DeepFace Face Recognition in Python on GitHub

🚀 10 Python Libraries That Make AI Agents Work 🤖 Building AI agents is exciting, but turning prototypes into reliable systems requires more than just intelligent models. Failures often stem from missing infrastructure, not the model itself. That’s where Python’s ecosystem shines! 🌟 Here are 10 essential Python libraries that help stabilize and scale AI agents: 1️⃣ LiteLLM: Simplifies interaction with multiple model providers via a single interface. 2️⃣ Instructor: Ensures structured outputs with schema-based responses using Pydantic. 3️⃣ Tenacity: Adds retry logic for handling temporary API failures. 4️⃣ Logfire: Provides tracing and searchable logs for easier debugging. 5️⃣ DiskCache: Enables local caching to reduce repeated expensive calls. 6️⃣ Tiktoken: Manages token awareness for context windows and cost optimization. 7️⃣ Rich: Enhances terminal output for better debugging and visualization. 8️⃣ Watchfiles: Speeds up development with hot reload workflows. 9️⃣ Guardrails: Validates agent outputs for safety and reliability. 🔟 Ragas/TruLens: Offers metrics for evaluating agent quality and performance. These libraries form the backbone of dependable AI systems, transforming experimental prototypes into production-ready solutions. 💡 Let’s shift our mindset: AI agents aren’t just prompts wrapped around models—they’re layered systems supported by robust infrastructure. Python makes this approach practical, which is why it’s the go-to language for building serious AI agents. 🛠️ What are your favorite Python libraries for AI development? Let’s discuss! 👇 #AI #Python #MachineLearning #ArtificialIntelligence #AIAgents #TechInnovation #DataScience #Programming

Recently built Bonnie Bot, a simple AI coding agent that can read files, write code, run Python scripts, and use tool calls to complete tasks. Built as a small project, but a useful way to understand the real mechanics behind modern coding agents instead of treating them like a black box. It is intentionally lightweight, and that is part of the value. At a basic level, it follows the same core loop behind tools like Cursor or Claude Code. Under the hood, I kept the code modular with a main agent loop, prompt-driven behavior, function dispatch, sandboxed file operations, controlled Python execution, and separate testable tool modules. That helped me focus on the engineering behind agents, not just the final output. The biggest benefit of building something like this is clarity. You can see how reliability, security, and guardrails fit into the workflow. It currently uses Gemini, but the model layer can be switched to other LLMs as well. This agent and repository are free to use under the MIT License: https://lnkd.in/g7SHnCkm #AI #AIAgents #Python #SoftwareEngineering #Automation

Understanding How LLM APIs Work (Python Perspective) Most people use AI APIs. Very few understand how they actually work. Here’s the real workflow I learned 👇 🔹 Input → User sends a query 🔹 Processing → Python app structures the prompt 🔹 API Call → Request sent to LLM 🔹 Model → Processes using tokens & prediction 🔹 Response → Returns structured output 🔹 Output → App formats and delivers result result 💡 What this taught me AI is not just about using libraries. It’s about understanding the end-to-end system. 🔧 What I’m focusing on ✔ Prompt structuring ✔ API integration with Python ✔ Response handling & optimization ✔ Building real AI-based features 🚀 Next Working on building: AI chatbot API-based intelligent system 🔖#Python #AI #MachineLearning #LLM #SoftwareDevelopment #DevelopersIndia #TechCareers

🚀 Day 4 – My AI Engineer Journey Building stronger logic in Python step by step 🤖 📌 Today I practiced: ✔️ if-else with multiple real-life examples ✔️ Decision making using conditions ✔️ Logical operators (and) 💻 Code I worked on: # Example 1: Pass or Fail mark = int(input("Enter mark: ")) if mark > 35: print("pass") else: print("fail") # Example 2: Eligibility check income = int(input("Enter income: ")) if income > 7000: print("available") else: print("not eligible") # Example 3: Divisibility check a = int(input("Enter number: ")) if a % 3 == 0 and a % 5 == 0: print("divisible by 3 and 5") else: print("not divisible by 3 and 5") 📊 Learned how programs: ➡️ Make decisions ➡️ Handle real-world conditions ➡️ Combine logic using operators ✨ This is how intelligence starts in programming — logic first, then AI! 🔥 Consistency continues… Day 4 done! #AIEngineer #Python #MachineLearning #CodingJourney #100DaysOfCode #LearnInPublic

