How to be a computational scientist with code assistants

📊 LeetCode Challenge: Median of Two Sorted Arrays Today’s problem was one of the most elegant and widely discussed challenges in algorithmic problem solving — finding the median of two sorted arrays. 💡 Problem Summary You’re given two sorted arrays, nums1 and nums2, and you need to find the median of the combined sorted array. The catch? You must do it in O(log(m + n)) time — meaning, no simple merging allowed 🚫 🧠 The Intuition At its heart, this is a binary search problem disguised as a math puzzle. The trick is to find a partition between the two arrays such that: Every element on the left side of the partition is less than or equal to every element on the right side. Once that partition is found — the median lies either in the middle element(s) of these partitions. 🧩 Why It’s Interesting It transforms a two-array problem into a search problem on one array. It’s a brilliant example of reducing complexity through symmetry. And it highlights how binary search can be used far beyond just finding numbers. ⏱️ Complexity Time: O(log(min(m, n))) Space: O(1) 🌱 Key Takeaway This problem reminded me that powerful solutions often come from rethinking the approach, not just optimizing the code. Sometimes, stepping back and seeing the symmetry in a problem can simplify everything. #LeetCode #DSA #ProblemSolving #BinarySearch #Algorithms #CodingChallenge #Learning

The R/Pharma virtual conference is taking place next week 📆✨ And as every year, the program is packed with great R developers who share a bunch of knowledge of applying R in pharma and biotech! It’s free to attend, and put together by passionate volunteers that want to share all about R 💙. And there are also some workshops/talks for Python users! Some personal highlights of the program: 🔹 Workshop on programming with LLM APIs using 𝗲𝗹𝗹𝗺𝗲𝗿 by Sara Altman 🔹 Practical AI for data science (keynote) by Simon Couch, because we love some practical use cases. There’s also a Q&A with Simon where you can ask him all about what he’s up to next 👀. 🔹 Build Model Context Protocol servers and clients with 𝗺𝗰𝗽𝗿 by John Coene 🔹 Validating Shiny apps in regulated environments by Pedro Silva, which includes talking about “Litmusverse” from Jumping Rivers, which I think is a very interesting project 🔹 Workshop where you’ll learn to supercharge your Shiny app by offloading computations to a HPC cluster by Michael Mayer 🔹 Nicola Rennie is providing a workshop on building (better) R packages, which will take away the intimidating feeling of writing your first R package 🔹 Michael Chow is doing a workshop on 𝗽𝗼𝗹𝗮𝗿𝘀 for the Python lovers 🐍 And this is just a small part! R/Pharma will run from 3-7 November with both a Europe/US and an APAC track. Who’s going to be there? 👋🏻 Schedule and website in comments 🔗👇🏻

Just successfully "ported" our old AGENTS 0.9 platform — a concurrent constraint programming system for the AKL language I helped develop in the early 1990s as part of my doctoral research — to run natively on modern 64-bit platforms (x86-64 and ARM64/Apple Silicon). Using Claude Code as my AI pair programmer and workhorse for all the heavy lifting, we identified and fixed two critical bugs that had been dormant in the codebase for three decades: a signedness issue in integer classification and a parser reentrancy problem. The remarkable part? Only seven lines of code needed changing. The system now runs cleanly on Apple Silicon, proving that well-architected research software can span generations with minimal intervention. 🙂 Here’s the classic n-queens problem running on the ported system — near-instant execution of what was cutting-edge concurrent programming research from before the Internet was a thing. It was a great pleasure to work on this system with Johan Bevemyr, Johan Montelius, Kent Boortz, Per Brand, Bjorn Carlson, Ralph Haygood, Björn Danielsson, Peter Olin, Dan Sahlin, Thomas S., and Seif Haridi — remarkable how solid that foundation still is. 🔗 https://lnkd.in/denpPHxq

📊 The Ultimate Algorithms Roadmap for Developers 📊 Excited to share this comprehensive algorithms roadmap that I've found incredibly valuable in my technical journey! This visual guide by @debugwithshubham breaks down the essential algorithms every developer should know: 🔍 Searching: From simple linear search to complex interpolation search techniques 🔄 Sorting: Eight powerful methods to organize data efficiently 🌐 Graph Algorithms: Master network problems with Dijkstra's, Bellman-Ford, and other critical pathfinding approaches 📱 Array & String: Kadane's algorithm, pattern matching, and dynamic programming fundamentals 🧩 Basics & Others: Compression techniques and essential algorithms like A* Search Whether you're preparing for technical interviews or looking to strengthen your algorithmic thinking, this roadmap serves as an excellent reference point! What algorithm category do you find most challenging? Let's discuss in the comments! 👇 If you want to stay updated with more such amazing and exclusive opportunities follow for more Shubham Maurya #ProgrammingFundamentals #AlgorithmsAndDataStructures #TechLearning #DeveloperResources #CodingJourney

🔍 Transforming Smart Parking Systems with Computer vision and LLM's! I'm excited to share a project I've been working on. It integrates Computer Vision and large language models (LLMs) to revolutionize parking lot management. By combining Roboflow's object detection with open source LLM's like Phi4, I developed a system that detects occupied and available parking spaces in real time and generates detailed, data-driven reports for smarter decision-making. 🛠️ Key Features: ✅ Real-Time Detection: Using a YOLO model from Roboflow to identify occupied parking spots. ✅ LLM-Powered Analysis: Ollama LLM generates actionable insights and recommendations. ✅ Automated Reporting: Detailed Markdown reports with occupancy trends and AI-generated suggestions. ✅ Scalable & Customizable: Built to scale for large parking lots or smart city solutions. You can check the code here: https://lnkd.in/g8k3yrqm Don't Forget to drop a star ⭐ and also feel free to check out other projects in the same repo #deeplearning #opensource #python #computervision

