Forem: Taipy

Stop duct-taping your Python scripts: Handle Scheduling and Versioning natively

Rym — Thu, 19 Feb 2026 17:14:05 +0000

TL;DR

Building a great model in Python is fast. But turning that script into a reliable, multi-user application usually involves writing manual "glue code," duct-taping Linux cron jobs, and crossing your fingers during updates.

What if you could bypass the "Day 2" operational wall and handle background scheduling and versioning entirely natively?

Enter Taipy: an open-source Python framework that lets you build production-ready data applications without forcing you to become a full-time DevOps engineer.

If you find this open-source project helpful, don't forget to show your support!

Star ⭐ the repo

The Need for an Alternative to "Glue Code"

We all know the pattern. You build a fast prototype (sales forecasting, optimization, etc.) and it runs perfectly on your machine. Then, the business team needs it every day.

Suddenly, your simple script turns into an operational monster:

• The Scheduling Nightmare:
You tie the model execution to a button click, which freezes the UI. So, you resort to external orchestrators or messy cron jobs.

• The Versioning Chaos:
You start creating files like model_final_v3.py. You become terrified to deploy a new version because you might break the dashboard that executives are looking at.

This is the transition from a prototype to a shared application. Let's look at how Taipy solves these architectural bottlenecks natively.

1. The Native Cron Scheduler ⏱️

Executing Machine Learning or optimization models takes time. Taipy now lets you run background computations automatically through a clean, built-in scheduling API.

🦾 How it helps:

Instead of relying on an external system, scheduling becomes part of your application model. You can set your scenarios to run overnight or hourly without blocking the app.

🛠️ Key features:

• Total Decoupling: The UI stays lightning-fast even if a 4-hour computation is running in the background.

• Overnight Updates: Forecasts and simulations refresh automatically, so decision-makers start the day with up-to-date insights.

• Zero "Glue Code": Reduce manual run steps and brittle external scripts.

2. Built-In Version Management 🗂️

Once people depend on your app, updates start to feel risky. Taipy provides built-in version management so you can track exactly what changed and why, right from the core.

🦾 How it helps:

It preserves traceability between your development, testing, and production environments. You avoid the situation where the safest option is doing nothing because nobody wants to break what already works.

🛠️ Key features:

• Absolute Traceability: You preserve the link between your logic, the data ingested, and the results produced.

• Stress-Free Deployments: Safely evolve your applications as usage grows and more users rely on the results.

• Built-in Governance: Seamlessly move from an isolated "experiment script" to a governed system.

🚀 See the Code in Action (No Sales Pitch)

Reading about architecture is great, but seeing it in code is better.
On April 9th, the Taipy technical team is hosting a Developer Webinar dedicated exactly to this transition: From Python models to trusted decision applications.

👉 Register for the April 9 Webinar here

This is a 100% technical, developer-first session shaped directly by questions from our Discord community. No marketing slides, no sales pitch.

What we will break down live:

• The Code: How to implement the Automated Scheduler and Built-In Version Management.

• End-to-End Tracking: How to monitor scenario and data node events so you aren't guessing how people use your app.

• Real-World Demos: We'll look at high-performance decision applications used by Taipy customer teams who handle massive datasets without browser bottlenecks.

👉 Register for the April 9 Webinar here

👋🏻Goodbye Power BI! 📊 In 2025 Build AI/ML Dashboards Entirely Within Python 🤖

Rym — Tue, 17 Dec 2024 14:54:55 +0000

TL;DR

The rise of data-driven decision-making has pushed businesses to adopt tools to create dynamic dashboards and applications.

Many of these solutions, like Power BI, require users to learn proprietary languages such as DAX or PowerQuery.

What if you could avoid the hassle and stay entirely within Python —the language you already use for data science and machine learning?

Enter Taipy Designer & Taipy: a game-changing, open-source solution that lets you build dashboards and applications without ever leaving the Python ecosystem. And customization is your best friend here.

The Need for an Alternative

Popular tools like Power BI and Tableau dominate the market for business intelligence (BI) and visualization. But they often fall short for AI/ML practitioners due to:

Proprietary languages: DAX, PowerQuery, or other complex syntaxes require additional learning and reduce flexibility.
Limited integration: While some tools support Python scripts, their integration is often cumbersome and not designed for seamless ML workflows.
Fragmented ecosystems: Moving data between BI tools and Python-based ML workflows can lead to inefficiencies and potential data handling errors.

A 100% Python Solution for Data Applications

As an open-source tool, Taipy is free to use and customize, making it a cost-effective alternative to proprietary BI solutions. Its Python foundation ensures that users are not locked into a specific vendor or technology, providing both freedom and flexibility.

Star ⭐ the repo

In addition, they developed a new product, Taipy Designer, the first no-code GUI builder specifically for Python users.

Fully integrated with the Taipy Enterprise Suite, this tool empowers businesses to create interactive, AI-driven applications seamlessly.

Whether you’re building dashboards to visualize complex Machine Learning models or implementing Real-Time AI analysis for operational data, Taipy Designer enables you to:

Leverage Python’s vast ecosystem without switching to other tools.
Implement advanced algorithms and seamlessly integrate machine learning workflows.
Build user-friendly dashboards without writing a single line of code—but with the flexibility to add Python-based customizations when needed.

Taipy Designer eliminates all current tools issues by offering a native Python environment, ensuring your workflows remain streamlined and efficient.

Key Benefits of Taipy Designer

1. For Business Analysts & BI Teams

No coding expertise needed A drag-and-drop interface that democratizes Python’s capabilities, enabling analysts to build applications and dashboards without coding expertise.
Faster application delivery to improved productivity.
Seamless integration with Python’s robust libraries, unlocking endless possibilities for data manipulation and visualization.

2. For Data Scientists & AI Practitioners

The ability to visualize and operationalize machine learning models in intuitive, interactive dashboards.
Tools for rapid experimentation, making it easier to iterate and present results to stakeholders.
Direct integration with Python’s ML & AI frameworks like TensorFlow, PyTorch, or Scikit-learn.

Real-World Impact: Why Businesses Are Switching to Taipy Designer?

The adoption of new tools often depends on their ability to drive tangible benefits. Taipy Designer has already demonstrated its transformative potential by empowering businesses to overcome technical barriers, enhance productivity, and innovate seamlessly.

Here's what leaders have to say about its impact:

Gilles Corde, Program Manager at IFP Energies Nouvelles (IFP-EN), one of Avaiga’s partners, highlighted how Taipy Designer has transformed their workflows.

Beyond Visualization: A Complete Solution

Taipy’s ecosystem doesn’t stop at dashboards. With Taipy you can orchestrate data workflows and create advanced user interfaces. Besides, the platform supports every stage of building enterprise-grade applications. Additionally, Taipy’s integration with leading platforms such as Databricks, Snowflake, IBM WatsonX, and Amazon SageMaker ensures compatibility with your existing data infrastructure.

Ready to Transform Your AI/ML Dashboards?

If you’re looking for a solution that allows you to:

Build scalable, interactive dashboards.
Stay entirely within the Python ecosystem.
Integrate machine learning and AI seamlessly.

Then, it’s time to say goodbye to Power BI and embrace Taipy.

Explore the GitHub repo and discover how this open-source alternative is redefining what’s possible in the world of business intelligence and AI-driven applications.

Have you tried building AI/ML dashboards in Python?

Let me know your thoughts or challenges in the comments below!

Build a 2025 Stock Dashboard in less than 40 lines of Python code!🤓

Rym — Thu, 05 Dec 2024 08:18:36 +0000

Building interactive data dashboards can seem intimidating.

Especially if you're unfamiliar with frontend technologies like HTML/CSS/ JS.

But what if you could create a fully functional, production-ready data science dashboard using just Python?

Enter Taipy, an open-source library that simplifies the process of creating data apps.

Star ⭐ Taipy repo

In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle.

Our app will dynamically filter data, display graphs, and handle user inputs—all from scratch.

Ready to dive in? Let’s get started!

Step 1: Setting Up Your Environment

First, we need to create a new Python environment. If you use Conda, you can set it up as follows:

conda create -n ds_env python=3.11
conda activate ds_env
pip install taipy pandas plotly

Clone the resources for this project:

git clone https://github.com/MariyaSha/data_science_dashboard.git
cd data_science_dashboard/starter_files

This will serve as our project root directory. Inside, you’ll find images, a wireframe, and a Python file (main.py) to start.

