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Understanding Big O Notation: Measuring Efficiency in Algorithms! In software development, understanding Big O notation is essential for evaluating how efficiently an algorithm performs, particularly in terms of time and space complexity. It helps us quantify the worst-case scenario, guiding us to write scalable, optimized code. What Big O Represents Big O describes the growth rate of an algorithm as the input size, denoted as n n, increases. It categorizes algorithms based on their order of complexity: Order of n (Linear): Algorithms like simple loops over an array (for loop) have linear complexity, O(n) O(n), meaning the time or space grows proportionally with input size. Order of n² (Quadratic): Nested loops over data structures, such as a nested for loop, have quadratic complexity, O(n2) O(n2). Order of log n (Logarithmic): Algorithms like binary search have logarithmic growth, O(log⁡n) O(logn), efficient even with very large data. Order of constant (O(1)): Operations like accessing an element by index, or checking if an element exists in a set, take constant time regardless of input size. Pattern Recognition in Code Recognizing patterns—such as single loops, nested loops, and data structure operations—allows developers to estimate the complexity quickly. For example: A single for loop over an array implies linear complexity. Nested loops over the same data signify quadratic complexity. Set operations like push/pop and includes/find have different complexities depending on implementation, with hash-based structures often offering near constant time. Why It Matters: Understanding these complexities enables us to predict performance bottlenecks and make informed trade-offs, especially when working with large datasets or real-time systems. It’s also fundamental for writing optimized and scalable code across different data structures like arrays, hash maps, and sets. Mastering Big O notation and recognizing pattern complexities ensures your algorithms are efficient, maintainable, and ready for growth in real-world applications. #Algorithms #BigONotation #CodeEfficiency #DataStructures #ProblemSolving

🚀 Day 111 of 180 — Exploring Low-Level Design (LLD) of URL Shortener System Today’s learning focused on the Low-Level Design (LLD) of URL shortener systems — understanding how long URLs are transformed into short, unique identifiers efficiently and safely. 🔐 URL Encoding Techniques To generate short, unique URLs, we use hashing or encoding algorithms like Base62, MD5, or a Counter-based approach. 1️⃣ Base62 Encoding Uses A–Z, a–z, 0–9 (62 total characters). Supports ~3.5 trillion (62⁷) unique combinations. Scalable and random, but prone to collisions in multi-server setups if not synchronized. 2️⃣ MD5 Encoding MD5 generates a 128-bit hash. Only first 43 bits are used for a 7-character URL. Requires a collision check in the database to prevent data corruption. Advantage: same long URL → same short URL → deduplication → less storage use. 3️⃣ Counter-Based Approach A distributed counter generates unique incremental IDs for every request. Ensures no collisions across multiple servers. Ideal for scalable systems when combined with Base62 encoding to convert numeric IDs into short strings. 🧠 Database Storage & Concurrency Handling When shortening URLs, we store mappings like: { short_url → long_url, created_at, expiration_time } Challenges in distributed systems: Race conditions if multiple servers generate the same short code simultaneously. Solved via centralized counters, unique key constraints, or distributed locks. 💡 Reflection This session helped me understand how something as simple as a short link relies on hashing algorithms, data consistency models, and concurrency control. It’s a perfect example of low-level design thinking — balancing simplicity, performance, and scalability. Grateful to algorithms365 and Mahesh Arali Sir for the continuous mentorship in developing structured system design thinking. #Day111 #SystemDesign #LowLevelDesign #URLShortener #Hashing #Base62 #MD5 #Concurrency #Scalability #BackendEngineering #Algorithms365 #LearningJourney

Recursion vs Iteration — A Performance Perspective Both recursion and iteration can solve the same problems, but their performance impact differs significantly. 🔹 Iteration works within a single function frame — updating variables repeatedly through loops. 🔹 Recursion, on the other hand, creates a new stack frame with every function call, storing local variables and return addresses. Because of this, recursion generally: Uses more memory (O(n) stack space) Runs slightly slower due to function call overhead Can overflow the stack if the recursion depth is large Iteration avoids these costs by running entirely within one frame — making it faster and more memory-efficient. However, modern compilers can optimize certain recursive functions through Tail Recursion Optimization (TRO), effectively transforming them into loops internally — offering the clarity of recursion with the efficiency of iteration. In short: + Use iteration for performance-critical or large data processing tasks. - Use recursion when the problem is naturally recursive (like tree traversal or divide-and-conquer) and code clarity matters more.