Becoming Quantum Computing Something* (Week 62, 2025-10-31) This week was again about learning how to learn in the age of AI. After struggling to get proper math equations from PDFs in Heptabase, I turned to a more familiar environment: Visual Studio Code. I built a lightweight prototype of a Test-Driven Learning setup that uses AI to generate questions directly from study materials and quiz me continuously. Not as a standalone app, but as a crafted prompt inside .github/copilot-instructions. Everything is then handled by a Coding Agent in VS Code. The system has a directory for resources, another for memory. It generates tables of contents, tracks progress, and marks completed topics. Quite easy to set up and quite effective for active recall. Still, it’s missing some essential elements: proper spaced repetition, visualization of the learning graph, and better cross-linking between concepts. Math parsing also needs improvement. GPT-5 and Gemini both handled the questioning phase fairly well, though Gemini sometimes struggles to persist progress for some reason (it just lies about saving the files). Each query consumes around 0.3% of my Copilot monthly limit, so efficiency matters. And to close the week, I submitted my first homework for Quantum Computing 1. A small but meaningful milestone. #QuantumComputing #AIForLearning #TestDrivenLearning #CTU #ContinuousLearning

🚀 Algorithm Books Every Programmer Should Know 🚀 If you want to master algorithms and data structures, these are my essential recommendations. 1️⃣ “Introduction to Algorithms” by Cormen, Leiserson, Rivest & Stein The gold standard in academia. Over 1,000 pages of rigorous theory with detailed pseudocode. Dense but comprehensive. 2️⃣ “Grokking Algorithms” by Aditya Bhargava Perfect for beginners. Visual and intuitive explanations without intimidating math. My favorite for getting started! 3️⃣ “Algorithms” by Robert Sedgewick & Kevin Wayne Comprehensive textbook with Java code. 4️⃣ “The Algorithm Design Manual” by Steven S. Skiena Practical approach with real world examples. Ideal for seeing how algorithms are used in industry. 5️⃣ “The Art of Computer Programming” by Donald Knuth The legendary (and dense) work of the 1974 Turing Award winner. Slow but essential reading. Links in comments!

How Fortran Outperformed Our Billion-Dollar AI Chips | Dax | October 2025 https://lnkd.in/g5DfnGHv Uncovering Surprising Performance in AI Programming In a surprising revelation, we discovered that a 1970s Fortran loop outperformed our meticulously optimized CUDA kernels by 1.4×! What began as a light-hearted benchmarking exercise turned into a lesson in the enduring power of simplicity. Key Insights: Old vs. New: Traditional Fortran code can still compete with modern GPU optimizations. Benchmarking Fun: The experiment highlights the necessity of testing beyond current best practices. Performance Paradox: An ancient loop showed that sometimes, less is more in programming efficiency. This intriguing exploration challenges our assumptions about technology and optimization in AI applications. It’s a call to appreciate legacy code and recognize the fundamentals in our pursuit of innovation. 🚀 Curious to learn more? Dive into the details of this unexpected performance story and rethink your approach to optimization! Share your thoughts below! Source link https://lnkd.in/g5DfnGHv

⚙️ Day 72 of #100DaysOfCode ⚙️ 💡 Problem: GCD of Odd and Even Sums – LeetCode ✨ Approach: Calculated the sum of the first n odd and even numbers separately, then determined their GCD through iterative checking. A neat blend of arithmetic patterns and number theory logic! 🔢 📊 Complexity Analysis: Time Complexity: O(n)** — summing and iterating up to the limit Space Complexity: O(1)** — constant auxiliary space ✅ Runtime: 575 ms ✅ Memory: 42.79 MB 🔑 Key Insight: Even the simplest mathematical patterns can reveal deep logic — coding is where math meets creativity! ✨ #LeetCode #100DaysOfCode #ProblemSolving #DSA #NumberTheory #LogicBuilding #AlgorithmDesign #ProgrammingChallenge #Efficiency #CodeJourney #DeveloperGrowth #CodingDaily

Day 5 of #30DaysOfCode Solving "Loud and Rich" - A Graph Theory Challenge Just tackled an interesting problem that combines graph traversal with dynamic programming! The Problem: Given people with different wealth levels and quietness scores, find the quietest person among those who are equally or more wealthy than each individual. Key Insights: ✓ Model wealth relationships as a directed graph ✓ Use DFS/BFS with memoization to traverse richer individuals ✓ Track the quietest person at each level The Approach: 1. Build an adjacency list where edges point from richer to poorer individuals 2. For each person, traverse all people who are richer (or equal) 3. Use memoization to avoid redundant calculations 4. Return the person with minimum quiet value in each subtree Time Complexity: O(n + e) where e is the number of wealth relationships Space Complexity: O(n) for the memoization cache This problem beautifully demonstrates how graph algorithms can model real-world hierarchical relationships! What's your favorite approach to solving graph traversal problems with optimization constraints? #DSA #GraphTheory #ProblemSolving #SoftwareEngineering #Algorithms #TechChallenges #30DaysOfCode #educative Educative