Step 2: Designing the GUI with Taipy

Let’s add a header and a logo to our app. Open main.py and start coding:

import taipy.gui as tgb

with tgb.page("Stock Dashboard"):
    # Add a logo
    tgb.image("images/icons/logo.png", width="10vw")

    # Add a title
    tgb.text("# S&P 500 Stock Value Over Time", mode="md")

Run your app:

taipy run main.py

Navigate to http://localhost:5000, and you’ll see your basic app!

Step 3: Adding User Inputs

To filter data by date, add a date range selector:

import datetime

dates = [datetime.date(2023, 1, 1), datetime.date(2024, 1, 1)]

with tgb.page("Stock Dashboard"):
    # Existing elements...

    # Add date range selector
    tgb.date_range(
        value="{dates}",
        label_start="Start Date",
        label_end="End Date",
    )

Step 4: Dynamic Data Handling with Taipy

Let’s load our dataset and filter it dynamically based on user inputs.

import pandas as pd

# Load the stock data
stock_data = pd.read_csv("data/sp500_stocks.csv")

def filter_data(state, name, value):
    if name == "dates":
        start, end = state.dates
        filtered_data = stock_data[
            (stock_data["Date"] >= str(start)) & 
            (stock_data["Date"] <= str(end))
        ]
        state.filtered_data = filtered_data

tgb.add_callback("filter_data", filter_data)

Step 5: Visualizing the Data

Finally, let’s plot the data with Plotly:

import plotly.graph_objects as go

def create_chart(data):
    fig = go.Figure()
    fig.add_trace(
        go.Scatter(
            x=data["Date"],
            y=data["High"],
            name="Stock Value",
            mode="lines"
        )
    )
    return fig

with tgb.page("Stock Dashboard"):
    # Existing elements...

    # Display the chart
    tgb.chart(figure="{create_chart(filtered_data)}")

Final Thoughts

And voilà!
You’ve built a stock dashboard with Taipy, handling dynamic user inputs and data visualization—all without writing a single line of HTML, CSS, or JavaScript.

Want to take it further?

Explore Taipy Scenarios to enable even more dynamic backend interactions. Check out the official Taipy GitHub repository and contribute to their open-source initiatives!

PS: you can watch the video tutorial here.

🤓 Top 12 Open Source Repositories to Watch in 2025 to Become the Ultimate Developer

Rym — Tue, 03 Dec 2024 07:26:48 +0000

If you're a developer, staying updated with the best open-source tools is a surefire way to boost productivity and deliver impactful projects.

Here, I've curated a list of 12 open-source tools that are game-changers for various aspects of modern software development—from AI integration to app building. Whether you’re working on data-driven apps, AI tools, or collaboration software, these are some of the most exciting tools you should explore in 2025.

1. Taipy - Build Python Data & AI web applications

Taipy is an open-source Python library that helps you build AI & Data web application including data-driven GUIs and automate scenario management.

➕ It provides a complete solution for building data apps, saving developers significant time and reducing complexity. It combines an intuitive GUI tool for designing dashboards with comprehensive scenario management capabilities, perfect for data scientists and analysts.

Key features:

Test multiple data simulations seamlessly.
Build fully customizable dashboards entirely in Python.
Real-Time Data Visualization

Star ⭐ the repo

2. Composio - Access +150 Tools in Just One Line of Code

For those aiming to build AI applications, you know that it's a nightmare to accomplish complex automation. You have to pass with success the connection of external tools such as GitHub, Jira, Notion, Discord... to your AI agent.

➕ Composio makes it super easy to connect over 150 tools, from system tools to SaaS apps and applications with AI models to accomplish agentic automation.

Key features:

Managed Auth
90+ Tools - Ready to Connect
Powerful RPA tools

Star ⭐ the repo

3. Postiz - Schedule your posts like a pro with AI.

Postiz is an open-source tool for automating and optimizing social media post scheduling.

➕ It offers a straightforward way to manage and debug APIs, improving collaboration and reducing development time. It provides a centralized platform to manage and debug APIs without hassle.

Key features:

Optimal Timing Analysis
Hashtag Generation
Team Collaboration

Star ⭐ the repo

4. LLMWare - Deploy & manage large language models with ease

LLMWare provides the tools necessary to integrate, deploy, and monitor large language models in production environments.

➕ It makes integrating AI models simple, enabling developers to create smarter applications with minimal effort. It focuses on simplifying the way these models interact with user data.

Key features:

Optimize models for environments with limited resources.
Utilize dashboards to monitor model performance in real time.
Implement models quickly with a straightforward API.

Star ⭐ the repo

5. Copilot Kit - Add a layer of AI assistance to your projects with just a few lines of code.

Copilot Kit is a modular development kit designed to integrate AI-powered assistants into your applications or systems.

➕ It enhances productivity by providing real-time coding suggestions, reducing bugs and speeding up development. It utilizes open AI models to assist coding and provide real-time suggestions

Key features:

Multi-language support for optimal customization.
Easy Integration with OpenAI and other large language models.
Comprehensive Documentation with interactive examples.

Star ⭐ the repo

6. Julep AI - Build scalable AI workflows in minutes

Julep is a minimalist yet powerful framework for building fast and lightweight web applications.

➕ It improves data reliability by offering quality monitoring, making data workflows more efficient and robust. It provides a user-friendly interface to manage, monitor, and improve data workflows.

Key features:

A lightweight and performant routing system.
Built-in support for RESTful APIs.
Extensible architecture through a robust plugin ecosystem.

Star ⭐ the repo

7. Traceloop - Debug distributed applications in real time.

Traceloop is a real-time debugging tool for distributed applications, offering clear data flow visualizations.

➕ It helps developers solve issues faster by providing real-time tracing without disrupting services. It traces processes in real time, helping you understand what’s going wrong without disrupting services.

Key features:

Real-time tracing of distributed systems.
Visualizes data interactions for better insight.
Supports multiple cloud environments for flexibility.

Star ⭐ the repo

8. Classiq - Easily design and simulate quantum circuits

Classiq simplifies quantum circuit design, enabling researchers and developers to simulate quantum operations.

➕ Python developers appreciate its robust integration with libraries like Scikit-learn and TensorFlow. It simplifies quantum computing, allowing developers to create quantum algorithms without deep expertise. It helps developers design quantum algorithms without deep quantum expertise.

Key features:

Intuitive tools for designing quantum circuits.
Integrates with major quantum platforms for a seamless workflow.
Real-Time simulation and analysis capabilities.

Star ⭐ the repo

9. ToolJet- Build custom Business Applications. Fast

ToolJet is an open-source low-code platform for creating business applications, compatible with various databases and APIs.

➕ It enables rapid development of internal tools without extensive coding, making it ideal for non-developers. It integrates with different data sources and provides a visual builder to create interfaces without coding from scratch.

Key features:

Drag-and-Drop Builder for quick UI creation.
API Integrations with external APIs for extended functionality.
Role-Based Access Control for user management

Star ⭐ the repo

10. Keep- Swiss-knife for managing alerts/events at scale

Keep offers a secure way to manage secrets and configurations for modern applications.

➕ It ensures critical alerts are never missed, improving incident response times and team coordination. It helps ensure you never miss critical notifications by integrating with various services.

Key features:

Robust encryption for sensitive data.
Integration with popular cloud platforms.
Versioning and auditing of configurations.

Star ⭐ the repo

11. Digger- Deploy cloud infrastructure quickly with simplified IaC.

Digger simplifies infrastructure-as-code workflows, enabling rapid cloud resource deployment.

➕ It streamlines infrastructure deployment, reducing manual tasks and speeding up the CI/CD process. It offers out-of-the-box compatibility with major cloud providers.

Key features:

Terraform integration for infrastructure automation.
Pre-configured templates for common use cases.
Collaboration tools for team-based deployments.

Star ⭐ the repo

12. Skyvern- Automate browser-based workflows with LLMs and vision

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows on a large number of websites, replacing brittle or unreliable automation solutions.

➕ It relies on prompts in addition to computer vision and LLMs to parse items in the viewport in real-time, create a plan for interaction and interact with them.

Key features:

Automated data extraction from web pages.
Customizable workflows using AI.
Integration with headless browsers for scaling.

