DEV Community: Aiza Rashid

How I Created a Inbound Voice AI Agent for Customer Support with Bland AI: A Beginner’s Guide

Aiza Rashid — Wed, 04 Mar 2026 16:24:45 +0000

Bland AI: How to Build No-Code AI Voice Agents for Your Business(Step-by-Step Beginner Guide)

I’ve built voice AI agents on multiple platforms over the past year.

Some were great. Some were overpriced. Some sounded robotic.

Basically every platform has it’s own problems and Bland has it’s own. It’s good, but again it’s not the best out of all other platforms out there.

I don’t want to demotivate you by mentioning it’s problems at first. We will talk about them later in article.

So here we are going to built a fully functional inbound voice agent for a Universal Fitness location, just to test how far their platform has come.

In this article, I’ll break down:

What makes Bland AI different
How to structure your prompt properly
The exact configuration that actually works
And the small settings most beginners mess up If you’re serious about building or selling voice AI agents, this will save you a lot of trial and error.

If you’re interested and want to learn more/work together, talk about potential projects, or just connect, feel free to reach out to me on LinkedIn!

Connect with me on LinkedIn
Email me at aizarashid17@gmail.com
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Any Clap or comment for this article would be of help!
So, let’s dive right in.

Why Bland AI Is Big in the Voice AI Space

Bland AI has raised over $65M in funding and is considered one of the top players in voice AI right now.

But funding aside, what actually matters is:

Realistic voices
Low per-minute pricing
Simple UI
Good documentation Before signing up, Bland AI also let you receive a live test call from one of their AI agents. You just enter your phone number and it calls you.

Understanding the Dashboard

Once you log in, the dashboard is clean and minimal.

On the left side, you’ll see:

Home
Analytics
Call logs
Send call (for outbound)
Phone numbers
Voices
Knowledge base
SMS
Documentation The UI is simple. No clutter. No unnecessary complexity.

The most important sections for building are:

Phone Numbers
Voices
Knowledge Base
Configuration settings

Inbound vs Outbound (Very Important)

Before building, understand this:

Outbound calls = rigid
Inbound calls = flexible

If you’re doing outbound lead generation, you should use conversational pathways because you have a defined goal and script.

If you’re building an inbound agent (like a gym answering customer questions), it needs to be flexible because you don’t know what the caller wants.

That difference changes how you structure the prompt.

Step 1: Purchase a Phone Number

Go to Phone Numbers → Purchase New Number

It costs:

$15/month for the number
$0.09 per minute usage

Step 2: Choose the Right Voice

Go to the Voices section and test them.

In my testing, Paige sounded the most natural.

Bland is known for having some of the most realistic voices in the industry, which matters a lot if you’re selling this to real businesses.

Always test before finalizing.

Step 3: Structure Your Prompt Correctly

This is where most people fail.

Bland actually provides a prompt engineering guide. And if you follow it properly, your agent performs significantly better.

Your prompt should be structured like this:

Goal What is the agent trying to accomplish?

Example:

Assist callers with inquiries about memberships, pricing, and hours.

Call Flow Step-by-step behavioral instructions.

Example:

Greet the caller
Let them explain their reason for calling
Answer clearly and concisely
Ask if they need anything else
Close politely
Background
Give the agent context.
Example:
Name
Business it works for
What it’s responsible for
This improves naturalness significantly.

Example Dialogue Show the AI what a good interaction looks like.

For example:

Caller: What are your membership options?
Agent: We offer two main memberships…

This reduces hallucinations and keeps responses aligned.

I tested variations of this prompt structure dozens of times. This format consistently performs best.

Step 4: First Sentence (Static Message)
You must define a starting message.

Example:

Thanks for calling Universal Fitness. This is Taylor. How can I help you?

Keep it natural. Keep it simple.

Step 5: Model Settings (Don’t Ignore This)
Here are the settings that matter:

Temperature (Set Around 0.5)
This controls randomness.

Lower temperature = more rigid
Higher temperature = more creative

Too high → unpredictable responses
Too low → robotic

0.5 is a good balance for inbound calls.

Interruption Threshold
This controls how sensitive the AI is when someone interrupts.

If too high:

Background noise may stop the AI mid-sentence.
If too low:

It may ignore real interruptions.
Keep it around the middle.

Step 6: Knowledge Base
This is where you upload business information.

Go to:
Knowledge Base → Upload File

Upload:

Pricing sheets
FAQs
Service lists
Policies Then attach that knowledge base to your agent inside configuration.

