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Building pathways for co-designing UI/UX: Ideas and tools

Co-creation between designers and users can transform design possibilities and ensure products and services are meaningful and equitable.
Conventional design processes often position the designer as the sole expert, falling short of leveraging the creative potential that exists among impacted users.
Design biases often reproduce the status quo, inadvertently marginalizing those whose needs are rendered invisible throughout the design process.
New Economics Foundation’s alternative ‘Ladder of Participation’ illustrates the spectrum of user involvement in design, from minimal consultation to full co-design and shared ownership.
Effective collaboration can take place by involving community members as equal partners, acknowledging their lived and experiential knowledge.
Co-design is about building reciprocal relationships and creating space for meaningful dialogue where users have an opportunity to influence strategic decision-making.
Co-creation enables us to deliver more meaningful impact, expanding our circle of influence by involving a diverse set of stakeholders and creating a sense of shared purpose that drives sustained engagement.
Common challenges in facilitating co-design: time constraints, power imbalances, cultural barriers, resource allocation and conflicting priorities.
Building effective pathways for co-creation means actively transforming our culture, mindset, and practice as designers across multiple levels of systems change.
Co-design isn’t just a method; it’s a mindset. By investing in relationships and collaboration, we can create designs that are not only functional but also transformative.

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Medium

231

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How Does Outsourcing Look in Practice?

Outsourcing is a practical strategy that helps businesses stay competitive and agile.
The process of outsourcing starts with recognizing a gap and finding the right outsourcing partner.
Clear communication, well-defined processes, and tools like project management software are essential for effective outsourcing.
Outsourcing offers access to specialized talent, improved efficiency, and the ability to focus on core priorities.

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Hackernoon

276

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New .NET Library Does Deep Cloning Right

A new .NET library called FastCloner has been created to address the pain point of deep cloning objects in .NET Core.
The library aims to provide a solution that just works out of the box and covers various edge cases such as cyclic references, delegates, and read-only collections.
FastCloner can be easily installed using the command 'dotnet add package FastCloner' and allows for deep cloning of objects.
The library also supports excluding fields and properties from cloning by using the [FastClonerIgnore] attribute.

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Dev

249

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Must-Know Python Open Source Projects for 2025

Python's open-source landscape is richer than ever, offering a smorgasbord of projects that can elevate your skills, boost your productivity, and make your life a whole lot easier in 2025.
FastAPI is a python framework for building APIs that offers blazing performance without compromising on simplicity. It leverages Python's type hints to validate data and generate automatic interactive documentation, which makes it incredibly developer-friendly.
PyTorch is a machine learning tool known for its intuitive design and strong community support in 2025. It makes it easier than ever to implement cutting-edge models and leads the ML framework space.
Pandas 2.0 is the go-to tool for handling massive datasets. It offers speed improvements and features designed for modern data types, making it easier to analyze or build reports on a large dataset.
Django 5.0 is a framework for web developers that blends stability with innovation. It supports Python’s latest features, like pattern matching, making coding smoother and more efficient and is an ideal choice for developers of all levels.
Rich is a library that helps beautify your terminal by bringing color and interactivity to it, making it functional and visually appealing in 2025.
Streamlit is a game-changer for data scientists in 2025. It allows you to create interactive dashboards and applications with minimal effort and no need for front-end skills, making it easy to visualize data seamlessly.
Apache Airflow is the go-to tool for orchestrating complex workflows in 2025. With version 3.0, it’s better than ever, offering a more intuitive user interface, faster task execution and better resource-management.
Poetry is the best tool for managing Python dependencies. It simplifies package management and virtual environments and ensures consistent environments across different setups in 2025.
JupyterLab is an essential tool for interactive data exploration in 2025, and version 4.0 brings significant improvements, better support for extensions and real-time collaboration and faster loading times and smoother user experience.

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Dev

358

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ServBay v1.7.2 Released: New Node.js Configuration Interface and UX Improvements

ServBay v1.7.2 has been released with UX improvements and bug fixes.
New features include a Node.js configuration interface for command-line tools and new PHP configuration options.
User experience enhancements include saving navigation menu collapse state and optimized installation and detection mechanisms for ServBay Helper.
Bug fixes include resolving issues with the PHP Imagick module.

