متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
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منشورات القناة
JupyterLab Templates plugin
👨🏻💻 If you use JupyterLab a lot, you probably create a new notebook from scratch every time you start a data analysis project. But with the JupyterLab Templates plugin , there is no need to do this anymore. This tool allows you to create different templates and use them whenever you need.
💰 For example, suppose you have a similar structure for each data analysis or EDA project. Using JupyterLab Templates, you can create this structure once and then use that template every time you want to start a new project, without having to start all over again!
⏪ Installing and using this plugin is very simple.👇
pip install jupyterlab_templates| 2 |  👩💻 algorithms is a useful repository with a collection of algorithms implemented in Python!
🌟 It covers a wide range of algorithmic topics, including sorting, searching, graph manipulation, data structures, dynamic programming, cryptography, and more. The main goal of the repository is to provide an educational resource for learning algorithms and improving programming skills.
🔐 License: MIT
🖥 Github | 897 |
| 3 | 📅Duration: 3 Months
⏱Commitment: 20 Hours per Week
📍Location: Addis Ababa (Bole) + Remote
🔗Apply: https://forms.gle/Vatxkg2yQqceRwoF8
📝Deadline: June 10, 2026 | 1 832 |
| 4 |  ⚡️ Google Recaptcha Solver
Google reCAPTCHA solution tool.
Solves captcha in less than 5 seconds! 🚀
This is a Python script to solve the Google reCAPTCHA challenge using the DrissionPage library.
sudo apt-get install ffmpeg | 1 430 |
| 5 | Python vs R: Must-Know Differences
Python:
- Usage: A versatile, general-purpose programming language widely used for data analysis, web development, automation, and more.
- Best For: Data analysis, machine learning, web development, and scripting. Its extensive libraries make it suitable for a wide range of applications.
- Data Handling: Handles large datasets efficiently with libraries like Pandas and NumPy, and integrates well with databases and big data tools.
- Visualizations: Provides robust visualization options through libraries like Matplotlib, Seaborn, and Plotly, though not as specialized as R's visualization tools.
- Integration: Seamlessly integrates with various systems and technologies, including databases, web frameworks, and cloud services.
- Learning Curve: Generally considered easier to learn and use, especially for beginners, due to its straightforward syntax and extensive documentation.
- Community & Support: Large and active community with extensive resources, tutorials, and third-party libraries for various applications.
R:
- Usage: A language specifically designed for statistical analysis and data visualization, often used in academia and research.
- Best For: In-depth statistical analysis, complex data visualization, and specialized data manipulation tasks. Preferred for tasks that require advanced statistical techniques.
- Data Handling: Handles data well with packages like dplyr and data.table, though it can be less efficient with extremely large datasets compared to Python.
- Visualizations: Renowned for its powerful visualization capabilities with packages like ggplot2, which offers a high level of customization for complex plots.
- Integration: Primarily used for data analysis and visualization, with integration options available for databases and web applications, though less extensive compared to Python.
- Learning Curve: Can be more challenging to learn due to its syntax and focus on statistical analysis, but offers advanced capabilities for users with a statistical background.
- Community & Support: Strong academic and research community with a wealth of packages tailored for statistical analysis and data visualization.
Python is a versatile language suitable for a broad range of applications beyond data analysis, offering ease of use and extensive integration capabilities. R, on the other hand, excels in statistical analysis and data visualization, making it the preferred choice for detailed statistical work and specialized data visualization.
Hope it helps :)🔥 | 1 558 |
| 6 | 🔥 Sorting Algorithms📊💻
Sorting is one of the most frequently asked topics in coding interviews.
📌 What is Sorting?
Sorting means arranging data in a specific order:
- Ascending → 1, 2, 3, 4
- Descending → 4, 3, 2, 1
Used in:
- Searching
- Data analysis
- Databases
- Optimization problems
🧠 Important Sorting Algorithms
1️⃣ Bubble Sort
- Concept: Repeatedly compares adjacent elements and swaps them if they are in the wrong order.
- Example: [5, 3, 2] → compare 5 & 3 → swap → [3, 5, 2]
- Key Point: Simple but inefficient
- Time Complexity: O(n²)
2️⃣ Selection Sort
- Concept: Find the smallest element and place it at the beginning.
- Example: [4, 2, 1] → pick 1 → place at start → [1, 2, 4]
- Key Point: Fewer swaps than bubble sort
- Time Complexity: O(n²)
3️⃣ Insertion Sort
- Concept: Builds sorted list one element at a time.
