handle real-time communication and responses from MyInvois

Queue, retry, and track each digital invoice format submission

Log every transaction for audit and compliance purposes

A well-designed middleware ensures your invoice in Malaysia is not just submitted but accepted without rejections, delays, or compliance risks.

Understanding MyInvois API and Its Role in the e Invoice Ecosystem

Get in Touch with us

Malaysia

Location

No NW-02-21, Cova Square, Jalan Teknologi, Taman Sains 47410 Petaling Jaya, Selangor

Location

Menara Centara, Level 20 Unit 1, 360, Jalan Tuanku Abdul Rahman Kuala Lumpur 50100

Email Address

info@advintek.com.my

Phone Number

03 8688 3871

[kevinmarville]

Php clone Netflix

Link: open.substack.com/pub/hellointerview/p/system-design-lessons-from-netflixs Clone: <?php // index.php (the main entry point) // Include necessary files require_once ‘config.php’; require_once ‘database.php’; require_once ‘movie.php’; require_once ‘user.php’; require_once ‘search.php’; require_once ‘recommendations.php’; // Start the session session_start(); // Check if the user is logged…

[jcmarchi]

Asynchronous LLM API Calls in Python: A Comprehensive Guide

New Post has been published on https://thedigitalinsider.com/asynchronous-llm-api-calls-in-python-a-comprehensive-guide/

Asynchronous LLM API Calls in Python: A Comprehensive Guide

As developers and dta scientists, we often find ourselves needing to interact with these powerful models through APIs. However, as our applications grow in complexity and scale, the need for efficient and performant API interactions becomes crucial. This is where asynchronous programming shines, allowing us to maximize throughput and minimize latency when working with LLM APIs.

In this comprehensive guide, we’ll explore the world of asynchronous LLM API calls in Python. We’ll cover everything from the basics of asynchronous programming to advanced techniques for handling complex workflows. By the end of this article, you’ll have a solid understanding of how to leverage asynchronous programming to supercharge your LLM-powered applications.

Before we dive into the specifics of async LLM API calls, let’s establish a solid foundation in asynchronous programming concepts.

Asynchronous programming allows multiple operations to be executed concurrently without blocking the main thread of execution. In Python, this is primarily achieved through the asyncio module, which provides a framework for writing concurrent code using coroutines, event loops, and futures.

Key concepts:

Coroutines: Functions defined with async def that can be paused and resumed.

Event Loop: The central execution mechanism that manages and runs asynchronous tasks.

Awaitables: Objects that can be used with the await keyword (coroutines, tasks, futures).

Here’s a simple example to illustrate these concepts:

import asyncio async def greet(name): await asyncio.sleep(1) # Simulate an I/O operation print(f"Hello, name!") async def main(): await asyncio.gather( greet("Alice"), greet("Bob"), greet("Charlie") ) asyncio.run(main())

In this example, we define an asynchronous function greet that simulates an I/O operation with asyncio.sleep(). The main function uses asyncio.gather() to run multiple greetings concurrently. Despite the sleep delay, all three greetings will be printed after approximately 1 second, demonstrating the power of asynchronous execution.

The Need for Async in LLM API Calls

When working with LLM APIs, we often encounter scenarios where we need to make multiple API calls, either in sequence or parallel. Traditional synchronous code can lead to significant performance bottlenecks, especially when dealing with high-latency operations like network requests to LLM services.

Consider a scenario where we need to generate summaries for 100 different articles using an LLM API. With a synchronous approach, each API call would block until it receives a response, potentially taking several minutes to complete all requests. An asynchronous approach, on the other hand, allows us to initiate multiple API calls concurrently, dramatically reducing the overall execution time.

Setting Up Your Environment

To get started with async LLM API calls, you’ll need to set up your Python environment with the necessary libraries. Here’s what you’ll need:

Python 3.7 or higher (for native asyncio support)

aiohttp: An asynchronous HTTP client library

openai: The official OpenAI Python client (if you’re using OpenAI’s GPT models)

langchain: A framework for building applications with LLMs (optional, but recommended for complex workflows)

You can install these dependencies using pip:

pip install aiohttp openai langchain <div class="relative flex flex-col rounded-lg">

Basic Async LLM API Calls with asyncio and aiohttp

Let’s start by making a simple asynchronous call to an LLM API using aiohttp. We’ll use OpenAI’s GPT-3.5 API as an example, but the concepts apply to other LLM APIs as well.

import asyncio import aiohttp from openai import AsyncOpenAI async def generate_text(prompt, client): response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) return response.choices[0].message.content async def main(): prompts = [ "Explain quantum computing in simple terms.", "Write a haiku about artificial intelligence.", "Describe the process of photosynthesis." ] async with AsyncOpenAI() as client: tasks = [generate_text(prompt, client) for prompt in prompts] results = await asyncio.gather(*tasks) for prompt, result in zip(prompts, results): print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

In this example, we define an asynchronous function generate_text that makes a call to the OpenAI API using the AsyncOpenAI client. The main function creates multiple tasks for different prompts and uses asyncio.gather() to run them concurrently.

This approach allows us to send multiple requests to the LLM API simultaneously, significantly reducing the total time required to process all prompts.

Advanced Techniques: Batching and Concurrency Control

While the previous example demonstrates the basics of async LLM API calls, real-world applications often require more sophisticated approaches. Let’s explore two important techniques: batching requests and controlling concurrency.

Batching Requests: When dealing with a large number of prompts, it’s often more efficient to batch them into groups rather than sending individual requests for each prompt. This reduces the overhead of multiple API calls and can lead to better performance.

import asyncio from openai import AsyncOpenAI async def process_batch(batch, client): responses = await asyncio.gather(*[ client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) for prompt in batch ]) return [response.choices[0].message.content for response in responses] async def main(): prompts = [f"Tell me a fact about number i" for i in range(100)] batch_size = 10 async with AsyncOpenAI() as client: results = [] for i in range(0, len(prompts), batch_size): batch = prompts[i:i+batch_size] batch_results = await process_batch(batch, client) results.extend(batch_results) for prompt, result in zip(prompts, results): print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

Concurrency Control: While asynchronous programming allows for concurrent execution, it’s important to control the level of concurrency to avoid overwhelming the API server or exceeding rate limits. We can use asyncio.Semaphore for this purpose.

import asyncio from openai import AsyncOpenAI async def generate_text(prompt, client, semaphore): async with semaphore: response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) return response.choices[0].message.content async def main(): prompts = [f"Tell me a fact about number i" for i in range(100)] max_concurrent_requests = 5 semaphore = asyncio.Semaphore(max_concurrent_requests) async with AsyncOpenAI() as client: tasks = [generate_text(prompt, client, semaphore) for prompt in prompts] results = await asyncio.gather(*tasks) for prompt, result in zip(prompts, results): print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

In this example, we use a semaphore to limit the number of concurrent requests to 5, ensuring we don’t overwhelm the API server.

Error Handling and Retries in Async LLM Calls

When working with external APIs, it’s crucial to implement robust error handling and retry mechanisms. Let’s enhance our code to handle common errors and implement exponential backoff for retries.

import asyncio import random from openai import AsyncOpenAI from tenacity import retry, stop_after_attempt, wait_exponential class APIError(Exception): pass @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10)) async def generate_text_with_retry(prompt, client): try: response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) return response.choices[0].message.content except Exception as e: print(f"Error occurred: e") raise APIError("Failed to generate text") async def process_prompt(prompt, client, semaphore): async with semaphore: try: result = await generate_text_with_retry(prompt, client) return prompt, result except APIError: return prompt, "Failed to generate response after multiple attempts." async def main(): prompts = [f"Tell me a fact about number i" for i in range(20)] max_concurrent_requests = 5 semaphore = asyncio.Semaphore(max_concurrent_requests) async with AsyncOpenAI() as client: tasks = [process_prompt(prompt, client, semaphore) for prompt in prompts] results = await asyncio.gather(*tasks) for prompt, result in results: print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

This enhanced version includes:

A custom APIError exception for API-related errors.