Star ⭐ the repo

These 12 tools offer something for everyone, whether you're focusing on AI integration, app development, or workflow automation. Which tools have you used or are excited to try? Let’s keep the conversation going in the comments!

💭 By contributing to and integrating these tools into your projects, you not only advance your own skills but also support the vibrant open-source community that drives continuous progress in our industry.

9 Open-Source Tools to Build Better Data Apps in 2025

Rym — Mon, 18 Nov 2024 12:45:31 +0000

Python developer always lookout for tools that streamline workflow, help ship faster, and make data apps more powerful.

Here are 9 incredible open-source tools that will help you build better data applications. Whether you're into data visualization, scenario management, or full-on data orchestration, these tools are must-haves for your 2025 toolbox.

1. Taipy - Simplify Complex Data Workflows

Taipy is an open-source Python library that helps you build AI & Data web application including data-driven GUIs and automate scenario management.

🦾 It’s perfect for developers who need to create custom analytics dashboards without dealing with frontend headaches. Plus, it integrates easily with other tools like Databricks or IBM Watson, so no need to change your coding environment, and uses other libraries charts and components like Matplotlib, Plotly... saving you loads of development time.

Scenario Management
User-Friendly GUI
Python Integration

Star ⭐ the repo

2. Composio - Access +150 Tools in Just One Line of Code

🦾 Composio makes it super easy to connect over 150 tools, from system tools to SaaS apps and applications with AI models to accomplish agentic automation.

Managed Auth
90+ Tools - Ready to Connect
Powerful RPA tools

Star ⭐ the repo

3. Shadcn - Streamline Your Environment

Beautiful, ready-to-use components for your applications
Creating visually appealing custom components can be a time-consuming task. Shadcn simplifies this by offering beautifully designed, reusable components built with Radix and Tailwind. You can easily copy, paste, and customize them to fit your apps.

🦾 This saves significant time compared to building similar components from scratch. Just find the component you need in Shadcn, copy it, paste it into your app, and you're all set.

Shadcn supports most popular front-end libraries and frameworks, including React, Next.js, Astro, Gatsby, and Remix.

Consistent environments across machines
Easy packaging of applications and dependencies
Simplifies deployment and setup

Star ⭐ the repo

4. FastAPI - API Builder in Python

FastAPI is a high-performance framework for building APIs with Python.

🦾 If you need to serve your machine learning models or any backend functionality, this is the fastest, developer-friendly option out there.

High Performance
Automatic Documentation
Ease of Use

Star ⭐ the repo

5. Postman - API Testing Made Easy

APIs are the backbone of any full-stack application, and Postman makes testing them a breeze.

🦾 With its clean interface, you can easily send requests and verify responses. Whether you’re working with complex authorization flows or just testing a simple GET request, Postman keeps everything organized and easy to manage. It’s essential for quick API development and testing.

Clean and intuitive interface for testing APIs
Supports complex authorization flows
Organizes and saves requests for easy management

Star ⭐ the repo

6. GitHub Copilot - Your AI Coding Buddy

Ever wish you had a coding buddy to help with boilerplate code or suggest the best way to refactor a function? GitHub Copilot is here to do just that. This AI-powered tool integrates with VS Code and gives you smart code suggestions in real time.

🦾 It can suggest entire code blocks or help you find the right function name, cutting down on repetitive tasks and making your coding sessions much more productive.

Real-time code suggestions
Helps write boilerplate and repetitive code
Integrates seamlessly with VS Code

Star ⭐ the repo

7. DVC (Data Version Control) - Collaborative Design Tool

🦾 Version control is vital for managing machine learning projects, and DVC brings Git-like capabilities to data. Whether you’re tracking datasets or sharing results across teams, DVC integrates perfectly with your usual Python tools.

Data Versioning
Pipeline Management
Storage Agnostic

Star ⭐ the repo

8. MLflow - End-to-End Machine Learning Lifecycle Management

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It covers everything from experimentation and reproducibility to deployment.

🦾 Python developers appreciate its robust integration with libraries like Scikit-learn and TensorFlow.

Experiment Tracking
Model Registry
Integration with ML Libraries

Star ⭐ the repo

9. Airflow - Automate your Workflow

Apache Airflow is a powerful workflow automation tool.

🦾 While it takes more setup than some other tools, it offers incredible flexibility and is ideal for orchestrating complex data applications.

Directed Acyclic Graphs (DAGs)
Extensibility
Scheduler and Monitoring

Star ⭐ the repo

These tools, especially when used alongside Taipy, make it easier for Python developers to move fast and create sophisticated, production-ready data applications. Whether you're a data scientist, a backend developer, or just curious about building great data-driven experiences, these tools will save you time and make your projects more impactful.

💭 Which of these tools have you already tried? Did I miss your favorite time-saving tool?
Drop it in the comments, and let’s help each other code smarter, not harder!

🎉 HacktoberFest 2024: How to Contribute & Win Exclusive Prizes? 🚀

Rym — Mon, 07 Oct 2024 09:24:58 +0000

TL;DR

Have you heard about Hacktoberfest? 🤔
This month-long celebration of open-source contributions is the perfect opportunity for developers of all skill levels to give back, learn, and snag some awesome swag! 🤯

What is HacktoberFest?

HacktoberFest is an annual month-long event where developers contribute to open-source projects throughout October.
The goal? To grow and improve the open-source ecosystem while helping contributors learn new skills, meet fellow coders, and, yes, earn awesome rewards!🎁🎁🎁

Why Should You Join Hacktoberfest? 🚀

Hacktoberfest is all about collaborating, learning, and having fun while contributing to the projects you care about. Plus, you get to expand your GitHub profile and win swag as a reward for your hard work! It’s an awesome way to improve your coding skills and boost your portfolio while being part of a global event.

Which Repository Should You Contribute To?

One great way to get started with Hacktoberfest is by contributing to one of the best-prepared open-source projects. And one stands among others: Taipy! With more than 50 issues of all levels available, there are plenty of opportunities to dive in and help out while earning exclusive Taipy swag.

Star ⭐ the Taipy repository

What’s in It for You? 🎁

As you contribute, you’ll accumulate points based on the issues you solve. These points can be exchanged for exclusive Taipy swag!

Difficulty	Number of points	Rewards
Easy	100 💎	Stickers + Bottle
Intermediate	200 💎💎	Stickers + Scott Hoodie + Mousepad
Advanced	300 💎💎💎	Stickers + Jack & Jones Hoodie + Bag

➕➕➕ if you give Taipy a star on our ⭐GitHub Repo⭐, you could be one of 10 randomly selected stargazers to win an additional swag! 🎁🎁🎁

Taipy is Proud to Sponsor Quira’s Hacktoberfest Event! 🎮

This year, Taipy is excited to be one of 15 organizations that have partnered with Quira for Hacktoberfest 2024. Quira, a company that focuses on building sustainable communities for next-gen dev tools, is helping to turn open source into a positive-sum game 🎮 by organizing events that encourage individuals to improve their coding skills in a fun and engaging way.

In addition, Quira is one of the three official sponsors of HACKTOBERFEST, making this year’s event even more special!

How Do You Do?

Here’s how you can participate:

➡️ Step 1: Choose an Issue

Visit Taipy GitHub Repository.
Look for issues tagged with "hacktoberfest" and choose the issue that best matches your skills and interests.

Difficulty	Number of points	Rewards
Easy	100 💎	Beginner-friendly issues
Intermediate	200 💎💎	Mid-level tasks
Advanced	300 💎💎💎	Challenging tasks for experienced coders

➡️ Step 2: Fork the Repository

Once you’ve selected an issue, start by forking the Taipy repository to your GitHub account. This creates a copy of the repository that you can work on without affecting the original codebase.

git clone https://github.com/YourGitHubUsername/taipy.git

➡️ Step 3: Clone Your Fork Locally

After forking, clone the repository to your local machine by running the following command in your terminal:

git checkout -b issue-#<issue-number>-description

➡️ Step 4: Create a New Branch

Always create a new branch for your work. It’s good practice to name your branch based on the issue you're working on. For example:

git checkout -b bug/#<issue#>-comment

➡️ Step 5: Work on the Issue

Start coding and solving the issue! Follow the guidelines mentioned in the issue, and make sure to test your changes locally. If you have any questions, feel free to ask for help in the issue’s comment section or reach out to our community.