- Example: [3, 1, 2] Insert 1 in correct position → [1, 3, 2]
- Key Point: Efficient for small datasets
- Time Complexity: O(n²), but good for nearly sorted data
4️⃣ Merge Sort
- Concept: Divide array into halves, sort them, then merge.
- Example: [4,2,1,3] → split → [4,2] & [1,3] → sort → merge
- Key Point: Very efficient
- Time Complexity: O(n log n)
- Uses extra memory
5️⃣ Quick Sort
- Concept: Pick a pivot and place smaller elements on left, larger on right.
- Example: [4,2,5,1] → pivot = 4 → [2,1] 4 [5]
- Key Point: Very fast in practice
- Average: O(n log n)
- Worst: O(n²)
🎯 When to Use What
- Small dataset → Insertion Sort
- Large dataset → Merge / Quick Sort
- Nearly sorted → Insertion Sort
- Memory constraint → Quick Sort
⚠️ Common Interview Questions
- Which sorting is fastest? 👉 Quick Sort (average case)
- Which is stable? 👉 Merge Sort
- Which uses divide & conquer? 👉 Merge & Quick Sort
⭐ Real Insight
Interviewers test:
- Understanding of logic
- Time complexity
- When to use which algorithm | 1 604 |
| 7 | 🔥 Data Structures
This is one of the most important topics for coding interviews.
📦 What is a Data Structure?
A Data Structure is a way of organizing and storing data efficiently so it can be:
• accessed quickly
• modified easily
• processed effectively
👉 Choosing the right data structure can optimize performance significantly.
🧠 Types of Data Structures
1️⃣ Linear Data Structures
Elements are arranged sequentially
• Array
– Fixed size
– Fast access using index
– Example use: storing marks
• Linked List
– Elements connected via pointers
– Dynamic size
– Slower access, faster insertion
• Stack (LIFO)
– Last In First Out
– Operations: push, pop
– 👉 Example: Undo feature
• Queue (FIFO)
– First In First Out
– 👉 Example: Ticket system
2️⃣ Non-Linear Data Structures
Elements are arranged hierarchically
• 🌳 Tree
– Parent-child structure
– Used in databases, file systems
• 🌐 Graph
– Nodes connected via edges
– Used in networks, maps
⚡ Key Operations
Every data structure supports:
• Insertion
• Deletion
• Traversal
• Searching
• Sorting
🎯 When to Use What
Problem Type → Data Structure
• Fast lookup → HashMap
• Ordered data → Array / List
• Undo operations → Stack
• Scheduling → Queue
• Hierarchical data → Tree
• Network problems → Graph
⚠️ Common Interview Mistakes
• ❌ Using wrong data structure
• ❌ Ignoring time complexity
• ❌ Not considering edge cases
• ❌ Overcomplicating solution
⭐ Real-World Usage
Data structures are used in:
• Databases
• Search engines
• Social networks
• Navigation systems
• Machine learning
🧠 Important Interview Questions
• Difference between Array Linked List
• Stack vs Queue
• What is HashMap?
• Tree traversal types
• BFS vs DFS | 1 312 |
| 8 | 50 Must-Know Web Development Concepts for Interviews 🌐💼
📍 HTML Basics
1. What is HTML?
2. Semantic tags (article, section, nav)
3. Forms and input types
4. HTML5 features
5. SEO-friendly structure
📍 CSS Fundamentals
6. CSS selectors & specificity
7. Box model
8. Flexbox
9. Grid layout
10. Media queries for responsive design
📍 JavaScript Essentials
11. let vs const vs var
12. Data types & type coercion
13. DOM Manipulation
14. Event handling
15. Arrow functions
📍 Advanced JavaScript
16. Closures
17. Hoisting
18. Callbacks vs Promises
19. async/await
20. ES6+ features
📍 Frontend Frameworks
21. React: props, state, hooks
22. Vue: directives, computed properties
23. Angular: components, services
24. Component lifecycle
25. Conditional rendering
📍 Backend Basics
26. Node.js fundamentals
27. Express.js routing
28. Middleware functions
29. REST API creation
30. Error handling
📍 Databases
31. SQL vs NoSQL
32. MongoDB basics
33. CRUD operations
34. Indexes & performance
35. Data relationships
📍 Authentication & Security
36. Cookies vs LocalStorage
37. JWT (JSON Web Token)
38. HTTPS & SSL
39. CORS
40. XSS & CSRF protection
📍 APIs & Web Services
41. REST vs GraphQL
42. Fetch API
43. Axios basics
44. Status codes
45. JSON handling
📍 DevOps & Tools
46. Git basics & GitHub
47. CI/CD pipelines
48. Docker (basics)
49. Deployment (Netlify, Vercel, Heroku)
50. Environment variables (.env) | 1 543 |
| 9 | 🚀 Senior Backend Engineer (DeFi)
🏢 Confidential Client
📍 Remote (EU-friendly time zones)
🕒 Full-Time
🎯 Role Overview
We’re looking for a Senior Backend Engineer to build and scale backend systems for DeFi applications, working with smart contracts, blockchain data, and high-performance infrastructure.
