Ade Python SDK Open Sourced for Document Extraction

𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗜𝗻 𝗚𝗼: 𝗔 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗲 v'𝘀 𝗚𝘂𝗶𝗱𝗲 You've written Python for years. You know how to use pip and asyncio. But then someone says "we're moving to Go." This guide is not an introduction to Go. It's about changing your mindset. Python and Go are different on the surface and underneath. In Python, you have the GIL and the event loop. You've used threading and asyncio. Go is different. It has goroutines and channels. Goroutines are lightweight threads. Channels are how goroutines talk to each other. Here's an example of fetching multiple URLs concurrently in Python and Go: - Python uses asyncio and aiohttp - Go uses goroutines and channels Go has no event loop. It's truly parallel. Every goroutine can run on a separate CPU core. In Go, context.Context is important. It carries deadlines and cancellation signals. You must cancel a context to avoid memory leaks. You'll notice some things are missing in Go: - No try/except. Errors are values, not exceptions. - No None. Go uses zero values. - No default function arguments. Use config structs instead. - No context manager. Use defer instead. - No enums. Use iota with a const block. - No decorators. Use closures instead. Python hides references. Go makes them explicit. You need to think about value vs reference semantics. The biggest difference: Python's slice syntax gives you copies. Go slices share backing memory. Some quick mappings: - list → slice or array - dict → map Go enforces a strict DAG for imports. No circular imports. The compiler will stop you. You can achieve everything you did with Python classes in Go, just differently. Use structs with methods attached. No constructor. Write a factory function instead. Encapsulation works through naming. Lowercase is private, capitalized is public. Interfaces are implicitly satisfied. You don't write implements. Polymorphism is interface-based, not inheritance-based. Composition is the default. Go has a simple toolchain: - go fmt formats your code - go test runs your tests - go build compiles everything - go vet catches suspicious constructs - go mod manages dependencies Source: https://lnkd.in/gKeYeMFS

A step-by-step guide on how to create a team of AI Agents that can analyze, translate, and test legacy code into modern Python code. Building a Multi-Agent AI System to Modernize Legacy Code. https://lnkd.in/gTtfc7A3 #AI #AIAgents #MultiAgentSystem #MAS #ModernizeLegacy

Curious how to integrate your Python code into a C++ project? I took some time to flesh out my previous article on calling Python scripts from with C++ with input- and output-arguments. I added figures, added content, made the text more readable, and added a section on multithreading. No generative models used. You can find the updated article here, and more posts to follow soon: https://lnkd.in/dWFngEvV

Build enterprise-grade RAG agents with Foundry IQ Knowledge Bases in ~20 lines of Python. Learn how the Azure AI Search Context Provider brings intelligent, multi-hop retrieval to the Microsoft Agent Framework—no fragme...

“At first glance, this sounds simple, but PDFs were never designed to be machine-readable in the way modern formats are. . (..) Extracting text from PDFs in Python is easier than it was a few years ago, but the fundamental challenges of document structure and layout remain. Choosing tools that handle these complexities well can significantly improve the quality of downstream systems, from search to RAG to analytics.” In this blog post, you'll learn how to extract text from PDFs in Python, how to handle more complex use cases like scanned documents, extracting tables, and more. Plus, a message on why we built Kreuzberg. https://lnkd.in/dCMurfbr

Python vs Mojo: Evaluating the Future of AI Development Python vs. Mojo: Is It Time to Switch for AI Development? Mojo markets itself as the language that gives developers Python’s friendly syntax while unlocking C-like speed. The promise is unmistakably attractive to AI engineers who wrestle with enormous datasets, latency-sensitive inference, and GPU/TPU pipelines. In this article, we explore whether Mojo’s performance claims translate into tangible benefits for production-grade AI systems or if the mature Python ecosystem still rules the stack....

5 Useful Python Scripts for Synthetic Data Generation Image by Editor   # Introduction  Synthetic data, as the name suggests, is created artificially rather than being collected from real-world sources. It looks like real data but avoids privacy issues and high data collection costs. This allows you to easily test software and models while running experiments to simulate performance after release. While libraries like Faker, SDV, and…...

Textstat Python library helps quantify text readability for ML tasks 📊. Flesch Reading Ease, Gunning Fog, and more metrics analyzed. Find the right one for your text analysis! #Python #TextAnalysis #DataScience

📘 Python for PySpark Series – Day 15 🎭 Polymorphism in Python ✨ What is Polymorphism? Polymorphism means “many forms”. It allows the same method or function to behave differently based on the object. ➡️ Promotes flexibility and dynamic behavior in code 🔹 Why Polymorphism? ✔ Improves code flexibility ✔ Reduces complexity ✔ Enhances readability ✔ Supports method reusability 🔹 Types of Polymorphism ✔ Method Overriding (Runtime) ✔ Method Overloading (Compile-time – limited in Python) ✔ Operator Overloading 🔹 Syntax (Method Overriding) class Parent: def show(self): print("Parent method") class Child(Parent): def show(self): print("Child method") 🔹 Example class Animal: def sound(self): print("Some sound") class Dog(Animal): def sound(self): print("Bark") class Cat(Animal): def sound(self): print("Meow") animals = [Dog(), Cat()] for a in animals: a.sound() ➡️ Same method sound() → different outputs 🔗 Why Polymorphism in PySpark? ✔ Same operations work on different data types (RDD, DataFrame) ✔ Functions behave differently based on input ✔ Helps in writing generic and reusable code 🏫 Real-Life Analogy (Remote Control 📺) One remote → multiple devices ➡️ Same button (action) → different behavior (TV, AC, etc.) 🧠 Interview Key Points ✔ Polymorphism = one interface, multiple implementations ✔ Achieved using method overriding ✔ Supports dynamic method dispatch ✔ Increases flexibility and scalability 🧠 Key Takeaway Polymorphism allows writing flexible and reusable code where the same operation behaves differently for different objects. 🔖 Hashtags #python #pyspark #dataengineering #oop #polymorphism #pythonbasics #learningjourney #coding

703 Python files. 300 Markdown files. I am a Python developer who writes 30% documentation. I pulled my language distribution from 19 days of AI-assisted work. The numbers did not match my identity. Python was first. Expected. But Markdown — planning docs, architecture decisions, agent instructions — was second. Not YAML. Not JSON. Prose. The AI needed written instructions more than it needed configuration files. Then the tools told a stranger story. 2,254 bash commands. 760 file reads. 416 edits. For every line I changed, I ran five commands investigating what to change. Before AI tools, my ratio was closer to 2:1. The AI made me a better reader, not a faster writer. That distinction matters for how you invest. If you evaluate AI coding tools on "lines generated per hour," you are measuring the tail, not the dog. The value is in investigation — 2,254 commands identifying what to change before a single edit. 📊 Three changes after seeing this: I optimized prompts for codebase exploration, not code generation. I wrote better CLAUDE.md instructions — Markdown, not Python. And I stopped timing "how fast can it write this function" and started timing "how fast can it understand this module." My exploration sessions — 8 out of 81 — produced zero code and the best decisions of the month. What does your read-to-edit ratio look like — and have you ever measured it? — Ernest Hemingway, Journalist AI #DeveloperProductivity #AITools #SoftwareEngineering