Optimize Sliding Window Calculations with Incremental Updates

Variable-Size Sliding Window: When to Expand vs Contract Unlike fixed-size windows, finding the longest substring without repeating characters requires dynamic window adjustment. The key insight: expand greedily by advancing the right pointer, but when a duplicate is encountered, contract from the left until the window is valid again. A HashSet tracks current window contents, enabling O(1) duplicate detection and removal. This pattern of "expand until invalid, then contract until valid" is foundational to constraint-based window problems. Why This Works: The right pointer moves exactly n times, and the left pointer also moves at most n times across the entire execution (never backtracks). Amortized analysis shows each character enters and exits the window at most once, yielding O(n) total operations despite the nested loop appearance. This amortized complexity analysis is critical for understanding why certain "nested loop" solutions are actually linear. Time: O(n) amortized | Space: O(min(n, charset_size)) #SlidingWindow #AmortizedAnalysis #HashSet #SubstringProblems #Python #AlgorithmOptimization #CodingInterview #SoftwareEngineering

Llama.fs – LLM Inference in #fsharp 🦙🦙 From-scratch implementation using TorchSharp + .NET 10. No Python, no Ollama—just F#, CUDA (GPU accel), and direct loading of Meta's .pth checkpoints. Key bits: • Full architecture: RoPE, SwiGLU, RMSNorm, GQA (32/8 heads) • KV cache with efficient views • BFloat16 weights + top-p sampling • Interactive terminal chat (Llama 3 instruct template) Runs locally on RTX 4050 (~3 GB VRAM), loads in ~25s first time. Repo: https://lnkd.in/dFZGKSGd Quick start: 1. Grab Llama-3.2-1B-Instruct weights 2. Set modelFolder in Program.fs 3. dotnet run --project src -c Release PRs welcome! (Maybe I should even send one to the TorchSharp examples repo)

PyStormTracker v0.5.0.dev0 is available on TestPyPI. 🎯 New Tracking Algorithm: Initial Implementation of the Hodges (TRACK) algorithm. 🧮 Spectral Backends: Established ducc0 as the primary spectral backend, validated against NCL/SPHEREPACK, and experimental GPU-accelerated support via JAX. ☁️ Data I/O Upgrades: Upgraded the data loader with automatic format detection, adding native support for remote Zarr datasets alongside NetCDF and GRIB. 🗺️ Track Visualization: Web-based interactive track explorer with real-time filtering, supported by a new utility for converting JSON track data. #Python #AtmosphericScience #ScientificComputing #OpenSource

Lately I was doing some #research on a #BLE enabled device and I had to write a fuzzer that run on Macos and allowed me to quickly review the available characteristics. So I put together GaBL-E a simple python script that quickly helps scan, enumerate and write/read to characteristics. I also included some fuzzing iterations that could use to exploit specific scenarios. As a note, this is specificlly designed to work in Mac with its own way of managing bluetooth devices. So do not expect it to work anywhere else. Link: https://lnkd.in/et2V-cug

🚀 Day 5: Cracking the Missing Number Logic 💡 How I solved it: Instead of sorting or using extra space, I used the XOR Bitwise Operator. * XORed all numbers in the range [0, n]. * Then XORed that result with all elements present in the array. *Because a oplus a = 0, all numbers present in the array cancel out, leaving behind only the missing value. 🧠 Key Takeaway: *Efficiency: Achieved O(n) time complexity and O(1) auxiliary space. *Property of XOR: A number XORed with itself is 0, and XORed with 0 is itself. It’s an incredibly powerful tool for finding unique or missing elements in a dataset. One step closer to mastering Data Structures and Algorithms! 💻🔥 The logic is getting sharper every day! 📈🤝 #100DaysOfCode #DSA #Python #ProblemSolving #StriverA2ZSheet #CodingJourney

Visualizing Multithreading: From Race Conditions to Thread Safety 🧵💻 I’ve always believed that the best way to master complex Operating System concepts is to build them from scratch. I recently developed a Multithreading Task Manager in Python to simulate how modern OSs handle concurrent tasks. Key features I implemented: ✅ Thread Lifecycle Simulation: Visualizing threads moving from New → Ready → Running → Terminated. ✅ Race Condition Demo: Showing how data corruption occurs without proper locking mechanisms. ✅ Mutex Locks: Using threading.Lock() to ensure critical sections are protected. ✅ Producer-Consumer Pattern: Implementing a thread-safe Task Queue. Building this helped me bridge the gap between theoretical OS concepts and practical, thread-safe Python code. Check out the demo below! 👇 #Python #Multithreading #OperatingSystems #ComputerScience #SoftwareEngineering #BackendDevelopment #Concurrency

