Myers Diff Algorithm Solves Noise Diff Problem

☠️ Intelligent Claude Code is dead. 🎉 Long live Intelligent Code Agents. 💡 With Skills becoming more and more a reality and a necessity, Intelligent-Claude-Code turned into an obsolete project, as it only targets ONE Platform, whereas Skills (and Hooks) are supported by multiple platforms. 🧠 Enter Intelligent Code Agents. 🎈 This Open-Source Project allows for an easy and simple way of installing Skills and Hooks using a shiny Dashboard. It supports multiple Agents and IDEs, can sync, and even allows for integration of custom private GIT repos, so that users can easily use their custom skills with that nice dashboard as well. 🛠️ To get started, all that is required is Docker and NodeJS. Then it is as simple as running the Shell-Commands provided in the GIT Repo. Or build from source. Or use the CLI to manage your skills. The sky is the limit. 🤝 Also, the Skills Repository is open to contributions. Just create a PR with the skill to be added, and after a manual security check, we will add the skills to the official list. 🔊 And there is more to come, so stay tuned. 👉 https://lnkd.in/dqeWUgd7 #AI #Skills #Hooks #ClaudeCode #CodexCLI #GeminiCLI #Cursor #VSCode #OpenSource #ICA #ICC

The Git Commands Nobody Teaches You (But Everyone Needs) Pixabay Everyone learns git add, git commit, git push. The basics. But real Git fluency comes from the commands people don't learn—the ones that save you when things go wrong, or make you dramatically more efficient. After years of Git usage and many rescued disasters, here are the commands I use constantly that most developers never learn. Fixing Mistakes Undo the Last Commit (Keep Changes) `bash git reset --soft HEAD~1 ` You committed too early. You want to add more changes. This undoes the commit but keeps your changes staged. I use this weekly. Committing, realizing I missed something, undoing, adding, and committing again. Undo the Last Commit (Discard Changes) `bash git reset --hard HEAD~1 ` Nuclear option. Undoes the commit and throws away the changes. Use carefully. Fix the Last Commit Message `bash git commit --amend -m "New message" ` Typo in your commit message? Fix it without creating a new commit. Add More to the Last Commit `bash git add forgotten-file.js git commit --amend --no-edit ` Forgot to include a file? Add it to the previous commit. The --no-edit keeps the same message. Undo Changes to a Specific File `bash git checkout -- path/to/file.js ` You've messed up one file and want to restore it to the last committed state. Everything else stays as is. Understanding What Happened See What You're About to Commit `bash git diff --staged ` Before you commit, review exactly what's staged. Catches accidental includes and debug code. See What Changed in a Commit `bash git show <commit-hash> ` Shows the commit message and all changes. Essential for understanding what someone did. Find Who Changed a Line `bash git blame path/to/file. https://lnkd.in/gHyCVRDX #DataAnalysis #DataScience #Python #Portfolio #Analytics This article was refined with the help of AI tools to improve clarity and readability.

Completed my microservices integration testing project! Built two communicating services (FastAPI + Flask) with Docker orchestration and comprehensive integration tests. What I built: - User Service (FastAPI on port 8001) - Order Service (Flask on port 8002) - validates users before creating orders - 13 integration tests verifying cross-service communication - Docker Compose setup for isolated environments - Tests run 76x faster in containers (0.56s vs 43s local) Stack: Python, FastAPI, Flask, Docker, Docker Compose, pytest, requests Took about one week. The complexity was managing service dependencies and ensuring Order Service properly validates users by calling User Service before accepting orders. Most interesting part: Docker networking configuration. Services communicate via container names (user-service:8001) instead of localhost. Tests verify business logic across services - like rejecting orders for inactive users. This project demonstrates real-world microservices patterns beyond just testing isolated APIs. GitHub: https://lnkd.in/d7tWGipM #QA #Microservices #Docker #IntegrationTesting #FastAPI #Flask #Python