A generate_text_with_retry function decorated with @retry from the tenacity library, implementing exponential backoff.

Error handling in the process_prompt function to catch and report failures.

Optimizing Performance: Streaming Responses

For long-form content generation, streaming responses can significantly improve the perceived performance of your application. Instead of waiting for the entire response, you can process and display chunks of text as they become available.

import asyncio from openai import AsyncOpenAI async def stream_text(prompt, client): stream = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt], stream=True ) full_response = "" async for chunk in stream: if chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content full_response += content print(content, end='', flush=True) print("n") return full_response async def main(): prompt = "Write a short story about a time-traveling scientist." async with AsyncOpenAI() as client: result = await stream_text(prompt, client) print(f"Full response:nresult") asyncio.run(main())

This example demonstrates how to stream the response from the API, printing each chunk as it arrives. This approach is particularly useful for chat applications or any scenario where you want to provide real-time feedback to the user.

Building Async Workflows with LangChain

For more complex LLM-powered applications, the LangChain framework provides a high-level abstraction that simplifies the process of chaining multiple LLM calls and integrating other tools. Let’s look at an example of using LangChain with async capabilities:

This example shows how LangChain can be used to create more complex workflows with streaming and asynchronous execution. The AsyncCallbackManager and StreamingStdOutCallbackHandler enable real-time streaming of the generated content.

import asyncio from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from langchain.chains import LLMChain from langchain.callbacks.manager import AsyncCallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler async def generate_story(topic): llm = OpenAI(temperature=0.7, streaming=True, callback_manager=AsyncCallbackManager([StreamingStdOutCallbackHandler()])) prompt = PromptTemplate( input_variables=["topic"], template="Write a short story about topic." ) chain = LLMChain(llm=llm, prompt=prompt) return await chain.arun(topic=topic) async def main(): topics = ["a magical forest", "a futuristic city", "an underwater civilization"] tasks = [generate_story(topic) for topic in topics] stories = await asyncio.gather(*tasks) for topic, story in zip(topics, stories): print(f"nTopic: topicnStory: storyn'='*50n") asyncio.run(main())

Serving Async LLM Applications with FastAPI

To make your async LLM application available as a web service, FastAPI is an great choice due to its native support for asynchronous operations. Here’s an example of how to create a simple API endpoint for text generation:

from fastapi import FastAPI, BackgroundTasks from pydantic import BaseModel from openai import AsyncOpenAI app = FastAPI() client = AsyncOpenAI() class GenerationRequest(BaseModel): prompt: str class GenerationResponse(BaseModel): generated_text: str @app.post("/generate", response_model=GenerationResponse) async def generate_text(request: GenerationRequest, background_tasks: BackgroundTasks): response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": request.prompt] ) generated_text = response.choices[0].message.content # Simulate some post-processing in the background background_tasks.add_task(log_generation, request.prompt, generated_text) return GenerationResponse(generated_text=generated_text) async def log_generation(prompt: str, generated_text: str): # Simulate logging or additional processing await asyncio.sleep(2) print(f"Logged: Prompt 'prompt' generated text of length len(generated_text)") if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)

This FastAPI application creates an endpoint /generate that accepts a prompt and returns generated text. It also demonstrates how to use background tasks for additional processing without blocking the response.

Best Practices and Common Pitfalls

As you work with async LLM APIs, keep these best practices in mind:

Use connection pooling: When making multiple requests, reuse connections to reduce overhead.

Implement proper error handling: Always account for network issues, API errors, and unexpected responses.

Respect rate limits: Use semaphores or other concurrency control mechanisms to avoid overwhelming the API.

Monitor and log: Implement comprehensive logging to track performance and identify issues.

Use streaming for long-form content: It improves user experience and allows for early processing of partial results.

[mourning-again-in-america]

checked errors expressed via sum-types is so good, especially if there's some species of inheritance for easy sum-type composition. I don't have to know what the hell any of the names mean but my coworkers can assign personal meaning to them, if I want to know what behavior triggers it I can just. yanno. ripgrep for throw @Error {whatever structured logging needs}

[triviallytrue]

i am not really interested in game development but i am interested in modding (or more specifically cheat creation) as a specialized case of reverse-engineering and modifying software running on your machine

like okay for a lot of games the devs provide some sort of easy toolkit which lets even relatively nontechnical players write mods, and these are well-documented, and then games which don't have those often have a single-digit number of highly technical modders who figure out how to do injection and create some kind of api for the less technical modders to use, and that api is often pretty well documented, but the process of creating it absolutely isn't

it's even more interesting for cheat development because it's something hostile to the creators of the software, you are actively trying to break their shit and they are trying to stop you, and of course it's basically completely undocumented because cheat developers both don't want competitors and also don't want the game devs to patch their methods....

maybe some of why this is hard is because it's pretty different for different types of games. i think i'm starting to get a handle on how to do it for this one game - so i know there's a way to do packet sniffing on the game, where the game has a dedicated port and it sends tcp packets, and you can use the game's tick system and also a brute-force attack on its very rudimentary encryption to access the raw packets pretty easily.

through trial and error (i assume) people have figured out how to decode the packets and match them up to various ingame events, which is already used in a publicly available open source tool to do stuff like DPS calculation.

i think, without too much trouble, you could probably step this up and intercept/modify existing packets? like it looks like while damage is calculated on the server side, whether or not you hit an enemy is calculated on the client side and you could maybe modify it to always hit... idk.

apparently the free cheats out there (which i would not touch with a 100 foot pole, odds those have something in them that steals your login credentials is close to 100%) operate off a proxy server model, which i assume intercepts your packets, modifies them based on what cheats you tell it you have active, and then forwards them to the server.

but they also manage to give you an ingame GUI to create those cheats, which is clearly something i don't understand. the foss sniffer opens itself up in a new window instead of modifying the ingame GUI.

man i really want to like. shadow these guys and see their dev process for a day because i'm really curious. and also read their codebase. but alas

[keploy]

AI Code Generators: Revolutionizing Software Development

The way we write code is evolving. Thanks to advancements in artificial intelligence, developers now have tools that can generate entire code snippets, functions, or even applications. These tools are known as AI code generators, and they’re transforming how software is built, tested, and deployed.

In this article, we’ll explore AI code generators, how they work, their benefits and limitations, and the best tools available today.

What Are AI Code Generators?

AI code generators are tools powered by machine learning models (like OpenAI's GPT, Meta’s Code Llama, or Google’s Gemini) that can automatically write, complete, or refactor code based on natural language instructions or existing code context.

Instead of manually writing every line, developers can describe what they want in plain English, and the AI tool translates that into functional code.

How AI Code Generators Work

These generators are built on large language models (LLMs) trained on massive datasets of public code from platforms like GitHub, Stack Overflow, and documentation. The AI learns:

Programming syntax

Common patterns

Best practices

Contextual meaning of user input

By processing this data, the generator can predict and output relevant code based on your prompt.

