Breaking the backend job experience loop with Python30 Challenge

You’ve learned backend. - You’ve watched tutorials. - You’ve built a few mini-projects. But you’re still not getting backend job offers Let’s fix that 👇 The real problem isn’t your skill. It’s proof. Recruiters don’t hire you for what you “know.” They hire you for what you can show. If you can’t point to deployed APIs, databases, and production-ready logic, they can’t trust you yet. Learning is not equal to Doing. You’ve probably built projects inside tutorials. But did you ever build something on your own — from scratch, with your own decisions, bugs, and refactors? That’s how you become a real backend engineer. Here’s the roadmap that works. ✅ Build 30 backend projects in 30 days. ✅ Each project focuses on one key concept (APIs, authentication, file upload, caching, etc.) ✅ Then, optimize your resume and learn DSA for interviews. Do this for 90 days — and you’ll be unstoppable. We built a challenge to guide you step-by-step. It’s a 90-Day Python challenge You’ll: - Ship 30 projects in 30 days - Study DSA & system design - Prepare with resume kits, mock interviews & community 2K+ have escaped “tutorial hell” through this: https://lnkd.in/d5iN7AX6

You can know Python. You can know Flask. You can even know databases, REST, and Docker... And still not land a backend job. Here’s the hard truth no tutorial tells you 🧵 Most developers confuse consuming information with gaining experience. But recruiters don’t hire what you know — they hire what you’ve done. You can’t “tutorial” your way into a real job. You need projects. Knowing what an API is is not the same as building one that scales Knowing what authentication means is not the same as implementing JWT securely Knowing SQL is not the same as designing schemas that actually work You dont learn those from videos. U learn them by shipping code Why most never make it past this stage Because building from scratch is hard. It’s confusing. It’s messy. You’ll fail a lot. But that’s exactly what prepares you for real backend work. The solution - Build one project a day. - Even small ones. - Each one teaches you something new: databases, routes, errors, and deployment. Do this for 30 days, and you’ll feel the shift. From “learner” → “engineer.” That’s why we built the Python30 Challenge ✅ 30 projects in 30 days ✅ DSA interview prep ✅ Job-ready backend toolkit ✅ Access to resume, mock interviews & community Stop studying like a beginner. Start shipping like a backend engineer https://lnkd.in/dEsaR2bN

If you’ve been “learning Backend” for months but still can’t build anything on your own... You’re not bad at coding — You’re stuck in Tutorial Hell. 🧵 1. What is Tutorial Hell? It’s when you: - Follow endless YouTube videos - Copy code without understanding - Never finish personal projects - Feel productive but never progress You’re learning passively — not actively. 2. Why did it happen Your brain loves short-term wins. Finishing a tutorial feels like progress, but it doesn’t teach you real problem-solving. Building from scratch does. 3. The way out Stop watching. Start shipping. - Every time you hit a bug, Google it. - Every time you get stuck, debug it. - Every time you finish something, document it. That’s how real backend engineers learn. 4. The formula: Build small Python APIs - Connect databases - Add authentication - Deploy live - Repeat daily After 30 projects, you’ll think like an engineer — not a tutorial follower. That’s exactly what we built in this 30 Python Projects in 30 Days Challenge. You’ll: ✅ Ship 30 backend projects in 30 days ✅ Learn by building, not watching ✅ Access interview & DSA prep kits ✅ Join 5,000+ devs who escaped Tutorial Hell Start here→ https://lnkd.in/dEsaR2bN

