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5 Types of Applications you can Build with Python

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5 Types of Applications you can Build with Python

Have you been deciding on developing an app and you are confused about what language you should use? You don’t need to fret anymore because python is at your service.

Python development businesses now have a plethora of options because of the technical, digital, and development environment. Python has offered developers a variety of employment beyond only designing apps as a staple.

Yet, other languages are abundant and it can be difficult for entrepreneurs to select the right one. According to a survey by stack overflow in 2020, python is the most used language for developing web and mobile applications. Let us look at some pros and cons and also the type of apps you can build using python.

PYTHON FRAMEWORK

If you are an industrialist and looking for the best python development company, it is necessary to know the basic framework of python. Frameworks are the modules and libraries in a programming language to write codes for developing applications. The python framework saves you from the mental anguish of protocols and thread management.

Frameworks make the developing process easier and more efficient by allowing the reuse of the code for the same HTTP operations. Some of the basic frameworks of python are Django, Flask, bottle; helping in creating scalable web applications.

PROS AND CONS

Like every other programming language, Python has some advantages and limitations. But if it has limitations then why use it? Because the advantages of python overpower the limitations, makes it the best choice available in the programming market.

Python is versatile, along with high readability and quick-to-use advantages. However, the execution speed of python is what limits its usage. Since python executes each line separately; it takes much longer to run a code. Yet, it is faster than modern languages and compatible with numerous operating systems.

Additionally, Python is open-source making it easier to find source code and develop apps hassle-free. With vast libraries and increased productivity makes it the most likeable coding language.

APPLICATIONS THAT CAN BE DEVELOPED USING PYTHON

Now that we have covered the concept of frameworks and skimmed over the pros and cons, we can acknowledge the types of applications that can be built using Python. Python with its design features and libraries is the most used language for developing applications.

GAMING APPLICATIONS

Python offers a large library and game designing tools, reducing the efforts of the developers. Additionally, it allows real-time testing and the facility for providing rapid-game prototypes. Hence, gaming enthusiasts love and rely on it to create top-notch games.

Games like Battlefield 2, EVE Online, and SIMS 4 were all created using the python programming language. It was also utilised to construct the Panda 3D game engine, which serves as the foundation for Disney’s Pirates of the Caribbean game. Its track record of success makes it ideal for creating game applications.

Blockchain applications have recently developed a trend in the market. Entrepreneurs and manufacturers prefer it because of the protection it provides. If you’re interested in blockchain apps, look for a Python Development Company because it’s the ideal language for them.

The python frameworks like flask and bottle have made the work of developers easy. They can interact with their blockchain over the internet using HTTP requests. The blockchain may be generated in Python with only 50 lines of code, making the procedure quick. The straightforward syntax of python makes the development process a walk in the park.

  • MACHINE LEARNING APPLICATIONS

Applying artificial intelligence and machine learning in development can be quite tricky. The time taken for machine learning is a factor that leaves the developers bewildered. But, python is here to save the day. Numpy, Scikit, Scipy, Pandas are some of the libraries offered by python. These libraries have different roles to play and make the machine learning process faster and more efficient.

Python code is easily understood and can be used for some complex machine learning tasks. Python development companies use these libraries to create some complex machine learning applications in half the time with python.

With python, the developers can focus on creating efficient and effective machine learning applications. This helps in avoiding spending time on the technicalities of the programming language.

Console applications otherwise referred to as command-line applications are used from the text user interface. Typing the commands in the console or shell, the console applications are navigated. With its extensive library and flexibility, Python is used for creating console applications.

Python also offers a virtual environment successfully creating applications that require packages not included in the standard library.

The addition of UI/UX interfaces to command lines has changed the designing method of applications. These command lines are being used by some famous python development companies. Python modules allow the creation of interactive command-line interfaces and node applications.

Amidst the pandemic, the e-commerce market has flourished tremendously. The offers, discounts, try-on facilities at the comfort of your home have influenced the customers. People do not want to stand in long lines to buy groceries or pay bills.

With frameworks like Django, Flask, bottle; python can create e-commerce applications super-fast. To customise the e-commerce applications, Python is the best available option. Most business companies use Python to create their applications. Along with the packages and modules, python also offers access controls. These protect the data while granting access where necessary. Python is also a scalable language, meaning it can handle a huge volume of traffic without slowing down the application.

CONCLUSION

Python is a highly scalable and adaptive language that creates web applications easily and effectively. The developers do not have to brainstorm for creating source code since they are readily available. To flourish in their market, the companies should hire python development companies to design and develop the applications and websites.

Python’s rich library, visuals, and design principles enable users to create their bespoke applications. Python can always be relied on for profitable and efficient applications, no matter what industry your firm is in.


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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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