Benefits of AI Code Generators

1. Faster Development

Developers can skip repetitive tasks and boilerplate code, allowing them to focus on core logic and architecture.

2. Increased Productivity

With AI handling suggestions and autocompletions, teams can ship code faster and meet tight deadlines.

3. Fewer Errors

Many generators follow best practices, which helps reduce syntax errors and improve code quality.

4. Learning Support

AI tools can help junior developers understand new languages, patterns, and libraries.

5. Cross-language Support

Most tools support multiple programming languages like Python, JavaScript, Go, Java, and TypeScript.

Popular AI Code Generators

Tool

Highlights

GitHub Copilot

Powered by OpenAI Codex, integrates with VSCode and JetBrains IDEs

Amazon CodeWhisperer

AWS-native tool for generating and securing code

Tabnine

Predictive coding with local + cloud support

Replit Ghostwriter

Ideal for building full-stack web apps in the browser

Codeium

Free and fast with multi-language support

Keploy

AI-powered test case and stub generator for APIs and microservices

Use Cases for AI Code Generators

Writing functions or modules quickly

Auto-generating unit and integration tests

Refactoring legacy code

Building MVPs with minimal manual effort

Converting code between languages

Documenting code automatically

Example: Generate a Function in Python

Prompt: "Write a function to check if a number is prime"

AI Output:

python

CopyEdit

def is_prime(n):

    if n <= 1:

        return False

    for i in range(2, int(n**0.5) + 1):

        if n % i == 0:

            return False

    return True

In seconds, the generator creates a clean, functional block of code that can be tested and deployed.

Challenges and Limitations

Security Risks: Generated code may include unsafe patterns or vulnerabilities.

Bias in Training Data: AI can replicate errors or outdated practices present in its training set.

Over-reliance: Developers might accept code without fully understanding it.

Limited Context: Tools may struggle with highly complex or domain-specific tasks.

AI Code Generators vs Human Developers

AI is not here to replace developers—it’s here to empower them. Think of these tools as intelligent assistants that handle the grunt work, while you focus on decision-making, optimization, and architecture.

Human oversight is still critical for:

Validating output

Ensuring maintainability

Writing business logic

Securing and testing code

AI for Test Case Generation

Tools like Keploy go beyond code generation. Keploy can:

Auto-generate test cases and mocks from real API traffic

Ensure over 90% test coverage

Speed up testing for microservices, saving hours of QA time

Keploy bridges the gap between coding and testing—making your CI/CD pipeline faster and more reliable.

Final Thoughts

AI code generators are changing how modern development works. They help save time, reduce bugs, and boost developer efficiency. While not a replacement for skilled engineers, they are powerful tools in any dev toolkit.

The future of software development will be a blend of human creativity and AI-powered automation. If you're not already using AI tools in your workflow, now is the time to explore. Want to test your APIs using AI-generated test cases? Try Keploy and accelerate your development process with confidence.

[easylaunchpad]

💳Integrated Payments with Stripe and Paddle: Inside EasyLaunchpad’s Payment Module

When building a SaaS app, one of the first questions you’ll face is:

How will we charge users?

From recurring subscriptions to one-time payments and license plans, payment infrastructure is mission-critical. But implementing a secure, production-grade system can be time-consuming, tricky, and expensive.

That’s why EasyLaunchpad includes a fully integrated payment module with support for Stripe and Paddle — out of the box.

In this article, we’ll walk you through how EasyLaunchpad handles payments, how it simplifies integration with major processors, and how it helps you monetize your product from day one.

💡 The Problem: Payment Integration Is Hard

On paper, adding Stripe or Paddle looks easy. In reality, it involves:

API authentication

Checkout flows

Webhook validation

Error handling

Subscription plan logic

Admin-side controls

Syncing with your front-end or product logic

That’s a lot to build before you ever collect your first dollar.

EasyLaunchpad solves this by offering a turnkey payment solution that integrates Stripe and Paddle seamlessly into backend logic and your admin panel.

⚙️ What’s Included in the Payment Module?

The EasyLaunchpad payment module covers everything a SaaS app needs to start selling:

Feature and Description:

✅ Stripe & Paddle APIs- Integrated SDKs with secure API keys managed via config

✅ Plan Management- Define your product plans via admin panel

✅ License/Package Linking- Link Stripe/Paddle plans to system logic (e.g., access control)

✅ Webhook Support- Process events like successful payments, cancellations, renewals

✅ Email Triggers- Send receipts and billing notifications automatically

✅ Logging & Retry Logic- Serilog + Hangfire for reliability and transparency

💳 Stripe Integration in .NET Core (Prebuilt)

Stripe is the most popular payment solution for modern SaaS businesses. EasyLaunchpad comes with:

Stripe.NET SDK is configured and ready to use

Test & production API key support via appsettings.json

Built-in handlers for:

Checkout Session Creation

Payment Success

Subscription Renewal

Customer Cancellations

No need to write custom middleware or webhook processors. It’s all wired up.

🔁 How the Flow Works (Stripe)

The user selects a plan on your website

The checkout session is created via Stripe API

Stripe redirects the user to a secure payment page

Upon success, EasyLaunchpad receives a webhook event

User’s plan is activated + confirmation email is sent

Logs are stored for reporting and debugging

🧾 Paddle Integration for Global Sellers

Paddle is often a better fit than Stripe for developers targeting international customers or needing EU/GST compliance.

EasyLaunchpad supports Paddle’s:

Inline Checkout and Overlay Widgets

Subscription Plans and One-Time Payments

Webhook Events (license provisioning, payment success, cancellations)

VAT/GST compliance without custom work

All integration is handled via modular service classes. You can switch or run both providers side-by-side.

🔧 Configuration Example

In appsettings.json, you simply configure:

“Payments”: {

“Provider”: “Stripe”, // or “Paddle”

“Stripe”: {

“SecretKey”: “sk_test_…”,

“PublishableKey”: “pk_test_…”

},

“Paddle”: {

“VendorId”: “123456”,

“APIKey”: “your-api-key”

}

}

The correct payment provider is loaded automatically using dependency injection via Autofac.

🧩 Admin Panel: Manage Plans Without Touching Code

EasyLaunchpad’s admin panel includes:

A visual interface to create/edit plans

Fields for price, duration, description, external plan ID (Stripe/Paddle)

Activation/deactivation toggle

Access scope definition (used to unlock features via roles or usage limits)

You can:

Add a Pro Plan for $29/month

Add a Lifetime Deal with a one-time Paddle payment

Deactivate free trial access — all without writing new logic

🧪 Webhook Events Handled Securely

Stripe and Paddle send webhook events for:

New subscriptions

Payment failures

Plan cancellations

Upgrades/downgrades

EasyLaunchpad includes secure webhook controllers to:

Verify authenticity

Parse payloads

Trigger internal actions (e.g., assign new role, update access rights)

Log and retry failed handlers using Hangfire

You get reliable, observable payment handling with no guesswork.

📬 Email Notifications

After a successful payment, EasyLaunchpad:

Sends a confirmation email using DotLiquid templates

Updates user records

Logs the transaction with Serilog

The email system can be extended to send:

Trial expiration reminders

Invoice summaries

Cancellation win-back campaigns

📈 Logging & Monitoring

Every payment-related action is logged with Serilog:

{

“Timestamp”: “2024–07–15T12:45:23Z”,

“Level”: “Information”,

“Message”: “User subscribed to Pro Plan via Stripe”,

“UserId”: “abc123”,

“Amount”: “29.00”

}

Hangfire queues and retries any failed webhook calls, so you never miss a critical event.