I hate learning theory first. So I built a project instead. Module 1 of my Gen AI Engineer roadmap — Python for AI. Instead of reading about async/await, decorators, and generators... I built an Async Wikipedia Scraper that fetches 100 pages concurrently and summarizes each one using Gemini API. Here's what I learned by actually building: → async/await → 100 API calls in 4s instead of 90s → Dataclasses → clean structured data instead of messy dicts → Generators → memory efficient pipelines → Decorators → added timing to any function in 3 lines → Secrets management → API keys never touch your code Every concept showed up naturally. No boring theory. 6 months. 6 phases. 12 projects. This is week 1. #Python #GenAI GITHUB LINK--->https://lnkd.in/gdp9cFUA

Most AI agents still work like interns who need permission for every step. That is why so many workflows feel slower than the demo. Pydantic’s 𝗠𝗼𝗻𝘁𝘆 is a serious alternative. It's a minimal Python interpreter, written in Rust, built to let agents run constrained code inside a secure sandbox. So instead of chaining endless tool calls, the agent can actually execute bounded logic. That shift matters because the real bottleneck is often orchestration overhead, latency, and the risk of giving generated code too much access. Monty gets the architecture right: start from zero access, then grant only the capabilities you want to expose and audit. 🔗 Link to repo: github(.)com/pydantic/monty --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

✈️ Powering Agentic AI: Why Python Leads the Way As Agentic AI continues to evolve—where systems can plan, act, and make decisions autonomously—choosing the right programming language becomes critical. Python has emerged as the backbone of this transformation. Here’s why: 🔹 Rich AI/ML Ecosystem Libraries like TensorFlow, PyTorch, and scikit-learn make it easier to build, train, and deploy intelligent agents efficiently. 🔹 Seamless Integration Python integrates effortlessly with APIs, databases, and external tools—enabling agents to interact with real-world systems. 🔹 Rapid Development & Prototyping Its simplicity and readability allow faster experimentation, which is crucial in designing adaptive and iterative agent workflows. 🔹 Strong Community & Support A vast global community ensures continuous innovation, support, and availability of pre-built modules for complex tasks. 🔹 Agent Framework Compatibility Modern frameworks for Agentic AI (like LangChain, AutoGen, etc.) are predominantly Python-based, making it the default choice. In the journey toward building intelligent, autonomous systems, Python is not just a language—it’s an enabler. #AgenticAI #Python #ArtificialIntelligence #MachineLearning #AIEngineering #Innovation

🚀 Built my own Jarvis – AI Voice Assistant I believe the best way to showcase learning is by building real projects. So, while learning Python, I decided to apply my knowledge by creating something practical — a Jarvis-like assistant. This assistant can: • Take voice commands 🎤 • Respond using AI (via OpenAI API) 🤖 • Open websites & perform basic tasks 🌐 • Automate simple daily actions Through this project, I learned: – How to work with APIs – Handling real-world errors & debugging – Speech recognition and text-to-speech – Structuring a functional Python project 🔗 Check out the code here: https://lnkd.in/gGMN2tET #Python #AI #Projects #LearningByDoing #Developers #OpenAI #GitHub

Most “AI for data” conversations are missing the point. It’s not about dumping massive exports into a chatbot. It’s about using AI once to build the logic then letting your systems do the work. In this new Endertech blog post: ]Use AI to generate focused Python scripts Test on a small dataset Refine the logic Run it locally on large STORIS and Inntopia data The result: Less token spend More control Repeatable outputs No need to expose large datasets This is where AI becomes practical in real-world commerce and integration work. Read more https://lnkd.in/e6RGqKMu #AI #Ecommerce #DataEngineering #Shopify #APIs #Python

🚀 𝐎𝐮𝐭𝐥𝐢𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐏𝐲𝐎𝐃 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 In real-world datasets, not all data points follow the pattern — some are outliers. Detecting them is crucial for building accurate and reliable models. 📊 Recently, I explored 𝐏𝐲𝐎𝐃 (Python Outlier Detection) — a powerful library that provides multiple algorithms for detecting anomalies. 📚 𝐖𝐡𝐚𝐭 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝: 🔍 𝐎𝐮𝐭𝐥𝐢𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 • Identifies unusual or rare data points • Improves model performance and data quality ⚙️ 𝐏𝐲𝐎𝐃 𝐋𝐢𝐛𝐫𝐚𝐫𝐲 • Offers multiple algorithms (KNN, Isolation Forest, LOF, etc.) • Easy to implement and compare different methods 📈 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 • Clear separation of normal vs anomalous data • Helps in better understanding data behavior 💡 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Handling outliers properly is essential for robust machine learning models and better decision-making. 🌍 From geoscience data to financial systems, anomaly detection plays a key role in real-world applications. #Python #DataScience #MachineLearning #PyOD #OutlierDetection #AI #LearningJourney