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Dev

190

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Research Paper Series: Using Lightweight Formal Methods to Validate a Key-Value Storage Node in Amazon S3

The paper 'Using Lightweight Formal Methods to Validate a Key-Value Storage Node in Amazon S3' presents a pragmatic approach to ensuring the correctness of ShardStore, a key-value storage node in Amazon S3.
ShardStore is a key-value storage node in Amazon S3, responsible for efficiently storing and retrieving objects using extents.
The paper describes the key components and workflow of ShardStore, including interactions with extents, the key-value abstraction, and read and write workflows.
The authors propose the use of lightweight formal methods, such as property-based testing, to validate ShardStore and have successfully prevented multiple issues in production.

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Dev

376

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Fine-Tuning Large Language Models (LLMs) with .NET Core, Python, and Azure

In this article, we'll explore how to fine-tune an LLM using Python, then integrate and deploy the resulting model into a .NET Core C# application, all on Microsoft Azure for scalability and convenience.
Fine-tuning allows developers to tailor these large models to a specific context or industry, improving accuracy and relevance.
Large Language Models (LLMs) have gained significant traction for their ability to understand and generate human-like text.
Performance Boost: Fine-tuning often reduces errors and increases relevance in use cases like customer service, research, and analytics.
Reduced Costs: Rather than building a model from scratch, you customize an existing powerful LLM.
Time Efficiency: You leverage pre-trained weights and only adjust final layers or parameters, expediting the process.
The solution comprises popular technologies like Python, .NET Core C#, and Azure services, including Azure Machine Learning for training and model management, Azure Storage for data and model artifacts, and Azure App Service or Azure Functions for hosting the .NET Core application.
The article serves as a guide to set up the required environment, train and fine-tune the LLM using Python, integrate the fine-tuned model into .NET Core C#, and deploy it to Azure App Services.
The article includes best practices with data privacy, monitoring & logging, security, model versioning, and prompt engineering.
This combination is an excellent choice for organizations seeking to leverage the best of Python’s AI ecosystem and .NET’s enterprise capabilities, all backed by the scalability of Azure.

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Dev

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My First RAG Chatbot: What I Built and How

HelloGitHub has been hearing from users that their search function isn’t cutting it for finding open-source projects.
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a language model by retrieving relevant information from a knowledge base before generating a response.
OceanBase’s open-source RAG chatbot is designed to deliver spot-on answers to user's document related queries through natural conversation.
To build a RAG system from scratch 'OceanBase' is a good choice due to the training tutorial tailored for beginners.
To boost the question-answering game, the data is optimized deep within where imported tables are made into cleaner and more precise content.
The RAG chatbot has gone through process optimization using the Tongyi Qianwen text-embedding-v3 model for debugging.
To optimize RAG, data quality is crucial, with retrieval making sure the relevant content is pulled up quickly and accurately.
OceanBase’s distributed architecture shines when dealing with massive data, making it ideal for RAG applications that require frequent data updates and synchronization.
In addition to vector data, RAG databases need to support hybrid searches of relational data, graph search (knowledge graph), and real-time queries with low-latency responses, transaction processing, and high availability.
The future of OceanBase is promising in RAG technology.

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Medium

169

Python Interview Questions — Day 34 of 100 Days of Data Engineering, AI and Azure Challenge

