Problem Solving & Adaptability in Software Engineering

We analyzed 790 CLAUDE.md files from GitHub. Not hand-picked examples. Real files from real repos — C++, Go, Python, TypeScript, Terraform, Rust, and 4 non-English languages. Here's what the data actually says: Structure convergence is real. → 65% of multi-section files start with Identity then Commands → 92% use code blocks (the single most consistent pattern across all domains) → The canonical structure: Identity → Commands → Architecture Brevity correlates with quality. → Median file: 4,536 characters (~100 lines) → Files under 5K chars pass quality assessment at 2x the rate of files over 8K → 86% stay under 300 lines → The 14% that exceed it are project diaries or status trackers, not config files What surprised us most: → MUST/NEVER emphasis markers are rare — under 10% of files use them (our smaller sample had overestimated this) → Non-English files (11% of corpus) follow identical structure patterns — the template is language-agnostic → The highest-scoring file is just 63 lines and 2,502 characters (a Go CLI tool) What developers actually put in CLAUDE.md: → 54% lead with project identity → 30% put commands second → 18% include naming/style conventions → 14% have formal constraint sections The takeaway that surprised me: The best CLAUDE.md files look nothing like comprehensive documentation. They look like a one-page cheat sheet a senior dev would hand a new team member on their first day. Short. Specific. Structured. The worst files read like READMEs, project diaries, or wish lists of how they want AI to behave. What does your CLAUDE.md look like — a cheat sheet or a diary? #AIEngineering #DataDriven #ClaudeCode #DeveloperExperience #CodingAssistants

LLM coding agents need a thinking medium. Right now they write code blind. They produce a function, run the test suite, read the failure, iterate. Every execution is a fire-and-forget shell command. No persistent state. No way to build up context incrementally. No scratchpad. A REPL changes this. Instead of "write then verify," an agent can "verify then write." Eval an expression to check an assumption before committing it to a file. Import a module and inspect a return type. Test an edge case. All in a persistent session where state accumulates across calls, the same way a developer thinks at a REPL. Bash gives agents hands. A REPL gives them a place to think. I built replsh: an open-source REPL tool designed for LLM agents. It manages named sessions across Python, Node.js, and Clojure, speaking each backend's native wire protocol. All output is structured JSON. Sessions persist across invocations. Timeouts return partial output instead of errors, so even an interrupted eval teaches the agent something. Every backend is zero-dependency. Python uses a stdlib-only bridge script that replsh auto-deploys and runs with your project's interpreter: venv, Poetry, conda, whatever. No Jupyter, no pip install. Node.js uses the built-in REPL module. Clojure uses nREPL, which ships with every Clojure toolchain. replsh plugs into whatever you already have. It also ships with an agent skill document which is a structured reference that teaches LLM agents when and how to use replsh and the REPL. Drop the skill into your agent's context and it knows how to install replsh, launch sessions, eval code, stream output, and manage background tasks. Tools like E2B and Open Interpreter are in this space but target cloud sandboxes or agent UIs. MCP REPL servers like repl-mcp exist but screen-scrape terminal prompts. replsh is protocol-native and local-first -- it runs in your project's environment with your dependencies. Github: https://lnkd.in/d4MbGWj5 #llm #ai #python #developer-tools #open-source

A while back I was working on a document management feature. Users could create documents, but naturally they should only be able to edit or delete their own. Managers, on the other hand, needed broader access so they could edit any document within their department. And then there were reports that had to be publicly readable by anyone, even without logging in. Django’s built-in permissions work well when things are global, like saying “managers can edit all documents”. But the moment you step into real-world rules like “this user can only edit their own documents” or “this specific person can access this one record”, you end up writing custom checks everywhere. It quickly becomes repetitive and a bit fragile. I kept running into the same problems, so I wanted something cleaner and reusable for: - Per-object permissions for individual records - Automatic ownership rules (users managing their own content) - Public access for specific endpoints without authentication - DRF action mapping so permissions are consistent across endpoints - Permission caching to avoid unnecessary database hits on every request Yes, django-guardian exists – but it's heavy and requires extra wiring for DRF. I wanted something lighter, DRF‑native, and drop‑dead simple. That is what led me to build django-user-permissions, a lightweight package that plugs into Django REST Framework. It gives you a simple mixin for viewsets, a way to attach permissions directly to objects, and a few controls for ownership and public access. The idea was not to build another heavy role system, but to fill in the gaps Django leaves at the object level. If you have ever had to implement “edit only your own posts” or “share a single private document with someone”, you probably know how quickly this logic spreads across a codebase. This is just my attempt to keep that part of Django a bit more manageable. 📦 PyPI: [https://lnkd.in/gR63kZdB) 💻 GitHub: https://lnkd.in/geSY5Mfj pip install django-user-permissions #Django #DRF #Python #OpenSource #Permissions

