AAU Software Engineering community

Senior Full-Stack Software Developer (Dashboard Development) DNA TECH | Data Neutral Analysis Technology PLC 📍 Location: Addis Ababa 💼 Job Type: Project-Based Contract 💰 Salary: Negotiable (Based on Experience & Project Scope) 🎯 Experience: 4+ Years 📩 How to Apply • Send your CV to: tamrataraya@dataneutralanalysistechnology.com

🚀 Three.js & Next.js Developer Location: Remote Job Type: Full-Time Salary: ETB 20,000–30,000/month (Negotiable) Vacancy : 10 Experience: Intermediate to Expert 📩 How to Apply • Apply here: https://docs.google.com/forms/d/e/1FAIpQLScrKTqdNMU_TancFISfhoxw97Cmxy0Tn88IyXYvc6J0Wb2ryw/viewform

"ይህ ቀን የዓመታት ድካማችሁና የፅናት ውጤታችሁ ብቻ አይደለም፤ አዲሱ የትምህርት ሪፎርማችን ከተጀመረ በኋላ ያስመረቅናችሁ የመጀመሪያዎቹ ተመራቂዎች በመሆናችሁ፣ ዕለቱ በኢትዮጵያ የትምህርት ታሪክ ውስጥ አዲስ ምዕራፍ ሲያበስር የታሪኩ ባለቤት ናችሁ!" ፕ/ር ብርሀኑ ነጋ የኢፌዴሪ ትምህርት ሚኒስትር እንዲሁም የአዲስ አበባ ዩኒቨርሲቲ ቻንስለር በቀዳሚ አሻራ የጸናን፣ በልህቀት የምንጓዝ! #AAU #aaugraduation2026

76ኛው የአዲስ አበባ ዩኒቨርሲቲ የምረቃት ሥነ ስርዓት በቀዳሚ አሻራ የጸናን፣ በልህቀት የምንጓዝ!

የእንኳን ደስ አላችሁ መልእክት ከአዲስ አበባ ዩኒቨርሲቲ ፕሬዝደንት ዶ/ር ሳሙኤል ክፍሌ በቀዳሚ አሻራ የጸናን፣ በልህቀት የምንጓዝ Rooted in Legacy, Advancing Through Excellence

🚀 Summer Internship Program 🏢 Swenetix Tech PLC 📍 Addis Ababa 💼 Full-Time Internship | Onsite 📅 Duration: July – September 2026 (3 Months) 📅 Application Deadline: June 30, 2026 ✅ Requirements • Student or recent graduate • Basic knowledge of HTML, CSS, JavaScript, or any programming language • Familiarity with JavaScript frameworks is a plus • Strong communication and teamwork skills • Passion for learning and technology 📩 How to Apply • Apply here: https://docs.google.com/forms/d/e/1FAIpQLSeNfg8YN1jCL7zZ-xyrra0bLQGBsMLfKfNkrLLC8hT6AyXx-A/viewform?usp=send_form

🚀 Remote Internship Program 🏢 Oasis Infobyte 🌍 Remote 🎓 Students & Fresh Graduates ⏳ Duration: 4–6 Weeks 📌 What You'll Do • Work on live industry projects • Collaborate with mentors • Build practical, job-ready skills • Showcase creativity and innovation 📩 How to Apply • Apply here: https://docs.google.com/forms/d/e/1FAIpQLSfwL_cwk_k0YzMS9iUz_onHs2VBNAKet3hgSrrUuEKrC_s6sQ/viewform

I don't think we should be paying 9$/m for load testing using machines we own. so i built loadcell (FOSS), run http requests, save them as a load testing profile, draw ramp up curves and run them concurrently with go. https://x.com/Robimez/status/2061939732049801224?s=20 github.com/robimez/loadcell @rb_wk http://github.com/robimez/loadcell Postman😭🔥 l like thissss🙌 use the customized features for scaling

🖥 How to hide secret text inside an image using Python? Nothing complicated, you just need the Stegano library:

# pip install stegano

from stegano import lsb
secret = lsb.hide('image.png', 'very secret text')
secret.save('secret_image.png')

print(lsb.reveal('secret_image.png'))

A quick question: how is this ability to store text in a picture implemented? How easy is it to detect such text hiding? 🖥 GitHub

Senior UI/UX Designer 📍 Addis Ababa, Ethiopia Part-Time 📅 Deadline: July 2, 2026 🧩 Key Responsibilities • Design user-friendly web and mobile interfaces • Create wireframes, prototypes, and design systems • Conduct user research and journey mapping • Build and maintain component libraries • Collaborate on product and user experience improvements 📩 How to Apply • Submit your CV and portfolio via the application form • Application Form: https://forms.gle/A4BX7E1c7BkjS2YT9

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

👩‍💻 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

📅Duration: 3 Months ⏱Commitment: 20 Hours per Week 📍Location: Addis Ababa (Bole) + Remote 🔗Apply: https://forms.gle/Vatxkg2yQqceRwoF8 📝Deadline: June 10, 2026

⚡️ 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

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 :)🔥

🔥 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

🔥 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

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)

🚀 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

--- 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