Rod Johnson on Embabel: A new framework for JVM

One of the biggest reasons developers ❤️ Rust is simple yet powerful — it offers memory safety without using a garbage collector. 🧠⚙️ Most programming languages handle memory cleanup automatically at runtime, which is convenient but can lead to unpredictable pauses, slowdowns, or even crashes. Rust takes a different path — it enforces memory rules before your program even runs. Thanks to Rust’s borrow checker, every variable has clear ownership, and data access rules are verified at compile time. 🧩 This means you can’t accidentally create dangling pointers, data races, or invalid memory references. The result? You catch memory errors early, not in production. 🚫💥 This system design gives Rust the speed of C/C++ with the safety of high-level languages — making it a favorite for systems programming, game engines, embedded devices, and even web assembly projects. ⚡ Rust isn’t just a language; it’s a mindset — encouraging developers to think precisely about resource ownership, lifetimes, and concurrency. The learning curve might feel steep at first, but once you “get” the borrow checker, there’s no going back. 🦀💪 #Rust #Programming #SystemProgramming #Developers #SoftwareEngineering #Coding #Performance #TechExplained #BackendDevelopment #LowLevelProgramming #OpenSource #DevLife

Why does Functional Programming (FP) matter in today’s world of distributed and data-heavy systems? ⚙️ At its heart, FP is built on two core principles: pure functions and immutability. 🧩 A pure function always gives the same output for the same input and has no side effects — meaning no hidden state or unexpected behavior. Immutability ensures data can’t be changed once created, eliminating bugs caused by shared or modified state. These ideas lead to more predictable, testable, and parallel-friendly code — a huge advantage when working with massive datasets or concurrent tasks. 💪 That’s why big data frameworks like Apache Spark, Scala, and Flink embrace FP concepts. They can safely process data across multiple nodes without worrying about race conditions or inconsistent states. ⚡ But FP isn’t just for big data — it’s a mindset. Thinking functionally encourages cleaner design, modularity, and fewer surprises in your codebase. In short, Functional Programming isn’t a trend — it’s an evolution in how we reason about code, built for a world that runs on data and parallelism. 🌍💻 #FunctionalProgramming #Programming #SoftwareEngineering #BigData #ParallelComputing #Developers #TechExplained #DataEngineering #CleanCode #ModernTech

The CAP Theorem ⚖️ — simple to say, tricky to master — quietly defines how every major distributed system works. It stands for Consistency, Availability, and Partition Tolerance — the three properties every system wants, but here’s the catch: you can only have two. 🚫 🔁 Consistency → All nodes show the same data at the same time. ⚡ Availability → Every request gets a response, even if some nodes fail. 🌐 Partition Tolerance → The system keeps running even if parts can’t communicate. When the network splits (and it always will, someday), your system must choose: stay consistent or stay available. 💾 CP systems (like MongoDB in certain modes) favor accuracy — they pause to stay consistent. ⚙️ AP systems (like Cassandra) keep serving data, even if it’s a little outdated. There’s no perfect combo — just trade-offs based on what your app values most: reliability or responsiveness. So next time someone says their system is “highly available and consistent”, remember — CAP doesn’t let you have your cake and eat it too. 🎂 #SystemDesign #DistributedSystems #SoftwareEngineering #BackendDevelopment #Programming #Developers #DatabaseDesign #TechExplained #Coding #Scalability

🔍 Day 38 of #160DaysOfChallenge 📌 Topic: Search in a Row-Column Sorted Matrix 📚 Category: DSA – Matrix Problems Today’s challenge: You’re given a matrix where each row and each column is sorted in ascending order. The task is to determine whether a given target value exists in the matrix. Sounds simple? The trick is in optimizing the search. Instead of going row-by-row, we can use a smarter approach starting from the top-right corner, adjusting direction based on comparisons — reducing time complexity to O(m + n). ⚡ 🔗 Takeaway: This problem sharpens your skills in matrix traversal and binary search intuition. It’s a great example of applying logic to reduce brute-force time. ✅ Small wins add up. Day 38 down, 122 to go! Have you tackled this problem before? Share your approach or optimizations! 👇 #160DaysOfCode #DSA #CodingChallenge #Leetcode #MatrixProblems #ProblemSolving #TechCommunity #CodeNewbie #LinkedInCodingChallenge

