Invisible Bug Costs a Day of Debugging

Most operational software I encounter wasn't built to talk to anything else. With FastAPI, you can build a lightweight API layer on top of almost any system, whether it's a database, a legacy application, or a third-party platform. Once that layer is in place, other systems can pull data from it, push data to it, or trigger actions automatically. The result isn't just a technical improvement. It means processes that used to require manual exports, emails back and forth, or someone running a report every morning can simply run on their own. The only thing required is a small Python application. Deployed, maintained, and adapted when business requirements change. No large dev team needed. How many manual actions does your most painful data process require? Drop a number below! D-Data #Python #FastAPI #DataEngineering #SoftwareEngineering #BusinessAutomation #APIIntegration

If you're a Claude Code user, check out these terminal tools! Glad to see Starship and CShip getting the love they deserve!

6 ways to silently destroy your Python async code: 1. Blocking call inside an async function.   time.sleep(2) inside async def.   Your entire event loop freezes for 2 seconds.   All other requests wait. Nobody tells you why. 2. Forgetting await.   result = fetch_user(id)   result is now a coroutine object, not user data.   No error. Just wrong data passed downstream. 3. Creating tasks and not tracking them.   asyncio.create_task(process())   Exception raised inside. Silently swallowed.   Your task failed. You never knew. 4. Running CPU-bound code in async.   Parsing a 50MB JSON file in async def.   One request monopolizes the event loop.   All other requests queue up behind it. 5. Opening a new database connection per request.   No connection pool. 500 concurrent users.   500 open connections. PostgreSQL screams.   async doesn't mean free. 6. Mixing sync and async without thinking.   requests.get() inside an async handler.   Works fine alone. Under load — blocks everything.   httpx exists for a reason. async/await is not a performance silver bullet. It's a tool. Wrong usage makes things worse, not better. Which one bit you hardest? 👇 #Python #AsyncIO #Backend #SoftwareEngineering #Programming

I recently worked on a project where I built a web scraper for IMDb’s Top 250 movies 🎬 The goal was to automate the process of collecting movie data instead of manually browsing through the site. Using Python, I was able to extract key details such as movie titles, release years, IMDb ratings, and rankings, and store them in a structured CSV format. One of the key challenges was handling dynamic content. I used Selenium for browser automation and adapted the scraper when the website structure changed. To improve reliability, I implemented regular expressions for extracting year data instead of depending on dynamic class names. This project helped me better understand how real-world web scraping works — especially the importance of writing adaptable and maintainable code. 🔗 GitHub Repository: https://lnkd.in/ePrJtgdB Looking forward to exploring more in automation, data analysis, and AI. #Python #WebScraping #Selenium #DataScience #Projects #GitHub

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

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

My agent kept dying at row 199 of 200. I blamed token limits. Token limits weren't the problem. Here's what was actually happening. Claude read "process 200 leads" and did the smart thing. It wrote one Python script. To bulk-process all 200 in a single run. Because that's what a real engineer would do. The script worked. The script's output was the problem — one massive stream that hit my routine's idle timeout before it finished writing back. Row 199 of 200. Every single time. Two changes fixed it: 1. Drop the batch from 200 to 30. Smaller payload. Finishes inside the timeout window. Done. 2. Add to the prompt: "Do NOT write a script that does this in bulk. Process one row at a time." Force the inefficient path. It's been running every 3 hours for 4 days now. 240 leads a day. Zero timeouts. Here's the counterintuitive bit. The agent works better when I tell it to be less efficient. Because "efficient" for a coding agent means "write the cleverest script possible." And the cleverest script is usually the one that hits the wall hardest when something goes wrong. Boring loops ship. Clever scripts crash. P.S. Anyone else find that "tell the model to do less" outperforms "tell the model to do more"?

When code runs millions of times a day, even minor enhancements lead to significant compute savings. So I built xmltodict-fast. 🦀🐍 xmltodict is a Python library many of us use without a second thought. With ~5K GitHub stars, it’s a quiet workhorse powering ETL pipelines, SOAP clients, and invoice processors. It’s a drop-in replacement that maintains the same public API, but rewrites the performance-critical sections in Rust using PyO3 and quick-xml. Importantly: if the Rust extension isn't available on a platform, it seamlessly reverts to the original Python implementation. It's completely safe for incremental adoption. local benchmarks :  🚀 parse(): 2.1 × faster on typical XML  🚀 unparse():5.9 × faster (massive for serialization-heavy workflows)  On pathologically deep XML (500+ nesting levels), the Rust version is actually slower. :( (Side note: Thanks to my kind and patient AI coding assistant for helping me building this!) If you work with XML in Python, I welcome your feedback, testing, and pull requests! 🔗 Repo & Benchmarks: https://lnkd.in/exhfBuD7 #Python #RustLang #PyO3 #OpenSource #DataEngineering #PerformanceOptimization

50 orders. 51 database queries. That's what I found when I finally checked the query count on an endpoint I'd shipped two weeks earlier. Looked fine in local. Response times were normal — but I was testing on maybe 8 records. Real data hit it and the thing crawled. 4+ seconds for a simple order list. One JOIN. Done. The 4-second response dropped to under 80ms. But here's the thing — the broken code reads fine. There's nothing obviously wrong with it. You'd write it without blinking. I did. The ORM hides the cost so well that you only find out at the wrong moment. I've got django-debug-toolbar running locally now. Not optional anymore. For M2M or reverse FK relations it's prefetch_related — different mechanism, same idea. Worth knowing which to reach for before you need to. How are you catching N+1s before staging — toolbar, SQL logging, something else? #django #python #djangorestframework #backenddev #pythondev

I spent a weekend building a tool I actually needed : A PDF-to-Flashcard pipeline that runs 100% locally. The Win: No subscriptions, no data exposure, and zero latency. Just Python and local intelligence. The Stack: → PyMuPDF : Clean text extraction → Ollama to run Llama 3 locally:: High-performance local LLM. → Streamlit for the interface (and Sithara Hayavadana — the standalone local UI is genuinely great for this kind of project) → Pandas: Instant Anki-compatible CSV exports. The Biggest Learning: Data preparation beats model size every time. I found that chunking strategy mattered more than prompt engineering or model choice. The stack is entirely free — and yes, Keming Wang, free and open source tools were enough to buIld this 😁 I have shared the full article and technical breakdown in the comments below! 👇 Have you experimented with Ollama for your local workflows yet?