Ever wondered why your backend APIs sometimes feel sluggish even though your code looks clean? One game-changing approach gaining traction for boosting API performance is **Asynchronous Python with asyncio**—and it’s time more developers gave it a serious look. Traditionally, Python’s synchronous code handles one task at a time. This works fine for simple scripts, but APIs that deal with multiple I/O operations—calling databases, external services, or complex file handling—can bottleneck your throughput. The server waits for each task to finish before moving on. Enter asyncio: Python’s native solution for async programming. It lets you write code that can handle multiple operations seemingly at once, without spinning up multiple threads or processes. Instead, it uses event loops and coroutines that pause work where waiting is necessary (like fetching data) and switch to other tasks meanwhile. The result? More efficient resource utilization and snappier responses under load. Why should backend engineers care about asyncio right now? Because modern frameworks like FastAPI and even Django (with recent updates) support asyncio natively. Even better: the community tools and libraries are catching up fast, making integration smooth and practical. Here’s a quick practical tip if you want to play around: - Use async def for your endpoint functions. - Leverage async database drivers (e.g., asyncpg for Postgres). - Await external API calls inside coroutines instead of blocking calls. You might hit a learning curve—async can be different from standard sequential thinking—but once you get the hang of it, your backend can handle far more requests simultaneously without the cost and complexity of threading. By embracing asynchronous Python today, you're not only writing cleaner code but future-proofing your services to handle scale efficiently. Have you experimented with asyncio yet? What challenges or wins did you experience? Let’s chat about the async revolution happening quietly in Python backends. #Python #AsynchronousProgramming #BackendDevelopment #APIPerformance #FastAPI #TechTrends #SoftwareEngineering #DeveloperTips

For a long time, I wrote almost everything in Rust, precise, strict, beautifully unforgiving. I did not build every system in Rust; I preferred it and delegated other stacks when it made sense. Over time, one thing became clear: real technical leadership requires breadth as much as depth. Every modern engineer should think fluently in at least three languages: 🦀 Rust: for building fast, reliable systems. Its strict type system and compiler discipline teach real engineering thinking. 🟦 TypeScript: once just a frontend tool, now the foundation of the growing AI SDK ecosystem (Claude, OpenAI, LangChain, and many others). 🐍 Python: the operating system of applied AI. Most agent frameworks are written in Python, which makes it the easiest path to assemble, run, and iterate on real agent workflows. It is also the default for model training and local ML. How to put this into practice this week: * Rust: ship a tiny CRUD service or CLI with Axum, SQLx, Serde. One health check, one write, one read. * TypeScript: use the Claude SDK to build a simple PDF text analyzer that returns structured JSON. * Python: deploy both using Pulumi for Python. Pulumi is an infrastructure-as-code tool like Terraform that supports many languages; use the Python SDK to define one stack, manage secrets cleanly, and get repeatable builds. Fluency across these three turns you from a single-instrument player into a conductor. You design the score, assign the parts, and ship on tempo. That is the skill set I hire for, develop in my teams, and hold myself accountable to.

Thousands of developers know Python. But only a handful get hired for backend roles. Here’s the truth: → Companies don’t hire Python learners. → They hire Python builders. Let’s talk about how to stand out — even without a CS degree 🧵 Stop chasing random tutorials. Every hour spent watching a “Python for Beginners” video is an hour not spent building. Tutorials give you knowledge. Projects give you proof. And in hiring, proof beats knowledge every time. Build what companies actually use. Skip the “guess-the-number” games. Start with projects that mirror real systems: ✅ Authentication APIs ✅ CRUD apps with databases ✅ File upload services ✅ Notification systems ✅ Job board or blog APIs When you can build those, your GitHub becomes your portfolio, not just code storage. Document everything. Each project should come with: - A README - Clear setup steps - API documentation Why? Because engineers who write and explain well are rare. And rare = valuable. Turn learning into momentum. Don’t overthink your next step. - Pick a project. - Ship it. - Repeat. That’s exactly how Python30 works — 30 real Python backend projects in 30 days. You don’t just learn — you transform. By the end of it, you’ll have: → 30+ projects to show recruiters → Confidence in backend fundamentals → A clear, job-ready portfolio Start your 90-day path to a Python backend role 👇 👉 https://lnkd.in/dEsaR2bN