#BuildingThePentester Day 66: Deeper into the CTF Mindset. My study of Gynvael Coldwind's "How to Win CTFs" continued today, and the second part was a deep dive into specific challenge categories and advanced tooling. Here are some of the key takeaways from today's session: Reverse Engineering (RE) Tactics: Gynvael broke down common RE tasks, from simple hard-coded flag comparisons to more complex scenarios involving decoding flags or exploiting byte-by-byte flaws. He also introduced two powerhouse Python libraries for automating RE: Z3 (an SMT solver) and Angr (a binary analysis framework). Brute-Forcing as a Strategy: The discussion wasn't just how to brute-force, but more importantly, when. Understanding when it's a viable strategy versus a time-wasting rabbit hole is a critical skill in itself. Steganography Challenges: He covered the different types of steganography tasks commonly found in CTFs, adding another layer to the problem-solving toolkit. Invaluable Resources: At the end, he shared some absolute gold for anyone serious about CTFs: ctftime.org/writeups: The single best place to read and learn from others' solutions. The "Handbook for CTFers": A book I'll definitely be adding to my reading list.

Coding with multiple agents at once sounded outrageous to me just a few months ago, and now here is Simon Willison canonicalizing it in his blog. I have tried working with multiple agents, too, and I find that it makes a lot of sense. I’m not talking about dozens of agents, all committing and shipping code to production at once; that still seems risky, and besides, that’s not my working style. I like to test and develop one thing, release it, and then move on to the next thing. The experience of using multiple agents on the same task reminds me a lot of developing in a compiled language. No matter what you do, you spend minutes to hours of your day just staring at the compiler console and waiting for it to either error or to give you a finished build. Working with an agent like Claude can feel like waiting for the compiler to compile. But in the agentic world, if we start another task, we can spin up another model. This is not so with compiled languages, because the amount of resources available to spin up “another compiler“ usually isn’t immediately available, and even if they were, what are you going to do with that other compiler? It’s not like you have an entire other code base. But when it comes to agents, there is always another aspect to the task at hand that can be explored while another agent is processing a request. Once I have written the prompt or prompt chain that elicits an extended, grinding response from Claude, I usually have raised several questions in my own mind about the details of the task that Claude is carrying out for me. One way to utilize multiple agents is to go to GPT and ask it questions about the expected outcomes of the process that Claude is currently running. Additionally, if I’m confident that the feature I am producing is nearly working, then I will want to develop documentation. Either GPT or Qwen is excellent at explaining in words what is going on in a piece of code. So I could spin up one or more agents to begin creating an explanation in words of what is happening in the code. Multiple agents can work on multiple different code features simultaneously as long as they are working in different directories and won’t overwrite each other’s code. For instance, when developing an API feature, another agent can develop the monitoring plan or the data pipeline for the API's back end. https://lnkd.in/ekdPNMWT

💻 Day 30 of #100DaysOfLeetCode Today’s Challenge: Integer to Roman 🔹 Problem: Convert an integer to its Roman numeral representation. Roman numerals are based on specific symbols combined using certain rules. SymbolValueI1V5X10L50C100D500M1000 Some special cases (subtractive notation): 4 → IV 9 → IX 40 → XL 90 → XC 400 → CD 900 → CM Example: Input: num = 58 Output: "LVIII" // L=50, V=5, III=3 Input: num = 1994 Output: "MCMXCIV" 🔹 Optimized Approach (Greedy Algorithm): Use the largest Roman numeral value possible at each step. Subtract its value from the number and add the corresponding symbol to the result. Time Complexity: O(1) (fixed set of Roman symbols). ✨ Key Takeaway: This problem teaches greedy algorithms — selecting the best immediate option leads to the optimal overall solution. link:[https://lnkd.in/gZRZpEhP] #100DaysOfLeetCode #Day30 #LeetCode #ProblemSolving #GreedyAlgorithm #Algorithms #DSA #CodingChallenge #CodeNewbie #InterviewPreparation #SoftwareEngineering #Programming #CodingCommunity #TechCareers #CareerGrowth

