Two Pointer Technique Beats Nested Loops in Two Sum II

I built a code converter a while back — Python to JS and back, with LLM under the hood. I've since rebuilt the pipeline significantly. Added test coverage checks, refinement loops, and a step where the model decides whether the converted code actually needs another pass. The reason: LLMs hallucinate. A single-pass conversion isn't reliable enough. So the pipeline now runs the actual test suite after each conversion, feeds the results back to the model, and loops until the tests pass and coverage meets the threshold. But loops cost time. And inference isn't instant to begin with. So now the pipeline takes a while. And a long-running pipeline with no feedback is flying blind — you don't know if it's thinking, stuck, or silently wrong. That's why I added a visualizer. Which got me thinking about the "GUI is dead, future is CLI" takes I've been seeing lately. I don't think the container matters — it can be a web UI, a TUI, a progress stream in the terminal. Even Claude Code streams every step rather than returning one big answer at the end. The interface will keep changing. The need to see what's happening won't. Here's what it looks like in action 👇 Demo: https://lnkd.in/gjxkTrEX #AIEngineering #LLM #AgenticAI

Spent three hours debugging why my web scraper was failing randomly... Turns out the target site was loading content with a 2-second delay, but only sometimes. Classic intermittent bug. Fixed it by adding proper wait conditions in Playwright instead of hardcoded sleeps. Now it waits for the actual element to appear, not just a fixed time. The scraper went from 70% success rate to 99.8% overnight. This is why I always tell people: async loading breaks everything if you're not careful. Modern websites are not your friend when building automation. What's the weirdest intermittent bug you've had to track down? --- Want to automate your workflows or build AI-powered systems for your business? DM me — I help teams ship automation that actually works. #BuildInPublic #WebScraping #Playwright #DevLife #Automation #Python #Debugging #SideProject

Just solved the Min Stack problem with an optimized O(1) approach for all operations. Key takeaway: Maintaining an auxiliary stack to track the minimum at each step ensures constant-time retrieval without additional traversal. Operations achieved: push → O(1) pop → O(1) top → O(1) getMin → O(1) This problem reinforces an important pattern: augmenting data structures to trade space for time efficiency. Always satisfying to see clean logic translate into solid performance. #DataStructures #JavaScript #ProblemSolving #LeetCode #Algorithms

512,000 lines of code. Almost 2,000 files. All it took was one tiny mistake in a package.json file for it all to go down the drain. Yesterday, March 31, 2026, ANTHROPIC, basically one of the richest AI companies out there, accidentally leaked the entire source code for "Claude Code." And the wild part is? Nobody hacked them. No one broke into their servers. It was just a simple file that shouldn't have been pushed. When we build apps with JavaScript, we use these files called "source maps." They’re basically like a map that helps developers fix bugs by linking the messy, unreadable code back to the clean stuff we actually wrote. They’re great for working on your own computer locally during development, but you’re never supposed to let them leave your local machines. Anthropic’s team accidentally left one in their public package. That map file pointed straight to a private zip folder on their storage. A researcher found it and it was all over. Within a few hours, over 40,000 people had already copied forked the whole code on github. It’s so easy to laugh and say, "How did these geniuses mess up something so basic?" But honestly, this happens to the best of us. We spend all our time worrying about high-tech security and hackers, but we forget to check the boring stuff, like a simple configuration file or a typo in our ".npmignore" list. The lesson is pretty clear: it doesn't matter how good your code is if your shipping process is sloppy. A single line nobody checked is the difference between a successful launch and giving your work away for free. If you’re a dev, do yourself a favor and always run a dry-run before you publish anything today. Anthropic can afford a mistake like this; most of us can't. Is it just me, or are we getting so focused on AI and complex architecture that we're forgetting the basics of shipping code? #SoftwareEngineering #Coding #JavaScript #Anthropic #TechTips #Programming

LLMs are powerful, but they hallucinate and don't know your private data. Retrieval-Augmented Generation (RAG) fixes both. By giving the LLM a search engine for your exact documents, you get 100% accurate, traceable answers with zero model fine-tuning costs. To really understand how it works under the hood, I built a complete, local RAG pipeline from scratch. What I built: 🔹 Engine: LangChain + FAISS + all-MiniLM-L6-v2 for sub-millisecond local vector search. 🔹 Brain: Llama 3 running via Groq for blazing-fast generation with sources & confidence scores attached. 🔹 UI: Pure CSS dark glassmorphism interface in React. 🔹 Live Terminal Sync: Wrote a custom FastAPI hook that streams Python chunking/embedding logs directly to the UI sidebar so you can see exactly what the backend is doing. Check out the demo video below to see the UI and the live terminal sync in action! 👇 #RAG #MachineLearning #FastAPI #React #LangChain