For any Python developers building modern, high-performance APIs, I've come across an exceptional 𝐅𝐑𝐄𝐄 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 that I had to share. This YouTube playlist is more than just a 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 tutorial; it's a comprehensive guide to the entire backend stack. It masterfully connects the dots between building, validating, and deploying your application. The playlist includes: 🐍 𝐅𝐚𝐬𝐭𝐀𝐏𝐈: Deep dives into asynchronous API development, performance, and best practices. ✅ 𝐏𝐲𝐝𝐚𝐧𝐭𝐢𝐜 𝐂𝐫𝐚𝐬𝐡 𝐂𝐨𝐮𝐫𝐬𝐞: A fantastic breakdown of the data validation, settings management, and parsing library that is the backbone of FastAPI's data models. 🐳 𝐃𝐨𝐜𝐤𝐞𝐫 𝐂𝐫𝐚𝐬𝐡 𝐂𝐨𝐮𝐫𝐬𝐞: Essential knowledge on how to containerize your FastAPI application, making your development and deployment workflows consistent and scalable. Finding a single, free playlist that covers the full development-to-deployment lifecycle (API logic, data validation, and containerization) is rare. This is a goldmine for leveling up your backend engineering skills. Check it out here: https://lnkd.in/dkqRyqtH #FastAPI #Pydantic #Docker #Python #BackendDevelopment #APIDevelopment #DevOps #Microservices #TechSkills #FreeLearning

🚀 Piton v0.5.0: Modernizing the Bridge Between Elixir & Python I'm excited to announce a major upgrade to Piton, the open-source library that lets you run Python code from Elixir while bypassing the GIL! After months of work, v0.5.0 is here with a completely modernized stack. 🎉 🔧 The Modernization: We've brought Piton into 2025 with: ✅ Elixir 1.19 + OTP 27 support ✅ Python 3 only (Python 2 retired) ✅ Built-in JSON - removed Poison dependency ✅ GitHub Actions CI/CD - automated testing & publishing ✅ Latest dependencies - erlport 0.11, ex_doc 0.39 All 13 tests passing ✅ | Fully automated | Production ready 💡 Why This Matters: The real power isn't just the tech stack - it's what you can build with it. Real-world scenarios where Piton shines: 🔹 ML/AI in Phoenix Apps Run TensorFlow or PyTorch models directly from your LiveView without blocking the BEAM 🔹 Data Science Pipelines Leverage NumPy, Pandas, and SciPy while maintaining Elixir's fault-tolerance 🔹 Legacy Python Integration Migrate to Elixir gradually - wrap existing Python services without rewriting everything 🔹 Parallel Processing True parallelism - run multiple Python algorithms concurrently, bypassing the GIL using Erlang's process model 🔹 API Enrichment Call Python NLP libraries, image processing tools, or scientific computing packages from your Phoenix APIs 🎯 The Elixir + Python Sweet Spot: You get: •🏃♂️ Elixir's concurrency without the GIL limitation •🐍 Python's rich ecosystem (350K+ packages) •🛡️ Fault tolerance - Python crashes won't take down your app •⚡ Performance - modern OTP 27 optimizations •🤖 DevOps ready - full CI/CD automation Whether you're building ML-powered Phoenix apps, migrating Python workloads, or just want the best of both worlds - Piton v0.5.0 is ready. 📦 Get it: https://lnkd.in/ecarHYk 📚 Docs: https://hexdocs.pm/piton 💻 GitHub: https://lnkd.in/dkk9W8M #Elixir #Python #OpenSource #MachineLearning #AI #WebDevelopment #Phoenix #DataScience #SoftwareDevelopment #DevOps #ElixirLang #FunctionalProgramming

“If you can code in one language, you can code in any language.” I’ve heard this many times — usually from well-intentioned managers or confident developers. Yes, programming concepts transfer easily. Logic is logic. But fluency — the ability to write idiomatic, maintainable, production-ready code — takes time. Each language has its own paradigms, libraries, and community norms. Knowing Python doesn’t mean you’ll instantly write clean Go, safe Rust, or elegant TypeScript. And here’s the hidden cost: When developers are pushed into a new language without time to properly learn it, they still deliver — but the code often just “works” rather than fits. That mismatch quietly builds technical debt that future teams pay for. What’s the ask here? Give developers the time to learn properly — or plan for the cost of rework later.