This makes the agent context-aware instead of guessing.

Step 7: Audio Settings (Highly Underrated)
Bland allows background audio:

Office
Cafe
Restaurant I prefer cafe.

It adds subtle realism that makes the call feel less robotic.

Turn on:

Noise cancellation
Recording
Keep:
Ignore button press OFF
Step 8: Post-Call Analysis & Webhooks
This is where it gets powerful.

Under Analysis → Post Call, you can define what data to extract.

Example:

Name
Phone number
Reason for calling Bland will structure the payload accordingly.

Then inside the Advanced section, you add your webhook URL.

This allows you to:

Send data to GoHighLevel
Trigger automations
Book appointments
Send follow-up SMS

Why Bland AI Stands Out

From a practical standpoint:

Cheap pricing
Strong voices
Clean UI
Solid documentation
Good outbound pathway system Is it perfect?

No.

There are platforms like Vapi or Retell that may outperform it in certain use cases.

But Bland is definitely in the top three.

Perfomance Bottlenecks

So now let’s talk about some of the limitations I’ve experienced with Bland AI , I want to be really real about this. If you’re building or selling AI voice agents for your own business, some of these issues can be deal breakers.

One big one is the “Barge-In problem”, when a caller interrupts the AI, Bland AI tends to keep talking for a solid one to two seconds before stopping. It makes the whole interaction feel like you’re talking to a voicemail machine.

Another issue I’ve seen is numbers getting flagged as spam, especially on T-Mobile, which can make calls feel obviously automated and less professional.

For practical business use cases, these things can be huge headaches. Personally, I don’t prefer Bland AI because of this.

Even though I’ve used Vapi AI the most to build my own agents, I’d say Retell.ai is currently the most reliable option. Some of the same issues you see on Bland might pop up occasionally in Vapi , but Vapi can also be a bit developer-heavy, requiring more coding and troubleshooting. Retell.ai feels simpler, more stable, and beginner-friendly , which is why I’d recommend it if you want to build AI agents without all the extra headaches.

And yeah that’s it.

See you soon.

How I built an AI Phone Assistant Using Vapi AI in Just a Few Steps

Aiza Rashid — Wed, 05 Feb 2025 14:18:34 +0000

In this edition, we’ll be diving into how to build voice AI agents. Today, there are numerous no-code and low-code platforms available that allow businesses to create voice AI agents and solutions, especially for tasks like customer support and other business-specific automation. Popular platforms like VAPI AI and Bland AI are being widely used to simplify this process.

Personally, I’ve had extensive experience working with Vapi AI and Bland AI. While Bland AI is an excellent tool overall, my go-to has always been Vapi AI, it’s user-friendly, powerful, and an amazing platform for voice AI development.

The beauty of Vapi AI is its no-code nature. You don’t need to be a programmer to create a fully functioning voice AI agent. The process involves connecting Vapi to automation platforms like Make.com and databases like Airtable.com. Here’s an outline of how it works:

Automation Setup: Use Make.com to create automated workflows for your voice AI.
Database Connection: Connect Airtable (or Google Sheets) as your database. When a user calls, their voice is transcribed into text, which is then saved to your database for further use.

Airtable is ideal for managing databases, as it provides a structured and user-friendly grid for storing call data.

However, diving deep into the specifics of setting up Make.com automations or configuring Airtable databases would make this article unnecessarily lengthy. Instead, in this guide, I’ll focus solely on how to create your own voice AI agent using Vapi AI. Detailed steps for integrating Make.com and Airtable will be covered in a separate article.

So, let’s start by exploring Vapi AI and building your first voice AI agent with ease.

If you’re interested and want to learn more, talk about projects, or just connect, feel free to reach out to me on LinkedIn!

Connect to me on LinkedIn
Become a member of My AI Newsletter Family for Free
Any Clap or comment for this article would be of help!

We’re going to create an AI voice assistant for customer support. Follow these steps:

Go to Vapi.AI

Start by creating an account and logging in.

Access the Dashboard

Once logged in, go to your dashboard and click on “Create Assistant.”

Select a Template

You have two options:

Use a blank template.
Choose from the available templates. Since we’re creating a customer support voice assistant, we’ll select the “Customer Support” template and then click “Create Assistant.”

Customize the Assistant Name

After creation, you’ll see the assistant interface. Click on the top to rename your assistant to whatever you prefer.

Modify the First Message

You can change the first message to whatever you want it to be.

