Claude Code vs Antigravity: Deep Work vs Speed

Ran a testing round with 25 developers working across PHP, Node, Python, and Go. The results were insightful and encouraging: ✔️ 72% said they’d definitely use the tool. ✔️ The biggest surprise? Setup time was the top highlight. Developers consistently pointed out how seamless the experience was. On average, setup took under 5 minutes. This reinforced a key takeaway: when friction is low, adoption is high. Developers are more inclined to try tools that respect their time and fit into their workflows effortlessly. A big thank you to the participants for their feedback—it’s invaluable in shaping how we continue to improve. What’s your take? How important is a low-friction setup in driving adoption? 🚀 #BuildInPublic #APM #production

Terminology changes, but the patterns stay the same. 🔄 Many developers moving between Python and PHP get tripped up by naming conventions. Here is a "cheat sheet" comparing Celery and Laravel Queues. ✅ Task = Job ✅ Broker = Queue Connection ✅ .delay() = dispatch() Full breakdown in the image below. Save this for your next cross-language project! 📌 #CodingTips #FullStack #PythonDev #LaravelDev #SystemDesign

The Universal Adapter 🌍 In a world of fragmented software, the Browser is the universal adapter. Does it run in a browser? Yes. Can we automate it? Yes. It doesn't matter if the app was built in React, Angular, ASP.NET, or Java Applets. If a human can drive it with a mouse, Python + Playwright can drive it with code. This is the only integration strategy that is truly "technology agnostic." It works on everything. #Interoperability #TechStack #Automation #Python #Integration Truly yours Bot.

🐍🔖 Front-end Web Development Tutorials — In this section, you will learn how to make Python Web applications more user-friendly by leveraging the power of both the Front-end and Back-end https://lnkd.in/gfunftH

𝐄𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 𝐄𝐱𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐍𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐔𝐬𝐞𝐫 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐃𝐚𝐭𝐚 Exception notifications are more useful when they include key context. I just enhanced our system to add user details, log stream info, and CLI command context to exception emails. This gives our team much more info to quickly debug issues. Capturing user IDs helps us understand who was impacted. Including the specific log stream pinpoints the failing service. Adding the exact command-line arguments lets us reproduce errors locally. This richer context streamlines troubleshooting and speeds up resolution times. Providing developers with all the relevant information upfront makes a huge difference. What's your favorite way to add context to debugging? I'd love to hear your strategies for faster problem-solving. https://lnkd.in/dhGXgzfz #JavaScript #Python

Every modern project has a different way to start. That’s the problem. React → npm run dev Next.js → npm run dev Node → npm start Flask → flask run Django → python manage.py runserver Same goal. Different commands. So I built WebRun — a VS Code extension that auto-detects your project and runs the dev server with one click. No config. No guessing. Just build. 🔗 https://lnkd.in/d5874JjG #VSCode #WebDevelopment #OpenSource #DeveloperTools #JavaScript #Python #Productivity

#StopUsing print() in Production – Start Using Logging in Django.... When I first started with Django, I used print() everywhere. But sometime its very hard to find the error even after using print() so i went in research for final solution and i figure out logger which is saviour for me. In real applications, especially in production, print() is useless. You need proper logging. Here’s why logging in Django actually matters: ✅ Track errors without crashing the app ✅ Monitor user activity and system behavior ✅ Debug production issues without SSH panic ✅ Maintain clean and structured logs Django comes with Python’s built-in logging module — you just need to configure it properly. #django #logger #python #Developer #codedaily

It seems there’s value in trying to understand previously written code and make it more efficient. (Especially utilizing #JavaScript in #ReactJS to break up repetitive code and use logic to handle the components. But sometimes it seems easier to start from scratch, and try to understand the code that way, by completely rewriting it, forcing oneself to think through it again, perhaps in a new way (multiple ways to do same thing). I wonder which approach is better for deeply learning/practicing a programming language, and also which is more common in a typical professional industry (probably the former, but new applications have to be built too — unless they’re always built on top of existing codebases). More updates on these processes to come while continuing to document the journey with JavaScript, #Python, and other languages/technologies such as #Jest, different APIs, and performing CRUD operations for #fullStack projects.

Most API tools implement scripting as a thin JavaScript layer. And yes, this can be ok for small tests, but it quickly breaks down when you start working with real, multi-step systems. In Voiden, we designed scripting as a true runtime layer: - It supports multiple languages, so you can use the one that fits your stack. - It runs on real interpreters, not a limited sandbox. - You can import packages and reuse your existing logic. - Workflows are stateful, letting you chain requests and maintain context across them. Already works with JavaScript and Python. Tomorrow, more languages will follow. The result: orchestration-level automation right inside your API client. Are you aware of another API tool that goes this far today? Download here: https://voiden.md/download GitHub: https://lnkd.in/d4rG6PcU