"Dr. McCaffrey's JavaScript ANOVA tutorial: computing F-statistics and p-values"

🔼 Day 165 of #200DaysOfCode Today, I revisited a fundamental concept that plays a major role in data structures and algorithm design — sorting an array in ascending order using Bubble Sort (without built-in sort methods). 💡 Modern JavaScript gives us shortcuts like Array.sort(), but when we build the logic manually, we develop a much deeper understanding of: • Pairwise comparison • Value swapping in arrays • Nested looping structure • Time complexity (Bubble Sort → O(n²)) Sorting isn’t just a beginner concept — it’s the backbone of efficient searching, optimization, and real-world computational logic. 🔁 Going back to basics reminds me that advanced problem-solving ability is built on strong fundamentals, not shortcuts. 🌱 Every step forward in coding is supported by the basics we choose to master — and revisit. #JavaScript #200DaysOfCode #CodingChallenge #Sorting #Algorithms #WebDevelopment #DeveloperMindset #LearnInPublic #DSA #ProblemSolving

 #100daysCODINGchallenge Date 27 - 10 - 2025 Day 65 of #100DaysOfCoding challenge! 🚀 Aaj maine seekha JavaScript Primitive Data Types ke baare mein Primitive Data Types hote hain String Text data ke liye ( "Hello World") Number Numeric values ( 25, 3.14) Boolean True/False values ( true, false) Undefined Jab variable declare hota hai par value assign nahi hoti Primitive ka matlab hota hai simple aur immutable (unchangeable) data type Mujhe ye concept kafi interesting laga kyunki ye JavaScript ke base foundation ko samjhne mein help karta hai #JavaScript #CodingJourney #LearnWithMe #100DaysOfCode #FrontendDevelopment

🌐 Day 152 of 160 – GeeksforGeeks #gfg160 DSA Challenge 📌 Problem: Floyd-Warshall Algorithm – All-Pairs Shortest Path 💻 Language: JavaScript 📂 Topic: Graphs | Dynamic Programming | Shortest Path | Matrix 🧠 Problem Summary: Given a weighted directed graph with V vertices and E edges, find the shortest distance between all pairs of vertices. The graph may contain negative weight edges but no negative cycles. ✅ Approach Used: Floyd-Warshall Algorithm (Dynamic Programming on Graphs) Initialize a dist[][] matrix with Infinity for all pairs; set dist[i][i] = 0. Fill dist[u][v] with the edge weights if an edge (u, v) exists. Iteratively update dist[][] for each intermediate vertex k: For all i and j: dist[i][j] = min(dist[i][j], dist[i][k] + dist[k][j]) After all iterations, dist[i][j] contains the shortest path from i to j. ⏱ Time Complexity: O(V³) 📦 Space Complexity: O(V²) 🔍 Key Insights: A classic DP on graphs problem. Handles negative edge weights, unlike Dijkstra. Foundation for understanding all-pairs shortest path, transitive closure, and graph optimization. 💡 Takeaway: Floyd-Warshall elegantly shows how dynamic programming extends to graphs, helping solve complex network problems where paths between all nodes are required. #Day152 #DSAChallenge #FloydWarshall #AllPairsShortestPath #GraphDP #DynamicProgramming #JavaScriptDSA #GeeksForGeeks160 #160DaysOfCode #CodingChallenge #GraphAlgorithms #TechInterviewPrep #ProblemSolvingDaily #ShortestPathMatrix #AlgorithmLearning

⚡ Day 151 of 160 – GeeksforGeeks #gfg160 DSA Challenge 📘 Problem: Bellman-Ford Algorithm 💻 Language: JavaScript 📂 Topic: Graph | Shortest Path | Dynamic Programming 🧠 Problem Summary: Given a weighted directed graph with V vertices and E edges, find the shortest distance from a source vertex S to all other vertices — even if negative edge weights exist. If there’s a negative weight cycle, report it. ✅ Approach Used: Bellman-Ford Algorithm (Dynamic Programming + Relaxation) Initialize dist[] with infinity for all vertices, except dist[S] = 0. Relax all edges V - 1 times: For each edge (u, v, w), if dist[u] + w < dist[v], update dist[v] = dist[u] + w. Run one more iteration to check for negative weight cycles. If any edge can still be relaxed → negative cycle detected. 📊 Example: Edges = [[0,1,5],[1,2,-2],[0,2,3]] Shortest distances from 0: [0, 5, 3] ⏱ Time Complexity: O(V × E) 📦 Space Complexity: O(V) 🔍 Key Insights: Works for negative edges, unlike Dijkstra. Fails only if a negative cycle exists. Relies on the idea that shortest paths contain at most (V - 1) edges. 💡 Takeaway: Bellman-Ford beautifully blends graph theory with DP-style relaxation, teaching the importance of iterative updates and cycle detection in weighted graphs. #Day151 #DSAChallenge #BellmanFord #GraphAlgorithm #ShortestPath #DynamicProgramming #JavaScriptDSA #GeeksForGeeks160 #160DaysOfCode #NegativeWeightCycle #AlgorithmLearning #CodingChallenge #TechInterviewPrep #ProblemSolvingDaily #GraphTheory

