Django OCR Integration with Python and LLM

FastAPI Changed How I Build APIs — Here's What I Learned After years with Django, I finally dove deep into #FastAPI. The difference is night and day for AI applications. What makes FastAPI special: ⚡ Async-first —> Handle 100 concurrent LLM calls without blocking 📝 Auto-documentation —> Swagger UI generated from your code ✅ Type validation —> Pydantic validates requests automatically 💉 Dependency Injection —> Clean, testable, reusable code 🚀 Performance —> One of the fastest Python frameworks What's in the repo: → Path & Query parameters with automatic validation → Pydantic models for request/response schemas → Field validators and custom validation logic → Dependency Injection patterns (Django doesn't have this!) → Async endpoints for non-blocking operations → Router organization for scalable projects The code progresses from basics to advanced patterns. Each file builds on the previous one. If you're building AI applications, APIs for LLMs, or just want a modern Python framework — this is the stack to learn. 🔗 GitHub repository: https://lnkd.in/eQt2p8pz Clone it and run `uvicorn main:app --reload`. You'll have a working API in 30 seconds. What framework are you using for your AI backends? 👇 #FastAPI #Python #API #WebDevelopment #AsyncPython #OpenSource #LearningInPublic

agentu gets eval and observability 🤓 from agentu import evaluate test_cases = [   {"ask": "What's 5 + 3?", "expect": 8},   {"ask": "Weather in SF?", "expect": "sunny"} ] results = await evaluate(agent, test_cases) print(f"Accuracy: {results.accuracy}%") Four matching strategies: exact, substring, LLM-as-judge (for semantic similarity), and custom validators. Exports JSON for CI/CD integration. Real-time Observability Every tool call. Every LLM request. Every error. Tracked automatically with a dashboard. from agentu import serve, observe observe.configure(output="console") # or json | silent serve(agent, port=8000) # Dashboard at /dashboard, no external tools needed #AIAgents #LLMOps #Observability #Python #OpenSource

I cleaned up local scripts that I had been repeatedly using and I'm releasing it as a new python package named `RetroFlow`. It makes beautiful, minimalist ASCII flow charts from simple strings like the following. It supports title banners, complex cycles, and more. simple_diagram = """ A -> B B -> C C -> D C -> E E -> A """ Beyond their cool, retro look straight out of the "golden age of computing" era, they have a real advantage now that agentic software engineering has become the norm: These ASCII, pure text diagrams can happily live inline with: PRs and other technical settings. Yes, agents can read images, but in my experience they more easily internalize these tiny representations (vs an image or full deck). Yes, you could just have agents read from things like the code block above but this is a visual format both people and agents can easily understand. Let me know what you think! Blog post: https://lnkd.in/euPzKegk GitHub: https://lnkd.in/ekw-RAEg PyPi: https://lnkd.in/etPvPvVU #Python #AI #DataVisualization #OpenSource

Scrapy in 2026: a big step forward for async crawling ⚡ Scrapy just shipped a new release, and while it may look quiet on the surface, it changes a lot under the hood. With Scrapy 2.14.0, the framework finally leans into native Python async/await, moving away from legacy Twisted Deferreds. It’s not a flashy release, but it does make Scrapy easier to run, easier to integrate, and more future-proof. 👉 Read the full article: https://hubs.li/Q03-k-670 #Scrapy #WebScraping #Python #AsyncIO #DataEngineering #OpenSource

🎥 New Setup Video Released – AI Productivity Toolkit v3 Many users asked how to properly set up and run my AI Productivity Toolkit v3, so I created a complete step-by-step setup video. This video covers: ✅ Installing dependencies ✅ Running the Django server ✅ Understanding the project structure ✅ Common beginner mistakes & fixes If you’re learning Django or building productivity tools, this will save you time. 👉 Setup video link in the comments 👇 #buildinpublic #django #python #ai #productivity #webdevelopment #indiehackers

How many times have we tanked a script’s performance by abusing .apply() or iterating over rows in Pandas? 🐢📉 It happened to me so often that I decided to stop complaining and actually build something to catch it automatically. That’s how pandas_lint was born. 🛠️ It’s a static analysis tool (linter) I’ve been working on to spot common anti-patterns in Pandas and NumPy. The goal is simple: code shouldn't just run; it should be clean and efficient from line one. 🚀 What v0.1.0 does: ✅ Catches slow operations (like the classic iterrows). ✅ Suggests vectorization improvements. ✅ Helps keep code readable. It’s been a fun challenge diving into how to structure a Python package properly and really thinking about the "Developer Experience" (DX). If you work with data, I’d love for you to check it out, try to break it, and give me some feedback (or a ⭐ on GitHub if you dig it). Suggestions on how to improve the detection logic are more than welcome! Link to the repo in the first comment 👇 #Python #DataScience #DataEngineering #OpenSource #Pandas #CodeQuality

🚀 Introducing WebNexa — Ask Websites Questions Directly! Tired of scrolling through pages just to find one answer? WebNexa is a Python library that lets you query websites like a conversation. 💡 Key Features: Load any website and ask questions instantly AI-powered, context-aware responses Quick website summarization Easy setup: pip install webnexa Built on Hugging Face models, fully open-source Save hours of research time and get answers, not just raw data. Perfect for data scientists, researchers, or anyone working with web content. 📌 Sample usage: from webnexa import WebNexa # Initialize (API key via environment variable) chat = WebNexa(hf_token="your-huggingface-api-key") # Load a website chat.load_website("https://example.com") # Ask a question answer = chat.ask("What services do they offer?") print(answer) Simple, fast, and powerful. 🌐✨

I built snapconfig to speed up loading large configs across AI worker processes. Inspired by uv's use of rkyv, it compiles JSON/YAML/TOML/INI/env files to a binary cache and then memory-maps them for zero-copy reads. Cold loads are slower than Python's json module, but cached loads are 10-1000x faster. Perfect for Celery workers, serverless functions, AI workflows, or any setup where multiple processes read the same config. Visit our GitHub repo for benchmark results and learn more about SnapConfig. #LaravelCompany, #PythonPerformance, #RustProgramming, #EfficientConfigLoading, #FastAIWorkflows

A quick note for those looking to handle API key management for integrated AI agents: PLEASE.. be as specific as possible before diving into code, it will save you a lot of time. when i did this with my current project, I've made a new markdown file to remind myself of what needs to be done. it turns out that it was incomplete and lacked lots of details. I found that it was crumbling into errors. well, you can say Instead of firing up the server, I've set it up on fire :) the good side of this was after having a nice discussion with the agent, everything was pulled up together. and I've learnt from my mistakes. lessons learnt: keep agents around, in case your code decided to go for a grilling party :) #API #backend #development #server #python #fastapi #mistakes

🚀 Introducing WebNexa — a Python library that lets you ask questions directly to any website. 🔍 No more reading long docs or articles just to find one answer. 🤖 WebNexa scans the entire website and returns AI-powered, context-aware responses. ⚡ Easy setup with 'pip install webnexa'. 🌐 Load any URL and start querying instantly. 📡 Supports streaming responses for real-time apps and chatbots. 📝 Also includes quick website summarization. 🧠 Built on Hugging Face models and fully open-source. ⏱️ It’s already saving me hours of research time. 👇 Sample usage below. from webnexa import WebNexa # Initialize (API key can also be set via environment variable) chat = WebNexa(hf_token="your-huggingface-api-key") # Load a website chat.load_website("https://example.com") # Ask a question answer = chat.ask("What is this website about?") print(answer)