Flask Development Essentials for Beginners A couple of years ago, I was knee-deep in a project when I realized I was overcomplicating things with Flask. I spent hours trying to implement features that should've been straightforward, only to discover later that I had missed some fundamental concepts. That moment taught me just how critical it is to understand the basics before diving into complex solutions. Flask is a lightweight web framework in Python, and knowing the core principles can make your life a whole lot easier, especially if you're just starting out. Grasping these concepts early on has been a game-changer in my development process, and I wish I’d understood them sooner. 🔹 Routing: This is where it all begins. It ties your URLs to the functions that handle requests. Understanding how to set up routes properly can save you a lot of headaches down the line. 🔹 Templates: Flask uses Jinja2 for templating. This means you can separate HTML from Python code, making your applications cleaner and more maintainable. I once spent hours debugging mixed code until I learned how to leverage templates effectively. 🔹 Request and Response Objects: Grasping how to work with these objects is crucial. They allow you to handle incoming data and send responses back to users. A simple understanding here can elevate your handling of forms and APIs. 🔹 Error Handling: It’s easy to ignore this, but knowing how to manage errors gracefully can improve user experience significantly. I remember a time when I didn’t handle errors properly, leading to a frustrating experience for users. Now, I always ensure user-friendly error messages. 🔹 Extensions: Flask has a variety of extensions that can add functionality, like database integration or form validation. Learning how to use these can prevent you from reinventing the wheel. It took me a while to embrace extensions, but it’s become a vital part of my toolkit. Every bit of knowledge builds upon the last. Understanding these fundamentals has not only made me a better developer but has also allowed me to create applications that are more robust and easy to maintain. What’s one fundamental concept you wish you’d mastered early in your development journey? #Flask #PythonDevelopment #BackendEngineering #WebDevelopment #LearningJourney

This tool just automated the most painful part of building with Claude Code, turning any documentation source into a ready-to-install AI skill with automatic conflict detection built in. Skill Seekers does deep AST parsing on Python, JavaScript, TypeScript, Java, C++, and Go repositories, extracts every function, class, and method with parameters and types, then cross-references them against the documentation to find exactly where the docs lie and where the code has moved on without updating anything. It runs as an MCP server with 26 tools, so you can just tell Claude Code to scrape a GitHub repo, detect conflicts, merge the sources, and package the skill without touching the CLI once. The three-stream GitHub architecture splits repos into Code, Docs, and Insights streams and includes issues, labels, stars, and forks as weighted signals for better topic routing.

🚀 Starting a new project — and I’m trying to approach this one differently. I’ve begun building a CLI-based Task Scheduler 🧠⚙️ At a basic level, it’s a system where you can give tasks (as commands), and instead of executing them manually, the system takes control of how they should be handled and run. Sounds manageable… until you actually start thinking about how a system should behave 😅 🧩 What I’m building A small system-inspired tool that: Accepts tasks from the user Keeps track of tasks reliably over time Decides execution order Runs real terminal commands The goal isn’t just functionality — I want it to feel like something reliable and system-like, not just a script I run once. 🧠 Something I’m learning early This project is already forcing me to think differently : 👉 It’s easy to keep adding features 👉 It’s hard to stop and stay focused I’m trying to keep the scope tight on purpose — so I can actually finish something that is clean and well-designed, instead of endlessly expanding it. 🛠️ Why I chose this stack Python → makes it easier to experiment and build quickly SQLite → simple, but gives real persistence Also, this is my first time properly working with a database 🗄️ Earlier, I would’ve just stored data temporarily and moved on… But this time I wanted: Tasks to persist History to be available The system to feel consistent That’s what pushed me towards using a database — and I can already see the difference. ⚠️ What’s actually challenging Not the implementation. It’s: making the design make sense avoiding unnecessary complexity and building something that actually improves how I think Not just something that looks impressive on the surface Have you ever tried building something like this? I’d love to hear your experience. Let’s see how far I can take this 🚀

