Open Source Claude Code Gains 117k Stars

From the leaked Claude source code, people are porting the architecture and logic to a different language (python) to avoid legal issues. One example is this one: https://lnkd.in/eVpnWN-n I remember using claude code to reverse engineering it itself, the intention? to enable a custom llm proxy, and it worked! The llm proxy didn't follow the regular authentication mechanism (required extra headers, custom url pattern) plus a custom change in the response. It was the opencode before opencode 😅🤣

Day 28/90 I solved one LeetCode in four different ways, but my Django project stayed at zero today. I took April 2nd off for personal reasons. Yesterday, Day 28, I finished my DSA session, but I couldn't get started on the development work. I am converting my Sunday rest day into a working day again to make up for the lost time. DSA & LeetCode: • Solved LeetCode 268 (Missing Number). • I deliberately started with a Sorting approach. By sorting the array, I could see exactly where the index and the value stopped matching. It’s a simple way to find the gap, but the O(n log n) time complexity is slow for bigger arrays. • Next, I optimized it by using a Set for hashing. Turning the list into a Set gave me O(1) lookups and dropped the total time to O(n). It’s faster, but it still carries an O(n) space cost. • Then, I tried the Summation (Gauss Formula) method. I calculated the expected sum using (n * (n + 1)) // 2, where n is the length of the nums array, and subtracted the actual sum. This hit a 0ms runtime and O(1) space. • Finally, I used the Bitwise Exclusive OR method. Since any number XORed with itself cancels out (a ^ a = 0), I XORed every index from 0 to n with every value in the array. The pairs cancelled each other out, leaving only the missing number. It’s O(n) time and a perfect O(1) space, and unlike the math formula, it doesn't risk an integer overflow. Do you find value in solving the same problem multiple ways to understand trade-offs, or do you prefer moving quickly to the next challenge? #python #django #backenddevelopment #leetcode #dsa #90daysofcode #softwareengineering #bitwise #refactoring #consistency

Django's middleware doesn't run top to bottom. It runs top to bottom in and bottom to top out. Here's what actually happens: 1. Django wraps middleware like nested functions, a stack. 2. The first middleware in MIDDLEWARE is the outermost wrapper. The last middleware is the innermost, closest to the view. 3. A request enters the first middleware, passes through each one down to the view. 4. That response travels back out through middleware in reverse order. Last middleware processes the response first. It's intentional design, each middleware wraps the entire request-response cycle like a context manager. Get the order wrong and the consequences are subtle. No exception. No warning. Just wrong behavior that looks like a logic bug. What's the hardest middleware ordering bug you've debugged — and how long did it take to find? #Python #Django #BackendDevelopment #SoftwareEngineering

Django's middleware doesn't run top to bottom. It runs top to bottom in and bottom to top out. Here's what actually happens: 1. Django wraps middleware like nested functions, a stack. 2. The first middleware in MIDDLEWARE is the outermost wrapper. The last middleware is the innermost, closest to the view. 3. A request enters the first middleware, passes through each one down to the view. 4. That response travels back out through middleware in reverse order. Last middleware processes the response first. It's intentional design, each middleware wraps the entire request-response cycle like a context manager. Get the order wrong and the consequences are subtle. No exception. No warning. Just wrong behavior that looks like a logic bug. What's the hardest middleware ordering bug you've debugged — and how long did it take to find? #Python #Django #BackendDevelopment #SoftwareEngineering

🚨 𝟱𝟬𝟬,𝟬𝟬𝟬+ 𝗹𝗶𝗻𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗰𝗼𝗱𝗲... 𝗹𝗲𝗮𝗸𝗲𝗱 𝗯𝘆 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝗶𝘁𝘀 Claude 𝗖𝗼𝗱𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝘃𝗶𝗮 𝗮 `.𝗺𝗮𝗽` 𝗳𝗶𝗹𝗲 𝗶𝗻 𝗮𝗻 𝗻𝗽𝗺 𝗿𝗲𝗹𝗲𝗮𝘀𝗲 — 𝗿𝗲𝘃𝗲𝗮𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. (Axios) Within hours, the internet did what it does best: → mirrored it on GitHub → analyzed it → rebuilt it One repo stood out: 👉 https://lnkd.in/gEJzYmXx But the real twist? Developers moved beyond copying. They created clean-room reimplementations in Rust & Python (Claw Code) — replicating the architecture without using the original code. (Claw Code) https://lnkd.in/guiUu3Ch This is classic software history repeating itself. 💡 Lesson: It’s not always hacks that break systems — sometimes it’s a single config mistake. And sometimes, that mistake teaches the whole industry how your system works. #AI #DevOps #Security #OpenSource #SoftwareEngineering

A rewrite in Rust might solve your performance problems. But it will hurt your time-to-market. When existing backends in Python, Java or Node.js hit their limits, the usual options come up: vertical scaling, async, concurrency or maybe a full rewrite in Rust. That rewrite can quickly turn into a large-scale project with endless ticket backlogs. The team is blocked for months, new features stall and the overhead of agile coordination keeps growing without delivering measurable product value. A more pragmatic approach is incremental replacement. Architectural flexibility means replacing only the isolated bottleneck with Rust. The rest of the system stays untouched. The foundational mechanism for this is the C-ABI (Application Binary Interface) as a universal interface, but today there are considerably more elegant solutions available. Depending on the language, there are two paths: 1. Generalist approaches - supporting multiple languages uniFFI: The established standard boltFFI: A newer alternative that achieves higher performance by skipping serialization Diplomat: Specialized for complex data structures 2. Language-specific integrations Python: PyO3 and Maturin (builds native Python wheels directly from Rust) Node.js: NAPI Java: j4rs, robusta_jni There is also an organisational advantage. The majority of the feature team continues working in their familiar ecosystem and simply integrates the Rust code, either as a library or as a domain core. That part is handled by a small dedicated group within the team, supported by a Rust expert. (that's me 😊) How does your team handle performance bottlenecks? Have you already integrated Rust into your systems? #RustLang #SoftwareArchitecture #Python #Java #NodeJS

