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.

Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?

Here's a concise breakdown of each, designed to clarify your options:

### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.

Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer

### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.

Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator

### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.

Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher

### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.

Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst

### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.

Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.

Here are some telegram channels to help you build your career 👇

Web Development
https://t.me/webdevcoursefree

Jobs & Internships
https://t.me/getjobss

Blockchain
https://t.me/Bitcoin_Crypto_Web

Machine Learning
https://t.me/datasciencefun

Artificial Intelligence
https://t.me/machinelearning_deeplearning

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

Complete Roadmap to learn SQL in 2024 👇👇

1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).

2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.

3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.

4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.

5. Subqueries
- Use nested queries for complex data retrieval.

6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.

7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.

8. Indexes
- Understand how to create and use indexes to optimize queries.

9. Views
- Create and manage views for simplified data access.

10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.

11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.

12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.

Here are some free resources to learn  & practice SQL 👇👇

Udacity free course- https://imp.i115008.net/AoAg7K

SQL For Data Analysis: https://t.me/sqlanalyst

For Practice- https://stratascratch.com/?via=free

SQL Learning Series: https://t.me/sqlspecialist/567

Top 10 SQL Projects with Datasets: https://t.me/DataPortfolio/16

Join for more free resources: https://t.me/free4unow_backup

ENJOY LEARNING 👍👍

Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

How to Master Networking

Looking to expand your connections? Use these tips!

1. Be genuine and approachable in conversations.
2. Prepare a brief, engaging introduction about yourself.
3. Follow up with new contacts to build lasting relationships.
4. Offer help and value to others without expecting immediate returns.
5. Attend industry events and stay active on professional platforms.

List of top 10 hard skills:

1. Cloud Computing
2. Data Analysis
3. Digital Marketing
4. Cybersecurity
5. Artificial Intelligence (AI) and Machine Learning (ML)
6. Web Development
7. Database Management
8. Networking
9. Software Development
10. Graphic Design

Keyboard shortcuts for Telegram Desktop ⌨️

Action : Command

• Move to next chat : Ctrl + Tab
• Move to next chat : Ctrl + PageDown
• Move to next chat : Alt + Arrow Down
• Move to previous chat : Ctrl + Shift + Tab
• Move to previous chat : Ctrl + PageUp
• Move to previous chat : Alt + Arrow Up
• Go to Previous Folder : Ctrl + Shift + Arrow Up
• Go to Next Folder : Ctrl + Shift + Arrow Down
• Search selected chat : Ctrl + F
• Exit selected chat and search Telegram : Esc
• Exit display of current chat/channel   : Esc
• Delete currently selected message : Delete
• Quit Telegram : Ctrl + Q
• Lock Telegram (if Local Password is set) : Ctrl + L
• Iconify (Minimize) Telegram : Ctrl + M
• Iconify (Minimize) Telegram to System Tray : Ctrl + W
• Edit Previous Message : Arrow Up
• Start New Line in Input Area : Ctrl + Enter or Shift + Enter
• Move Cursor to Start of Multi-line Message : Ctrl + Home
• Make Text Italic : Ctrl + I
• Make Text Bold : Ctrl + B
• Make Text Underline : Ctrl + U
Make Text Striketrough : Ctrl + Shift + X
• Make Text Monospace : Ctrl + Shift + M
• Remove Text Formatting (Make Selection Plain Text) : Ctrl + Shift + N
• PH4N745M
• Add URL to Selected Text (Make Link) : Ctrl + K
• Send File : Ctrl + O
• Open Contacts : Ctrl + J
• Fast Scroll : Scroll with Ctrl or Shift pressed.
• Reply in another chat : Ctrl+Click on Reply in the menu.
• Jump to a message from the reply panel : Ctrl + LMB.
• Open conversation in a separate tab : Ctrl + click.
• Jump between Folders :  Ctrl + 1,2,3...


#Desktop #Shortcuts #Tips

🧠 Build your own ChatGPT

Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code

⬇️ step-by-step instructions ⬇️

1. Install the necessary Python Libraries

Run the following commands from your terminal to install the required libraries:

2. Import necessary libraries

• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions

3. Set up the Streamlit app and API key input.

• Creates a title for the app
• Adds a secure input field for the Together API key

4. Initialize Together AI clients.

• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients

5. Define the models and aggregator system prompt.

• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt

6. Implement the LLM call function.

• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response

7. Define the main function to run all LLMs and aggregate results.

• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.

8. Set up the user interface and trigger the main function.

• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"

HIGH-INCOME SKILLS TO LEARN💰

1. Artificial Intelligence
2. Cloud Computing
3. Data Science
4. Machine Learning
5. Blockchain
6. Data Analytics
7. Data Engineering
8. Applications Engineering
9. Web Development
10. Software Development
11. UX Design
12. Web Design
13. Graphic Design
14. Video Editing
15. Content Marketing
16. Digital Marketing

OpenAI New Model-01

Don't let snake-oil salesmen fool you. This new model released by OpenAI today doesn't "think." It just generates an extensive "chain of thought", which is just a discussion of the model with itself that looks like a person talking to themselves.

Previous models were trained to be used in one-shot. You ask a question, you get an answer. Because of randomness in next token generation, if you were unlucky, your answer might be wrong. This model was finetuned to generate a long (and hidden from the user by the UI) discussion on how to better solve the problem, what facts are known, what assumptions need to be made, and what constraints should be respected.

If you explicitly asked previous models to generate this discussion before answering your question, you would get a better quality result, because the final answer would be conditioned on the information contained in this discussion.

They seemingly optimized their model to generate good quality discussions (without the user asking for it) by using reinforcement learning on various problems that have a verifiable solution, so that a reward for finding the right answer could be automatically assigned. For example:

Question: 1+1 = ?
Discussion: we have 1 and we have 1 more. And we have a plus sign, so it's an addition. What happens if we add 1 and 1? It means 1 is incremented by 1. When we increment 1 by 1, what do we get? Let's count: 1, 2, 3, 4,... Ok, 2 comes after 1, so 1 + 1 must be 2.
Answer: 2
Reward: 1

Question: 1+1 = ?
Discussion: It's easy. 1+1=11
Answer: 11
Reward: 0

Once the model is trained, what the user sees:

Question: 1+1 = ?
(Discussion happens behinds the scenes.)
Answer: 2

Sure! Here's a more polished version of the statement:

Don't get swept away by the hype around AI; Stay grounded and approach it thoughtfully. 💯

In my teen's I thought:

• A fancy job title.
• A huge salary.
• A demanding job.
= WINNING

In my 20s I realized:

• Having control of your time.
• Doing work you enjoy.
• Seeing family more happy.
= WINNING

Life Looks Different Every Decade.