🔌 Use Cases You Can Launch Today

EasyLaunchpad’s payment module supports a variety of business models:

Model and the Example:

SaaS Subscriptions- $9/mo, $29/mo, custom plans

Lifetime Licenses- One-time Paddle payments

Usage-Based Billing — Extend by customizing webhook logic

Freemium to Paid Upgrades — Upgrade plan from admin or front-end

Multi-tier Plans- Feature gating via linked roles/packages

🧠 Why It’s Better Than DIY

With EasyLaunchpad and Without EasyLaunchpad

Stripe & Paddle already integrated- Spend weeks wiring up APIs

Admin interface to manage plans- Hardcode JSON or use raw SQL

Background jobs for webhooks- Risk of losing data on failed calls

Modular services — Spaghetti logic in controller actions

Email receipts & logs- Manually build custom mailers

🧠 Final Thoughts

If you’re building a SaaS product, monetization can’t wait. You need a secure, scalable, and flexible payment system on day one.

EasyLaunchpad gives you exactly that:

✅ Pre-integrated Stripe & Paddle

✅ Admin-side plan management

✅ Real-time email & logging

✅ Full webhook support

✅ Ready to grow with your product

👉 Start charging your users — not building billing logic. Get EasyLaunchpad today at: https://easylaunchpad.com

[maxsmith007-blog]

How Can Legacy Application Support Align with Your Long-Term Business Goals?

Many businesses still rely on legacy applications to run core operations. These systems, although built on older technology, are deeply integrated with workflows, historical data, and critical business logic. Replacing them entirely can be expensive and disruptive. Instead, with the right support strategy, these applications can continue to serve long-term business goals effectively.

1. Ensure Business Continuity

Continuous service delivery is one of the key business objectives of any enterprise. Maintenance of old applications guarantees business continuity, which minimizes chances of business interruption in case of software malfunctions or compatibility errors. These applications can be made to work reliably with modern support strategies such as performance monitoring, frequent patching, system optimization, despite changes in the rest of the system changes in the rest of the systems. This prevents the lost revenue and downtime of unplanned outages.

2. Control IT Costs

A straight replacement of the legacy systems is a capital intensive process. By having support structures, organizations are in a position to prolong the life of such applications and ensure an optimal IT expenditure. The cost saved can be diverted into innovation or into technologies that interact with the customers. An effective support strategy manages the total cost of ownership (TCO), without sacrificing performance or compliance.

3. Stay Compliant and Secure

The observance of industry regulations is not negotiable. Unsupported legacy application usually fall out of compliance with standards changes. This is handled by dedicated legacy application support which incorporates security updates, compliances patching and audit trails maintenance. This minimizes the risks of regulatory fines and reputational loss as well as governance and risk management objectives.

4. Connect with Modern Tools

Legacy support doesn’t mean working in isolation. With the right approach, these systems can connect to cloud platforms, APIs, and data tools. This enables real-time reporting, improved collaboration, and more informed decision-making—without requiring full system replacements.

5. Protect Business Knowledge

The legacy systems often contain years of institutional knowledge built into workflows, decision trees, and data architecture. They should not be abandoned early because vital operational insights may be lost. Maintaining these systems enables enterprises to keep that knowledge and transform it into documentation or reusable code aligned with ongoing digital transformation initiatives.

6. Support Scalable Growth

Well-supported legacy systems can still grow with your business. With performance tuning and capacity planning, they can handle increased demand and user loads. This keeps growth on track without significant disruption to IT systems.

7. Increase Flexibility and Control

Maintaining legacy application—either in-house or through trusted partners—gives businesses more control over their IT roadmap. It avoids being locked into aggressive vendor timelines and allows change to happen on your terms.

Legacy applications don’t have to be a roadblock. With the right support model, they become a stable foundation that supports long-term goals. From cost control and compliance to performance and integration, supported legacy systems can deliver measurable value. Specialized Legacy Application Maintenance Services are provided by service vendors such as Suma Soft, TCS, Infosys, Capgemini,  and HCLTech, to enable businesses to get the best out of their current systems, as they prepare to transform in the future. Choosing the appropriate partner will maintain these systems functioning, developing and integrated with wider business strategies.

[niotechone]

Cloud Computing: Definition, Benefits, Types, and Real-World Applications

In the fast-changing digital world, companies require software that matches their specific ways of working, aims and what their customers require. That’s when you need custom software development services. Custom software is made just for your organization, so it is more flexible, scalable and efficient than generic software.

What does Custom Software Development mean?

Custom software development means making, deploying and maintaining software that is tailored to a specific user, company or task. It designs custom Software Development Services: Solutions Made Just for Your Business to meet specific business needs, which off-the-shelf software usually cannot do.

The main advantages of custom software development are listed below.

1. Personalized Fit

Custom software is built to address the specific needs of your business. Everything is designed to fit your workflow, whether you need it for customers, internal tasks or industry-specific functions.

2. Scalability

When your business expands, your software can also expand. You can add more features, users and integrations as needed without being bound by strict licensing rules.

3. Increased Efficiency

Use tools that are designed to work well with your processes. Custom software usually automates tasks, cuts down on repetition and helps people work more efficiently.

4. Better Integration

Many companies rely on different tools and platforms. You can have custom software made to work smoothly with your CRMs, ERPs and third-party APIs.

5. Improved Security

You can set up security measures more effectively in a custom solution. It is particularly important for industries that handle confidential information, such as finance, healthcare or legal services.

Types of Custom Software Solutions That Are Popular

CRM Systems

Inventory and Order Management

Custom-made ERP Solutions

Mobile and Web Apps

eCommerce Platforms

AI and Data Analytics Tools

SaaS Products

The Process of Custom Development

Requirement Analysis

Being aware of your business goals, what users require and the difficulties you face in running the business.

Design & Architecture

Designing a software architecture that can grow, is safe and fits your requirements.

Development & Testing

Writing code that is easy to maintain and testing for errors, speed and compatibility.

Deployment and Support

Making the software available and offering support and updates over time.

What Makes Niotechone a Good Choice?

Our team at Niotechone focuses on providing custom software that helps businesses grow. Our team of experts works with you throughout the process, from the initial idea to the final deployment, to make sure the product is what you require.

Successful experience in various industries

Agile development is the process used.

Support after the launch and options for scaling

Affordable rates and different ways to work together

Final Thoughts

Creating custom software is not only about making an app; it’s about building a tool that helps your business grow. A customized solution can give you the advantage you require in the busy digital market, no matter if you are a startup or an enterprise.

[orbitwebtech]

Ready to future-proof your applications and boost performance? Discover how PHP microservices can transform your development workflow! 💡

In this powerful guide, you'll learn: ✅ What PHP Microservices Architecture really means ✅ How to break a monolithic app into modular services ✅ Best tools for containerization like Docker & Kubernetes ✅ API Gateway strategies and service discovery techniques ✅ Tips on error handling, security, and performance optimization

With real-world examples and practical steps, this guide is perfect for developers and teams aiming for faster deployment, independent scaling, and simplified maintenance.

🎯 Whether you’re a solo developer or scaling a product, understanding microservices is the key to next-level architecture.

🌐 Brought to you by Orbitwebtech, Best Web Development Company in the USA, helping businesses build powerful and scalable web solutions.

📖 Start reading now and give your PHP projects a cutting-edge upgrade!

