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Python
Jamiemaguire
2w
34
Building an AI Home Security System Using .NET, Python, CLIP, Semantic Kernel, Telegram, and Raspberry Pi 4 – Part 3: Creating a Local AI Custom Vision API for Training and Matching Images
- This article is part of a miniseries on building an AI home security system and cross-platform solution.
- The system's main requirements include motion detection, photo capture, messaging, and facial recognition.
- The .NET API implemented in this blog post provides endpoints for training and matching images using CLIP.
- The API encapsulates image processing and recognition logic for extensibility and ease of integration.
- Core functionalities of the API include getting vector embeddings, calculating cosine similarity, and finding the best match.
- API controller endpoints allow training the system with images and labels, as well as matching incoming image data.
- Testing the controllers and API can be done using tools like Postman to ensure functionality.
- The system matches incoming images with trained embeddings to determine if a match is found.
- The .NET API simplifies consumption of AI functionalities on Raspberry Pi, enhancing maintainability.
- Future plans in the series include extending the Telegram bot for managing embeddings and invoking APIs remotely.
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Medium
2w
268
Starting My AI Journey: Why I Began with Python
- Python is considered the 'universal language' of AI and machine learning, making it a popular choice for beginners entering the field.
- Starting with Python, even without a technical background, can provide the necessary foundation to understand AI concepts and build intelligent systems.
- The journey into AI is described as a process of continuous learning, embracing challenges, and using tools like ChatGPT for assistance in debugging and learning.
- While Python is just the beginning, it is seen as a powerful initial step towards exploring the possibilities of AI and technology in creating meaningful solutions.
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Dev
2w
251
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⚡ Supercharge Your Python Learning with These 9 Weekly Newsletters
- Python Trending Weekly, a paid newsletter, stands out for its thorough descriptions, curated compilations, and reasonable pricing, earning a top recommendation.
- Python Weekly, an OG newsletter since 2012, offers a mix of content with some inconsistency but impressive longevity, receiving a high rating.
- Pycoder's Weekly, known for effort in descriptions and featuring exclusive content, is appreciated for its quality and connection to Real Python.
- Python Hub Weekly Digest excels in curation with a clean design, mixing popular and older content, making it a recommended choice.
- Django News focuses on Django-related content, monetizing through ad pricing transparency, and targeting a niche audience effectively.
- Python on Microcontrollers Newsletter caters to hardware enthusiasts, covering IoT and single-board computers, filling a niche in Python newsletters.
- This Week in Python offers a simple format with no ads, showcasing genuine enthusiasm from the creator despite minimal formatting.
- Python weekly newsletter prioritizes quantity over quality, aggregating content from various sources, with inconsistent quality.
- Awesome Python Weekly, with a long operation history but limited quality and ads, is rated as questionable for its confusing content mix.
- Diverse Python newsletters provide varied content approaches, from automation to quality focus, catering to different preferences of users.
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Dev
2w
73
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Build an LLM Web App in Python from Scratch: Part 3 (FastAPI & WebSockets)
- The article discusses building a real-time AI chatbot web app using FastAPI for backends and WebSockets for live chat.
- Streaming AI in web apps creates a more engaging user experience with instant responses flowing in.
- FastAPI, a high-speed Python web framework, is used for the backend of the chatbot.
- WebSockets enable live, two-way communication between the server and the browser for real-time interactions.
- Async programming is crucial for integrating AI tasks into web apps without freezing the entire application.
- PocketFlow, in its async version, helps manage complex AI tasks by allowing multiple operations to run concurrently.
- The StreamingChatNode class streams AI responses word by word through WebSockets to provide a more dynamic chatbot experience.
- The article provides code snippets and explanations for implementing FastAPI, WebSockets, and asynchronous AI tasks in web apps.
- By combining FastAPI, WebSockets, and PocketFlow, developers can create a seamless real-time AI chatbot with interactive streaming capabilities.
- The tutorial guides developers through creating a live chat experience where AI responses are displayed word by word for a more natural conversation flow.
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Dev
2w
368
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Iterator in Python (4)
- Explanation of creating a class-based iterator in Python using __iter__() and __next__() methods.
- __next__() method can be a generator with one or more yield statements.
- __iter__() method can be a normal function returning 'Hello' or a generator.
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Dev
2w
312
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Python Trending Weekly #105: Dify Breaks 100K Stars, Full-Stack Development Best Practices for 2025
- Python Trending Weekly features 12 articles and 12 open-source projects in the latest issue.
- Articles & Tutorials cover topics such as creating a React + Flask project, optimizing Python code, and developing a PDF Chat App.
- Open-source projects include dify, sequor, bilive, NLWeb, and more, focusing on various platforms and tools for developers.
- Premium subscription at $4.99/month offers curated Python content from 400+ sources to help professionals stay updated in the tech space.
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18 Likes
Medium
2w
234
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Python Enums with Custom Data: Beyond Simple Constants
- Python Enums can carry custom data and behave like mini data classes, allowing related data to be grouped together.
- This pattern is useful for maintaining related constants, ensuring type safety, and preventing invalid combinations of values.
