Binary Search Algorithm: Efficient Sorting and Finding

Most tutorials teach you to build a model. Nobody teaches you what to do when it breaks in production. Here’s what actually goes wrong after deployment: → Input data format shifts slightly and your preprocessing crashes → A class your model never saw during training starts appearing → Confidence scores are high but predictions are wrong → Model works on your machine. Fails on the server. These aren’t ML problems. They’re software engineering problems. The gap between “model works in notebook” and “model works in production” is where most ML beginners get stuck. Bridging that gap is the actual skill nobody talks about. What’s the messiest production bug you’ve encountered? #MachineLearning #MLEngineering #Python #DeepLearning #SoftwareEngineering #ComputerVision #PyTorch #MLOps #AI #Programming

HNSW delivers impressive speed and recall for vector search — but it comes with trade-offs. In this short video we peel back the layers: why the graph structure drives higher memory and disk use, how index build/merge behavior impacts performance, and what that means for latency in Elasticsearch. Read the benchmark notes here: https://lnkd.in/etakF2xu #programming #ai #database https://lnkd.in/e87UuP8F

APL’s secret isn’t just its symbols-it’s how it treats math as code. The language doesn’t abstract away mathematics; it is mathematics. Looking at its source code reveals a deep truth: elegant solutions come from aligning syntax with concept, not layering abstractions. Modern languages still struggle with this balance. What happens when we stop treating programming as an afterthought to math-and make the two one? How do we build systems today that honor both the beauty of mathematical notation and the efficiency of computation? #APL #ProgrammingLanguages #MathInCode #SoftwareArchitecture #ArrayProgramming

For over 25 years I've been writing DSP algorithms, and for 25 years the same thing has driven me mad: you prototype in MATLAB, rewrite in C++ for production, then spend weeks chasing subtle differences between the two. Sometimes you add a Python version "just for fun". Every rewrite is a chance to introduce bugs that didn't exist in the original. Every rewrite is time not spent on the actual problem. I finally decided to stop complaining and build the tool I wish I'd had all along. The language I'm working on is based on a subset of MATLAB/Octave, designed specifically for audio DSP – with initial applications in synthesizer development and matrix-based live coding. The idea is simple: write code in the subset language, and if you ever need MATLAB's advanced debugger or analysis tools, your code just works there too. I've named it Semitone (and if you find a musical pun there, you may keep it). Why build a new language instead of just using Octave? Because I need something that can run inside a synth plugin. Something lightweight, embeddable, and eventually real-time safe. Octave is a fantastic tool, but it's not something you ship inside a VST (besides the fact that Octave's license is GPL). Also, I've implemented a few features to the interpreter that allow certain types of code to run at speeds that are only 2 or 3 times slower than C++ code compiled with -O3 – try doing that with MATLAB or Octave! The plan is to release the language interpreter itself under a permissive license. The tools built around it – e.g. synthesizer plugins where you define your own signal chain in code – those will be commercial products. It's early days. Matrices, indexing, basic linear algebra, and a growing set of built-in functions are working. There's a long road ahead, but for the first time in a while, I'm writing code where simplicity is the goal, not the enemy. More updates to come. If you're into audio programming, DSP, language design, or just enjoy watching someone build something from scratch — stick around. #programming #dsp #audio #synthesizers #livecoding #matlab #languagedesign

Programming is not just about writing instructions anymore—it’s about building systems that can make decisions. In MATLAB, this idea comes alive through conditional logic. At first, we write code that simply executes line by line. But real-world problems are never that simple. Data changes. Conditions vary. Systems need to respond intelligently. This is where conditional thinking becomes important. Instead of forcing every step, we define rules: 👉 If a condition is true → do something 👉 If not → move in a different direction Behind the scenes, MATLAB simplifies everything into logical evaluation (true = 1, false = 0). This simple concept becomes the foundation for powerful applications like data filtering, simulation control, and algorithm design. What I find most interesting is that this mirrors human decision-making. We don’t follow fixed paths in life—we react based on conditions. MATLAB does exactly the same in computation. Once you understand this shift, coding stops being just “writing commands” and becomes designing behavior. That’s the real power of conditional logic. 💡 Learning MATLAB is not just learning syntax—it’s learning how to think in logic. #MATLAB #Programming #DataScience #MachineLearning #Engineering #Coding #TechLearning https://lnkd.in/dtPreNJn