🔥 𝐘𝐨𝐮𝐫 𝐀𝐩𝐩 𝐑𝐮𝐧𝐬 𝐋𝐨𝐜𝐚𝐥𝐥𝐲. 𝐍𝐨𝐰𝐡𝐞𝐫𝐞 𝐄𝐥𝐬𝐞. 𝐇𝐞𝐫𝐞'𝐬 𝐖𝐡𝐲. [Docker Deep Dive — Day 1/5] Your app works perfectly. Your teammate runs it. It crashes. Different OS. Different Python. Different everything. Docker fixes this with one file — the Dockerfile. Think of a shipyard blueprint. Write it once, any shipyard in the world builds the identical ship. The Dockerfile is that blueprint for your app. FROM picks your hull — the base OS and runtime. RUN installs your tools. COPY loads your code as cargo. CMD sets it sailing. dockerfile FROM python:3.11 RUN pip install flask COPY . /app CMD ["python", "app.py"] docker build reads the blueprint and creates a Docker image — your ship, frozen in dry dock. docker run launches it as a container — the live ship hitting open water. 𝐅𝐀𝐐: Q: Why is FROM always first? Every ship needs a hull before anything else. No FROM, nothing to build on. Q: What's the difference between RUN and CMD? RUN works in dry dock during build. CMD fires when the ship hits water at runtime. Q: Can FROM pull any language? Python, Java, .NET, Node — thousands of base images exist on Docker Hub. Pick your hull. Tomorrow: image vs container — what really happens when you delete one? #DevOps #Docker #Dockerfile #CloudEngineering #DockerDeepDive #DevOpsInterview

𝗨𝗡𝗟𝗘𝗔𝗦𝗛𝗜𝗡𝗚 𝗧𝗛𝗘 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗣𝗢𝗪𝗘𝗥𝗛𝗢𝗨𝗦𝗘: 𝗧𝗛𝗘 𝗦𝗘𝗖𝗥𝗘𝗧 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗𝗜𝗡𝗚 𝗟𝗜𝗚𝗛𝗧𝗡𝗜𝗡𝗚 𝗙𝗔𝗦𝗧 𝗔𝗣𝗜𝗦 𝗥𝗘𝗩𝗘𝗔𝗟𝗘𝗗 As we head into 2026, building scalable microservices requires a rock-solid understanding of how your code is organized from the first line. This guide demystifies the structural foundations of FastAPI to ensure your backend remains maintainable as your codebase grows. THE FASTAPI INSTANCE The core of every application begins with initializing the FastAPI class. This instance acts as the central router and configuration hub, serving as the bridge between your incoming HTTP requests and your internal application logic. Understanding how to instantiate this object correctly is the first step toward managing middleware, dependencies, and route decorators effectively. ROUTING AND PATH OPERATIONS Path operations are the fundamental building blocks of your API. By using decorators linked to your FastAPI instance, you define how the server responds to specific HTTP methods like GET or POST. This video breaks down how these functions map directly to URL paths, allowing for clean, readable code that handles client communication without unnecessary complexity. PARAMETER HANDLING AND TYPE HINTS FastAPI leverages Python type hints to perform automatic data validation and documentation. By defining expected types for path parameters, query parameters, and request bodies, you enable the framework to enforce data integrity before your logic even executes. This approach significantly reduces the surface area for bugs while providing built-in Swagger UI documentation for your endpoints. As a Senior Engineer, I cannot overstate that the structure of your application in 2026 is just as important as the logic inside it. FastAPI enforces good habits early by requiring explicit type definitions and clear route mapping, which saves hundreds of hours in debugging and refactoring down the line. Treat your project structure as your primary documentation. Tags: #FastAPI #Python #API #BackendDevelopment 📺 Watch the full breakdown here: https://lnkd.in/d7YeSp75

Decoupling logic in Django is always an interesting architectural challenge. Recently, I’ve been relying more on Django Signals to keep my models clean and enforce a strict separation of concerns. For those who haven't dug into how they work under the hood: Django signals essentially implement the Observer design pattern. There is a central dispatcher, when a specific action occurs in the application (the sender), the dispatcher routes that event to any function "listening" for it (the receiver), allowing them to execute their own logic independently. In the snippet below, I’m using the post_save signal. Whenever a new Student instance is successfully created, this receiver catches the signal and automatically generates a CreditWallet for them. Why use a signal here instead of just overriding the save() method on the Student model? It comes down to encapsulation. Overriding save() works fine for simple apps, but as a project grows, it can lead to massive, bloated models. By using signals, the Student model remains strictly responsible for student data, while the financial/wallet logic is encapsulated in its own domain. It makes the codebase much easier to maintain, scale, and test. I’m curious to hear from other developers on here: What is the most complex, creative, or technically challenging way you have utilized Django signals in a project? I'd love to learn from your experiences! #Django #Python #SoftwareEngineering #WebDevelopment #Architecture #Coding

Built an Dual-App Emergency Dispatch System with Python and Django! 🚀 This project really pushed my backend skills. It features: ✅ Real-time incident reporting on a live map ✅ Automated dispatch math to find the closest available unit ✅ A highly secured, staff-only HQ Control Room ✅ Custom User Models and role-based authentication Overcoming the complex URL routing to safely separate the public map from the admin portal was a massive win. GitHub link: [https://lnkd.in/g4yx4kmy] #WebDev #Django #Python #SoftwareDeveloper #Tech