➡️ Step 6: Commit & Push Your Changes

Once you’re satisfied with your work, commit your changes and push the branch to your fork:

git add
git commit -m "description of the fix"
git push origin your-branch-name

➡️ Step 7: Submit a Pull Request (PR)

Go back to the original Taipy repository. You’ll see an option to submit a pull request. Make sure your PR description clearly references the issue you worked on and provides a brief explanation of what you fixed.

Important Guidelines to Follow

Stick to the issues labeled "hacktoberfest" and with a difficulty tag to ensure they count towards the event.
Do not submit PRs on newly created issues unless they have been approved by the Taipy team.

Follow these simple rules to ensure your contributions are counted, and you'll be on your way to grabbing that cool swag! 🎁

Event Timeline 📅

Starts: October 1, 2024
Ends: October 31, 2024

Get Started Now! 🎯

Head over to the Taipy GitHub Repository, choose an issue, and start contributing. Don’t forget to star our project for a chance to win more rewards!

Together, let’s make HacktoberFest 2024 an epic open-source celebration! 🌍✨

🚀Top 5 Open Source Projects to Contribute to During Hacktoberfest 2️⃣0️⃣2️⃣4️⃣

Rym — Fri, 04 Oct 2024 08:37:45 +0000

Hacktoberfest is an exciting event for developers to contribute to open-source projects, sharpen their skills, and earn awesome swag.
However, it's always so difficult to find the right project, the one that suits their level.

If you're looking for great projects to get involved with, here are five fantastic repositories offering swag and welcoming contributors of all levels.

1. Taipy

Taipy is a Python-based platform that simplifies the creation of production-ready AI & Data web applications.

By contributing to Taipy during Hacktoberfest 2024, you not only get to work with state-of-the-art tools but also have the chance to earn some 🎁🎁🎁very cool swag!🎁🎁🎁

Star ⭐ the Taipy repository

🎁🎁🎁 Prizes 🎁🎁🎁:

Taipy offers contributors some unique and exciting swag during Hacktoberfest 2024, based on the difficulty of merged pull requests
that solve hacktoberfest-tagged issues

Difficulty	Number of points	Rewards
Easy	100 points 💎	Stickers + Bottle
Intermediate	200 points 💎💎	Stickers + Scott Hoodie + Mousepad
Advanced	300 points 💎💎💎	Stickers + Jack & Jones Hoodie + Bag

⭐⭐⭐ Bonus ⭐⭐⭐

Star the repo to be among the 10 stargazers winning a special prize.

2. Appwrite

Appwrite is an open-source backend-as-a-service (BAAS) platform that allows developers to quickly build secure web and mobile applications. Appwrite is a big supporter of Hacktoberfest 2024 and offers a variety of swags, such as hoodies and stickers, to contributors who make valid pull requests.

Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

The top team will each walk away with The Appwriter, an exclusive award for the best overall project.
2nd and 3rd-place teams will receive the Appwrite swag kit.

3. CopilotKit

CopilotKit is an open-source tool that extends GitHub Copilot's capabilities, giving developers more flexibility with AI-assisted coding. This is an ideal project for developers who are interested in AI-powered development environments. CopilotKit is actively participating in Hacktoberfest 2024 with swag offerings for valid contributions.
Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

A bit harder with this library. You have to create a whole application to win some surprise swag (we know nothing about it yet)
Demos will be ranked from 0 to 100 based on:

Criteria	Points
Innovation & Creativity	0-25
Usefulness & Practicality	0-25
Quality & Completeness	0-25
User Experience & Design	0-25

Bonus Points

4 Ways to Earn 10 bonus points for each (Swag is only available if you complete a demo) :

⭐ Write and share a tutorial about your app on dev.to that receives up to 30 or more reactions.
⭐ Post about your project on Twitter and get at least 10 reactions.
⭐ Solve good first issues that get merged.
⭐ Create a YouTube video promoting your CopilotKit project that receives up to 100 views.

4. Auth0 (by Okta)

Auth0 by Okta provides authentication and authorization services. Developers can work on issues related to security and identity management during Hacktoberfest 2024.
Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

Contributors who submit two pull requests to the Auth0 projects can earn a limited-edition t-shirt as part of their “Authtoberfest” campaign.

5. Hyperswitch

Hyperswitch is a powerful open-source payment processor created by Juspay. It simplifies integrations with various payment gateways. During Hacktoberfest 2024, Hyperswitch offers exclusive swag, including stickers for 1 merged pull request and a special swag kit with t-shirts or hoodies for 3+ merged PRs.

Star ⭐ the repository

🎁🎁🎁 Prizes 🎁🎁🎁:

Contributors who submit a PR that solves a hacktoberfest-tagged issue will earn some nice shirts and hoodies.

Final Thoughts

Hacktoberfest 2024 is a great opportunity to grow your skills and connect with the open-source community. Not only will you have a meaningful impact by contributing to these projects, but you’ll also get rewarded with some exciting swag and most important, if you're a student this will help you in your future career. Make sure to register, track your contributions, and have fun hacking this October 2024!

🚀 Turning all Databricks Jobs Into Something Your Non-Tech-Savvy Team Can Actually Use 🤖

Rym — Tue, 24 Sep 2024 08:31:05 +0000

If you’re a Databricks user, you know the struggle is real.

You’ve got your data pipelines humming, your machine learning models crunching numbers, and your notebooks looking flawless. But then comes the dreaded part: turning all that backend magic into something your non-tech-savvy team can actually use.

Building interactive applications on top of Databricks? Not exactly a walk in the park.

Cue Taipy, your new best friend. We’re now a Databricks Technology Partner (yes, we’ve got the badge to prove it! 👇🏻👇🏻👇🏻) and we’re here to make your life easier.🦾🦾🦾

The Struggles You’re Facing

🤦🏻‍♂️ Building a Front-End: Databricks is awesome at backend stuff, but making that data accessible to non-tech folks? That’s a headache.
🤦🏻‍♂️ "What-If" Analysis: Your boss wants to tweak some variables and see different outcomes… but building a tool for that is a whole new project.
🤦🏻‍♂️ Collaboration Across Teams: Sharing insights across departments while keeping things easy for everyone? Easier said than done.

How Taipy Saves the Day

📊 No More Front-End Drama: Taipy’s low-code platform lets you turn those Databricks notebooks into slick, user-friendly web apps in no time. No need to be a front-end wizard!
📉📈 Scenario Management for the Win: Your boss can finally run those “What-If” scenarios with ease. Taipy lets you orchestrate Databricks jobs and visualize different outcomes like a pro.
🫱🏼‍🫲🏼 Teamwork, Dreamwork: Multi-user support means your whole team can jump in, collaborate, and make data-driven decisions together.

So How Does It Work?🤔

🦾 Run Databricks Jobs Through Taipy: You keep doing what you do best—setting up jobs in Databricks. Taipy handles the rest, linking those jobs to its super-friendly scenarios.
🤖 Automated Awesomeness: Taipy orchestrates all the heavy lifting, running your Databricks tasks and giving you (and your team) real-time results in a clean interface.

Go try it yourself and let us know what you think about it!

No more fussing with front-end code or manual workflows. With Taipy, you’ll be turning your Databricks magic into interactive apps faster than you can say "big data."

Ready to give it a go? Check out more details in this article, our tutorial, and video guide to see how easy it is to integrate Taipy with Databricks.

How I built my Python open-source AI & Data builder

Vincent Gosselin — Mon, 16 Sep 2024 16:33:44 +0000

It all started with the lack of production-ready tools in Python

At Taipy, we set out to solve one of the most demanding problems in the world of AI: connecting high-performing algorithms to friendly applications for end users.

A few years ago, we had a strong vision: offer companies solid tools for developing their applications in Python. But as we delved deeper, we realized that the Python ecosystem didn’t deliver the kind of user-focused, collaborative, production-ready data & AI web applications we wanted to facilitate. So we built Taipy 😊

What it brings to the table

Taipy shares similarities with popular tools like Streamlit, Gradio, Dash, and Reflex, but it distinguishes itself through features specifically designed to support the development of robust, production-ready data and AI applications. Our mission is to make AI accessible, impactful, and easy to integrate into business processes.