- Adding methods to Enums can enhance their functionality and allow for more complex behaviors.
- Using Python Enums with custom data leads to cleaner and more maintainable code, providing elegant solutions for handling related data.
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Medium
2w
21
5 Python Things I Wish I Knew Earlier
- enumerate() simplifies loops and makes code cleaner and less error-prone. No more messing with manual counters!
- The walrus operator in Python 3.8 allows assigning values to variables as part of an expression, reducing redundant code and improving readability.
- List comprehensions in Python are shorter, faster, more Pythonic, and can include conditionals for increased flexibility.
- pytest, a modern testing framework, enhances testing experience and coverage, making it more enjoyable than the built-in unittest module.
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1 Like
Medium
2w
95
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Python: The Swiss Army Knife of Programming
- Python has become a favorite among computer science students and professionals due to its simplicity and versatility.
- Python's straightforward syntax and abundance of libraries make it suitable for various domains such as data science, machine learning, and web development.
- Its ease of use and adaptability allow users to focus on problem-solving rather than dealing with unnecessary complexity.
- Python's role in simplifying tasks like cleaning datasets for machine learning projects and its compatibility with JavaScript for full stack development highlight its significance in the programming world.
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Dev
2w
173
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🧩 Guide to Solving "Lexicographically Minimum String After Removing Stars" LeetCode 3170 (C++ | JavaScript | Python)
- Decode LeetCode problem 3170: Lexicographically Minimum String After Removing Stars in C++, JavaScript, and Python.
- Problem: Given a string with letters and asterisks, remove * by eliminating the smallest non-* character to its left to get the lexicographically smallest string.
- Approach: Use greedy decision-making by tracking the smallest character encountered, indexing characters, marking deleted characters, and building the final answer string.
- Code snippets provided for C++, JavaScript, and Python along with test cases and time/space complexity analysis.
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Dev
2w
73
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Python Development
- Python development has a unique appeal with clean code, elegant syntax, and less debugging hassles.
- The journey with Python started smoothly, with its readability and friendliness standing out compared to other languages like Java.
- The allure of automation led to scripting tasks like renaming files, scraping flight deals, and sending motivational quotes.
- Despite challenges like debugging and learning frameworks like Django, the supportive Python community makes the journey worthwhile for developers.
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Dev
2w
0
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I built a File Sharing App in Python (with Auto Discovery + Drag & Drop). Here is what I learned.
- The article discusses the author's experience in building a Python file-sharing app over local networks.
- The app features a graphical interface using Tkinter, with separate tabs for sender and receiver functionalities.
- Sender side includes auto-discovery of hosts, drag and drop support, port selection, and file integrity check.
- Receiver side allows choosing listening ports, selecting save folders, and start/stop receiving options.
- The project utilizes Python libraries like socket, threading, tkinterdnd2, with no external dependencies.
- The author built the app to overcome perfectionism, learn practical networking concepts, and avoid overthinking.
- Challenges encountered included broadcasting issues with VMs, real-world experimentation, and utilizing GitHub Copilot.
- Lessons learned include the importance of modularity in code structure, debugging, and breaking projects into modules.
- The GitHub repository 'File-Share' contains the full code for the peer-to-peer file-sharing app with various features.
- The project summary showcases the app's capabilities like one-to-one file transfers, host discovery, drag & drop, and integrity checks.
- The article encourages sharing work without fear, highlighting the joy of learning, progress, and celebrating small victories.
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Dev
2w
299
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Build a Policy-Based Access System in Python
- A developer shares the frustration of scattered permission checks in the API codebase, proposing a policy-based access system in Python to simplify implementation.
- Requirements include the ability to compose different rules, easy integration into existing codebase, and ensuring fast rejection of queries.
- Implementation involves decorators for applying access policies, policies for defining access rules, and the use of pre-execution and post-execution checks for efficient access control.
- The policy-based access system addresses challenges like scattered permission logic, code duplication, resource-dependent access control, and enhances maintainability.
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18 Likes
Medium
2w
317
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Python vs. JavaScript: Which Path Should You Choose in 2025?
- Python and JavaScript are both powerful and popular programming languages, each with its own strengths and applications.
- Python's emphasis on readability and simplicity makes it a leader in data science, machine learning, and scientific computing.
- JavaScript, born for web interactivity, powers modern web applications from user interfaces to server-side development.
- Choosing between Python and JavaScript depends on aligning language strengths with personal interests and project goals.
- The article delves into core differences, ecosystems, performance, scalability, use cases, and job markets of Python and JavaScript.
- Python's clean syntax and 'batteries-included' philosophy enhance readability and enable rapid prototyping.
- JavaScript's flexibility, event-driven nature and multiple paradigms contribute to its versatility and dominance in web development.
- Both languages offer multi-paradigm support, with Python excelling in data science and AI, while JavaScript shines in frontend and full-stack development.
- JavaScript's ecosystem includes robust frontend frameworks like React, Vue.js, Angular, and backend power with Node.js.
- Python's rich ecosystem includes libraries like NumPy, Pandas, and TensorFlow for data analysis, machine learning, and backend web development.
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