Ever wondered why Java ☕ still dominates the enterprise world, even after decades and the rise of newer languages? The answer lies in its foundation, ecosystem, and reliability. 💼 At its core, Java runs on the Java Virtual Machine (JVM) — the secret weapon that makes code “write once, run anywhere.” 🌍 Whether it’s a Windows server, a Linux cluster, or a cloud container, the JVM ensures consistent behavior across platforms. Then comes the ecosystem — thousands of well-maintained libraries, frameworks like Spring, and tools that power everything from banking systems to Android apps. Java isn’t just a language; it’s an entire ecosystem built to scale, secure, and sustain complex applications. ⚙️ Enterprises also love backward compatibility — Java code written a decade ago can often compile and run today with minimal changes. That stability is priceless when your systems handle millions of transactions daily. 💰 And while newer languages like Kotlin, Go, and Rust get attention, many of them actually run on the JVM or interoperate with Java seamlessly — proving just how deeply it’s embedded in modern software stacks. In short, Java isn’t “old” — it’s mature, stable, and battle-tested. And in the enterprise world, that’s exactly what reliability looks like. 🔥 #Java #SoftwareEngineering #BackendDevelopment #Programming #TechExplained #Developers #EnterpriseSoftware #JVM #SpringBoot #Coding #SystemDesign #DevLife

📌 Day 137 (Daily Problem) – Pacific Atlantic Water Flow (LeetCode – Medium) 🎯 Goal: Find all coordinates in a matrix where water can flow to both the Pacific and Atlantic oceans, given that water can only flow from higher to lower or equal heights. 💡Approach & Debugging: Used DFS from both oceans’ borders (top/left for Pacific, bottom/right for Atlantic). Maintained two sets (pac, atl) to track cells reachable by each ocean. After both DFS traversals, collected cells present in both sets: these can flow to both oceans. The trick: instead of flowing water downhill, think of it as reverse flow i.e. water moves from ocean borders to higher/equal cells. ✔️ Time Complexity: O(n × m) - each cell visited at most twice. ✔️ Space Complexity: O(n × m) - visited sets and recursion stack. 🧠Key Takeaways: Always look for reverse DFS/BFS opportunities in “reachability” problems. Set-based solutions simplify overlap detection. #Day137 #LeetCode #GraphTheory #DFS #FloodFill #GridProblems #ProblemSolving #Coding #Java #100DaysOfCode #DSA #CodingChallenge #Algorithms #InterviewPrep #SoftwareEngineering #ConsistentCoding

Recursion isn’t just a coding trick — it’s a way of thinking. 🧠 When you call a function inside itself, it might look like a loop in disguise. But underneath, recursion reflects how complex problems naturally break into smaller parts. Think of trees 🌳 — each node is a smaller version of the whole. Or graphs — where visiting one node often means exploring connected ones recursively. Even famous strategies like merge sort, quick sort, and binary search rely on recursion to divide and conquer efficiently. ⚙️ The real beauty of recursion is that it matches how we reason: Take a big problem → split it → solve smaller pieces → combine results. That’s why it’s not just a topic in DSA textbooks — it’s a mental model for problem solving. Once you “get” recursion, you start spotting it everywhere — in file systems, backtracking algorithms, even UI trees in frameworks like React. Recursion matters because it helps you stop thinking like a human typing code and start thinking like a computer executing logic step by step. That’s not a trick — that’s mastery. 🔁 #Recursion #DSA #Programming #SoftwareEngineering #CodingMindset #ProblemSolving