Work on the Prompt

Since you’re using a template, it comes with a pre-written prompt. You can copy this prompt and paste it into GPT. Ask GPT to modify the prompt according to your business needs (e.g., a dental clinic, restaurant, or any other business) by providing your business information. Once GPT generates a new prompt, proofread it to make sure it aligns with your requirements.

Select a Provider

I selected OpenAI as the provider because of its advanced capabilities in understanding and generating natural language.
For the model, I chose GPT-4O because it offers better performance in understanding context and generating accurate responses for customer support.

Adjust the Temperature

Set the temperature to 0.5 or 1.0 based on your needs.

Temperature controls the randomness of the responses:

A lower value (e.g., 0.5) makes the assistant more focused and consistent.
A higher value (e.g., 1.0) makes it more creative but potentially less accurate. I went with 0.5 to ensure the assistant gives reliable and precise answers.

Set the Transcriber

I chose Deepgram as the provider because it is cost-effective and offers accurate transcription.
Set the language to English.
For the model, select Nova 2, as it provides high-quality transcription that aligns well with customer support needs.

Configure the Voice

I selected 11labs as I frequently use it and appreciate its extensive voice library.
For the model, I chose Eleven Turbo V2.

Add Background Sounds (Optional)

This feature lets you add background sounds to your assistant. However, I chose No BG sound for simplicity and clarity.

Enable Functions

Click on the “Functions” tab. Here, you can create functions to perform specific actions. We’ll cover custom functions in a later article.

For now, enable the End Call function so the assistant can end calls on its own when necessary.

Set Up Call Forwarding

You can forward calls to a human if the assistant cannot handle certain queries.

To do this, prompt the assistant by saying something like:
“If the user asks for technical advice (or any other topic you specify), forward the call to [insert number].”

After setting up the initial configurations, click on Advanced next to Functions. Here, you’ll find various options to fine-tune your assistant.

Privacy Settings

In this section, you can configure the privacy settings for your assistant. For instance, I’ve enabled Audio Recording, which records conversations with the assistant. This allows me to listen to the recordings later for quality checks or improvements.
Start Speaking Plan
This plan defines when the assistant should start speaking during a conversation. For example, I’ve set the assistant to wait 0.4 seconds before it begins speaking. This delay ensures a smooth interaction and avoids overlapping with the user.
There are also advanced options like:

On Number of Seconds
On No Punctuation Seconds
On Punctuation Seconds For most pre-built templates, like the Customer Support template I’m using, these settings are already optimized, so you don’t need to change them unless you have specific requirements.

Stop Speaking Plan
Similar to the Start Speaking Plan, the Stop Speaking Plan defines when the assistant should stop speaking. These settings are also pre-configured in templates, so adjustments are rarely needed.

Call Timer Settings
Inactivity Timer
Maximum Call Duration Advanced Settings: Server URL (Why It’s Important) In the Advanced Settings, you’ll come across an option to add a Server URL. While we won’t be setting this up in this article, let me explain why it’s important and what purpose it serves.

The Server URL is where the assistant sends the data it receives during calls, such as user inputs or call events. Without a Server URL, the assistant can only handle basic interactions using the predefined responses you’ve set. However, adding this URL and setting up an external system like Make.com allows the assistant to do much more. For example:

Store user data in a database (like Airtable).
Fetch specific information based on user queries.
Trigger workflows, like sending emails or forwarding calls. In our case, we’ll use Make.com to create an automation that connects the assistant to a table (such as Airtable) to store or retrieve data during calls. This integration enables the assistant to handle more dynamic tasks that go beyond static responses.

In the Advanced Settings, you’ll also find Client Messages and Server Messages options. Let me break them down for you so there’s no confusion.

Client Messages: These are messages sent to the client-side interface. For example, I selected Transcript, it means the assistant will send the text of the conversation (a transcript) to the client interface. But what does “client-side” mean here? It depends on how you’ve connected your assistant. If we set up an automation using Make.com later (which we’ll cover in the next article), these transcripts can be sent to Make.com and then saved in a platform like Airtable. Essentially, this setting helps send conversation details to wherever you’ve configured it , such as a dashboard, app, or database.

Server Messages: These are messages sent to the backend or server for processing. For example, I enabled End of Call Report, it means the assistant will send a summary of the call (e.g., duration, user inputs, outcomes) to the server. In our case, when we connect the assistant to Make.com, this report will also be sent there, allowing us to save or process the data as needed.