What if you could draw in the air using just your finger? I created a real time air drawing application using Computer Vision that tracks hand movements and lets you draw on screen without touching anything. Features: • Hand tracking using MediaPipe • Draw with index finger • Multiple color options 🎨 • Clear screen functionality • Smooth drawing using interpolation • Real-time FPS display 🛠 Tech Stack: Python • OpenCV • MediaPipe This project helped me understand how powerful computer vision can be in real-world applications. 🔗 Code: https://lnkd.in/dC7RSAp2 Would love your feedback! 👇 #Python #OpenCV #AI #ComputerVision #Projects #Coding #Developers #LinkedInCreators

Antigravity + Claude combo is crazy. I just used Opus latest Model to transform idea into reality and more projects and ideas on the way. Building > overthinking We’re entering an era where AI can handle a lot of the heavy lifting. Many top engineers and recruiters say this clearly: 👉 The value is shifting from writing every line of code to thinking, designing, and building systems. 👉Problem Solving and System Design is the future. So instead of waiting to “learn everything first”, I tried something different, I started building. This made me realize: 👉 In today’s world, we need to become builders & architects, not just coders. Would love your thoughts on this 👇 #BuildInPublic #AI #Developers #Python #Projects #Learning #SystemDesign

Most people don’t struggle with DSA because they’re “bad at coding.” They struggle because they try to memorize hundreds of problems instead of learning the small set of patterns behind them. Once I stopped asking: ❌ “Which LeetCode problem is this?” and started asking: ✅ “Which pattern is hiding here?” everything changed. This cheat sheet covers the core DSA patterns that solve the majority of interview questions: • Two Pointers • Sliding Window • Prefix Sum • Binary Search • Fast & Slow Pointers • Monotonic Stack • Tree Traversal • Heap / Priority Queue • Top K Frequency • Merge Intervals • Hashmaps • DFS / BFS The biggest realization? The same pattern keeps showing up again and again in different forms. A “Longest Substring Without Repeating Characters” problem teaches you Sliding Window. A “Top K Frequent Elements” problem teaches you Heaps. A “Find Peak Element” problem teaches you Binary Search. A “Next Greater Element” problem teaches you Monotonic Stack. You don’t need to master 300 problems. You need to master the patterns. If I had to start over, I’d spend 7 days like this: Day 1: Arrays & Strings Day 2: Binary Search Day 3: Linked Lists Day 4: Stacks & Queues Day 5: Trees Day 6: Heaps / Priority Queues Day 7: Re-solve everything without notes That one week would be more valuable than months of random practice. #DataStructures #Algorithms #DSA #CodingInterview #LeetCode #SoftwareEngineering #Programming #InterviewPrep #ComputerScience #Tech

A future where AI agents run our labs and simulations is already upon us. Experimenting with OpenClaw to run simulations and program instruments completely took me by surprise. Give it the right API keys and a secure environment, and programming lab instruments or testing theoretical models becomes effortless. Add to that the possibility to read research papers and to try their models via Python. Despite the current serious security concerns and the skepticism about the generated code, the massive amount of time this saves means scientists can redirect a big part of their energy into idea generation and learning.

Most people don’t struggle with DSA because they’re “bad at coding.” They struggle because they try to memorize hundreds of problems instead of learning the small set of patterns behind them. Once I stopped asking: ❌ “Which LeetCode problem is this?” and started asking: ✅ “Which pattern is hiding here?” everything changed. This cheat sheet covers the core DSA patterns that solve the majority of interview questions: • Two Pointers • Sliding Window • Prefix Sum • Binary Search • Fast & Slow Pointers • Monotonic Stack • Tree Traversal • Heap / Priority Queue • Top K Frequency • Merge Intervals • Hashmaps • DFS / BFS The biggest realization? The same pattern keeps showing up again and again in different forms. A “Longest Substring Without Repeating Characters” problem teaches you Sliding Window. A “Top K Frequent Elements” problem teaches you Heaps. A “Find Peak Element” problem teaches you Binary Search. A “Next Greater Element” problem teaches you Monotonic Stack. You don’t need to master 300 problems. You need to master the patterns. If I had to start over, I’d spend 7 days like this: Day 1: Arrays & Strings Day 2: Binary Search Day 3: Linked Lists Day 4: Stacks & Queues Day 5: Trees Day 6: Heaps / Priority Queues Day 7: Re-solve everything without notes That one week would be more valuable than months of random practice. Quick challenge 👇 Comment with the ONE DSA pattern that changed the way you solve problems. For me, it was Sliding Window — once it clicked, so many problems became easier. What’s yours? Let’s build the best pattern list in the comments 🚀 #DataStructures #Algorithms #DSA #CodingInterview #LeetCode #SoftwareEngineering #Programming #InterviewPrep #ComputerScience #Tech