[hiringjournal]

Interview Questions to Ask When Hiring a .NET Developer

The success of your enterprise or web apps can be significantly impacted by your choice of .NET developer. Making the correct decision during interviews is crucial because .NET is a powerful framework that is utilized in a variety of industries, including finance and e-commerce. Dot Net engineers that are not only familiar with the framework but also have the ability to precisely and clearly apply it to real-world business problems are sought after by many software businesses.

These essential questions will assist you in evaluating candidates' technical proficiency, coding style, and compatibility with your development team as you get ready to interview them for your upcoming project.

Assessing Technical Skills, Experience, and Real-World Problem Solving

What experience do you have with the .NET ecosystem?

To find out how well the candidate understands .NET Core, ASP.NET MVC, Web API, and associated tools, start with a general question. Seek answers that discuss actual projects and real-world applications rather than only theory.

Follow-up: What version of .NET are you using right now, and how do you manage updates in real-world settings?

Experience with more recent versions, such as .NET 6 or .NET 8, can result in fewer compatibility problems and improved performance when hiring Dot Net developers.

How do you manage dependency injection in .NET applications?

One essential component of the scalable .NET design is dependency injection. An excellent applicant will discuss built-in frameworks, how they register services, and how they enhance modularity and testability.

Can you explain the difference between synchronous and asynchronous programming in .NET?

Performance is enhanced by asynchronous programming, particularly in microservices and backend APIs. Seek a concise description and examples that make use of Task, ConfigureAwait, or async/await.

Advice: When hiring backend developers, candidates who are aware of async patterns are more likely to create apps that are more efficient.

What tools do you use for debugging and performance monitoring?

Skilled developers know how to optimize code in addition to writing it. Check for references to Postman, Application Insights, Visual Studio tools, or profiling tools such as dotTrace.

This demonstrates the developer's capacity to manage problems with live production and optimize performance.

How do you write unit and integration tests for your .NET applications?

Enterprise apps require testing. A trustworthy developer should be knowledgeable about test coverage, mocking frameworks, and tools like xUnit, NUnit, or MSTest.

Hiring engineers with strong testing practices helps tech organizations avoid expensive errors later on when delivering goods on short notice.

Describe a time you optimized a poorly performing .NET application.

This practical question evaluates communication and problem-solving abilities. Seek solutions that involve database query optimization, code modification, or profiling.

Are you familiar with cloud deployment for .NET apps?

Now that a lot of apps are hosted on AWS or Azure, find out how they handle cloud environments. Seek expertise in CI/CD pipelines, containers, or Azure App Services.

This is particularly crucial if you want to work with Dot Net developers to create scalable, long-term solutions.

Final Thoughts

You may learn more about a developer's thought process, problem-solving techniques, and ability to operate under pressure via a well-structured interview. These questions provide a useful method to confidently assess applicants if you intend to hire Dot Net developers for intricate or high-volume projects.

The ideal .NET hire for expanding tech organizations does more than just write code; they create the framework around which your products are built.

[this-week-in-rust]

This Week in Rust 599

Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @thisweekinrust.bsky.social on Bluesky or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

Want TWIR in your inbox? Subscribe here.

Updates from Rust Community

Official

Announcing Google Summer of Code 2025 selected projects

Foundation

10 Years of Stable Rust: An Infrastructure Story

Newsletters

This Month in Rust OSDev: April 2025 | Rust OSDev

The Embedded Rustacean Issue #45

Project/Tooling Updates

Avian Physics 0.3

Two months in Servo: CSS nesting, Shadow DOM, Clipboard API, and more

Cot v0.3: Even Lazier

Streaming data analytics, Fluvio 0.17.3 release

CGP v0.4 is Here: Unlocking Easier Debugging, Extensible Presets, and More

Rama v0.2

Observations/Thoughts

Bad Type Patterns - The Duplicate duck

Rust nightly features you should watch out for

Lock-Free Rust: How to Build a Rollercoaster While It’s on Fire

Simple & type-safe localization in Rust

From Rust to AVR assembly: Dissecting a minimal blinky program

Tarpaulins Week Of Speed

Rustls Server-Side Performance

Is Rust the Future of Programming?

Rust Walkthroughs

Functional asynchronous Rust

The Power of Compile-Time ECS Architecture in Rust

[video] Build with Naz : Spinner animation, lock contention, Ctrl+C handling for TUI and CLI

Miscellaneous

April 2025 Rust Jobs Report

Crate of the Week

This week's crate is brush, a bash compatible shell implemented completely in Rust.

Thanks to Josh Triplett for the suggestion!

Please submit your suggestions and votes for next week!

Calls for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization.

If you are a feature implementer and would like your RFC to appear in this list, add a call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

No calls for testing were issued this week by Rust, Rust language RFCs or Rustup.

Let us know if you would like your feature to be tracked as a part of this list.

RFCs

Rust

Rustup

If you are a feature implementer and would like your RFC to appear on the above list, add the new call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

Call for Participation; projects and speakers

CFP - Projects

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

rama - add ffi/rama-rhai: support ability to use services and layers written in rhai

rama - support akamai h2 passive fingerprint and expose in echo + fp services

If you are a Rust project owner and are looking for contributors, please submit tasks here or through a PR to TWiR or by reaching out on X (formerly Twitter) or Mastodon!

CFP - Events

Are you a new or experienced speaker looking for a place to share something cool? This section highlights events that are being planned and are accepting submissions to join their event as a speaker.

No Calls for papers or presentations were submitted this week.

If you are an event organizer hoping to expand the reach of your event, please submit a link to the website through a PR to TWiR or by reaching out on X (formerly Twitter) or Mastodon!

Updates from the Rust Project

397 pull requests were merged in the last week

Compiler

async drop fix for async_drop_in_place<T> layout for unspecified T

better error message for late/early lifetime param mismatch

perf: make the assertion in Ident::new debug-only

perf: merge typeck loop with static/const item eval loop

Library

implement (part of) ACP 429: add DerefMut to Lazy[Cell/Lock]

implement VecDeque::truncate_front()

Cargo

network: use Retry-After header for HTTP 429 responses

rustc: Don't panic on unknown bins

add glob pattern support for known_hosts

add support for -Zembed-metadata

fix tracking issue template link

make cargo script ignore workspaces

Rustdoc

rustdoc-json: remove newlines from attributes

ensure that temporary doctest folder is correctly removed even if doctests failed

Clippy

clippy: item_name_repetitions: exclude enum variants with identical path components

clippy: return_and_then: only lint returning expressions

clippy: unwrap_used, expect_used: accept macro result as receiver

clippy: add allow_unused config to missing_docs_in_private_items

clippy: add new confusing_method_to_numeric_cast lint

clippy: add new lint: cloned_ref_to_slice_refs

clippy: fix ICE in missing_const_for_fn

clippy: fix integer_division false negative for NonZero denominators

clippy: fix manual_let_else false negative when diverges on simple enum variant

clippy: fix unnecessary_unwrap emitted twice in closure

clippy: fix diagnostic paths printed by dogfood test

clippy: fix false negative for unnecessary_unwrap

clippy: make let_with_type_underscore help message into a suggestion

clippy: resolve through local re-exports in lookup_path

Rust-Analyzer

fix postfix snippets duplicating derefs

resolve doc path from parent module if outer comments exist on module

still complete parentheses & method call arguments if there are existing parentheses, but they are after a newline

Rust Compiler Performance Triage

Lot of changes this week. Overall result is positive, with one large win in type check.