To clarify, these settings won’t do anything until the assistant is connected to an external platform like Make.com. Once connected, these messages will ensure that all the necessary data is properly sent and stored for tracking or analysis. We’ll dive into this setup step by step in the next article!

Why We’re Not Setting This Up Yet

Since setting up the Server URL and Make automation involves a bit of work, I’ve decided not to include it in this article to keep things concise. But don’t worry! In the next article, I’ll walk you through:

Generating a webhook URL from Make.com.
Connecting it to your assistant as the Server URL.
Setting up an Airtable to store and organize user data.
Testing the automation to ensure everything works seamlessly.

This setup will take your assistant to the next level, making it capable of handling more advanced tasks efficiently. For now, just remember that this URL is crucial for enabling dynamic workflows and data integration.
Now, you can hit the publish button of your vapi AI assistant, and click on the the talk with assistant and see how it responds.

Finalizing the Assistant

Once all settings are configured, your assistant is ready. Now you can assign it a number.

If you’re in the US or Canada, you can directly get a number from VAPI.AI.
Alternatively, you can purchase a number from providers like Twilio and add it to your assistant. That’s it for today. Stay tuned for the next article, where we’ll dive deep into setting the make.com automation step-by-step!

I Tested 3 No-Code Platforms to Create Chatbots!

Aiza Rashid — Sun, 02 Feb 2025 13:58:49 +0000

Hey there, friend.

I had been working as a Python Django Full Stack Developer and WordPress developer.

But, Lately, with all the hype and buzz around AI, I couldn’t help but get curious about it. I wanted to understand how AI works and how it’s being used everywhere, so I decided to dive in.

I started by taking courses on platforms like Coursera and got enrolled in the IBM AI Engineering program , a six-month journey to explore AI in depth. Honestly, I didn’t have a clear direction at that time. I was just following my curiosity, wanting to learn how things work. After completing several courses, I discovered something fascinating: the world of low-code and no-code AI platforms.

These platforms are incredible. They let people, even without advanced coding skills, build AI-powered solutions for businesses. From automation tools like Make, Zapier, n8n and Airtable to AI-enhanced Voice AI platforms like Vapi and Bland, and even chatbot creators like Voiceflow, there’s so much you can do. These tools simplify complex tasks and make creating AI solutions more accessible.

That’s what led me here. I’ve been exploring and building with these low-code and no-code platforms for a long time and today, I want to share my experience with three such platforms. Let’s dive in and see what they offer!

If you’re interested and want to learn more, talk about projects, or just connect, feel free to reach out to me on LinkedIn!

Connect to me on LinkedIn
Become a member of My AI Newsletter Family for Free
Any Clap or comment for this article would be of help! Alright, so let me tell you about these three platforms I’m going to talk about, ChatBase.co, DanteAI, and meetcody.ai. These platforms are great when it comes to prototyping chatbots.

Now, let me explain why I say that and why they’re so handy when you’re working with clients.

Let’s assume that your client comes to you and says they want a chatbot for their business, but they’re not sure how it’s going to work or if it’s even possible. That’s where these platforms shine. You can quickly create a basic prototype of the chatbot, something that shows how the idea would function. Within minutes, you can set up a simple version and present it to your client, saying, “Look, this is how it could work!” It’s like creating a demo to show them that their vision is achievable.

So, why are these platforms better suited for prototyping? Well, let me break it down for you:

Ease of Use: They’re straightforward, allowing you to build a chatbot with minimal effort.
Quick Setup: Time is crucial when you’re prototyping. These platforms let you create something functional within minutes.
Focused Features: They keep it simple, so you can quickly build something that works without worrying about integrations or coding.
Presentation-Ready: These platforms often come with built-in options to test the chatbot or show it in action, making it perfect for presenting.

Now, again, don’t get me wrong , these platforms are not the go-to for building fully functional or highly customized chatbots. For that, you’d look into something like voiceflow, which allows for deeper integrations and more complex solutions. But for quick demos and getting your client onboard with the idea, ChatBase.co, DanteAI, and Cody AI are the real deal.

That’s why I say these platforms are best for prototyping. They let you take an idea and bring it to life in no time, without overcomplicating things. So if you’re exploring chatbot ideas for a client, these are great places to start!

Chatbase

First, let’s talk about ChatBase.co and see how you can build a custom chatbot, step-by-step, with a simple example.

Getting Started

Head over to chatbase.co and create an account if you don’t already have one. Once you’re logged in, go to the Chatbots section and click on Create New Chatbot.

