Overcoming Modern Web Scraping Challenges with Python

We just launched JSON Craft - a free, AI-powered JSON toolkit that does something no other tool does. You know the pain. You're debugging an API response that's 500 lines deep. Nested objects inside arrays inside objects. You find the key you need, but now you have to manually figure out the path to access it. **data.response.users[0].address.coordinates.lat** You count brackets. You scroll up. You mess it up. You try again. We fixed this. Click any key in the tree view → instantly get the exact access path in 8 languages. JavaScript, Python, Java, C#, Ruby, PHP, Go - one click. Copy. Done. No more counting brackets. No more guessing. No more wasted time. And that's just one feature. JSON Craft also includes: 🔧 Editor — Format, validate, minify, and search with JSONPath 🔍 Diff — Side-by-side comparison with visual highlighting ⚡ Transform — Filter, sort, flatten, group, convert case — all in-browser 📊 Visualize — Tree graphs, tables, and charts — with fullscreen mode Oh, and when your JSON is broken? One click → AI fixes it for you. Completely free. No signup. No limits. No tracking. Your data never leaves your browser (except the optional AI fix). 🔗 Try it: https://lnkd.in/dtk4Qdnv Built by the team with love ❤️ at KSPR Technologies. #JSON #DeveloperTools #WebDev #API #FreeTools #AI #Programming #JavaScript #Python #SoftwareEngineering #DevEx #OpenSource #Productivity #KSPRTECH

Claude Code Gave You the Code; Now What? (Models Part 17) Generated code is not a pipeline. It is potential. Part 17 closes the gap between “code in a chat window” and a running system. Three things actually matter here. Not theory. Not architecture. Execution. 1. Environment before code Nothing runs without a clean Python setup. • Python 3.11 only • Virtual environment created and activated • Dependencies installed from requirements.txt If this is wrong, everything downstream fails in ways that look unrelated. Most early failures are here. 2. Structure before execution Every script assumes a directory structure. data/raw → normalized → cleaned → formatted → chunked vectorstore, finetune, logs, reports, models If the structure does not exist, scripts fail with path errors. Create it once. Never think about it again. 3. Sequence is not optional This pipeline is order-dependent. You do not “try things.” You run: Download → normalize → clean → format → chunk → embed → store → serve → assemble If embeddings run before chunking, it fails. If retrieval runs before embeddings, it returns nothing. If serving starts without the model, it crashes. This is not flexible. It is deterministic. ----------- What running actually looks like • Setup scripts print confirmations • Ingestion shows steady progress logs • Services start, then go quiet • Logs update only when queries arrive Silence is success. Red text is failure. ----------- The three failure patterns 1. Missing dependency   ModuleNotFoundError → install it 2. Bad path   FileNotFoundError → wrong directory or skipped step 3. GPU memory   CUDA out of memory → reduce batch size or fix quantization Everything else is a variation of these. ----------- How to use Claude Code correctly Do not summarize errors. Paste the full traceback. Ask for a fix against the specific script. That turns debugging from guessing into resolution. ----------- What this article actually does It removes friction. Not conceptual friction. Execution friction. The pipeline was already designed. This is what gets it running. ----------- Open the browser. Hit the endpoint. Ask a real question. If the answer comes back grounded in your data, the system exists. #InHouseAI #ClaudeCode #LLMDeployment #PythonPipeline #AIInfrastructure

𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝗶𝘀𝗻'𝘁 𝗳𝗮𝘀𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗙𝗮𝘀𝘁𝗔𝗣𝗜.  𝗜𝘁'𝘀 𝗳𝗮𝘀𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝘄𝗵𝗮𝘁'𝘀 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵. Most people stop at "FastAPI is faster than Flask." Few ask 𝘸𝘩𝘺. Here's what's actually happening: 𝗙𝗹𝗮𝘀𝗸 runs on 𝗪𝗦𝗚𝗜. One request = one thread = blocked until done.  Your thread waits while the DB responds. It does nothing. Just sits there. 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 runs on 𝗔𝗦𝗚𝗜. One thread handles 𝘵𝘩𝘰𝘶𝘴𝘢𝘯𝘥𝘴 of connections. While one request waits for DB, the thread picks up another. No idle time. But FastAPI doesn't do this alone. The real stack: • 𝗨𝘃𝗶𝗰𝗼𝗿𝗻 — the ASGI server (built on uvloop) • 𝗦𝘁𝗮𝗿𝗹𝗲𝘁𝘁𝗲 — the async engine (handles requests, WebSockets, middleware)  • 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 — the developer layer (validation, docs, type hints) Think of it this way: Starlette = 𝘵𝘩𝘦 𝘦𝘯𝘨𝘪𝘯𝘦. FastAPI = 𝘵𝘩𝘦 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥.  Uvicorn = 𝘵𝘩𝘦 𝘧𝘶𝘦𝘭. Flask was built for a 𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀 world. FastAPI was built for an 𝗮𝘀𝘆𝗻𝗰-𝗳𝗶𝗿𝘀𝘁 world. The speed difference isn't a feature.  It's a 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 difference. Next time someone says "FastAPI is fast", ask them: 𝘐𝘴 𝘪𝘵 𝘍𝘢𝘴𝘵𝘈𝘗𝘐, 𝘰𝘳 𝘪𝘴 𝘪𝘵 𝘚𝘵𝘢𝘳𝘭𝘦𝘵𝘵𝘦? #FastAPI #Flask #Starlette #Python #AsyncProgramming #BackendEngineering #SystemDesign #SoftwareEngineering

They say 90% of software engineering is debugging, and today I definitely felt that! 😂 After a marathon session of untangling server conflicts, navigating API versioning updates, and restructuring database schemas on the fly, I am thrilled to finally share my latest project: NutriScan-AI. 🚀🍏 I wanted to build something that bridged the gap between raw data and practical, everyday AI. NutriScan-AI is a full-stack web application that allows users to snap a photo of any meal and instantly receive a complete nutritional breakdown and ingredient analysis. 🧠 How it works under the hood: Frontend: A clean, dark-mode UI built with HTML/CSS that handles user image uploads. Backend: A robust Python (Flask) server handling the API routing and logic. AI Integration: Integrated Google's Gemini 2.5 Flash Vision API to process the image pixels and accurately identify complex food items. Database: Engineered a PostgreSQL relational database to securely log user scans and perform fuzzy-search lookups for detailed macro-nutrients (Calories, Protein, Carbs, Fat). git - https://lnkd.in/gW7VqJrM Always learning, always building. On to the next challenge! #ArtificialIntelligence #Python #Flask #PostgreSQL #FullStackDevelopment #GeminiAI #SoftwareEngineering #TechJourney #StudentDeveloper

Your FastAPI backend is fast to build. But is it fast to run? Most developers find out the answer at the worst possible moment when real users hit it at the same time. Endpoints slow down. Requests pile up. Users drop off. Not because the code is wrong. Because it is blocking. Here is what blocking actually looks like in production: Your user hits an endpoint. FastAPI calls the database. That query takes 200ms. During those 200ms your server is frozen. Not slow. Frozen. Every other request sits in a queue waiting for that one query to finish. 100 users hit your API at the same time. User 1 gets served. Users 2 to 100 wait in line. That is sync. That is blocking I/O. FastAPI was built to never work that way. With async/await while your database query runs in the background, your server is already picking up the next request. And the next. And the next. 200ms of database wait becomes invisible to every other user. In real backend terms. SYNC — blocks: def get_orders(user_id: int): return db.query(user_id) ASYNC — non blocking: async def get_orders(user_id: int): return await db.query(user_id) Same logic. Same database. Same server. But now 100 users get served in the time it used to take to serve 1. This matters even more when your endpoints call external services. 1. Payment gateway 300ms wait. 2. AI model response 2 to 3 seconds wait. 3. Email service 500ms wait. Sync every user feels every millisecond of every one of those waits. with Async none of them do. FastAPI gives you non-blocking I/O natively. No extra setup. No plugins. No workarounds. Just write async. Add await. Let FastAPI handle the rest. Your backend was already fast to build. Now make it fast to run. Are you using async endpoints in your FastAPI projects? 👇 #FastAPI #Python #BackendDevelopment #AsyncProgramming #SoftwareEngineering #APIDesign #PythonDeveloper #WebDevelopment #TechIn2026 #BuildInPublic