I was so frustrated watching my Mac's disk space vanish into node_modules folders everywhere. So I decided to build something about it. I created a Python CLI called cleanup-nodemodule that recursively finds and removes build artifacts like node_modules, .next, and dist folders. The best part? It's completely safe by default with dry-run mode. This is also my first PyPI package. Thanks to AI, I figured out the entire publishing workflow so other devs can actually install and use it with pip. Honestly, publishing to PyPI felt intimidating, but breaking it down step by step made it doable. How to use it pip install cleanup-nodemodule # Safe dry-run first (shows what would be deleted) cleanup-nodemodule -p /path/to/project # Actually delete (when you're ready) cleanup-nodemodule -p /path/to/project --no-dry-run It's simple, safe, and saves a ton of disk space. I tested it on macOS and it works smoothly. If you try it and find bugs, please let me know or contribute. What other folders should I add? Thinking .turbo, .cache, .vercel, coverage. For more instruction click on comment link #python #javascript #nodejs #react #cli #opensource #devtools #productivity #firstproject #learning #ai #developer #coding

 Async I/O vs Threading: The Real FastAPI Performance Secret Many developers use FastAPI because it’s “fast”, but few understand why it’s fast. The real reason? Asynchronous I/O (async/await) Let’s break it down 👇 🧩 1️⃣ Threading: Traditional Approach Threading works by running multiple threads concurrently. Each thread can process one task, but before Python 3.14, Python’s GIL (Global Interpreter Lock) means only one executes at a time per core. That’s fine for CPU-heavy tasks, but not ideal for I/O-heavy work (like DB queries or API calls). ⚙️ 2️⃣ Async I/O: Modern Approach Async I/O uses a single event loop to handle thousands of concurrent requests without blocking. When one request waits on I/O, another starts immediately. That’s how FastAPI achieves massive throughput. Example 👇 from fastapi import FastAPI import httpx, asyncio app = FastAPI() @app.get("/weather") async def get_weather(): async with httpx.AsyncClient() as client: res = await client.get("https://lnkd.in/dw-UPWaG") return res.json() ✅ Why this works: async with and await let the event loop handle multiple requests concurrently. Perfect for I/O-bound workloads like network calls or DB queries. 🧠 When to Use What Use Async I/O for APIs, web scraping, DB, or network-heavy apps. Use Threading/Multiprocessing for CPU-bound tasks (ML inference, heavy computation). Takeaway: FastAPI’s performance doesn’t come from magic, it comes from asynchronous design done right. Mastering async and await is how you unlock real backend scalability. #FastAPI #Python #BackendEngineering #AsyncIO #Concurrency #Microservices #SoftwareArchitecture #PerformanceEngineering #ScalableSystems

💡 Problem-Solving Post: Surrounded Regions in a Grid Today I worked on an interesting problem that really tested my understanding of graph traversal and boundary conditions. 🧩 Problem Summary: Given a 2D grid containing 'O's and 'X's, the goal is to replace all 'O's that are completely surrounded by 'X's. In other words, any 'O' that is not connected to the border should be converted into 'X'. ⚙️ Key Idea: At first glance, it looks like a simple replacement problem — but it’s not! The challenge is to differentiate between ‘O’s that are truly surrounded and those that are connected to the border. 🧠 Approach: Identify all 'O's on the borders (they can never be surrounded). From each border 'O', perform a DFS/BFS traversal to mark all connected 'O's as “safe.” After marking, flip the remaining 'O's (which are not safe) into 'X'. 🚀 What I Learned: The importance of thinking beyond the direct condition — sometimes the solution lies in what’s connected to the edge. How depth-first or breadth-first traversal can simplify problems that initially seem complex. A reminder that clean logic and clear reasoning often matter more than complex code. 👨💻 Complexity: Time: O(n × m) Space: O(n × m) (due to recursion or queue storage) It’s a simple yet elegant application of graph traversal that blends problem-solving and optimization thinking. Have you solved a similar boundary-based traversal problem recently? Would love to hear your approach or variations! #ProblemSolving #DSA #GraphAlgorithms #CodingJourney #LearningEveryday #Java

MetaResolve is excited to announce HarveyVaultApp, a small open-source desktop utility built in Python that allows users to bulk-upload documents directly into their Harvey AI vault via API. Read more about why we decided to build it and how you can benefit in our press release: https://lnkd.in/gKb5czXE

I wrote a short blog post introducing MetaResolve’s new open-source Harvey.ai bulk uploader. Easy, straightforward, and simple solution for those that have been plagued by the platform’s limitations. Go give it a read, you might find the app useful!

💡 Exploratory Data Analysis on USA Housing Dataset 🇺🇸 Just uploaded my latest project on GitHub — an in-depth EDA of the USA Housing dataset! This project focuses on uncovering meaningful insights about housing prices using Python, pandas, NumPy, Matplotlib, and Seaborn. 🔍 Highlights: Data cleaning and preprocessing Correlation analysis between features like area, bedrooms, and price Visualizations to explore patterns and relationships Heatmaps and regression plots for better feature understanding 📊 This EDA helps identify the most influential factors affecting housing prices — a great foundation for future machine learning prediction models. 🧠 Tech Stack: Python | pandas | NumPy | Matplotlib | Seaborn | Jupyter Notebook 🔗 Check out the full notebook and visuals here: 👉 https://lnkd.in/girNxqgd #DataScience #EDA #MachineLearning #Python #DataAnalysis #GitHub #Visualization #USA #HousingMarket