Uploading a Custom Knowledge Base

Now, let’s say we’re building a customer support chatbot for a gym. This chatbot will help answer common customer queries like:

“What are the pricing plans for yoga classes?”

“Do you offer personal training sessions? How much do they cost?”

“What are the gym timings on weekends?”

To make the chatbot smart, you’ll need to upload a custom knowledge base. This could be a PDF or a document containing all the relevant details about your gym, such as pricing plans, schedules, and membership benefits.

If your gym’s website already has this information, you can scrape the website’s data to train your chatbot, but keep in mind that this feature is only available on paid plans on chatbase.co

What Are Custom Knowledge-Based Chatbots?

In case, if you don’t know , these are chatbots that use your specific content to answer questions. Instead of pulling generic data, they rely on the information you provide, like your gym’s policies, pricing, or any other details unique to your business.

Giving It a Personality

After uploading your knowledge base, you can:

Name Your Chatbot: Let’s call it “FitBot.”

Set a Prompt: Give the chatbot a purpose, like:
“You’re a friendly assistant for Gym XYZ. Your job is to help users with pricing, schedules, and membership inquiries in a professional yet approachable tone.”

This is just an example and it’s not a good one also, there are a plenty of instructions you can mention in a prompt to make the chatbot give it’s best.

You can also add instructions for its conversational tone (e.g., friendly but professional) and how to format links when recommending URLs.

Choose a Model

Now you get to choose a model for your chatbot, like GPT-3.5 Turbo. These models determine how your chatbot processes and answers questions.

You’ll also see an option to set a temperature. Temperature controls the chatbot’s creativity.

A low temperature (e.g., 0.2) makes it more focused and precise great for answering straightforward questions.
A high temperature (e.g., 0.8) makes it more creative and varied — useful for open-ended conversations. For our gym chatbot, sticking to a low temperature is better since it needs to give accurate, factual responses.

Integrations

ChatBase.co lets you integrate your chatbot with different platforms, like:

Facebook Messenger, Zapier and a bunch of other integrations.

You can also embed the chatbot directly onto your gym’s site using a script or iframe tag.

To embed it, copy the script tags from ChatBase, go to your website’s HTML code, and paste them at the bottom before the closing

5 Free AI Design Tools For Designers!

Aiza Rashid — Wed, 15 Jan 2025 15:43:38 +0000

For Product Designers, Graphic Designers and Web/App Designers!
Hey there, friend!
Okay, picture this: You’re sitting at home, scrolling through YouTube, diving deep into the latest AI trends (you know, as we all do). And suddenly, you feel like you’re in a war. AI is coming for you. It’s like one of those movies where the villain is everywhere, and you’re trying to block all the attacks with your hand—like, You are in a defensive stance, holding a sword and possibly a shield, "Oh no! AI is going to take my job, my career, my life!"

The arrows are gonna pierce my heart(it’s historical, man).

I'm not exaggerating; I was getting such thoughts after watching some YouTube videos on AI while listening to the hype background music.

If you have not watched such videos, yet, I can even suggest you some.

Ok, I get it(not the videos, but the AI landscape).

The whole AI revolution can feel a little overwhelming. It’s like we’re all on this wild rollercoaster, and we're trying to hold on for dear life while the landscape around us shifts in ways we’ve never seen before.

But I want to clear the air—AI isn’t here to replace you.

In fact, if you’ve been worried about AI taking jobs, let’s clear that up right now. AI doesn’t eliminate opportunities—it shifts them. For example, companies that once needed 10 designers might now rely on 3 or 4 who are equipped with AI tools. It’s not about cutting people out; it’s about doing more with less, and those who adapt will always have a place in this evolving landscape.

So, I want to share some amazing AI tools specifically for designers(UI/UX designers, product designers & graphic designers) that can help you stay ahead in the game—tools that can supercharge your work, streamline your workflows, and let you focus on what truly matters: being creative, being efficient, and being a true professional in whatever field you’re in.

Even if you're not a designer, these tools can still be valuable to you.

So, Let's dive in!

1. Figma – Design Collaboration Made Smarter

Figma is a cloud-based design tool widely used for interface design, prototyping, and collaboration in web and mobile applications.

If you’ve used Figma before, you already know it’s the gold standard for collaborative design, but here are some of it’s AI features:

First Draft: Imagine having a tool that takes your thoughts and sketches and turns them into polished prototypes in minutes. That’s what Figma’s AI is doing. It understands your design needs and comes up with the first draft—ready to be refined.

