Overcome JavaScript Scraping Challenges with Crawlbase

Recently completed a project for a business and it’s already running in production Completed the development in 15 days. Spent the next 15 days learning why it's called "production-level" the hard way Anyways, the project was to build a custom backend using Python to integrate CyberLink FaceMeSDK. The system handles multiple video streams from different cameras, recognizes users in real-time, generates events, and exports them to registered webhooks. The project was responsible for: - device registration + handling live video streams - User registration with face validation - Real-time face recognition & event generation - Secure event storage in database - Webhook-based event export and many other things like handling timezone consistency in database, security and scalable solution. so what I learned during this project. - SQLAlchemy instead of raw queries (bye-bye SQL injection) - Database versioning with Alembic - Multi-layer architecture: API → Service → Repository layers - Pydantic schemas for validation - Separate security layer for encryption/decryption - FastAPI + PostgreSQL stack and finally understood what webhooks actually are (and how messy they can get) Overall, it was a great learning doing this project real production challenges I faced: - handling multiple things at the same time: - face recognition - event exporting - device registration/updates - user updates/deletion - webhook operations - events being generated in seconds - making sure nothing breaks under load handled all of this using proper task handling & system design. and then… the final boss appeared: I built everything on Windows… and then got hit with: “it’s not working on Jetson” 💀 so yeah, had to rework parts of the system to make it compatible. overall, learned a lot from this project, not just coding, but what actually happens when things go live. thanks for reading this far (mujhe pata ha yahan tak koi nahi aata 😄) THANK! M. Awais Nazir!!! #python #fastapi #backenddevelopment #softwareengineering #systemdesign #computervision #facerecognition #artificialintelligence #webhooks #postgresql #sqlalchemy #developers #freelancerlife #learningbydoing #buildinpublic

🚀 Day 9 of My LeetCode Journey — Building Data Structures from Scratch Today’s challenge: Design Linked List (LeetCode 707) 💡 What this problem is about Instead of just using a linked list, I had to design and implement it from scratch 👉 Operations implemented: get(index) addAtHead(val) addAtTail(val) addAtIndex(index, val) deleteAtIndex(index) 🧠 What I Learned: How nodes are connected using pointers (next references) Difference between singly vs doubly linked list Handling edge cases like: Empty list Invalid index Adding/deleting at head or tail ⚡ Key Insight: Arrays are easy… but Linked Lists teach you how data is actually managed in memory This problem really improved my understanding of: Traversal Node manipulation Writing clean, structured code 🔥 Takeaways: Designing data structures builds real problem-solving skills Edge cases matter more than the main logic Implementation > Theory Big thanks to Namaste DSA and Akshay Saini 🚀 for the amazing learning path Day 10 loading… 💪 #LeetCode #DataStructures #Algorithms #CodingJourney #100DaysOfCode #SoftwareEngineering #Programming #InterviewPrep #JavaScript #CodingLife #TechGrowth #ProblemSolving #Developers #LearnToCode #LinkedList #DSA #NamasteDSA #AkshaySaini

Laying the foundation for FlowState API: Multi-tenant Workspace & Project models • UUID primary keys for security & horizontal scaling • RESTRICT cascades to protect owner-data integrity • Composite unique constraints for workspace-scoped slugs • Explicit db_table & index tuning for predictable DB performance • Admin optimized with select_related to kill N+1 queries Trade-off: Keeping tenant scoping explicit at the API layer instead of implicit model managers. Background workers, tests, and Django admin stay predictable while DRF handles request isolation. #Django #Python #BackendEngineering #SystemDesign #DevOps #OpenSource

Let’s talk about something fun and interesting I did quite a while ago. I optimized a keyword-driven query system, focusing on improving throughput and stability under constraints. The core problem: Maximize queries/hour while avoiding conflicts, throttling, and system instability. Key optimizations: • Parallel processing with controlled concurrency • Keyword-based query pipeline for structured input distribution • User-agent rotation to distribute request patterns • Retry + backoff mechanisms for handling transient failures • Idempotent execution to avoid duplicate processing One interesting tweak that made a noticeable difference: I introduced a keyword expansion strategy - combining each keyword with incremental alphabet variations (e.g., keyword + a, keyword + b, ...). This helped: • Increase result coverage without changing the core keyword set • Avoid repetitive query patterns • Improve overall discovery efficiency per keyword After multiple iterations, the system stabilized at ~70 leads/hour from about ~15–20 leads/hour with consistent performance. This was one of the most interesting things I had worked on, may not be as flashy but interesting for sure that such a small change can have such a great impact! Curious to know your thoughts! #Optimizations #Python #Software #SaaS

