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Golang-Beego Framework and its Applications

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4 minutes read

There are usually some concerns when implementing a new technology such as what would happen if we get stuck somewhere and end up wasting our time and effort. It’s possible that we’ll have to go back to the original solution. We faced similar issues a year ago but after long and in-depth research, we found a solution that was more secure and safe-Golang and its frameworks. The way it is documented is really helpful. However, we were quite certain that we would not find all the answers online which was a challenge we accepted in the spirit of Lailah Gifty Akita’s renowned adage, “THERE IS ALWAYS A SOLUTION TO EVERY CHALLENGING SITUATION.”

This blog mainly talks about Golang-Beego framework and its applications. We’ll be discussing how Golang is used in Web Development and why most of the developers shift from Python, Node, or other languages to Go.

Let’s understand the Golang framework in order to know how it works.

What is Golang?

First appearing in 2009, Golang (popularly known as Go) quickly gained popularity among developers, becoming a preferred language for more than 90% of users. Its ancestor languages are C and C++ programming languages which is quite evident by looking into its syntax and compiling features. 

Primarily used for backend development, Go has 4 other use cases- 

  1. Cloud & Network Services
  2. Command-line Interfaces (CLIs) 
  3. Web Development
  4. Development Operations & Site Reliability Engineering. 

Here are some of the main features of Golang that make this framework the preferred choice for developers:

1. Simplicity 

Go syntax is straightforward as shown here and its compiler can smell trouble and raise errors during the build process — that is before the program is run.

Go Syntax in Golang-Beego Framework

The flexibility, usability, and incredibly cool concept behind Go (how it handles native concurrency, garbage collection, and safety+speed) are some of the features that are quite useful for developers.

2. Speed

Built-in concurrency ( Goroutines and Channels ) is one of the main reasons for its high performance. Analyzing this stack overflow will allow us to estimate its speed.

“I may have implemented this incorrectly because the results do not make sense. I have a Go program that counts to 1000000000; it finishes in less than a second. On the other hand, I have a Python script; it finishes in a few minutes. Why is the Go version so much faster? Are they both counting up to 1000000000 or am I missing something?” 

If you’re still unsure about the speed, here’s a comparison between Go, Node JS, Java, and Python that will help in gaining more clarity about its usage:

My Device Specification:

Device name- LAPTOP-Q8U9LM8P

Processor- Intel(R) Core(TM) i5-10210U CPU @ 1.60GHz   2.10 GHz

Installed RAM- 16.0 GB (15.6 GB usable)

System type- 64-bit operating system, x64-based processor

N-body print:

Source Time To Count 

Go: 6.34   seconds

Python3: 545.25 seconds

GO

Output:

Factorial   Time To calculate factorial

10000       0.008 seconds

50000       0.506 seconds

100000      3.154 seconds

500000      82.394 seconds

1000000     284.445 seconds

NodeJS (Javascript )

Output:

Factorial   Time To calculate factorial

10000       0.113 seconds

50000       1.974 seconds

100000      22.730 seconds

500000      477.534 seconds 

1000000     1175.795 seconds 

Python

Output:

Factorial   Time To calculate factorial

10000       0.046 seconds

50000       1.187 seconds

100000      6.051 seconds

500000      388.607 seconds 

1000000     813.725 seconds 

JAVA

Output:

Factorial   Time To calculate factorial

10000       0.064 seconds

50000       1.607 seconds

100000      5.363 seconds

500000     141.076 seconds

1000000     585.868 seconds

3. Safety:

GARBAGE Collector:

Go prefers to allocate memory on the stack, so most memory allocations will end up there. This means that it has a stack per goroutine and when possible it will assign variables to this stack.

Golang mark and sweep garbage collector has two phases: Mark, and Sweep. First, it will mark all unused and used variables, then sweep unused ones.

The statistics and the description above suggest why one should work with Go. Golang framework that is best for creating APIs also accelerates and facilitates development.

Why do we use Beego Framework?

Be it Go or Beego, both are fantastic for developing high-performance REST APIs. 

Beego is a “battery included” framework, with built-in tools ( bee tool ), ORM, and libraries compared with other frameworks like Gin-gonic which is not a “battery included” type and contains most essential libraries and features not good for server-side features.

Beego uses a typical Model-View-Controller (MVC) framework which has turned out to be good for people (like us) who work on Python-Django before and Beego is quite similar.

Why do we use Beego Framework?

Conclusion: 

That’s how we started our application with Golang and Beego. We worked on PDF, Image handling with ImageMagick, AWS-SNS, AWS-SES SMTP, IVR calls, Fax, Digital signatures, Reports generation with ORM, and many more. And we haven’t found any blockage while working with third-party features like Twilio or AWS. It is really simple to write code on Golang as mentioned by their creators. There are certain challenges in using this framework but there are solutions as well. We really enjoyed it working on this framework. BEST OF LUCK for your upcoming Golang applications.

About the Author

Piyush Raj graduated from IIT Kharagpur in Chemical Dept. He started his career with ML and AI, and now works at Mantra Labs as a software developer. In his free time, he likes to explore new paths in the real world or on paper through traveling and painting.

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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