Plugins with AI: From layer renaming to generating mock content, there are tons of Figma plugins that bring AI magic into your workflow.

Auto-Layout: Automatically adjusts your design elements(like texts, buttons, etc) to fit perfectly saving time on resizing and aligning.

What’s my takeaway here?
AI in Figma is changing the game. It’s not just for designers, but for anyone who needs to create a polished product, whether you’re building a website, crafting a pitch deck, or anything in between.

Subscribe for free to receive such content directly into your inbox, weekly!
LET ME VIEW IT FIRST!

2. Muzli – Your Personalized AI Inspiration Board

What it is: Muzli is a design inspiration platform that provides the latest creative content, trends, and design resources.

How it works: You can access Muzli through its website or install it as a browser extension (for Chrome or other browsers). It offers a personalized feed every time you open your browser or visit the site.

Personalized Feed: Muzli learns what you like (and don’t like) and serves you design inspiration that’s relevant to your work and your aesthetic.

AI Trend Spotter: It’s also on top of all the emerging trends, so you don’t have to stress about whether you’re behind the curve. AI finds what’s hot and feeds it straight to you.

So, it’s like Pinterest for designers!

Think about it: So, Whether you're in business, tech, or just curious about how the creative world is evolving, Muzli gives you that jolt of inspiration in a totally personalized way.

3. Khroma – AI Color Palettes that Actually Work

Khroma is a color palette generator that uses artificial intelligence to create unique and personalized color schemes.

By learning from your preferences and selections, Khroma allows allow you to explore a wide array of harmonious color combinations tailored to your taste.

I’ve been using Coolors for color palette generation for a while, but recently, I gave Khroma a try—and wow, I’m really impressed. The color palettes it generates are spot on for my aesthetic, and it just feels more intuitive. Definitely a great tool for anyone looking to step up their design game with AI-driven color choices!

4. Uizard – AI-Driven Prototyping for Everyone

Here’s the thing: prototyping can feel daunting. Whether you’re designing a website, app, or even a simple product, getting the prototype just right is a huge part of the process. Enter Uizard—AI that simplifies it all.

How it works: You can upload sketches, use templates, or even provide text descriptions, and Uizard’s AI will generate designs and layouts that match your vision. It's perfect for fast prototyping without needing design expertise.

Sketch to Digital: Draw something on a napkin, and Uizard’s AI will instantly turn that sketch into a digital prototype. Yep, no more staring at your paper sketches, wondering if you can actually make that work digitally.

Smart Suggestions: It helps you refine your prototype with intelligent layout suggestions. Uizard takes your basic idea and improves it with suggestions that you can either accept or tweak.

It’s not just for designers, it’s for anyone who needs a prototype to take a concept and turn it into something tangible.

5. Looka: AI-Powered Logo Design Made Simple

Looka is a fantastic tool I tried a month ago. It uses AI to generate logo options based on your business name, industry, and design preferences. The best part? You don't need any design skills to get started.

AI-generated logos: Just input your business name and Looka generates professional logo options in minutes.

Customization: Easily tweak fonts, colors, and layout to fit your style.

Full branding package: Along with logos, you get matching fonts, color palettes, and business card designs.

It’s an incredible tool for anyone who needs a solid starting point or want to test different ideas without breaking the bank.

Wrapping it Up

So, that’s it for the AI tools.

Look, no one knows what the future holds, but AI is already here, and it’s not going anywhere.

And while it might feel like you’re standing there, shielding yourself from an incoming storm, the truth is: AI can be your shield, too. It’s all about embracing it, adapting, and integrating these powerful tools into your workflow to make things easier, faster, and more creative.

In fact, recent studies show that only about 3** in 10 U.S. citizens** can correctly identify the six key uses of AI. This highlights how many people are still behind when it comes to understanding and leveraging AI. But if you can use AI tools in your own work and daily life, you're already ahead of more than 60% of people out there.

So, while it might seem daunting at first, integrating AI into your workflow is not only going to make things easier for you — it’s going to set you apart. Whether you’re designing, creating, or just managing tasks, adopting AI could be your ticket to staying ahead of the curve and gaining an edge over others in your field.

So, don’t panic. Adapt. And let these AI tools help you take your work to the next level.

Let me know your thoughts, and tell me: What’s your favorite AI tool? Or are you still in defense mode like I was? 😊

Sending all the good vibes from my corner of internet to yours!

See you, soon!

Aiza

What init.py has to do with Python?