“Your architecture is only as strong as your API design.“ Behind every scalable system lies a well-thought-out API — one that balances ,speed, structure, and developer experience. Yet, API design often gets ignored in the race to ship faster. In reality, it’s *the blueprint* that determines how your product scales and how easily teams can integrate with it. Let’s break it down : Choosing the Right Design Approach • REST:Best for standard CRUD operations and web-based integrations. Simple and well-supported. • GraphQL: Ideal when clients need flexible data fetching — request only what’s needed. Great for mobile and AI dashboards. • gRPC: Built for microservices and real-time systems. High-speed communication with low latency. Each has its place. The key is knowing *when* to use which — that’s what separates good systems from great ones. How DSA Shapes Better APIs • Pagination → Queues & modular logic reduce payload size. • Search & Sorting → Efficient algorithms (binary search, merge sort) improve response speed. • Caching → Hashmaps speed up repeated requests. Well-designed APIs: * Reduce latency and cost. * Enable faster iteration. * Make collaboration seamless across teams. In 2025, learning API design is as crucial as learning a new framework. Because **APIs aren’t just endpoints — they’re the language of your system.** #API #Developers #SystemDesign #DSA #GraphQL #gRPC #BackendDevelopment #ScalableSystems #DeveloperCommunity #Python #SoftwareEngineer #BackendDeveloper #CloudServices

How to Debug a Slow API? Your API is slow. Users are complaining. And you have no idea where to start looking. Here is the systematic approach to track down what is killing your API. Start with the network: High latency? Throw a CDN in front of your static assets. Large payloads? Compress your responses. These are quick wins that don't require touching code. Check your backend code next: This is where most slowdowns hide. CPU-heavy operations should run in the background. Complicated business logic that needs simplification. Blocking synchronous calls that should be async. Profile it, find the hot paths, fix them. Check the database: Missing indexes are the classic culprit. Also watch for N+1 queries, where you are hammering the database hundreds of times when one batch query would do. Don't forget external APIs: That Stripe call, that Google Maps request, they are outside your control. Make parallel calls where you can. Set aggressive timeouts and retries so one slow third-party doesn't tank your whole response. Finally, check your infrastructure: Maxed-out servers need auto-scaling. Connection pool limits need tuning. Sometimes the problem isn't your code at all, it’s that you are trying to serve 10,000 requests with resources built for 100. The key is being methodical. Don't just throw solutions at the wall. Measure first, identify the actual bottleneck, then fix it. Over to you: What is the weirdest performance issue you have tracked down? -- just launched the all-in-one tech interview prep platform, covering coding, system design, OOD, and machine learning. Launch sale: 50% off. Check it out: https://lnkd.in/euwKh6u8 #systemdesign #coding #interviewtips

update after another ~3 months. still seeing steady growth. some commentary: i have been bearish on mcp because it doesn't have a solution for tool pollution. i think the pipe-dream for everyone is to have this chatbot that can access all their data from any system of record and synthesize it for good use. the fundamental limitation is the expressiveness of the tools available and the LLMs capability to compose those tools. an analogy from my prof that i really liked is this: "when an engineer sits down to write code that solves a problem, they don't write out the individual functions then compose those together. instead they do the opposite, they write the complete script to perform the solution then after the fact, they may refactor to composable units." so all that to say, this code execution direction from Anthropic is compelling: https://lnkd.in/gxBV5-Zq what's more expressive than a programming language? data source: https://lnkd.in/gFpKBUA8 original post: https://lnkd.in/gF3ACufb update 1 post: https://lnkd.in/gkE9G6cA

𝐁𝐢𝐠 𝐎 𝐍𝐨𝐭𝐚𝐭𝐢𝐨𝐧: 𝐌𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 𝐇𝐨𝐰 𝐘𝐨𝐮𝐫 𝐂𝐨𝐝𝐞 𝐒𝐜𝐚𝐥𝐞𝐬 In backend engineering, it’s not just about making code work, it’s about making it scale. Big O Notation helps us measure how efficiently an algorithm grows as input increases, time, space, or both. It’s how we predict performance under load before it ever hits production. 🔹 𝐎(1) – Constant Time: The operation takes the same time, no matter the input size. Example: Accessing an element by index in an array. Fastest and most predictable, ideal for lookups in hash maps. 🔹 𝐎(log n) – Logarithmic Time: Work grows slowly as data grows. Example: Binary search or balanced tree lookups. Excellent for scalable systems, commonly used in databases and indexing. 🔹 𝐎(n) – Linear Time: Performance grows directly with input size. Example: Looping through an array to find an element. Simple, but can become costly with large data sets. 🔹 𝐎(n log n) – Linearithmic Time: Typical for efficient sorting algorithms like Merge Sort or Quick Sort. Common in data-heavy backend operations like pagination, aggregation, or analytics. 🔹 𝐎(n²) – Quadratic Time: Workload increases dramatically with input size. Example: Nested loops, comparing every element to every other. Avoid this in performance-critical systems, it won’t scale under heavy traffic. 🔹 𝐎(2ⁿ) and beyond – Exponential Time: Usually seen in recursion-heavy or brute-force algorithms. Avoid at all costs for production-scale systems. Understanding Big O helps you design predictable and scalable backends, from API response times to database query efficiency. It’s the foundation behind decisions like caching, indexing, batching, and parallel processing. Pro Tip: When optimizing code, don’t just test the current performance, analyze how it grows with scale. That’s what separates a good backend from a scalable one. Big O Notation isn’t just theory, it’s how backend engineers think in scale. #BackendEngineering #Performance #Scalability #SystemDesign #BigO #BackendDevelopment #SoftwareEngineering #Algorithms #DatabaseDesign #TechLeadership #Programming #CodeOptimization #TechTips #CleanCode #CloudComputing

