How to Boost Telegram Post Views

A Tale of Triumph and Technology

Have you ever wondered how some Telegram posts skyrocket in views overnight?

I certainly did. That curiosity sparked a thrilling journey of discovery, innovation, and relentless problem-solving, culminating in a project that combined technology and tenacity to achieve the seemingly impossible: increasing views on Telegram posts using automation. This blog takes you through my exhilarating journey, from the initial idea to a working solution that, while not perfect, showcased the power of persistence and ingenuity.

The Curiosity: How It All Began

One evening, while scrolling through various Telegram channels, I noticed how some posts garnered massive views within a short span of time. Intrigued, I wondered if it was possible to artificially boost these numbers. The idea seemed simple yet challenging enough to pique my interest.
With a basic understanding of Python and some enthusiasm, I embarked on this project, armed with curiosity and a determination to learn.

The Research: Diving into Technology

My first step was to understand how Telegram tracks views. I scoured through online forums, StackExchange posts, and technical documentation. I even sought advice from AI tools like ChatGPT. Gradually, I pieced together a plan: by using different proxies, I could make Telegram’s server think that each view was from a unique user. The concept was simple, but the execution would be far from straightforward.

Building the Foundation: Writing the Code

Armed with newfound knowledge, I started coding. My project consisted of several components:

- A main driver script to coordinate everything.

- A module to handle Telegram API interactions.

- A system for fetching and managing proxies.

- Helper functions for configuration and logging.

These parts worked together to form the backbone of my project.

The First Hurdle: Initial Errors and Low Accuracy

Excitedly, I ran my first complete script. The result? A mere 5% success rate. Out of 100 requests to increase views, only 5 were successful. This was a major blow. The project was far from the seamless success I had envisioned.

But giving up was not an option. I dived deeper into the code, debugging, and tweaking. One major issue was proxy validation. Many proxies were either dead or unreliable, causing a high failure rate. I needed a robust way to validate proxies before using them.

The Breakthrough: Proxy Validation

To tackle the proxy problem, I implemented a validation function to test each proxy against a known URL to ensure it was active and reliable. With this improvement, the success rate jumped to 40%, a significant leap forward. However, this came at the cost of time. Processing 100 requests now took nearly 3 hours.

The Challenge: Fetching and Validating Proxies

Getting new proxies for the program was not easy. I took free proxies from different sources, which involved scraping fresh free proxies from websites and GitHub repositories using a regex function. This added another layer of complexity but was crucial for improving the success rate.

The Challenge: Reducing Time

With accuracy somewhat under control, the next challenge was time efficiency. I realized that validating proxies and sending requests could be optimized by running multiple tasks at once. By parallelizing the process using python’s multithreading, I managed to cut down the time significantly without compromising the accuracy.

The Climax: Reaching a Functional Solution

Finally, after numerous iterations, sleepless nights, and countless cups of coffee, I had a working solution. It wasn’t perfect, but it was a testament to perseverance. The project now had a decent success rate and operated within a reasonable timeframe.

The Reflection: Lessons Learned

Looking back, this journey was a rollercoaster of emotions and learning. It taught me the importance of persistence, the value of detailed research, and the power of technology. Each hurdle was a stepping stone, each failure a lesson, and each success a motivation to keep pushing forward.

The Technical Background: Telegram Bot API and Python

For the tech enthusiasts, here’s a deeper dive into the technologies I used:

- Python: The core programming language.

- Requests: For sending HTTP requests.

- Threading: To handle multiple proxies simultaneously.

- ConfigParser: For managing configuration files.

- Regex: For extracting necessary data from responses.

- Logging: To keep track of errors and debug information.

- Fake UserAgent: To simulate different user agents.

A significant part of the journey was learning how to make the bot functional using the Python Telegram Bot library. This library provides an easy-to-use interface for the Telegram Bot API, allowing me to create a bot that could interact with users, collect URLs, and initiate the view-boosting process.

Throughout this process, I encountered numerous errors. From understanding the basics of the Telegram Bot API to dealing with unexpected issues, each problem required careful reading of the Telegram Bot API documentation, trial and error, and sometimes a bit of creativity to solve.

Conclusion: A Journey Worth Taking

From a simple question to a fully functional project, this journey was a thrilling adventure. It was a blend of technical challenges and creative problem-solving. The project may not have turned out exactly as I initially imagined, but the process was invaluable.

Never forget the need of perseverance if you ever encounter a similar problem. Accept the trip, take in all you can from every step, and don’t be scared to go far into the unknown. Worth it are the benefits.

Thus, the next time you come along a Telegram post with an abnormally large amount of views, you might just be curious in the background. And who knows — you could be motivated to begin your own fascinating project.

Happy Hacking!