Aiza Rashid — Thu, 09 Jan 2025 09:16:34 +0000

Understanding init.py in Python!

Hi Guys! I hope that you all may be doing well. So here I am with an interesting topic in Python which is init.py, it is interesting and worth-thinking of at the same time. As I learned python, three-four months ago, and I am not currently programming, it’s really essential to always keep hold of such basic and core concepts, and I am trying to do the same by revising my concepts.

We are going to talk about init.py file in Python. When working with Python, you may have stumbled upon a special file named init.py. If you have ever wondered that what this init.py(as it is an important concept) file has top do in programming ,then you must read this. You may be curious about this init.py file. So let's convert this curiosity into knowledge. This file plays a crucial role in the organization and functionality of Python packages. So today, we will explore together what init.py is, its purpose, its functionalities, and how you can effectively utilize it in your projects.

What is init.py?

init.py is a Python file that is used to mark a directory as a Python package. In essence, it allows Python to recognize a folder as a package, enabling you to structure your code in a modular way. Introduced in Python 2, its importance has only grown, although it's worth mentioning that as of Python 3.3, init.py is no longer strictly required to recognize a directory as a package. However, including it offers significant advantages.

Purpose of init.py

1.Package Initialization: The primary purpose of init.py is to initialize a Python package when it is imported. This is where you can define what happens when the package is loaded, such as importing specific modules or setting up initial configurations.

2.Exports: You can use init.py to control what is exposed to the outside world when your package is imported. By defining the all list, you can specify a list of modules or classes that should be accessible when someone imports your package using the from package import * syntax.

3.Scoped Module Visibility: By using init.py, you can manage the visibility of modules within a package. For instance, you can keep certain helpers private by not importing them directly in init.py.

Functionalities of init.py

Module Imports: Within init.py, you can import specific classes, functions, or modules to make them readily available when the package is imported. For example:

from .module_a import FunctionA

from .module_b import ClassB

Package Metadata: You can define metadata for your package such as version numbers or author information directly inside init.py. This can be helpful for documentation and maintenance purposes.

version = ‘1.0.0’

author = ‘Your Name’

Initialization Code: Any initialization code that you want to run when the package is imported can be placed in init.py. This could include setup tasks or environment checks.

Best Practices

1.Keep It Simple: Avoid putting too much logic in init.py. Its primary job is to initialize the package, so keep the code simple and focused.

2.Use all Wisely: If your package has many modules and you only want to expose a few, define an all list. This helps in controlling what your users can access.

all = [‘FunctionA’, ‘ClassB’]

3.Consistent Structure: Maintain a consistent structure for your packages. Organizing your modules logically will make it easier for users to understand and navigate your package.

4.Documentation: Don’t forget to document your package well. Providing a clear explanation of what is available in init.py and how to use it can save time and confusion for users.

Conclusion

In summation, init.py is an essential file for organizing and defining the functionality of Python packages. Although it's not strictly necessary in Python 3.3 and above, including it allows for better package management, initialization, and control of exported symbols.

That’s it. I hope you understood it as it’s a really unique file and line of code in Python.

Stay happy and blessed!

Happy Coding!

Python Decorators: The Secret Sauce for Cleaner Code !

Aiza Rashid — Mon, 06 Jan 2025 11:22:20 +0000

Making Sense of Python Decorators — One Wrapper at a Time !

Hey Everyone!
I hope you are doing well!
Have you ever come across Python decorators and thought, “Oh no, not another complicated topic!” Well, let me tell you, decorators aren’t as scary as they look. In fact, once you get the hang of them, they’re like adding a cherry on top of your Python skills.

Let’s break it down, step by step, and wrap (pun intended) our heads around decorators.

What Are Python Decorators?

A decorator in Python is like a magical tool that lets you tweak or extend the functionality of a function without touching its code. It’s like adding filters to your Instagram post — you don’t change the photo; you just enhance it.

Imagine you have a function that prints something. What if you want it to log a message before and after it runs? Instead of rewriting the function, you use a decorator to add that functionality.

A Simple Example

Here’s a quick example:

def my_decorator(func):  
    def wrapper():  
        print("Starting the function...")  
        func()  
        print("Function has ended!")  
    return wrapper  

@my_decorator  
def say_hello():  
    print("Hello, world!")  

say_hello()

And the output?

Starting the function...  
Hello, world!  
Function has ended!

Breaking It Down

Let’s walk through it, line by line:

1.** def my_decorator(func)**:This is the decorator function. It takes another function (func) as input.