So, here’s what makes Taipy stand out:

1. Callback:

Lets users automatically trigger custom actions following certain events or the completion of specific tasks. Callbacks allow our software to apply flexible, event-driven automation, which is great for interactive applications.

2. Scenario Management:

Allows for organizing and running different workflow configurations, complete with version control and automation. It also allows for comparing the results of multiple runs for a given analysis to see what works best.

3. Multi-user Collaboration:

Enable several users to work together on the same application, each with safe, private access to a version of the app that is theirs alone.

By offering these features, Taipy ensures that companies can bridge the gap between prototyping and deploying scalable, production-grade AI applications.

A Connected Solution in the Broader AI Ecosystem

Not only does Taipy simplify AI development, but it also seamlessly integrates with other major tools such as IBM Watson, Dataiku, Databricks, and Google Colab, expanding its versatility and ease of use.
Furthermore, Taipy is an official technology partner of Databricks, reinforcing our commitment to providing cutting-edge solutions in the AI and data science ecosystem.

Help us out

Taipy is an open-source project. It's 100% free. We're participating in the HacktoberFest 2024, so stay tuned and contribute to the project on GitHub!

Star our repo ⭐️

What-if I told you complex Data Integration makes good Task Orchestration?

Rym — Thu, 12 Sep 2024 15:24:17 +0000

No matter how complex your data problem is, you can always Divide & Conquer it. 🦾🦾🦾

In this article, we’ll break down how to solve intricate data challenges, data treatment, data management using a fully open-source Python library: Taipy. Whether you're cleaning data, orchestrating tasks, or exploring different outcomes, Taipy's intuitive design empowers you to handle it all.

Taipy's Scenario Management basis

Let’s go through three steps to show how Taipy can help streamline your data workflow:

1. Data Integration: Get the Data You Need

Every AI, ML, and data-driven project starts with data, obviously!
And it’s rarely as simple as a single, clean dataset. In most cases, you'll be pulling data from databases, APIs, flat files, or other external sources, and sometimes, all in the same project. This is where Taipy's data integration comes in—gathering and unifying data from these different sources.

In Taipy, this process is simplified with a key abstraction: the Data Node.

A Data Node represents your data but doesn’t store it directly. Instead, it holds all the necessary metadata to read and write the actual data, whether it’s a CSV file, a database table, or even an API response.

Here’s a quick example of defining a Data Node for a CSV:

from taipy import Config
initial_dataset_cfg = Config.configure_data_node(id="initial_dataset",
                                                 storage_type="csv",
                                                 path="data/dataset.csv",
                                                 scope=Scope.GLOBAL)

With this abstraction, Taipy takes care of data management, allowing you to focus on transforming and processing your data.

2. Task Orchestration: What Are You Doing with All That Data?

Now that your data is in place, what do you do with it? In any data workflow, the next step involves defining tasks that process and transform the data. This is what we call the Task Orchestration

A Task in Taipy is like a function, taking Data Node(s) as inputs, performing transformations, and then outputting Data Node(s).

For instance, you may want to filter some data or calculate new metrics. Here's an example of creating a Task to calculate the sum of a column:

clean_data_task_cfg = Config.configure_task(id="clean_data",
                                            function=clean_data,
                                            input=initial_dataset_cfg,
                                            output=cleaned_dataset_cfg,
                                            skippable=True)

Once you've defined your tasks, you can arrange them into a pipeline to ensure the steps execute in the correct order. This lets you easily build workflows that transform data, build models, generate reports, and more.

3. What-If Analysis: Explore Different Scenarios

Once your data and workflows are set up, you can begin exploring different scenarios. This is where the What-if analysis comes into play.

A Scenario in Taipy represents a specific instance of a problem you’re trying to solve, allowing you to test different parameters and see how they impact your results. By adjusting input data or assumptions, you can model various scenarios without starting over from scratch.

Here's how you can define a Scenario in Taipy:

scenario_cfg = Config.configure_scenario(id="scenario", task_configs=[clean_data_task_cfg, predict_task_cfg,evaluate_task_cfg], frequency=Frequency.MONTHLY)
tp.Core().run()
my_first_scenario = create_and_run_scenario(dt.datetime(2021, 1, 25))
predictions = my_first_scenario.predictions.read()
print("Predictions\n", predictions)

This makes it easy to perform sensitivity analysis or optimize outcomes, all within the same framework. Want to test different discount rates on your sales model? Just create new scenarios, tweak the parameters, and rerun them.

Taipy vs. Other Pipeline Management Tools

You may be wondering, how does Taipy compare to other popular pipeline orchestration tools like Apache Airflow, Luigi, or Prefect? While those tools are great for managing task scheduling across distributed environments, Taipy stands out by focusing on Python simplicity, especially when it comes to scenario management and what-if analysis.

Airflow/Luigi/Prefect: Generally focus on orchestrating ETL processes, scheduling, and monitoring workflows.
Taipy: It not only provides workflow orchestration but also simplifies what-if analysis with its unique scenario abstraction, allowing you to model various outcomes seamlessly.

For developers looking to handle complex data workflows in Python with minimal setup, Taipy offers a more straightforward, code-first approach.

Conclusion: Divide and Conquer with Taipy

The Divide and Conquer strategy wins every time, no matter the size or complexity of your data problems! With Taipy, you can handle everything from data integration to task orchestration and what-if analysis, all in one place. And you can finalize with data visualization as well.

Ready to give Taipy a try? Check out the GitHub repo and see how it can streamline your data workflows today!

Star Taipy ⭐️

Don’t forget to leave a star ⭐ and share your feedback or scenarios you've worked on in the comments below!

21 Developer Tools to Improve Your Daily Life as Developer ⚡🎁

Anmol Baranwal — Tue, 10 Sep 2024 14:35:08 +0000

There are thousands of developer tools and packages but the common problem is that most of them are not up to standards.

That is why, I'm covering 21 actually useful ones that can improve your daily life as a developer.

Some of these are tools, some are projects and others might include packages.

Let's jump in.

1. Taipy - Data and AI algorithms into production level web apps.

Normally, when I need an interface for Python, I use Streamlit. However, it isn't very efficient and has a lot of performance-based problems.

Taipy (open source) on the other hand is the perfect Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools.

To be clear, Taipy is used to create a GUI interface for Python-based applications and improve data flow management.

The key is performance and Taipy is the perfect choice for that.

While Streamlit is a popular tool, its performance can decline significantly as I told you earlier especially when handling large datasets, making it impractical for production-level use.

Taipy, on the other hand, offers simplicity and ease of use without sacrificing performance.

Taipy has a lot of integration options and connects effortlessly with leading data platforms.

Get started with the following command.

pip install taipy

You can read the docs.

Another useful thing is that the Taipy team has provided a VSCode extension called Taipy Studio to accelerate the building of Taipy applications.

If you want to read a blog to see codebase structure, you can read Create a Web Interface for your LLM in Python using Taipy by HuggingFace.

It is generally tough to try out new technologies, but Taipy has provided 10+ demo tutorials with code & proper docs for you to follow along. I will discuss some of these projects in detail!

Taipy is close to 11k stars on GitHub and is on the v3.1.1 release.

Star Taipy ⭐️

2. Mitosis - write components once, run everywhere.

If you're someone who doesn't want to maintain a consistent design system across multiple frameworks or avoid the pitfalls of web components by compiling native framework code, then this one is for you!

Mitosis is a tool that transforms JSX components into fully functional components for frameworks like Angular, React, Qwik, Vue, Svelte, Solid and React Native.

By writing your UI components once in JSX, you can deploy them across any platform, removing the need to rewrite code for each framework.

The concept is very unique and they use a static subset of JSX, inspired by Solid. It can parse it to a simple JSON structure, then easily build serializers that target various frameworks and implementations. You can read more on how it works.

To create a new Mitosis project from scratch, run the following command.

npm create @builder.io/mitosis@latest

It's simple, very powerful and easy to use. Many organizational-level open source projects maintain a codebase of different frameworks, so mitosis can be very useful for them.

You can read the docs and check the live playground.

The most amazing feature is that it can also integrate with Figma to keep your design system in sync with your code system. Wow!

They have 12k stars on GitHub and are built using TypeScript.

Star Mitosis ⭐️

3. responsively - modified web browser for 5x faster responsive web development.