Sometimes the biggest bottleneck in your code isn't the algorithm itself, but how the language handles memory. 💻 Just crushed a complex query problem with a 100% runtime. To get the execution time this low (389ms), I had to step away from standard JavaScript practices and optimize for the machine: Instead of creating new arrays and triggering heavy GC pauses, I allocated a BigUint64Array exactly once and overwrote it. By combining this with Square Root Decomposition for modular arithmetic, the processing time plummeted. A great reminder that when dealing with large datasets, thinking about memory allocation and data types is just as important as the logic itself. #CodingJourney #JavaScript #DataEngineering #Optimization #LeetCode

💡 A small algorithmic discovery while revisiting sorting algorithms While revisiting sorting concepts like Bubble Sort and Insertion Sort, I tried implementing my own approach to sort an array. Interestingly, the logic that came to my mind turned out to be very similar to the idea behind Insertion Sort. The approach was simple: • Start from the second element of the array • Compare the current element with previous elements • Shift larger elements to the right • Insert the current element in its correct position Here is the JavaScript snippet I came up with: const getSortedArr = (arr) => { const n = arr.length; for (let i = 1; i < n; i++) { let curr = arr[i]; let prev = i - 1; while (arr[prev] > curr && prev >= 0) { arr[prev + 1] = arr[prev]; arr[prev] = curr; prev--; } } return arr; }; 📊 Complexity Analysis • Time Complexity - Best Case: O(n) - Average Case: O(n²) - Worst Case: O(n²) • Space Complexity - O(1) (in-place sorting) #JavaScript #Algorithms #Sorting #Learning #SoftwareEngineering #ProblemSolving

Solved Leetcode 497 - Next Greater Element I Here’s the key insight: Use a monotonic decreasing stack. Traverse from right to left. For each element, remove all smaller elements from the stack. The top of the stack becomes the next greater element. To make it efficient for queries, store the results in a map: number → next greater element This reduces the complexity from O(n²) to O(n). What clicked for me was this: The stack is used for computation, and the map is used for fast retrieval. Once you understand this separation of roles, a whole category of problems becomes easier: Next Greater Element Previous Greater Element Stock Span Daily Temperatures #DataStructures #Algorithms #JavaScript #CodingInterview #LeetCode #ProblemSolving

HolyClaude is a preconfigured, containerized AI development workstation that runs the real Claude Code CLI together with a browser-based UI, headless Chromium (Playwright), and a consolidated developer toolchain (TypeScript, Python, DB clients, multiple AI CLIs). The project emphasizes operational reliability: images and configuration tweaks address common Docker/browser failure modes such as shared-memory limits, Xvfb compatibility, UID/permission mismatches, and SQLite locking on network mounts. Images are published in variants to balance included tooling and image size. Target users are developers and teams that already hold a Claude Code subscription or API key and need a reproducible environment for web-based interactions, automated browser testing, and combined CLI workflows without repeated container troubleshooting. Limitations: HolyClaude packages and configures third-party components but requires the user's existing Claude Code subscription and credentials. Runtime and subscription constraints for Claude Code remain governed by the upstream service. #HolyClaude #ClaudeCode #containerization https://lnkd.in/dtpmNqh9

🚀 Finding the Middle of a Linked List in O(n) Time & O(1) Space One of the most elegant patterns in algorithm design is the Fast & Slow Pointer technique (also known as the tortoise and hare 🐢🐇). Here’s a simple example in JavaScript to find the middle node of a singly linked list; 💡 How it works: The slow pointer moves one step at a time The fast pointer moves two steps at a time When fast reaches the end, slow is at the middle ✅ Why this is powerful: No extra memory needed (O(1) space) Only one pass through the list (O(n) time) Widely used in problems like: Detecting cycles Finding middle elements Splitting linked lists ⚠️ Bonus insight: For even-length lists, this approach returns the second middle node — which is often the expected behavior in coding interviews. 📌 Check out more patterns and exercises here: https://lnkd.in/ej4fNeZs Mastering patterns like this is key to leveling up in data structures & algorithms. #JavaScript #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #100DaysOfCode