2.def wrapper(): Inside the decorator, we define a new function called wrapper that adds some extra behavior.

func(): This calls the original function (say_hello).
@my_decorator: The @ symbol is shorthand for applying the decorator to the function. It’s the same as writing:

say_hello = my_decorator(say_hello)

Why Are Decorators Useful?

Let’s look at a real-world scenario. Suppose you want to log every time a function is called. You can write a decorator for that:

def log(func):  
    def wrapper(*args, **kwargs):  
        print(f"Calling {func._name_}...")  
        return func(*args, **kwargs)  
    return wrapper  

@log  
def add(a, b):  
    return a + b  

print(add(3, 5))

This outputs:

Calling add...  
8

Simple, isn’t it? Instead of manually adding print statements in every function, you just use one decorator.

Real World Use Cases

Here’s how decorators can work for you in practical, everyday coding scenarios:

1. Logging Everything Your Functions Do

Ever wonder what exactly your functions are up to, or what data they’re handling? With decorators, you can automatically log every time a function is called, what input it receives, and what it returns.

For example: You’re building an app and want to track how many times a feature is being used. A decorator can log each call without cluttering the function itself.

2. Measuring Function Speed

How long does your function take to run? Is it slowing down your program? Instead of manually timing each one, a decorator can measure the execution time of your functions automatically.

For example: You’re optimizing a data processing script and want to find bottlenecks. A decorator can tell you how long each part of the process takes.

3. Managing User Access
If you’re building an app or a website, there are times when you need to restrict certain features to specific users — like admins or logged-in users. A decorator can handle these checks seamlessly.
**
For example: **If a user tries to access an admin dashboard, a decorator can verify if they have the right permissions before letting them in.

4. Repeating Tasks with Ease

Some functions perform tasks that are the same across different parts of your program — like saving data to a database or sending notifications. A decorator can ensure those tasks are handled uniformly and with minimal repetition.

For example:Imagine you’re saving data to multiple tables in a database. A decorator can add consistency and error handling to every save operation.

One Last Thing

If decorators still feel a bit tricky, don’t worry! Like everything else in Python, practice makes perfect. Start small, try writing a few decorators, and soon you’ll be using them like a pro.

What’s your take on decorators? Let me know in the comments — or better yet, tell me what other Python concepts you’d like me to break down!

That’s it for now. Go ahead, try wrapping some functions, and make your Python code shine!
Happy Coding!
Sending all the good vibes from my corner of internet to yours!

DEV Community: Aiza Rashid

How I Created a Inbound Voice AI Agent for Customer Support with Bland AI: A Beginner’s Guide

Why Bland AI Is Big in the Voice AI Space

Understanding the Dashboard

Inbound vs Outbound (Very Important)

Step 1: Purchase a Phone Number

Step 2: Choose the Right Voice

Step 3: Structure Your Prompt Correctly

Why Bland AI Stands Out

Perfomance Bottlenecks

How I built an AI Phone Assistant Using Vapi AI in Just a Few Steps

Go to Vapi.AI

Access the Dashboard

Select a Template

Customize the Assistant Name

Modify the First Message

Work on the Prompt

Select a Provider

Adjust the Temperature

Set the Transcriber

Configure the Voice

Add Background Sounds (Optional)

Enable Functions

Set Up Call Forwarding

Privacy Settings

Why We’re Not Setting This Up Yet

Finalizing the Assistant

I Tested 3 No-Code Platforms to Create Chatbots!

Chatbase

Getting Started

Uploading a Custom Knowledge Base

What Are Custom Knowledge-Based Chatbots?

Giving It a Personality

Choose a Model

Integrations

5 Free AI Design Tools For Designers!

1. Figma – Design Collaboration Made Smarter

2. Muzli – Your Personalized AI Inspiration Board

3. Khroma – AI Color Palettes that Actually Work

4. Uizard – AI-Driven Prototyping for Everyone

5. Looka: AI-Powered Logo Design Made Simple

Wrapping it Up

What __init__.py has to do with Python?

What is init.py?

Purpose of init.py

Functionalities of init.py

Best Practices

Conclusion

Python Decorators: The Secret Sauce for Cleaner Code !

What Are Python Decorators?

A Simple Example

And the output?

Breaking It Down

Why Are Decorators Useful?

Real World Use Cases

1. Logging Everything Your Functions Do

2. Measuring Function Speed

4. Repeating Tasks with Ease

One Last Thing

What init.py has to do with Python?