How to troubleshoot application crashes due to full partitions.

🔎 As someone who interacts and works with data every day, I’ve been curious about how files are stored and transferred efficiently. I explored ZIP files and LZ77 to understand how repeated patterns can be represented efficiently without losing any information — principles that also inspire storage and transfer systems at companies like Google, Amazon and Dropbox. 📁 Check out my latest blog post for a step-by-step look at LZ77 and the principles behind lossless compression → https://lnkd.in/g4iTNZpK Looking to connect and learn with others in data and tech! #Learning #Data #Compression #CloudComputing #Storage

Simple LZW Compression Algorithm Implementation in C A straightforward C code of the Lempel-Ziv-Welch (LZW) compression algorithm in C. LZW is a popular lossless data compression method that substitutes repeated data occurrences with references to a single instance. #C #lzw #algorithm #compression

Go beyond vague 400s and 500s and learn how to implement structured, standards-based error responses in your Quarkus applications with just a few lines of code. https://buff.ly/FaOJ4bF

Right window- list directory and read file. It says “can you read this”. Left side run go file that encrypts files inside a directory and saves a private key to decrypt. Right side try to read the same file but it has .encrypted extension and it displays cipher text.

In my last post, I talked about Escape Analysis and how Go tries to minimize heap allocations to reduce GC work. But what if we know we’ll need the same objects repeatedly? 👉 That’s where sync.Pool comes in. A sync.Pool is a concurrent-safe pool of reusable objects. Instead of allocating a new object each time (and later letting GC clean it up), Go lets you borrow from the pool and put it back when done. Example : https://lnkd.in/ecTZGPzG 🔹 Without sync.Pool: every request allocates a new buffer → GC pressure. 🔹 With sync.Pool: buffers get reused → fewer allocations, less GC overhead. 📌 Takeaway: sync.Pool isn’t for caching forever — it’s for short-lived, temporary objects where reuse can dramatically improve performance. #Golang #Performance #BackendEngineering #Concurrency

Use nameof() to Keep Code Clean Sometimes we write variable names as text: Console.WriteLine("user"); ❌ Problem: if you rename user, this text will not change → possible bugs. ✅ Solution: use nameof() void Save(User user) { ArgumentNullException.ThrowIfNull(user, nameof(user)); } Now if you rename user, this code updates automatically. Less bugs, cleaner code!

Tips for Claude Code Memories 1️⃣ After implementing a large feature or fixing a non-obvious bug, prompt Claude with "Save everything we learned (or implemented) in this session and update the memory" 2️⃣ Create separate CLAUDE.md in subdirectories that can be more specific. Claude will only use these when needed (avoid context rot/bloating) 3️⃣ Take advantage of @path/to/import in memory files ie @docs/migration-instructions.md. Especially useful if you're doing 1️⃣. Of course, you can ask CC to break up your large memory files for you automatically 😎

An ID Generator in C I wrote a C program that generates a 16-character ID from: 10 digits (0–9) 26 uppercase letters (A–Z) 26 lowercase letters (a–z) That gives us 62 possible characters for each slot. Since the ID length is 16 characters, the total number of unique possible IDs is: 62 raised to the power of 16 which is approximately 40000000000000000000000000000 (40 octillion) possible combinations. In other words, the probability of generating the same ID twice (at random) is practically 0 for all real-world purposes. Here’s the program:

Ping with a graph – a CLI tool that visualizes latency and packet loss in real time with custom colors. https://lnkd.in/dsUAK_eq

Day 155/160 of GFG 160 Days of DSA 🔥 Today’s topic: Implement Trie (Prefix Tree) 🌲 🔹 Approach used: Each node stores references (links) for 26 lowercase characters. insert(word) → Traverse character by character, create new nodes if missing, and mark the last node as an end. search(word) → Traverse, return true only if the final node is marked as end. isPrefix(word) → Traverse, return true if all characters exist (even if it’s not an end node). 🔹 Key Insight: Tries shine in prefix-based lookups (e.g., autocomplete, spell check). They are faster than sets/maps when handling large word collections. 🔹 Complexity: Insert/Search/Prefix check → O(L) where L is the length of the word. This data structure is widely used in search engines, dictionaries, and predictive typing systems 🚀 #GFG160 #GeekStreak2025 #Trie #PrefixTree #DSA