I’d like to share my experience working on a project for processing XLSX documents and building business analytics. The project went through several stages of evolution - from a classic backend approach to more modern solutions using WebAssembly. Stage 1: Python + Pandas (server-side processing) Initially, all processing was done on the server using Python and Pandas. This approach is simple and efficient from a development standpoint, but in practice it introduced serious limitations: the need to transfer business documents over the network and store them on the server creates security and confidentiality risks. Stage 2: Pyodide + WebAssembly To avoid data transfer, I tried moving the processing to the browser using Pyodide (Python compiled to WebAssembly). This made it possible to run analytics entirely on the client side. However, new limitations appeared: - large WASM bundle size (it includes the Python interpreter) - low performance when working with large files - especially noticeable issues on mobile devices Stage 3: Rust + WebAssembly The final solution was to implement the processing and analytics module in Rust and compile it to WebAssembly. Yes, this required significant effort - essentially rewriting the logic from Python to Rust. But the result was worth it: - significantly smaller WASM size - high performance (even faster than the server-side version) - acceptable performance on mobile devices Conclusion WebAssembly truly opens up new possibilities for client-side analytics, but the choice of technology is critical. In my case, Rust turned out to be the most balanced solution in terms of performance, security, and bundle size. I’d be interested to hear if anyone has faced similar challenges and what approaches you used.

Microsoft just pushed Agent Framework 1.0 to GA, unifying AutoGen and Semantic Kernel into one production-ready framework for .NET and Python. That matters. But the bigger lesson is architectural: the sample runs a six-agent travel planner on Azure App Service with a continuous WebJob, Service Bus, Cosmos DB, Azure OpenAI, and built-in OpenTelemetry. That is the insight too many teams miss: Multi-agent complexity usually comes from infrastructure choices, not agent logic. In Jordan S. design it uses: ✅ 3 agents gather context in parallel ✅ itinerary, budget, and coordination run sequentially ✅ long-running work moves off HTTP with async request-reply ✅ chat history stays client-side ✅ telemetry is one builder call away with UseOpenTelemetry() 👉 No cluster management. 👉 No orchestration theater. 👉 No “we’ll need platform engineering before we can prototype.” Just a stable GA agent framework, plain code, background processing, and a deploy path that real teams can actually own. That is the real unlock: Serious agent apps get easier when you stop treating infrastructure complexity as a badge of sophistication. Learn more here: https://lnkd.in/eBMYY4Sa

𝗗𝗷𝗮𝗻𝗴𝗼 𝗮𝗻𝗱 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝗳𝗼𝗿 𝗘𝗻𝗲𝗿𝗴𝘆 𝗔𝗜 We simplified our AI stack. We moved to a unified Python web app. We use Django for the runtime. We use MongoDB for storage. Why this change? Split frontend and backend stacks cause friction. They drift apart. Django now handles the UI, API, and auth in one place. This removes sync errors. MongoDB fits AI data. Chat history and user sessions change shape. Rigid tables slow you down. MongoDB uses flexible documents. It stores chat sessions and training logs easily. The AI is energy-aware. Simple requests take a light path. Complex requests take a deep path. This keeps the system efficient. We added web search. The AI finds live info for new questions. It does not rely only on model memory. The team built this: - @haindav_lyada - @rithvik_sakinala_f5814d71 - @himesh_131 - @harshith_varma_k - @pvr_bharath_3e54935013654 Thanks to @chanda_rajkumar for the guidance on system design. This architecture makes the system easy to test and publish. It improves reliability for your users. Source: https://lnkd.in/gN4HjRnY

📈Reliability Hack!📈I STRUGGLED WITH THIS FOR YEARS - PLEASE CONSIDER THIS WORKFLOW IF YOU ARE AN IGNITION DEVELOPER! With AI being way better now, it hit me that I can perform robust testing on NamedQueries in ignition. To do this, I package the data I'm modelling into sqlite (You can export as csv and ingest with a python script/AI-assisted), copy the NamedQuery into a .sql file, and then package it all in a directory and ask AI to produce a SQLalchemy script to run the query, and perform tests. I can now run a bunch of tests on the queries to ensure reliability of the query AND perform robust refactorization if I want to change something like the navigation model of an entire platform (More on that to come -- excited to share the new best way to perform navigation!!)

When we launched Data Legion, the API was the only interface. A few months later there are four: REST API, Python and Node.js SDKs, an MCP server, and now a CLI. Each layer exists because a different consumer needed a different way in. SDKs for application code. MCP for AI assistants like Claude and ChatGPT. The CLI for AI coding agents and shell scripts. The interesting part: we built the CLI last, after watching how agents actually interact with data tools. Here's why we built each layer and what we learned. https://lnkd.in/gM2KjGzR #B2BData #AIAgents #DeveloperTools #MCPServer #APIDesign