⚡ What if your computer could organize itself? Inderjeet Kumar and Prahalad Kumar just built something crazy — a PC Automation Toolkit using Python 🤖 With a single command, the system can: 🗂️ Automatically organize messy desktop files 📊 Monitor CPU & RAM in real time 🌐 Check network status and IP info 🚫 Block distracting websites 🔐 Reveal saved WiFi passwords 📸 Take screenshots & record the screen 🍅 Run a Pomodoro timer for productivity Basically, we tried turning a normal laptop into a smart assistant for everyday tasks using Python automation. Small project. Big learning. This is why I love building — because a few lines of code can turn boring manual work into instant automation. More experiments coming soon. 🚀 #Python #Automation #BuildInPublic #Programming #TechProjects

⚡ Excited to share a project I built with my groupmate Hansani Hathurusinghe! We developed SCHEDVIZ — a CPU Scheduling Simulator that implements and compares all major CPU scheduling algorithms for any given set of processes! 🖥️ 🔧 Algorithms implemented: ✅ First Come First Served (FCFS) ✅ Round Robin (configurable time quantum) ✅ Shortest Process Next (SPN) ✅ Shortest Remaining Time First (SRTF) ✅ Priority Scheduling 📊 Key Highlights: The simulator takes process inputs, runs all selected algorithms, generates Gantt charts for each, computes average waiting time & turnaround time, and automatically recommends the best algorithm for the given workload! 🎯 🛠️ Tech Stack: Python · HTTP Server · HTML/CSS · JavaScript A great hands-on experience understanding how algorithm choice directly impacts CPU performance! 💡 #CPUScheduling #OperatingSystems #Python #Algorithms #StudentProjects #Engineering #SystemsProgramming

#Today’sFocus: Circular Linked List 🔄 Today I explored Circular Linked Lists (CLL) and implemented two fundamental operations: Creation and Display. Unlike traditional linked lists, a Circular Linked List connects the last node back to the first node, forming a loop 🔁 This means there is no NULL pointer — traversal can continue endlessly! 🔍 What I Practiced: 🔸 Creation of Circular Linked List → Created nodes dynamically → Linked each node sequentially → Connected last node back to head 🔸 Display Operation → Started traversal from head → Used loop to stop when reaching head again → Ensured no infinite loop ⚠️ 💡 Key Insight: Handling traversal in CLL requires careful stopping conditions — since there is no NULL, we must detect when we return to the starting node. 🎯 Why This Matters: ✔ Efficient round-robin scheduling ✔ Multiplayer gaming loops 🎮 ✔ Circular queues ✔ Continuous data processing systems #DataStructures #LinkedList #CircularLinkedList #DSA #Algorithms #Programming #Coding #SoftwareEngineering #ComputerScience #Python #LearnToCode #CodingJourney #TechSkills

Headline: AI Agents on 4GB RAM? I’ve spent the last few days pushing the limits of my 7-year-old laptop (Intel i5-7200U, 4GB RAM). Running Fedora 43 and Python 3.14, I realized that modern AI frameworks are simply too heavy for older hardware in that, they leak memory, crash on new Python versions, and struggle with small models. So, I built "NOEL." (Native Operations & Execution Layer) NOEL is a lightweight, "Direct-to-Shell" AI Assistant. Instead of relying on bloated Python agents, it uses a custom Bash bridge to communicate directly with local LLMs via Ollama. Key Project Highlights:  Bypassed Python 3.14 compatibility issues with a custom metadata shim.  Optimized for the 1.5B Qwen2.5-Coder model to ensure fast inference on 2 CPU cores.  Native Fedora integration: Noel handles journalctl logs and DNF package management seamlessly. 100% Private & Local: No data leaves my SSD. This project proves that the "Small Language Model" (SLM) revolution isn't just for high-end servers—it's for anyone with a terminal and a curious mind. Plans to do a test run to convert it to a full personal assistant AI is considered when i get a hold of better hardware resources. So at the moment we remain on the testing and breaking stage. Any support is appreciated 😁. Check out the full technical breakdown and the source code on my GitHub! https://lnkd.in/dyM37XhD #AI #Linux #Fedora #OpenSource #Ollama #EdgeComputing #Python #HardwareHacking #SelfHosted