Super happy to share the open-source version of DRS similar to the one used at Meta built by our academic colleagues (Audris and Peter) for the broader benefit of the developer community. Paper on DRS: https://lnkd.in/gCH_e8wv Post on DRS: https://lnkd.in/gTxddiMW Cc: Matt Steiner, Maher Saba, Peng F., James Everingham, Kelly Hirano, Syamla Bandla, Sahil Kumar, Brian Ellis, Gursharan Singh, Jason Kalich, Rui Maranhao Abreu, Akshay Patel, Audris Mockus and Peter Rigby

Excited to announce DRS-OSS, our open-source, Llama-based system for Just-In-Time Software Defect Prediction! In large-scale software projects, hundreds of commits land daily, each a potential source of defects. Just-In-Time Defect Prediction (JIT-DP) is used to assess the risk level of individual commits at submission time. By leveraging JIT-DP, teams can: • Prioritize code reviews and testing on high-risk commits. • Automatically gate or flag risky pull requests before deployment. • Optimize CI/CD resources to focus on what matters most—preventing regressions before they land. DRS-OSS is a deployable AI system for JIT-DP. It achieves state-of-the-art performance (F1 = 0.64, ROC-AUC = 0.895) and delivers real-time, reproducible risk feedback at scale. Beyond raw metrics, our simulations show that gating just the top 30% of risky commits (e.g., during periods of high usage or production sensitivity) can prevent up to 86.4% of defect-inducing code from landing in production, a significant step toward safer and more cost-effective continuous delivery. Our system uses Llama 3.1 8B as its predictor model, efficiently fine-tuned on the ApacheJIT dataset using parameter-efficient adaptation and CPU offloading. DRS-OSS integrates directly into developer workflows through: • a FastAPI gateway and LLM microservices for inference, • a React dashboard for manual commit analysis, and • a GitHub App that comments on PRs with risk labels and confidence scores. This project is an open-source replication and extension of the “Moving Faster and Reducing Risk: Using LLMs in Release Deployment” paper originally published by Meta. This is joint collaboration between Concordia University University(Ali Sayed Salehi) of Tennessee at Knoxville (Dr. Audris Mockus). Explore DRS-LLM: Website: https://lnkd.in/e_SPVJWy GitHub Bot: https://lnkd.in/eRmqfdPb Source Code: https://lnkd.in/e2P8yhf2 Research: https://lnkd.in/eFfnj_uM If you’re interested in AI for Software Engineering or agent-based CI/CD systems, we’d love to connect and collaborate! #AI #SoftwareEngineering #DefectPrediction #LLM #MLOps #OpenSource #Research #ConcordiaUniversity #UTK #Meta #DRSLLM

Exploring a Low-Cost, Open-Source RAG Architecture Lately, I’ve been exploring how to build an AI system that can learn directly from your own documents — PDFs, DOCXs, TXTs — and answer questions contextually, without depending on expensive APIs or closed platforms. The idea is simple: 💬 Ask: “What are the company’s time-off rules?” 🤖 Get: an accurate, context-based response — pulled right from your internal files. The architecture I’m planning (illustrated below 👇) combines LangChain / LlamaIndex, ChromaDB or Postgres + pgVector, and Ollama running open models like Llama 3 or Mistral 7B. Here’s how it works in three steps: 1️⃣ Ingestion Pipeline: Load, split, and embed your internal documents to build a searchable knowledge base. 2️⃣ Retrieval Phase: When a user asks a question, a semantic search retrieves the most relevant chunks from the vector store. 3️⃣ Generation Phase: A local LLM (via Ollama) uses those chunks to generate clear, contextual responses — powering FAQs, flashcards, or summaries via a simple UI (Gradio/Streamlit). 🔍 Why I find this exciting: • 100 % open-source — no vendor lock-in • Private & secure — all data stays within your environment • Lightweight — runs even on a low-cost GPU setup • Flexible — easily swappable components (models, databases, or frameworks) This is still in the design stage, but I’m excited to start prototyping soon — the goal is to create a private, low-cost RAG system that teams can deploy to turn their own content into a smart knowledge assistant. If you’ve experimented with Ollama, pgVector, or ChromaDB, I’d love to hear your thoughts or performance tips! #AI #RAG #OpenSource #LLM #LangChain #LlamaIndex #ChromaDB #Postgres #pgVector #Ollama #Mistral #Llama3 #GenerativeAI #MLOps #KnowledgeManagement