1. Read a CSV file and filter rows based on a specific column value. If data filtering were like social media, we’d just be blocking data younger than 25.
2. Merge two DataFrames using a common column. Merging DataFrames is like matchmaking — you need a common column (or a good algorithm) to bring them together!
3. Write a DataFrame to a CSV file without the index.
4. Handle missing data by replacing NaN values with the column mean. "NaN stands for Not a Number, but I like to call it ‘Not Available Now.’ Let’s fix that."
5. Convert a DataFrame column from string to datetime format. When strings pretend to be dates, we say, ‘Nice try, buddy,’ and use pd.to_datetime to set them straight.
6. Sort a list by length. Size does matter, especially when it comes to sorting lists!
7. Flatten a nested list. Turning layers into simplicity!
8. Merge dictionaries in Python. The 'b' key got an upgrade!
9. Access nested dictionary values with .get(). Safe navigation through deep data!
10. Handle outliers with IQR. Catching data that doesn’t behave!
11. Perform element-wise operations on NumPy arrays. Broadcasting your love of math is underrated!
12. Filter and mask arrays with conditional statements. Filtering the negativity out (literally)!
13. Use vectorized string operations in Pandas. Fast string processing, no loops needed!
14. Calculate eigenvalues and eigenvectors. For when linear algebra knocks on your door!
15. Use advanced array manipulation techniques. Repeating and tiling — because sometimes once isn’t enough!
16. Write data to a JSON file. JSON: Because plain text just isn’t hip enough anymore.
17. Append content to an existing file. Appending: The art of politely adding more stuff without overwriting.
18. Count word frequency in a text file. Word counting: The cousin of frequency analysis, but less mysterious.
19. Compress and decompress a file using gzip. Gzip: For when your file has a New Year’s resolution to lose weight.
20. Write a custom exception.
21. Explain context managers. Context managers: The cleanup crew of your Python script.
22. Log error messages instead of printing them. Logging: Turning your mistakes into elegant records of failure.
23. Use multiprocessing for parallel processing.
24. Use threading for concurrent execution.
25. Use sorting algorithms to sort lists. Sort by length because size does matter!
26. Perform list comprehension for compact and efficient code.
27. Read and write CSV files in Python.
28. Read and write JSON files in Python.
29. Utilize Pandas for data manipulation and analysis.
30. Utilize NumPy for numerical computing and array manipulation.
31. Use SQLAlchemy for database interaction.
32. Use psycopg2 for PostgreSQL database connection.
33. Use Apache Airflow for workflow management.
34. Use Luigi for building complex pipelines.
35. Use requests for making HTTP requests.
36. Use BeautifulSoup for web scraping.
37. Handle exceptions with try-except blocks.
38. Implement logging to record and track errors.
39. Utilize multiprocessing for parallel execution.
40. Utilize threading for concurrent execution.
41. Write unit tests using unittest framework.
42. Write tests using pytest framework.
43. Sort lists with custom keys for specific sorting criteria.
44. Reverse a linked list in Python. Reversing a linked list: Just like reversing a car, but with less honking.
45. Find the maximum difference between two elements in a list. Difference is key, whether in lists or life!
46. Check if a string is a palindrome. Palindrome: The word nerd’s version of a boomerang.
47. Find the intersection of two lists. Intersection: Where two lists meet and agree to disagree.

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Medium

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Mastering Git Stash: Your Ultimate Developer Tool

Stashing in Git acts as a safety net for big changes, allowing you to easily go back to your saved work if needed.
Stashing helps you temporarily set aside your changes without losing progress, allowing you to focus on other tasks.
Stashing allows you to start fresh by switching branches, getting updates, or testing code, while keeping your changes safe.
Git provides commands to save, list, apply, delete, and manage stashes effectively.

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Medium

449

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Will PHP Make You Rich in 2025?

PHP continues to power a significant portion of the web, making it a viable path to a lucrative career.
Legacy system modernization and building PHP products can create additional revenue streams.
Large enterprises still maintain massive PHP codebases, offering high-paying positions.
Strategic positioning and focusing on solving high-value business problems can lead to a comfortable income in 2025.

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27 Likes

Medium

113

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Goals for 2025

The author has created a starter kit for new engineering managers.
Sharing goals is about getting clarity for oneself.
The author has shared their goals for 2025 to stay focused.

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6 Likes

RealPython

117

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Build a Personal Diary With Django and Python

Creating a Django diary allows you to build a personal, secure web app on your computer without using external cloud services.
Building a diary is a great beginner project because it involves fundamental web app concepts like CRUD operations and authentication.
Class-based views provide a structured way to handle common web app patterns with less code.
You can leverage the Django admin site for authentication by reusing its login mechanism to secure your diary entries.

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7 Likes

Medium

281

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Step-by-Step Guide: How to Build a Facebook Posting Bot Using PuppeteerJS and Node.Js in 2025

Social media automation has changed how businesses interact with their customers.
A comprehensive guide on how to automate a Facebook posting bot using PuppeteerJS and Node.js.
Node.js is a preferred choice for building a Facebook automation bot.
PuppeteerJS is a powerful tool for web scraping and automation, perfect for deploying a Facebook bot.

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Dev

185

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Chaos Testing: Ensuring Resilience in Modern Systems

Chaos testing, also known as chaos engineering, is a proactive approach to testing the robustness of software systems by intentionally introducing failures to observe how they respond.
Modern systems rely on interconnected components, making chaos testing essential to ensure the system can handle disruptions and deliver uninterrupted service.
Chaos testing follows a structured approach of creating a hypothesis, injecting failures, monitoring and observing the system's behavior, and resolving any issues.
Benefits of chaos testing include improved reliability, faster incident response, and increased confidence in a system's ability to handle disruptions.

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