Your webhook handler is probably slowing down your system without you realizing it. ⚡ I used to process webhook events directly inside the request cycle. Receive request → process logic → update DB → return response. It worked… until traffic increased. Multiple webhook events started hitting at the same time. External services kept retrying if response was slow. And suddenly, the system was under pressure. The problem: Webhook providers don’t care about your processing time. If you’re slow, they retry. If they retry, you get duplicate load. The fix: Stop processing inside the request. Receive → validate → acknowledge fast Then push the actual work to a background queue Example approach: # views.py def webhook_handler(request):    data = request.data    process_webhook.delay(data) # async task    return Response({"status": "received"}) What changed: • Faster response to webhook provider • No blocking request threads • Better handling of traffic spikes • System stayed stable under load Important detail: Async alone is not enough. You still need idempotency to handle retries safely. The insight: Webhooks should be received fast, not processed fast. #SoftwareEngineering #BackendDevelopment #Django #Python #SystemDesign #Webhooks #Scalability #Performance #Developers

Building Something Powerful with Django REST I’ve been working on improving how APIs handle data, focusing on performance, flexibility, and clean architecture. Recently, I implemented a system where: 🔹 Clients can request only the fields they need 🔹 Nested data can be controlled dynamically (GraphQL-style) 🔹 Query performance is optimized using select_related & prefetch_related 🔹 Clean service-layer architecture keeps everything maintainable This approach helps: ✅ Reduce payload size ✅ Improve response time ✅ Avoid unnecessary database hits ✅ Keep APIs scalable and production-ready Instead of switching REST to GraphQL, I explored how far we can push Django REST Framework with the right design patterns. 💡 Key focus areas: Field-level filtering Dynamic query optimization Service layer separation Clean and reusable architecture I’ll be sharing more details soon about the implementation and challenges. Curious to know how you are handling flexible APIs in your projects? #Django #DjangoREST #BackendDevelopment #API #GraphQL #SoftwareEngineering #CleanArchitecture #Python

Building a CRUD API with FastAPI One of the first practical projects backend developers build is a CRUD API, which allows applications to Create, Read, Update, and Delete data. Using FastAPI, developers can build these APIs quickly while maintaining strong performance and clean code architecture. FastAPI uses Python type hints and modern asynchronous features to simplify both request validation and response handling. In a typical CRUD API, developers define models representing resources such as users, posts, or products. These models describe the structure of the data and help ensure that requests contain valid information. FastAPI integrates with libraries like Pydantic to automatically validate incoming data, reducing the risk of incorrect or malformed requests reaching the database. Beyond simplicity, FastAPI provides automatic API documentation using OpenAPI and Swagger UI. This allows developers to test endpoints directly in the browser without needing external tools. As a result, FastAPI not only speeds up development but also improves collaboration between backend developers, frontend developers, and API consumers. #FullStackDeveloper #WebEngineering #TechCommunity #BuildInPublic #LearnToCode

Just solved the Min Stack problem with an optimized O(1) approach for all operations. Key takeaway: Maintaining an auxiliary stack to track the minimum at each step ensures constant-time retrieval without additional traversal. Operations achieved: push → O(1) pop → O(1) top → O(1) getMin → O(1) This problem reinforces an important pattern: augmenting data structures to trade space for time efficiency. Always satisfying to see clean logic translate into solid performance. #DataStructures #JavaScript #ProblemSolving #LeetCode #Algorithms

Watching 5,000 tickets go from over an hour to 2.5 seconds is a reminder that the best project outcomes come from solving the root problem, not patching around it.

I once spent three days trying to optimize a high-concurrency data pipeline in Django, only to realize I was fighting the framework’s architecture, not the problem. Last week, on a client project involving real-time sensor data, we hit a wall where Django’s ORM and sync nature couldn't keep up with the throughput requirements. The lesson? Pick your Python weapon based on the job, not just what you know best. Django is unbeatable for complex admin panels, strict schema management, and rapid prototyping. It gives you the "batteries included" safety net that lets you ship features instead of building boilerplate. FastAPI, on the other hand, is for when you need to squeeze out every drop of performance. Its asynchronous nature is a massive win for I/O-bound tasks and heavy WebSocket integration. If you’re building a CRUD-heavy enterprise dashboard, stick with Django. If you’re building a high-scale microservice that needs to handle thousands of concurrent requests, move to FastAPI. Don't force a monolith into a microservice’s shoes. What’s the one project where you swapped backends midway because the first choice didn't scale? #Python #SoftwareEngineering #Django #FastAPI #SystemDesign