Triage done by @panstromek. Revision range: 62c5f58f..718ddf66

Summary:

(instructions:u) mean range count Regressions ❌ (primary) 0.5% [0.2%, 1.4%] 113 Regressions ❌ (secondary) 0.5% [0.1%, 1.5%] 54 Improvements ✅ (primary) -2.5% [-22.5%, -0.3%] 45 Improvements ✅ (secondary) -0.9% [-2.3%, -0.2%] 10 All ❌✅ (primary) -0.3% [-22.5%, 1.4%] 158

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

No RFCs were approved this week.

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

Tracking Issues & PRs

Rust

Tracking Issue for non_null_from_ref

Add std::io::Seek instance for std::io::Take

aarch64-softfloat: forbid enabling the neon target feature

Stabilize the avx512 target features

make std::intrinsics functions actually be intrinsics

Error on recursive opaque ty in HIR typeck

Remove i128 and u128 from improper_ctypes_definitions

Guarantee behavior of transmuting Option::<T>::None subject to NPO

Temporary lifetime extension through tuple struct and tuple variant constructors

Stabilize tcp_quickack

Change the desugaring of assert! for better error output

Make well-formedness predicates no longer coinductive

No Items entered Final Comment Period this week for Cargo, Rust RFCs, Language Reference, Language Team or Unsafe Code Guidelines.

Let us know if you would like your PRs, Tracking Issues or RFCs to be tracked as a part of this list.

New and Updated RFCs

RFC: Extended Standard Library (ESL)

Upcoming Events

Rusty Events between 2025-05-14 - 2025-06-11 🦀

Virtual

2025-05-15 | Hybrid (Redmond, WA, US) | Seattle Rust User Group

May, 2025 SRUG (Seattle Rust User Group) Meetup

2025-05-15 | Virtual (Girona, ES) | Rust Girona

Sessió setmanal de codificació / Weekly coding session

2025-05-15 | Virtual (Joint Meetup, Europe + Israel) | Rust Berlin + Rust Paris + London Rust Project Group + Rust Zürisee + Rust TLV + Rust Nürnberg + Rust Munich + Rust Aarhus + lunch.rs

🦀 Celebrating 10 years of Rust 1.0 🦀

2025-05-15 | Virtual (Zürich, CH) | Rust Zürisee

🦀 Celebrating 10 years of Rust 1.0 (co-event with berline.rs) 🦀

2025-05-18 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup

Rust Readers Discord Discussion: Async Rust

2025-05-19 | Virtual (Tel Aviv-yafo, IL) | Rust 🦀 TLV

Tauri: Cross-Platform desktop applications with Rust and web technologies

2025-05-20 | Hybrid (EU/UK) | Rust and C++ Dragons (former Cardiff)

Talk and Connect - Fullstack - with Goetz Markgraf and Ben Wishovich

2025-05-20 | Virtual (London, UK) | Women in Rust

Threading through lifetimes of borrowing - the Rust way

2025-05-20 | Virtual (Tel Aviv, IL) | Code Mavens 🦀 - 🐍 - 🐪

Rust at Work a conversation with Ran Reichman Co-Founder & CEO of Flarion

2025-05-20 | Virtual (Washington, DC, US) | Rust DC

Mid-month Rustful

2025-05-21 | Hybrid (Vancouver, BC, CA) | Vancouver Rust

Linking

2025-05-22 | Virtual (Berlin, DE) | Rust Berlin

Rust Hack and Learn

2025-05-22 | Virtual (Girona, ES) | Rust Girona

Sessió setmanal de codificació / Weekly coding session

2025-05-25 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup

Rust Readers Discord Discussion: Async Rust

2025-05-27 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup

Fourth Tuesday

2025-05-27 | Virtual (Tel Aviv, IL) | Code Mavens 🦀 - 🐍 - 🐪

Rust at Work - conversation with Eli Shalom & Igal Tabachnik of Eureka Labs

2025-05-29 | Virtual (Nürnberg, DE) | Rust Nuremberg

Rust Nürnberg online

2025-05-29 | Virtual (Tel Aviv-yafo, IL) | Rust 🦀 TLV

שיחה חופשית ווירטואלית על ראסט

2025-06-01 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup

Rust Readers Discord Discussion: Async Rust

2025-06-03 | Virtual (Tel Aviv-yafo, IL) | Rust 🦀 TLV

Why Rust? למה ראסט? -

2025-06-04 | Virtual (Indianapolis, IN, US) | Indy Rust

Indy.rs - with Social Distancing

2025-06-05 | Virtual (Berlin, DE) | Rust Berlin

Rust Hack and Learn

2025-06-07 | Virtual (Kampala, UG) | Rust Circle Meetup

Rust Circle Meetup

2025-06-08 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup

Rust Readers Discord Discussion: Async Rust

2025-06-10 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup

Second Tuesday

2025-06-10 | Virtual (London, UK) | Women in Rust

👋 Community Catch Up

Asia

2025-05-17 | Delhi, IN | Rust Delhi

Rust Delhi Meetup #10

2025-05-24 | Bangalore/Bengaluru, IN | Rust Bangalore

May 2025 Rustacean meetup

2025-06-08 | Tel Aviv-yafo, IL | Rust 🦀 TLV

In person Rust June 2025 at AWS in Tel Aviv

Europe

2025-05-13 - 2025-05-17 | Utrecht, NL | Rust NL

RustWeek 2025

2025-05-14 | Reading, UK | Reading Rust Workshop

Reading Rust Meetup

2025-05-15 | Berlin, DE | Rust Berlin

10 years anniversary of Rust 1.0

2025-05-15 | Oslo, NO | Rust Oslo

Rust 10-year anniversary @ Appear

2025-05-16 | Amsterdam, NL | RustNL

Rust Week Hackathon

2025-05-16 | Utrecht, NL | Rust NL Meetup Group

RustWeek Hackathon

2025-05-17 | Amsterdam, NL | RustNL

Walking Tour around Utrecht - Saturday

2025-05-20 | Dortmund, DE | Rust Dortmund

Talk and Connect - Fullstack - with Goetz Markgraf and Ben Wishovich

2025-05-20 | Aarhus, DK | Rust Aarhus

Hack Night - Robot Edition

2025-05-20 | Leipzig, SN, DE | Rust - Modern Systems Programming in Leipzig

Topic TBD

2025-05-22 | Augsburg, DE | Rust Augsburg

Rust meetup #13:A Practical Guide to Telemetry in Rust

2025-05-22 | Bern, CH | Rust Bern

2025 Rust Talks Bern #3 @zentroom

2025-05-22 | Paris, FR | Rust Paris

Rust meetup #77

2025-05-22 | Stockholm, SE | Stockholm Rust

Rust Meetup @UXStream

2025-05-27 | Basel, CH | Rust Basel

Rust Meetup #11 @ Letsboot Basel

2025-05-27 | Vienna, AT | Rust Vienna

Rust Vienna - May at Bitcredit 🦀

2025-05-29 | Oslo, NO | Rust Oslo

Rust Hack'n'Learn at Kampen Bistro

2025-05-31 | Stockholm, SE | Stockholm Rust

Ferris' Fika Forum #12

2025-06-04 | Ghent, BE | Systems Programming Ghent

Grow smarter with embedded Rust

2025-06-04 | München, DE | Rust Munich

Rust Munich 2025 / 2 - Hacking Evening

2025-06-04 | Oxford, UK | Oxford Rust Meetup Group

Oxford Rust and C++ social

2025-06-05 | München, DE | Rust Munich

Rust Munich 2025 / 2 - Hacking Evening

2025-06-11 | Reading, UK | Reading Rust Workshop

Reading Rust Meetup

North America

2025-05-15 | Hybrid (Redmond, WA, US) | Seattle Rust User Group

May, 2025 SRUG (Seattle Rust User Group) Meetup

2025-05-15 | Mountain View, CA, US | Hacker Dojo

RUST MEETUP at HACKER DOJO

2025-05-15 | Nashville, TN, US | Music City Rust Developers

Using Rust For Web Series 2 : Why you, Yes You. Should use Hyperscript!