🎁 Benefits
• Competitive salary + tokens
• Fully remote and flexible work setup
• Work on cutting-edge DeFi products
• Fast-paced and strong engineering team
📩 Apply Here
https://forms.gle/Xhq2ocf3sjcLTDAw7 | 1 555 |
| 10 | --- 𝗦𝗘𝗔𝗥𝗖𝗛𝗜𝗡𝗚 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠𝗦 — 𝗜𝗡𝗧𝗘𝗥𝗩𝗜𝗘𝗪 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡𝗦 𝗪𝗜𝗧𝗛 𝗔𝗡𝗦𝗪𝗘𝗥𝗦 ---
1. What is Linear Search?
Linear Search is a method where you check each element one by one until the target is found.
Example:
Find 5 in [2, 4, 5, 9]
-- check 2 -- check 4 -- check 5 (FOUND)
It works on unsorted data, but is slower for large datasets.
2. What is Binary Search?
Binary Search is a technique where you divide the sorted array into halves to find the target efficiently.
Example:
Find 7 in [2, 4, 7, 10]
-- middle = 7 -- found
It is much faster but requires sorted data.
3. What is the main difference between Linear Search and Binary Search?
Linear Search checks elements one by one, while Binary Search repeatedly divides the search space into halves.
Example:
- Linear -- may check all elements
- Binary -- reduces search area quickly
So Binary Search is faster for large datasets.
4. What is the time complexity of Linear Search?
Worst case: O(n)
Example:
If element is at the end or not present, all elements are checked.
5. What is the time complexity of Binary Search?
O(log n)
Example:
For 1000 elements:
- Linear -- up to 1000 checks
- Binary -- around 10 checks
6. Why does Binary Search require sorted data?
Because it relies on comparing the middle element to decide whether to search left or right.
If data is unsorted, this logic breaks.
Example:
Unsorted -- [7, 2, 10, 4] -- cannot decide direction correctly.
7. What are the common mistakes in Binary Search?
- Using it on unsorted data
- Incorrect calculation of middle index
- Infinite loops due to wrong conditions
- Not handling edge cases
8. What is the space complexity of Binary Search?
- Iterative version -- O(1)
- Recursive version -- O(log n) due to call stack
9. When should you prefer Linear Search?
- When data is unsorted
- When dataset is small
- When simplicity is preferred
10. When should you prefer Binary Search?
- When data is sorted
- When dataset is large
- When performance matters
[ BONUS INTERVIEW QUESTION ]
Q: Can Binary Search be used on linked lists?
Not efficiently, because linked lists do not support direct access to the middle element.
Binary Search works best with arrays.
( INTERVIEW TIP )
Always mention:
- Time complexity
- Condition (sorted or not)
- Why you chose that approach | 1 568 |
| 11 | 🚀 Multiple Positions Open
🏢 Ablaze Labs
📍 Not Specified
🕒 Full-Time
🎯 Role Overview
Ablaze Labs is expanding and looking for experienced professionals across multiple roles. If you want to work in a fast-paced environment on exciting projects, this is a great opportunity.
🧩 Open Positions
• Technical Lead
• Software Architect
• Frontend Engineer
• Backend Engineer
• Partnerships & Sales
• Social Media Manager
• Reception
📩 Apply Here
👉 @Ablaze_Labs | 1 337 |
| 12 | Why AI Can Simulate But Not Instantiate Consciousness
Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the AI welfare trap. What we actually need is a rigorous ontology of computation.
This explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding AI consciousness.
📍People who don't use AI will be left behind | 0 |
| 13 | Real-world Data Science projects ideas
1. Credit Card Fraud Detection
📍 Tools: Python (Pandas, Scikit-learn)
Use a real credit card transactions dataset to detect fraudulent activity using classification models.
Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation.
2. Predictive Housing Price Model
📍 Tools: Python (Scikit-learn, XGBoost)
Build a regression model to predict house prices based on various features like size, location, and amenities.
Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation.
3. Sentiment Analysis on Tweets or Reviews
📍 Tools: Python (NLTK / TextBlob / Hugging Face)
Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral.
Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification.
4. Stock Price Prediction
📍 Tools: Python (LSTM / Prophet / ARIMA)
Use time series models to predict future stock prices based on historical data.
Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis.
5. Image Classification with CNN
📍 Tools: Python (TensorFlow / PyTorch)
Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits).
Skills you build: Deep learning, image preprocessing, CNN layers, model tuning.
6. Customer Segmentation with Clustering
📍 Tools: Python (K-Means, PCA)
Use unsupervised learning to group customers based on purchasing behavior.
Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling.
7. Recommendation System
📍 Tools: Python (Surprise / Scikit-learn / Pandas)
Build a recommender system (e.g., movies, products) using collaborative or content-based filtering.
Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE).
NB:
👉 Pick 2–3 projects aligned with your interests.
👉 Document everything on GitHub, and post about your learnings on LinkedIn.
React ❤️ for more | 0 |
| 14 | 🚀 Web Hosting & DevOps Engineer
🏢 Ashewa Technologies
📍 Addis Ababa, Ethiopia
Role Overview
We’re looking for a DevOps Engineer to manage and optimize cloud and hosting infrastructure with a focus on availability, security, and performance.
Requirements
* 2+ years (MSc) or 4+ years (BSc) in DevOps / System Administration / Hosting
* Strong Linux, DNS, and cloud knowledge
* Experience with cPanel, WHM, or DirectAdmin
* Familiar with GitHub or GitLab
* Strong problem-solving skills
📩 Apply here:
https://docs.google.com/forms/d/e/1FAIpQLSeQK-WEnrbkO6q0yMB4qkivXduLJGymzkGLsfWDPH49GL8tkw/viewform | 0 |
| 15 | Thank you for your Premium support our VIPs 🙌 | 0 |
| 16 | https://t.me/boost/CTBEsoftwareengineers | 0 |
| 17 | Introducing CoreLink: Ethiopian Innovation and Talent Hub
Join now: corelink.et
CoreLink is a verified pool where Ethiopian professionals and students are brought together on one table. It is the space where talent gets discovered by building a deep-dive professional identity based on actual projects and daily progress.
By organizing scattered skills into one resource, CoreLink makes it possible to find the best talent for your ideas and connect with the right team members. Instead of searching through disconnected groups, you can see everyone's work in one place.
If you are a student, start building your profile now to get discovered by companies and individuals. Whether you want to collaborate on projects or be matched with startups and opportunities that fit your skills, CoreLink connects you to both local and global possibilities.
Join the pool of professionals and get discovered.
🔗 Link: corelink.et
📢 Telegram: @CoreLinkEthiopia | 0 |
| 18 | 01 | FRONT-END vs BACK-END
- Front-end: UI/UX (HTML, CSS, JS)
- Back-end: Server, DB, Logic (Node.js, Python)
02 | VARIABLE vs CONSTANT
- Variable: Value can change (let, var)
- Constant: Fixed value (const)
03 | NULL vs UNDEFINED
- Null: Intentional empty value
- Undefined: Variable exists but has no value
04 | FUNCTION vs METHOD
- Function: Independent block of code
- Method: Function tied to an object/class
05 | FOR vs WHILE LOOP
- For: Best for known number of iterations
- While: Runs until a specific condition is met
06 | SQL vs NoSQL
- SQL: Structured, table-based (MySQL)
- NoSQL: Flexible, document-based (MongoDB)
07 | API vs SDK
- API: The interface used to communicate
- SDK: The toolkit used to build the software
08 | LOCAL vs GLOBAL
- Local: Only exists inside a specific block
- Global: Accessible throughout the entire script
09 | RECURSION vs LOOP
- Recursion: A function that calls itself
- Loop: A control structure that repeats code
10 | HTTP vs HTTPS
- HTTP: Standard data transfer (Unsecured)
- HTTPS: Encrypted data transfer (Secure) | 0 |
| 19 |  لا يوجد نص... | 0 |
| 20 | I tried to change my Gmail password to 'ManchesterUnitedDefence'...and Gmail is telling me it's too weak?!😭😂 | 0 |
