‼️ USEFUL SITES TO MAKE YOUR WORK EASIER ‼️
1. Media.io – Online Free File Converter, Editor, Compressor
2. https://alternativeto.net/ - Lacks of softwares
3. https://geektyper.com/mobile/ - Pretend to be a hacker
4. http://Leetcode.com/ - Practice your coding skills
5. https://www.privacytools.io/ - Amazing site for privacy tools
6. http://Rainymood.com/ - Amazing site for Rainy & Thunderstorm sounds (to listen while sleeping)
🔔Unmute Notification & Share Channel For More Content ✅
1. Media.io – Online Free File Converter, Editor, Compressor
2. https://alternativeto.net/ - Lacks of softwares
3. https://geektyper.com/mobile/ - Pretend to be a hacker
4. http://Leetcode.com/ - Practice your coding skills
5. https://www.privacytools.io/ - Amazing site for privacy tools
6. http://Rainymood.com/ - Amazing site for Rainy & Thunderstorm sounds (to listen while sleeping)
🔔Unmute Notification & Share Channel For More Content ✅
❤4
Coding is just like the language we use to talk to computers. It's not the skill itself, but rather how do I innovate? How do I build something interesting for my end users?
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
In a recently leaked recording, AWS CEO told employees that most developers could stop coding once AI takes over, predicting this is likely to happen within 24 months.
Instead of AI replacing developers or expecting a decline in this role, I believe he meant that responsibilities of software developers would be changed significantly by AI.
Being a developer in 2025 may be different from what it was in 2020, Garman, the CEO added.
Meanwhile, Amazon's AI assistant has saved the company $260M & 4,500 developer years of work by remarkably cutting down software upgrade times.
Amazon CEO also confirmed that developers shipped 79% of AI-generated code reviews without changes.
I guess with all the uncertainty, one thing is clear: Ability to quickly adjust and collaborate with AI will be important soft skills more than ever in the of AI.
❤5👍1
5 Handy Tips to master Data Science ⬇️
1️⃣ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2️⃣ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3️⃣ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4️⃣ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5️⃣ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
1️⃣ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2️⃣ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3️⃣ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4️⃣ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5️⃣ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
❤7
🔰 10 Python Automation Project Ideas
🎯 File Organizer (sort files by type)
🎯 Bulk Image Resizer
🎯 Email Automation Tool
🎯 YouTube Video Downloader
🎯 PDF Merger/Splitter
🎯 Auto Rename Files
🎯 Instagram Bot (like/comment)
🎯 Weather Notification App
🎯 Currency Converter
🎯 Stock Price Tracker
React ❤️ for more like this
🎯 File Organizer (sort files by type)
🎯 Bulk Image Resizer
🎯 Email Automation Tool
🎯 YouTube Video Downloader
🎯 PDF Merger/Splitter
🎯 Auto Rename Files
🎯 Instagram Bot (like/comment)
🎯 Weather Notification App
🎯 Currency Converter
🎯 Stock Price Tracker
React ❤️ for more like this
❤10