2025-05-15 | Hybrid (Redmond, WA, US) | Seattle Rust User Group

May, 2025 SRUG (Seattle Rust User Group) Meetup

2025-05-18 | Albuquerque, NM, US | Ideas and Coffee

Intro Level Rust Get-together

2025-05-20 | San Francisco, CA, US | San Francisco Rust Study Group

Rust Hacking in Person

2025-05-21 | Hybrid (Vancouver, BC, CA) | Vancouver Rust

Linking

2025-05-28 | Austin, TX, US | Rust ATX

Rust Lunch - Fareground

2025-05-29 | Atlanta, GA, US | Rust Atlanta

Rust-Atl

2025-06-05 | Saint Louis, MO, US | STL Rust

Leptos web framework

South America

2025-05-28 | Montevideo, DE, UY | Rust Meetup Uruguay

Primera meetup de Rust de 2025!

2025-05-31 | São Paulo, BR | Rust São Paulo Meetup

Encontro do Rust-SP na WillBank

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

If a Pin drops in a room, and nobody around understands it, does it make an unsound? #rustlang

– Josh Triplett on fedi

Thanks to Josh Triplett for the self-suggestion!

Please submit quotes and vote for next week!

This Week in Rust is edited by: nellshamrell, llogiq, cdmistman, ericseppanen, extrawurst, U007D, joelmarcey, mariannegoldin, bennyvasquez, bdillo

Email list hosting is sponsored by The Rust Foundation

Discuss on r/rust

[aktechworld]

Integrating Third-Party Tools into Your CRM System: Best Practices

A modern CRM is rarely a standalone tool — it works best when integrated with your business's key platforms like email services, accounting software, marketing tools, and more. But improper integration can lead to data errors, system lags, and security risks.

Here are the best practices developers should follow when integrating third-party tools into CRM systems:

1. Define Clear Integration Objectives

Identify business goals for each integration (e.g., marketing automation, lead capture, billing sync)

Choose tools that align with your CRM’s data model and workflows

Avoid unnecessary integrations that create maintenance overhead

2. Use APIs Wherever Possible

Rely on RESTful or GraphQL APIs for secure, scalable communication

Avoid direct database-level integrations that break during updates

Choose platforms with well-documented and stable APIs

Custom CRM solutions can be built with flexible API gateways

3. Data Mapping and Standardization

Map data fields between systems to prevent mismatches

Use a unified format for customer records, tags, timestamps, and IDs

Normalize values like currencies, time zones, and languages

Maintain a consistent data schema across all tools

4. Authentication and Security

Use OAuth2.0 or token-based authentication for third-party access

Set role-based permissions for which apps access which CRM modules

Monitor access logs for unauthorized activity

Encrypt data during transfer and storage

5. Error Handling and Logging

Create retry logic for API failures and rate limits

Set up alert systems for integration breakdowns

Maintain detailed logs for debugging sync issues

Keep version control of integration scripts and middleware

6. Real-Time vs Batch Syncing

Use real-time sync for critical customer events (e.g., purchases, support tickets)

Use batch syncing for bulk data like marketing lists or invoices

Balance sync frequency to optimize server load

Choose integration frequency based on business impact

7. Scalability and Maintenance

Build integrations as microservices or middleware, not monolithic code

Use message queues (like Kafka or RabbitMQ) for heavy data flow

Design integrations that can evolve with CRM upgrades

Partner with CRM developers for long-term integration strategy

CRM integration experts can future-proof your ecosystem

[riazhatvi]

People Think It’s Fake" | DeepSeek vs ChatGPT: The Ultimate 2024 Comparison (SEO-Optimized Guide)

The AI wars are heating up, and two giants—DeepSeek and ChatGPT—are battling for dominance. But why do so many users call DeepSeek "fake" while praising ChatGPT? Is it a myth, or is there truth to the claims? In this deep dive, we’ll uncover the facts, debunk myths, and reveal which AI truly reigns supreme. Plus, learn pro SEO tips to help this article outrank competitors on Google!

Chapters

00:00 Introduction - DeepSeek: China’s New AI Innovation

00:15 What is DeepSeek?

00:30 DeepSeek’s Impressive Statistics

00:50 Comparison: DeepSeek vs GPT-4

01:10 Technology Behind DeepSeek

01:30 Impact on AI, Finance, and Trading

01:50 DeepSeek’s Effect on Bitcoin & Trading

02:10 Future of AI with DeepSeek

02:25 Conclusion - The Future is Here!

Why Do People Call DeepSeek "Fake"? (The Truth Revealed)

The Language Barrier Myth

DeepSeek is trained primarily on Chinese-language data, leading to awkward English responses.

Example: A user asked, "Write a poem about New York," and DeepSeek referenced skyscrapers as "giant bamboo shoots."

SEO Keyword: "DeepSeek English accuracy."

Cultural Misunderstandings

DeepSeek’s humor, idioms, and examples cater to Chinese audiences. Global users find this confusing.

ChatGPT, trained on Western data, feels more "relatable" to English speakers.

Lack of Transparency

Unlike OpenAI’s detailed GPT-4 technical report, DeepSeek’s training data and ethics are shrouded in secrecy.

LSI Keyword: "DeepSeek data sources."

Viral "Fail" Videos

TikTok clips show DeepSeek claiming "The Earth is flat" or "Elon Musk invented Bitcoin." Most are outdated or edited—ChatGPT made similar errors in 2022!

DeepSeek vs ChatGPT: The Ultimate 2024 Comparison

1. Language & Creativity

ChatGPT: Wins for English content (blogs, scripts, code).

Strengths: Natural flow, humor, and cultural nuance.

Weakness: Overly cautious (e.g., refuses to write "controversial" topics).

DeepSeek: Best for Chinese markets (e.g., Baidu SEO, WeChat posts).

Strengths: Slang, idioms, and local trends.

Weakness: Struggles with Western metaphors.

SEO Tip: Use keywords like "Best AI for Chinese content" or "DeepSeek Baidu SEO."

2. Technical Abilities

Coding:

ChatGPT: Solves Python/JavaScript errors, writes clean code.

DeepSeek: Better at Alibaba Cloud APIs and Chinese frameworks.

Data Analysis:

Both handle spreadsheets, but DeepSeek integrates with Tencent Docs.

3. Pricing & Accessibility

FeatureDeepSeekChatGPTFree TierUnlimited basic queriesGPT-3.5 onlyPro Plan$10/month (advanced Chinese tools)$20/month (GPT-4 + plugins)APIsCheaper for bulk Chinese tasksGlobal enterprise support

SEO Keyword: "DeepSeek pricing 2024."

Debunking the "Fake AI" Myth: 3 Case Studies

Case Study 1: A Shanghai e-commerce firm used DeepSeek to automate customer service on Taobao, cutting response time by 50%.

Case Study 2: A U.S. blogger called DeepSeek "fake" after it wrote a Chinese-style poem about pizza—but it went viral in Asia!

Case Study 3: ChatGPT falsely claimed "Google acquired OpenAI in 2023," proving all AI makes mistakes.

How to Choose: DeepSeek or ChatGPT?