Responsively is a modified browser built using Electron that helps in responsive web development.

I know Chrome development tools work very well but you will be amazed by how easy it is to use Responsively.

At first glance, anyone would think it of ordinary app but there are some very unique features:

✅ See all devices at once.

✅ Any click, scroll or navigation that you perform on one device will be replicated to all devices in real-time.

✅ It allows quick context switching by saving your favorite device combinations as Preview Suits to switch between them when you're testing.

✅ It comes with a large collection of device profiles out of the box. You can also add your own custom devices.

You must be thinking about the usual features that would be missing here but that's not the case.

⚡ You will get all the basic browser features like cookie management, local storage, session storage and bookmarks. You can also use the dev tools as in any browser.

⚡ You can also take screenshots of individual devices by clicking on the camera icon in the device toolbar with a quick screenshot of the viewport too.

⚡ The Unified Inspector allows you to inspect elements across all devices at once by pressing Cmd/Ctrl + I to activate it and hover over any element to see its details.

You can download the desktop app.

They have also provided browser extensions for Chrome, Edge, and Firefox to easily send links from your browser to the app and preview instantly.

I would highly recommend to at least check it out because it supports hot reloading as well. It might end up making your workflow much smoother.

Responsively has 22.3k stars on GitHub and is built using JavaScript.

Star responsively ⭐️

4. Documenso - sign digital documents in the easiest way.

If you're doing freelancing and need to sign an agreement, this is a perfect tool for that. This solves a huge problem for developers who don't want to end up wasting time and focusing on what matters.

This is technically quite easy today, but it also introduces a new party to every signature.

The tech stack for the project includes TypeScript, Next.js, Prisma, Tailwind CSS, shadcn/ui, NextAuth.js, react-email, tRPC, @documenso/pdf-sign, React-PDF, PDF-Lib, Stripe and Vercel.

The free tier lets you sign 10 documents per month which is good enough.

You can read this on how to set up the project.

You can read the docs.

I know it's not a very wide use case but you can still learn from the codebase so that is always a plus point.

I've tested it properly and it works very well. In a nutshell, this is how it works:

✅ You upload the document and start filling in the information.

✅ Insert the email of all the parties involved.

✅ You just insert the textbox of the email, name, or signature that you need in the document.

✅ You will receive an email when any of the parties signs it and it will be available to download once every member has signed it.

The document will contain the signature and one more attached page with digital information. Very unique!

blurred part will contain the email

They have 7.1k stars on GitHub and are built using TypeScript.

Star documenso ⭐️

5. Jam - one click bug reports.

I found Jam a few months back and I have used it too often.

Jam is a free Chrome extension (not open source) that you can use to report bugs efficiently.

Reporting a bug is a lengthy process and you may end up missing the essential data needed to solve it. That is why developers prefer using Jam.

Watch this video to see how Jam works!

As you can see, the best part is that it captures the console and network logs information which makes it convenient for other developers to look into it.

You will also get an AI debugger, backend tracing, repro steps and browser info. What more do you need?

I've used Jam for a long time, you will also get a dashboard to see all the jams that you've created to date. It's efficient and works really well.

It also works with a lot of popular tools so you don't have to change your environment at all.

Don't use the traditional way, you can simply comment on the Jam and improve the whole process to deal with it without hassle.

Visit Jam 🔥

6. Can I Use - check the support of frontend web technologies.

Can I use is a website that provides up-to-date browser support tables for support of front-end web technologies on desktop and mobile web browsers.

Frankly, a lot of developers don't even know about this.

For instance, typing local storage to see where I can use it will give proper stats and references.

You can also directly compare browsers on various types of data.

There are so many options to filter data which you can explore yourself, I loved the overall concept.

If you want to contribute towards the data, check out their open source repository.

It has 5.6k stars on GitHub and is used by 1.8M developers on GitHub.

Star Can I Use ⭐️

7. AppFlowy - open source alternative to Notion.

AppFlowy is an AI-powered secure workspace similar to the notion where you achieve more without losing control of your data.

I've covered Obsidian before, so I'm just skipping it here.

It is important to manage our tasks, and AppFlowy is a strong option for that.

You can organize and visualize your data in tables, boards and calendars. They also provide mobile apps so that's a plus.

You can read the docs and read about installation methods.

They support self-hosting AppFlowy using Supabase. This is ideal for users who use Supabase for their infrastructure.

You should also check this to learn more about data storage, markdown, shortcuts, themes, AI involved and plugins.

AppFlowy has 52.3k+ stars on GitHub.

Star AppFlowy ⭐️

8. Copycat - generate deterministic fake values.

This is not a tool but an efficient package that every developer can use in their workflow.

Snaplet (startup) was recently shut down but Paul Copplestone (Co-founder of Supabase) reached out to Peter to see if Snaplet was interested in open sourcing.

Open sourcing the stuff you have built is the best way to ensure your products outlive your business.

They released three tools out of which Copycat is one of those and the other is Seed. I've discussed both of them.

Copycat helps you to generate fake data. It's like faker.js, but deterministic.

This works statelessly, which means for the same input, the same value will be returned regardless of the environment, process, call ordering, or any other external factors.

A few examples would be:

import { copycat } from '@snaplet/copycat'

copycat.email('foo')
// => 'Raleigh.McGlynn56687@wholewick.info'

copycat.email('bar')
// => 'Amir_Kris69246@raw-lout.name'

copycat.email('foo')
// => 'Raleigh.McGlynn56687@wholewick.info'

You can read the docs including API reference and alternative approaches.

Copycat has 648 stars on GitHub and is built using TypeScript.

Star Copycat ⭐️

9. Litlyx - One line code Analytics with AI.

I don't trust Google Analytics that much because it's not open source so I was looking for another alternative. This one is developer oriented.

Litlyx is a single-line code analytics solution that integrates with every JavaScript/TypeScript framework.

A simple analytics solution for developers, they use a wrapper of GPT that users can query about the data collected with litlyx.

For instance, you can simply ask How many visits did I have 7 days ago?, How many visits I had last week?, how many custom events were triggered with this name or name of the event and so on.

I believe they are planning to generate charts and reports with it in the future.

It tracks 10+ KPIs and as many custom events as you want for your app. It also provides AI-powered dashboards for intuitive data insights, automatic reports sent straight to your email, and seamless CSV file downloads to work on your data.

You can install it using npm or CDN. The total lib size is less than 4kb so that's a plus point.

It supports 15+ frameworks as shown below.

Read the docs and check the live demo.

Trust me, using a good analytics system is perfect for your project!

Litlyx has 342 stars on GitHub and is built using Vue + TypeScript.

Star Litlyx ⭐️

10. Requestly - powerful HTTP interceptor for browsers.

We have all been there when the backend isn't ready with APIs so the frontend developers manually hardcode the response.

Requestly is an open source frontend development platform that helps developers test and debug their apps faster without needing multiple deployment cycles. It helps you to:

✅ Build features when backend API is not ready.

✅ Testing code changes in production without deployment cycles.

✅ Testing, Validating, Mocking API responses and many more.

🎯 How to install Requestly?

You can download the desktop app or find it on your preferred browser including Chrome, Safari and Brave.

the desktop version

As I discussed the hardcoding responses in the codebase, you no longer need to do it after setting up Requestly. With a mutual understanding of the backend and frontend, an API contract is decided and then defined on the Requestly dashboard for the desired API endpoint.

It serves the response in such a way that it's coming from the server to avoid making any changes in the codebase or any other complex configuration.

You can lots of other stuff like:

-→ Testing scripts or APIs directly on staging/production sites by redirecting HTTP requests(API Calls/Files) from one environment to another.

-→ Modify headers or Inject custom scripts/styles on external web pages.

-→ Intercept, Modify, and Mock GraphQL Query by applying extra targeting on operation name and query data.

It's extremely useful for QA Software engineers or frontend teams.
Let's see some of the exciting features:

✅ You can create a workspace to share rules, mocks, and sessions. Faster collaboration leads to solving problems quickly!

✅ You can record sessions and it's automatically done whenever you test a rule from the dashboard.

✅ You can enable/disable rules from the dashboard.

✅ You can mock any servers. Read more with sample apps and use cases.

You can read the docs.

They have 2.2k stars on GitHub.