Stop Reviewing Syntax. Start Reviewing Logic. 🚀 We’ve all been there: a Pull Request (PR) is 50+ comments deep, but 40 of them are nitpicks: ▪️ "We use 4 spaces, not 2." ▪️ "This variable should be camelCase." ▪️ "Remove unused imports." ❌ The Problem Manual code reviews are expensive. When humans act as "syntax checkers," they miss the big picture. Architectural flaws, security holes, and logic bugs slip through because the reviewer is distracted by the "lint." ✅ The Solution: Automated Linting Think of it as a spell-checker for your codebase. It ensures 100% consistency before a human eye even touches the code. 🛠️ How to "Fail the Build" (Stop Bad Code at the Gate) The secret to a clean codebase isn’t a "Style Guide" PDF that no one reads; it’s an automated gate. Here is how to enforce it: 1️⃣ In Your Local Workflow (Git Hooks) Don’t wait for the CI server. Use Git Hooks to scan code before it is committed. ▪️ Tool: pre-commit or a shell script in .git/hooks/pre-commit. ▪️ Result: If the linter finds an error, the git commit fails. The developer fixes it instantly on their machine. 2️⃣ In Your CI/CD Pipeline (GitHub Actions / Jenkins) This is your final line of defense. Add a "Lint" stage before your "Test" or "Build" stages. ▪️ Example (Gradle): Run ./gradlew checkstyleMain. ▪️ The Key: Configure your tool with ignoreFailures = false. ▪️ Result: If a developer bypasses local checks, the CI pipeline turns RED 🔴. The PR cannot be merged until the style is fixed.

#vibecoding #llm #codex A fundamental problem with the coding agents (or rather with their default behavior) is their obsession with getting the code work at all cost without trying to perform comprehensive analysis of the failures. LLM is confined by your code context and thus it is “fixing” the failures by modifying the code even if the problem is caused by an external factor and has nothing to do with the code at all. LLMs are notoriously bad in figuring out misconfiguration of runtime environment and in acknowledging the fact that build systems are awkward and buggy. In my today experiments with a Python extension module written in Rust, Codex continuously used a wrong build command (one intended to build the rust-only extension, not a mixed rust/python one, as it should be). Every time it failed the agent went to a panic mode and tried to add tons of nonsense code in an attempt to “fix” it. It didn’t even attempted investigating the build logs, which will clearly tell it that the problem is not in the code itself. It didn’t do what any sane human developer does – googling the error message and browsing though the related issues and discussions. It doesn’t think of a bigger system at all. Another example – deploying the Github actions. Codex wrote an actions yaml file perfectly but fell short in explaining what to do in order to make it work. It was not helpful at all in debugging the problem with Github access token permissions and gave completely wrong and utterly confusing advice on how to replace the access token in git repo (actually, the later is, quite surprisingly, a kind of arcane black magic, requiring googling through dozens of issues and StackOverflow threads). If your system is somehow misconfigured LLMs won’t help you to fix this. In the best case they will confuse you even more and in the worst one their confident advice will break everything completely. Probabilistic nature of LLM inference make them notoriously bad in keeping track of nuances like different distro and library versions. They are eagerly advising Arch commands for Fedora and give you useful recipes that worked perfectly for a kernel version from 2018. It is very impressive what this tools are able to do but they are not nearly as smart as we’d like them to be. Not yet at least.

`git blame` isn't a clean audit trail. It's often a lie that hides the real story behind your code. We've all been there: production is burning, you've narrowed it down to a single line, and the first thing you do is `git blame`. You see a name, maybe from years ago, and think you've found your culprit. "Why did Bob even touch this?" But Bob probably just fixed a linter error across 50 files, or re-indented a block, or worse, merged a massive PR where the actual problematic change was buried deep inside another refactor. `git blame` tells you who *last touched* that specific line, not who introduced the original logic, or the actual intent behind the change. It's a technical truth that's often a contextual falsehood. In 2026, we should be past this shallow interpretation. ❌ **The Trap:** Using `git blame` as a definitive source of ownership or intent. ✅ **The Reality:** Use `git blame` as a starting point. Then `git log -p <commit_hash>` and find the PR. That's where the real context lives. `git blame` is a finger-pointer. The commit history is the confession. Anyone else constantly digging through PRs because `git blame` sends them on a wild goose chase? #ProductionLogs #VersionControl #Git #CodeReview #SRE

The best developers I've worked with don't write more code. They write less — but every line has intention. After years of reading messy codebases, debugging spaghetti logic at 2 AM, and refactoring code that "worked but nobody understood" — I distilled 10 rules that separate clean code from clever code. Here's the thing nobody tells you: Clean code isn't about perfection. It's about empathy for the next person reading it. (That person is usually future you.) 've put together a visual cheat sheet ↓ covering everything from naming conventions to commit hygiene. And tell me — which rule do you break the most? Be honest 😄 I'll go first: I still catch myself writing magic numbers when I'm in the zone. #CleanCode #SoftwareEngineering #CodingBestPractices #ProgrammingTips #CodeQuality #DeveloperLife #SoftwareDevelopment #WebDevelopment #TechCommunity #CodeReview #RefactoringCode #LearnToCode #DevTips #EngineeringCulture #BuildInPublic

Tuesday , 24 February , 2026 ! ☑ 1st problem of the day done ! ╰┈➤ (Codechef Starters 210, Div 4) • Problem No. D (First Element Counting) ! • Problem Link : ⤵︎ https://www.codechef.com/problems/FIRSTCNT?tab=statement Observation & Intuition : 🕵🏻♂️ • In this problem, we are given a array for each element we have to find out that how many x integers are there which is closest to this array elements and farthest from all of others elements of this array ! • If any of the integer value x closeat to more than one elements simultaneously then the lower elements will own this integer value beacuse we'll consider that it's the nearest from this lower array value than higher ! Solution Approach : 🎯 • At first, we'll sort the array in ascending order and then we'll calculate left mid and right mid ! • for each v[i] if(v[i] - Lmid >= Lmid - v[i - 1]) then map[v[i]] += v[i] - Lmid - 1, otherwise map[v[i]] += v[i] - Lmid; • And also for right side and even itself this value the x count will be map[v[i]] += Rmid - v[i] + 1; Time Complexity : O(nlogn) 📝 Implementation Uses : Adhoc </> 👨🏻💻 ! C++ Code Link : ╰┈જ⁀➴ https://github.com/Ridwanulhaquekawsar/Codechef-Problems/blob/main/D-First_Element_counting-codechef-starters-210-div-4.cpp #programming #problemSolving #coding #consistency #codechef #starters210 #analyticalThinking #observationSkills #maths #DeepThinking

🚀 Just pushed an open-source project to git: ib-positions I’ve been building a lightweight, async-first Python package to retrieve and structure portfolio positions from Interactive Brokers (IB Gateway / TWS). The goal isn’t just data extraction — it’s to create a clean, extensible foundation for: • Systematic portfolio monitoring • Risk & exposure analysis • Research pipelines • Real-time position snapshots • Downstream alpha modeling The project uses a modern Python stack (uv, asyncio, pydantic, pytest, CI) and follows a production-grade src/ layout. It’s intentionally modular so others can build on top of it — adding factor exposure mapping, PnL attribution, risk metrics, execution wrappers, or real-time streaming. If you’re working with IB data and want a structured base to extend, feel free to fork it, suggest improvements, or add features. Open to collaboration — especially from quants and engineers building systematic infrastructure. #PortfolioManagement #Systematic #QuantitativeFinance #Research

🚀 𝗝𝘂𝘀𝘁 𝗹𝗲𝘃𝗲𝗹 𝘂𝗽 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗴𝗮𝗺𝗲! My senior gave us an interesting challenge: write unit tests that are so clear, developers rarely need to check GitHub issues or documentation to understand the code. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Traditional tests often require digging through GITHUB tickets, Slack threads, or design docs to understand 𝘄𝗵𝘆 something works the way it does. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Implement self-documenting tests using three key principles: 1️⃣ 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗡𝗮𝗺𝗲𝘀(𝗚𝗶𝘃𝗲𝗻-𝗪𝗵𝗲𝗻-𝗧𝗵𝗲𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻) Example: shouldEvictLeastRecentlyUsedEntry_whenCapacityExceeded_givenLRUPolicy() 2️⃣ 𝗜𝗻𝗹𝗶𝗻𝗲 𝗦𝘁𝗮𝘁𝗲 𝗖𝗼𝗺𝗺𝗲𝗻𝘁𝘀 Example: cache.put("A"); // Order: A cache.put("B"); // Order: B -> A cache.get("A"); // Order: A -> B (A moved to front) 3️⃣ 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗝𝗮𝘃𝗮𝗗𝗼𝗰 - Algorithm explanations - Real-world use cases - Comparison tables 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁: ✅ Zero dependency on external docs ✅ New developers can understand the system just by reading tests The tests will now serve as living documentation that never goes out of sync. 𝗕𝗶𝗴 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: If a developer can understand the feature just by reading the tests, you’ve written good tests. Below I have attached reference example code, which inlines with above mentioned points and gives a basic idea about Unit Testing. #SoftwareEngineering #UnitTesting #CleanCode #Java #BestPractices #AI #DeveloperProductivity 🚀