Pick ChatGPT if:

You need English content, coding help, or global trends.

You value brand recognition and transparency.

Pick DeepSeek if:

You target Chinese audiences or need cost-effective APIs.

You work with platforms like WeChat, Douyin, or Alibaba.

LSI Keyword: "DeepSeek for Chinese marketing."

SEO-Optimized FAQs (Voice Search Ready!)

"Is DeepSeek a scam?" No! It’s a legitimate AI optimized for Chinese-language tasks.

"Can DeepSeek replace ChatGPT?" For Chinese users, yes. For global content, stick with ChatGPT.

"Why does DeepSeek give weird answers?" Cultural gaps and training focus. Use it for specific niches, not general queries.

"Is DeepSeek safe to use?" Yes, but avoid sensitive topics—it follows China’s internet regulations.

Pro Tips to Boost Your Google Ranking

Sprinkle Keywords Naturally: Use "DeepSeek vs ChatGPT" 4–6 times.

Internal Linking: Link to related posts (e.g., "How to Use ChatGPT for SEO").

External Links: Cite authoritative sources (OpenAI’s blog, DeepSeek’s whitepapers).

Mobile Optimization: 60% of users read via phone—use short paragraphs.

Engagement Hooks: Ask readers to comment (e.g., "Which AI do you trust?").

Final Verdict: Why DeepSeek Isn’t Fake (But ChatGPT Isn’t Perfect)

The "fake" label stems from cultural bias and misinformation. DeepSeek is a powerhouse in its niche, while ChatGPT rules Western markets. For SEO success:

Target long-tail keywords like "Is DeepSeek good for Chinese SEO?"

Use schema markup for FAQs and comparisons.

Update content quarterly to stay ahead of AI updates.

🚀 Ready to Dominate Google? Share this article, leave a comment, and watch it climb to #1!

Follow for more AI vs AI battles—because in 2024, knowledge is power! 🔍

[alok401]

Python's Game-Changing Match-Case Statement

Python 3.10 introduced the match-case syntax — a powerful upgrade over if-elif chains that brings advanced pattern matching to the language.

From destructuring complex data to handling APIs, configs, and even building state machines — match-case lets you write cleaner, more declarative code.

 python case statement

Edit

match config: case "debug": return {"logging": "verbose"} case [first, *rest]: return {"first": first, "rest_count": len(rest)} case {"type": "cache", "ttl": int(ttl)} if ttl > 0: return f"TTL: {ttl}s" case _: return "Invalid config"

It’s like switch-case, but way smarter — supporting data types, guards, sequences, and class matching.

🎯 Use Cases:

Simplify conditionals

Clean API response handling

Build state machines effortlessly

Elegant error validation

Cleaner config processing

Match-case isn’t just syntax sugar — it’s a shift in how we think about control flow in Python.

💡 Bonus: It works beautifully with type hints, mypy, and IDE autocompletion.

🔧 Want to test smarter with powerful pattern-matched logic? Keploy supports your dev workflows with reliable testing tools for modern Python code.

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The Benefits of Using a WhatsApp API Chatbot Provider

In an era where instant communication is vital for customer satisfaction, businesses are turning to messaging platforms to enhance their engagement strategies. WhatsApp, with its extensive user base, offers an incredible opportunity for businesses to connect with their audience. Using a WhatsApp API chatbot provider can take this engagement to the next level by providing businesses with tools to automate, streamline, and optimize their communication efforts. Below, we explore the key benefits of using a WhatsApp API chatbot provider.

1. Seamless Integration

WhatsApp API chatbot providers simplify the process of integrating the API with existing business systems. These providers offer pre-built solutions and frameworks that reduce the need for in-house development resources. Businesses can connect their WhatsApp chatbots with:

Customer Relationship Management (CRM): Track customer interactions and manage leads efficiently.

E-commerce Platforms: Automate order updates, confirmations, and payment notifications.

Help Desk Tools: Streamline customer support by routing complex queries to human agents.

By offering seamless integration, chatbot providers allow businesses to save time and focus on their core operations.

2. 24/7 Customer Support

One of the primary advantages of using a WhatsApp API chatbot is its ability to operate around the clock. With a chatbot provider, businesses can ensure:

Instant Responses: Customers receive immediate replies, enhancing their satisfaction.

Consistent Service: Even outside business hours, inquiries are addressed promptly.

Reduced Workload: Human agents can focus on more complex tasks, while the chatbot handles repetitive queries.

This continuous availability significantly improves the overall customer experience.

3. Scalability

As a business grows, so does the volume of customer interactions. A WhatsApp API chatbot provider enables businesses to handle thousands of conversations simultaneously without compromising quality. This scalability ensures that:

Businesses can manage peak periods, such as holiday seasons or promotional campaigns.

Multiple customer inquiries are addressed in real-time, avoiding delays.

The chatbot’s infrastructure can scale up or down based on demand, optimizing resource usage.

4. Enhanced Customer Engagement

A WhatsApp API chatbot provider offers tools to create personalized and engaging interactions. Features like Natural Language Processing (NLP) and AI-powered recommendations help:

Personalize Responses: Tailored replies based on customer history and preferences.

Offer Real-Time Assistance: Guide customers through purchasing decisions or troubleshooting steps.

Collect Feedback: Conduct surveys to understand customer needs and improve services.

These capabilities strengthen the relationship between businesses and their customers.

5. Cost Efficiency

Investing in a WhatsApp API chatbot provider is cost-effective compared to maintaining a large customer support team. Chatbots help:

Automate Repetitive Tasks: Responses to FAQs, order status inquiries, and appointment bookings are handled automatically.

Reduce Human Intervention: Chatbots take care of basic queries, lowering staffing costs.

Minimize Errors: Automated responses are consistent and accurate, reducing potential misunderstandings.

This leads to significant savings while maintaining a high standard of customer service.

6. Security and Compliance

A reputable WhatsApp API chatbot provider ensures that businesses adhere to WhatsApp’s strict policies and guidelines. Key benefits include:

End-to-End Encryption: Protecting customer conversations from unauthorized access.

Data Privacy: Complying with data protection regulations such as GDPR.

Reliable Infrastructure: Providers handle updates, maintenance, and security patches, ensuring uninterrupted service.

By prioritizing security, businesses can build trust and confidence among their customers.

7. Analytics and Insights

Most WhatsApp API chatbot providers offer analytics tools to track and optimize performance. These insights help businesses:

Monitor Key Metrics: Measure response times, customer satisfaction, and conversation volumes.

Identify Trends: Understand customer behavior and preferences to refine strategies.

Improve Chatbot Performance: Continuously update workflows and templates for better outcomes.

With actionable data, businesses can make informed decisions to enhance their operations.

8. Support for Multilingual Communication

A global customer base often requires communication in multiple languages. Chatbot providers enable businesses to:

Offer Multilingual Support: Provide responses in customers’ preferred languages.

Expand Market Reach: Connect with diverse audiences without language barriers.

Enhance Accessibility: Ensure inclusivity for non-English-speaking users.

This feature is particularly beneficial for businesses aiming to expand their presence in international markets.

9. Streamlined Onboarding and Training

Using a chatbot provider simplifies the process of setting up and managing a WhatsApp API chatbot. Providers often offer:

Pre-Built Templates: For common use cases such as order tracking and customer support.

Comprehensive Documentation: Guiding businesses through integration and customization.

Ongoing Support: Ensuring smooth operations and troubleshooting any issues.

This support makes it easier for businesses to get started and maximize their chatbot’s potential.