Star Requestly ⭐️

11. Music for Programming - music while programming to inspire the mind.

Most of you would deny the importance of music. I know the truth is hard sometimes but music can improve your coding 😅

That is why, a lot of developers absolutely adore this site.

Music for programming presents a series of mixes intended for listening while programming to focus the brain and inspire the mind.

I love the feature of playing random music, it's sweet and focus oriented in my opinion.

Let me know in the comments if this can improve your coding skills :)

The only drawback is that it's not open source.

Visit Music for Programming 🔥

12. Obsidian - writing app for your style.

Obsidian is a private and flexible writing app that adapts to the way you think.

You can also see the list of plugins that can help you shape Obsidian to fit your way of thinking. I've checked the insane amount of options present there!

You can even collaborate and easily track changes between revisions, with one year of version history for every note.

You can publish those (I've never tried) and control the look and feel of your site with themes, custom domains, password protection, and more. It's a paid feature but you can read all about publishing with Obsidian.

You can read the detailed docs and check the live website. You can also build a custom plugin using this guide and use React or Svelte for that.

Download Obsidian based on the platform you're using.

They offer a free forever tier plan and don't charge based on features or usage. You only pay if you use Obsidian commercially.

One of the great alternatives that you can try is Capacities. It might even be better than Obsidian in some ways. I will cover it in one of my future articles.

The main repository has 9k stars on GitHub with 1600+ contributors. Another awesome project by the open source community.

Star Obsidian ⭐️

13. Vanna - Chat with your SQL database.

Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation.

Basically, it's a Python package that uses retrieval augmentation to help you generate accurate SQL queries for your database using LLMs.

It's perfect for developers like me who are not very fond of SQL queries!

Vanna works in two easy steps - train a RAG model on your data, and then ask questions that will return SQL queries that can be set up to run on your database automatically.

You don't need to know how this whole stuff works to use it.
You just have to train a model, which stores some metadata, and then use it to ask questions.

Get started with the following command.

pip install vanna

To make things a little easier, they have built user interfaces that you can use as a starting point for your own custom interface. Find all the interfaces including Jupyter Notebook and Flask.

You can read the docs and you can try this Colab notebook in case you want to see how it works after training.

Watch this demo for a complete walkthrough!

They have 10.5k+ stars on GitHub and are built using Python.

Star Vanna ⭐️

14. Seed - seed your database based on schema.

This is not a tool but a package that can be useful for your workflow.

Seed generates realistic synthetic data based on a database schema. It automatically determines the values in your database so you don't have to define each value.

You can get started by using this command.

npx @snaplet/seed init

For instance, if you want to generate 3 comments for one of your posts you simply point it at your schema and let it handle the rest:

It consists of the following parts:

a. Seed Client: Auto-generated and type-safe data client for Node.js & TypeScript.

b. Seed CLI: A CLI to generate and keep the data client in sync with your database 🛠️

c. Seed AI: Our custom model identifying the shape of your data 🤖

You can read the docs. Seed is compatible with PostgreSQL, SQLite and MySQL.

Watch this quick demo about Seed and how it works.

Seed was recently open sourced after Snaplet got shut down and has 298 stars on GitHub.

Star Seed ⭐️

15. Hyperswitch - payments switch written in Rust.

Hyperswitch is a community-led, open payments switch to enable access to the best payment infrastructure for every digital business.

It provides APIs to access payment ecosystems across 130+ countries and you can even use it to reduce processing fees with smart routing.

You can manage 200+ payment methods and 50+ payment processors by using a simple CRUD way. Plus, it's trusted by a lot of companies.

You can read the api reference docs and the quickstart guide.

It's open source with 11.3k stars on GitHub.

Star Hyperswitch ⭐️

16. Chat2DB - AI-driven data management platform.

Chat2DB is an AI-first data management, development and analysis tool.

Its core is AIGC (Artificial Intelligence Generation Code), which can convert natural language into SQL, SQL into natural language, and automatically generate reports, taking efficiency to another level.

Even operations that do not understand SQL can use it to quickly query business data and generate reports.

For instance, when you are doing database development, it will help you generate SQL directly in natural language, SQL optimization suggestions, analyze SQL performance, analyze SQL execution plan, and can also help you quickly generate SQL test data, system code, etc. Too much jargon but it's very powerful :)

They have excellent support for multi-data sources and can easily integrate up to 17 different database types including PostgreSQL, MySQL, MongoDB, and Redis.

You can download or try it in the browser.

Let's see some of the exciting features:

✅ Intelligent reports.

✅ Data Exploration.

✅ SQL Development.

You can read the quickstart guide on the official docs.

They have 14.7k stars on GitHub and are on the release v3.2.

Star Chat2DB ⭐️

17. Micro Agent - AI agent that writes (actually useful) code for you.

AI-assisted coding tools like GitHub Copilot and ChatGPT don't produce very reliable code. They often don't work correctly right out of the box, you find bugs, edge cases or even references to non-existent APIs.

This can lead to a frustrating loop of trying the generated code, finding issues, going back to the AI for fixes and repeating.

The time spent debugging can negate the time saved by using AI tools in the first place. But devs still use it!

Micro Agent claims to deliver the benefits of AI-assisted coding while mitigating the problems of unreliable code generation.

Give it a prompt and it'll generate a test and then iterate on code until all test cases pass.

You can install it using this command.

npm install -g @builder.io/micro-agent

# Next, set your OpenAI API key when prompted or manually using this.
micro-agent config set OPENAI_KEY=<your token>

# Then you can run to start a new coding task
micro-agent

Micro Agent will prompt you to describe the function you want, generate tests, and start writing code in your preferred language to make the tests pass. Once all the tests are green, you'll have a fully functional, test-backed function ready to use.

Let's explore some of the most mind blowing use cases:

⚡ 30-second demo of Micro Agent generating tests and code for a TypeScript function that groups anagrams together from an array of strings.

⚡ Using Micro Agent to generate a simple HTML to AST parser (it was achieved on two iterations).

⚡ Unit test matching.

⚡ Visual matching (experimental).

⚡ Integration with Figma.

Micro Agent can also integrate with Visual Copilot to connect directly with Figma to ensure the highest fidelity possible design to code!

Visual Copilot connects directly to Figma to assist with pixel-perfect conversion, exact design token mapping, and precise usage of your components in the generated output.

Then, Micro Agent can take the output of Visual Copilot and make final adjustments to the code to ensure it passes TSC, lint, tests, and fully matches your design including final tweaks. Amazing right :)

You can read the docs and the official blog where the team discussed everything about the micro agent.

It's open source with 2.5k stars on GitHub.

Star Micro Agent ⭐️

18. Miro AI - AI visual workspace.

Miro is the AI-powered visual workspace that brings teams together to collaborate and make faster decisions. Its intelligent canvas with interactive tools makes it easy for anyone to get work done, from journey mapping to diagramming and more. It's just too awesome!

Watch a quick demo to understand most of the stuff.

The concept of intelligent canvas is definitely new to me.

You can let AI use your brainstorms and ideas to create product briefs and summaries to move work forward — no complicated prompts needed. All you need to do is provide content from your canvas and let Miro AI handle the rest.

Watch the video to understand about that canvas!

There are intelligent templates, integrations and also an academy where you can learn all sorts of stuff about Miro. See all the courses and verified blogs at Miro Academy, and you can earn cool badges.

They have 70M users so I guess I was the only one who didn't know about it earlier. By the way, there is a free tier plan available but it's limited to a single workspace with 3 editable boards.

It's not open source but it will absolutely blow your mind.

Visit Miro 🔥

19. Codium AI - Quality-first AI code generation to help busy devs write, test and review code.

CodiumAI is a developer tool that uses AI to generate meaningful tests for code. It analyzes your code, docstring, and comments, and then suggests tests as you code. You don’t have to spend hours!

Watch the complete demo of Codium AI in under 1 minute!

You can download it from VSCode Marketplace and JetBrains extension.

You definitely have heard about a PR agent that assists you in reviewing PRs, that with other tools and Codiumate are products by the same company.

You can also see the difference between CodiumAI vs ChatGPT for generating unit-tests.

I recommend checking out docs to find comprehensive guides and details to harness the full potential of CodiumAI.

They offer a lot more, so check their website for the detailed features along with all of the things that you can do!

CodiumAI is completely open source with all of its tools.