Nobody posts about the boring part of learning to code. So here's me doing exactly that. I'm still learning Python. Right now I'm working through comparisons and conditional logic — if/else statements, evaluating expressions, controlling the flow of a program. It's not flashy. There's no project to show off yet. It's just me, Boot.dev, and a lot of repetition. A few weeks ago I shared that I built my first Python project from scratch — a tip calculator and bill splitter. That felt like a milestone. But what I didn't talk about was the stretch between milestones. The part where you're grinding through concepts, not building anything visible, and wondering if you're actually making progress. Here's what I've realized about this phase: → Fundamentals aren't exciting, but they're everything. Every complex system I've worked with in my homelab — Terraform configs, monitoring pipelines, anomaly detection models — is built on basic logic like the stuff I'm learning right now. Skipping this step is how you end up copying code you don't understand. → Progress doesn't always look like progress. Some days I fly through exercises. Other days I stare at a simple if/else block longer than I'd like to admit. But each concept is clicking a little faster than the last, and that's the signal that matters. → Consistency beats intensity. I'm not doing 4-hour coding marathons. I'm showing up regularly, working through the material, and trusting the process. The people who actually learn to code aren't the ones who sprint — they're the ones who don't stop. I'm not where I want to be yet. But I'm past where I started, and that's enough to keep going. Anyone else in the middle of learning something where the progress feels slow? How do you stay motivated through the fundamentals? #Python #LearnInPublic #BootDev #DevOps #ProfessionalDevelopment #ContinuousLearning

🚀 Starting DSA? Don’t skip this foundation. Before solving problems… Before jumping into LeetCode… 👉 You need to understand how data is stored. That’s where Data Structures come in. --- 🧠 Think of it like organizing your room: - Books on a shelf 📚 - Clothes in a wardrobe 👕 - Files in folders 📂 When everything is organized… 👉 You find things faster. 👉 You work more efficiently. Same in programming. --- 💡 What is a Data Structure? It’s a way to store and organize data so you can: - Access it quickly ⚡ - Modify it easily 🔧 - Build scalable apps 🚀 --- 🌍 Why this matters in real projects: - In React → managing lists & state - In APIs → structuring JSON responses - In databases → organizing records 👉 Better structure = better performance + clean code --- 🔥 Key Takeaway: Don’t just write code that works… 👉 Write code that handles data smartly. --- 💬 Question for you: Where have you used arrays/lists in your projects without realizing it's a data structure? --- #DSA #Python #SoftwareEngineering #Coding #LearnInPublic

Coding agents generate code like there is no tomorrow. Soon enough, they struggle under the weight of what they created. AI writes a new helper instead of reusing an existing one. Old functions stay around because tests still call them, even though production does not. The codebase grows, but the agent's ability to reason about it does not. On bigger projects, especially ones that have been heavily vibe-coded, this turns into chaos. The problem is not just messy code. It is slower reviews, weaker trust in the codebase, and agents that get less reliable as the surface area grows. We have put a lot of energy into making code generation faster. I think the next thing to get right is safe code removal. There is a reason senior engineers get excited about deleting code. It is a bit like never throwing away clothes you no longer wear. It seems fine at first. Then one day, you have five versions of everything, and finding what you actually need means digging through closets you forgot existed. I built a Claude Code skill to help with this. It gives Claude a methodology for dead code removal: classify what you are looking at, verify the cases static tools miss, and avoid drifting into refactor territory while you are in there. It is tuned for Python and TypeScript, but should be easy to adapt. Clone it, fork it, open a PR if you improve it. https://lnkd.in/ds5AcC5U #CodingAgents #CodeQuality

🚀 Master RESTful APIs: From Zero to Production API design is one of the most critical skills for any backend developer, yet it’s easy to get the "rules" tangled up. Whether you are working with Python, Flask, or any other framework, these core principles remain the same. I’ve put together a comprehensive breakdown of the RESTful API Masterclass cheat sheet. Here is what you need to know to build professional-grade interfaces: 🔹 The Anatomy of REST: It’s not just a library; it’s an architectural style based on Representational State Transfer. 🔹 Verbs matter: Use GET for reading, POST for creating, PUT for full replacements, and PATCH for partial updates. 🔹 Statelessness is key: Every request must contain all the information the server needs—no "guessing" based on previous interactions. 🔹 Clean URLs: Use plural nouns (/books) instead of verbs (/getBooks). Keep nesting shallow to stay readable. 🔹 Security: Transition from simple API Keys for server-to-server talk to JWT (JSON Web Tokens) for modern, scalable auth. Pro-Tip from the guide: Always keep your v1 running while launching v2. Never make breaking changes without versioning!. Check out the full cheat sheet attached below for a deep dive into status codes and Flask implementation. ⬇️ #Python #Flask #WebDevelopment #API #Backend #CodingTips #RESTAPI