Star CodiumAI ⭐

20. Opire - reward platform for developers.

Opire has a clear principle that states anyone can make a living from open source.

It's a platform for solving and creating bounties in open source.

As a developer, you can solve real problems which can be shown in your portfolio to build credibility.

As a project admin, this might be the most underrated way to grow your open source project.

Some of the very useful use cases can be:

⚡ Collaborate on open source projects and earn money for your contributions.

⚡ Include paid technical tests as part of the selection process in your company by adding rewards. This can help you evaluate your candidates' skills while valuing their time, which sends a very strong message.

⚡ Support maintainers of open source projects by contributing financially. They don't get enough recognition for the work they do!

🎯 How to create and solve those bounties?

You can install the bot that manages communication with the Opire API, ensuring that actions performed through commands will be reflected in the Opire dashboard.

You can use commands such as:

-→ /reward [amount] : to create a reward.

-→ /try : try to solve the issue.

-→ /claim #[issue_number] : to claim rewards.

-→ /tip [amount] @[user] : for tip in USD to the mentioned user.

If you don't want to install the bot, then you can also create those rewards from the dashboard. I have attached a snapshot.

You can read the docs.

Everyone knows about Algora so I'm just trying to help Opire as a new platform in the domain. But the main reason is that developers receive 100% of the rewards.

Opire is open source with docs and the website.

Star Opire ⭐️

21. Mantine Hooks - react hooks for state and UI management.

How many times have you been stuck in writing hooks from scratch?

Well, I'm never going to do it from now on thanks to mantine hooks.

It's not efficient to write more code since you would end up maintaining it later so it's better to use these production-level hooks to make your work a lot easier plus each of them has a good number of options.

This might be the most useful project that everyone can use rather than writing code from scratch.

Trust me, getting 60+ hooks is a big deal considering they have a simple way for you to see the demo of each of the hooks with easy docs to follow.

Get started with the following npm command.

npm install @mantine/hooks

This is how you can use useScrollIntoView as part of mantine hooks.

import { useScrollIntoView } from '@mantine/hooks';
import { Button, Text, Group, Box } from '@mantine/core';

function Demo() {
  const { scrollIntoView, targetRef } = useScrollIntoView<HTMLDivElement>({
    offset: 60,
  });

  return (
    <Group justify="center">
      <Button
        onClick={() =>
          scrollIntoView({
            alignment: 'center',
          })
        }
      >
        Scroll to target
      </Button>
      <Box
        style={{
          width: '100%',
          height: '50vh',
          backgroundColor: 'var(--mantine-color-blue-light)',
        }}
      />
      <Text ref={targetRef}>Hello there</Text>
    </Group>
  );
}

They almost have everything from local storage to pagination, to scroll view, intersection and even some very cool utilities like eye dropper and text selection. This is damn too helpful!

You can read the docs.

They have more than 25.9k stars on GitHub but it's not only for the hooks because they are a component library for React.

To get a better idea, the package has 500k+ weekly downloads along with the v7 release which proves their credibility.

Star Mantine Hooks ⭐️

I know not every tool, project or package here would be useful to you but you could try these in your workflow.

Everyone shares the same tools and projects so I have tried to cover some unique stuff. I hope you found some useful.

Have a great day! Until next time.

If you are looking for a friendly community of writers and developers, you can join us at dub.sh/opensouls.

If you loved this, please follow me on socials :)

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The Python alternative to Power BI, Qlik & Tableau

Anmol Baranwal — Mon, 22 Jul 2024 08:17:42 +0000

We have all seen the extensive features of popular analyzing and dashboarding tools like Power BI, Tableau, or Qlik. However, we Python data scientists, do not use these tools.

Why? The most challenging issue preventing us from using those tools is the environment. Exporting cleaned data into external tools for dashboard creation and visualization can be cumbersome and disrupt the workflow.

Moreover, using a Python based tool for dashboarding, will help you integrate AI to the process seamlessly.

That's why I'll share with you the first Python dashboarding/web app builder that stands out and has come out lately.

Let's jump in.

Taipy Designer

Taipy Designer is a drag-and-drop builder that creates dynamic GUIs on top of your Python code without any hassle. It addresses the previous challenge by enabling data scientists to create dynamic GUIs directly on top of their Python code. Yes, you read it well! "ON TOP OF YOUR PYTHON CODE! Interesting, isn't it?
Let's explore why Taipy Designer is a game-changer for Python data scientists.

It's not as performant as Power BI with its Dask language but it's perfect for Python developers looking for a WYSIWYG GUI environment!

Why Taipy Designer Stands Out

✅ Simple Drag and Drop User Interface.

Instead of traditional insertion methods or programming, users can simply drag and drop widgets from the toolbar on the canvas.

✅ Dynamic and Interactive Dashboards.

Connect widgets to your Python variables for instant data visuals and rendering. It will automatically be present on the dashboard!

✅ Define your own look and feel with a few clicks without any CSS.

✅ Comparing with Other Tools.

While Taipy Designer excels within the Python ecosystem, it's still a WYSIWYG tool. Recognizing the strengths of other popular tools like Power BI, Tableau, and Qlik is essential.

Watch this introductory video to understand what it does!

You can watch this complete walkthrough by Alex & Florian!
It has everything you need to use Taipy Designer and start with.

If you're a Python developer or data scientist, this might be the best GUI designer you can get. It's simple and efficient!

It's created by Taipy which has almost 10k+ stars on GitHub and is growing at a rapid pace.

Try Taipy Designer Now🔥

Now let's check the other tools!

Power BI and Tableau are two essential Business Intelligence (BI) tools used to collect, integrate, analyze, and present business information.

These technologies enable data analysis, manipulation, and visualization, helping you make sense of business data and extract valuable insights.

Let's cover them with the necessary resources to help you learn them. And check if you can use them in your projects rather than Taipy Designer.

Tableau

Tableau is a visual analytics platform that is transforming the way we use data to solve problems.

Tableau was founded in 2003 as a result of a computer science project at Stanford that aimed to improve the flow of analysis and make data more accessible to people through visualization.

Tableau provides a suite of products, including Tableau Desktop for creating and publishing visualizations, Tableau Server for sharing and collaborating on data and visualizations across an organization, and Tableau Prep for preparing and cleaning data before analysis.

Both Tableau Public and Power BI Desktop are free for users to explore and visualize data. The only thing is that your data with Tableau Public is not private and is shared with everyone in the community. Keep an eye on that!

Key things about Tableau:

⚡ Intuitive drag-and-drop interface but steeper learning curve for advanced features.

⚡ Large, active user community, extensive online resources, and professional support.

⚡ Strong support for embedding dashboards into other applications.

⚡ Works across various platforms and devices, including desktop, web, and mobile, ensuring accessibility for users.

Some of the resources to learn Tableau:

✅ Crash tutorial by freeCodeCamp.

✅ Tableau 6 hours Full Course by Edureka.

✅ Watch this tutorial to learn Tableau in 15 minutes and create your first report (FREE Sample Files)!

✅ The official team also provide a guide to help you get started with tableau.

✅ Complete Tutorial by Geeksforgeeks.

Power BI

Power BI is a business analytics service by Microsoft. It provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create reports and dashboards.

It can help you connect to multiple data sources, clean and transform data, create custom calculations, and visualize data through charts, graphs, and tables.

It's accessible via web browser, mobile device, or desktop application, Power BI can also integrate with other Microsoft tools like Excel and SharePoint.

Key things about Power BI:

⚡ Excellent integration with Microsoft products and a variety of other data sources.

⚡ Robust visualization capabilities, slightly less flexible than Tableau.

⚡ Built-in AI capabilities like Power BI Q&A, AI visuals, and integration with Azure ML.

⚡ Support of embedded analytics and follows associative data model.

Some of the resources to learn Power BI:

✅ Power BI 4-hour course by Edureka.

✅ Official guide by Microsoft.

✅ Complete tutorial by Geeksforgeeks.

You can read the key difference between Power BI and Tableau covered by Simplilearn.

As developers, we prefer doing things completely with code that's why Taipy Designer (combined to Taipy) might be the ultimate GUI tool that perfectly complements the Python data science environment.

Have a great day! Till next time.

You can join my community for developers and tech writers at dub.sh/opensouls.

If you loved this, please follow me for more :)

Follow Taipy for more content like this.

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From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability.