DEV Community: Vincent Gosselin

[Boost]

Vincent Gosselin — Thu, 19 Feb 2026 17:18:04 +0000

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

Rym ・ Feb 19

#python #programming #opensource #developers

Try it, you'll see

Vincent Gosselin — Thu, 19 Dec 2024 09:00:15 +0000

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

Rym for Taipy ・ Dec 17

#datascience #data #machinelearning #python

[Boost]

Vincent Gosselin — Thu, 05 Dec 2024 10:55:40 +0000

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

Rym for Taipy ・ Dec 5

#python #programming #beginners #devjournal

[Boost]

Vincent Gosselin — Tue, 03 Dec 2024 08:48:03 +0000

🤓 Top 12 Open Source Repositories to Watch in 2025 to become the ultimate developer

Rym for Taipy ・ Dec 3

#opensource #programming #llm #beginners

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 ⭐️

Simplifying AI Integration: Insights from Taipy's Journey

Vincent Gosselin — Mon, 18 Mar 2024 13:32:42 +0000

In the burgeoning realm of AI and data science, the journey from conception to successful implementation is fraught with complexities. Despite AI's transformative potential for organizations, the path to realizing its full benefits remains elusive for many.

As the CEO and co-founder of Taipy, my journey through the labyrinth of "smart software" projects has illuminated both the immense value these technologies offer and the significant hurdles that stand in the way.

The Roadblocks to AI Adoption:

The Chasm Between Potential and Implementation...

AI and data science promise to revolutionize industries, offering insights and efficiencies previously unattainable. Yet, outside the tech-savvy circles, many organizations struggle to navigate the AI landscape successfully. From data lakes to AI pilots, the quest for a substantial return on investment is often met with hurdles that impede progress.

Identifying the Gaps:

Overcoming Silos and Enhancing User Acceptance

Our focus at Taipy is not to dwell on the challenges but to spotlight solutions. Our analysis identifies two primary barriers:

Siloed environment of development teams
Elusive acceptance by end-users.

These challenges suggest a disconnect not only within the teams responsible for developing AI solutions but also between these teams and the end-users of these technologies.

Bridging the Divide: A Unified Approach

The first step towards overcoming these challenges is fostering a unified environment and language. Specialization has led to a fragmented landscape where data scientists, developers, and end-users operate in isolation. This disparity complicates collaboration and hampers the seamless integration of AI solutions into business processes.

Python emerges as a beacon of hope in this context. Its versatility and simplicity make it an ideal candidate for bridging the technological divide. Yet, the trade-off between ease of development and performance remains a stumbling block. Many Python libraries are available and provide an easy learning curve (including low code); unfortunately, they often suffer from performance issues and lack of customization.

This is where Taipy enters the scene, marrying the simplicity of Python with enhanced performance and customization capabilities, thus addressing the crucial need for a balanced approach.

Enhancing User Engagement: The Taipy Strategy

The second gap entails ensuring end-user acceptance. The success of AI initiatives hinges on their relevance and usability to the business users they are designed to assist. At Taipy, we've introduced innovative concepts like the 'scenario' feature, which facilitates interaction with AI models and enables a dynamic exploration of various outcomes, thereby enriching the user experience and fostering acceptance.

Taipy: A Solution to the AI Puzzle

Our dedication to making AI accessible and impactful has led to the development of Taipy Designer. This tool exemplifies our commitment to democratizing AI, making it approachable for data analysts and seamlessly integrating it into business processes.
Stay tuned for its release in a few weeks. Check our GitHub and star it if you do like our product.

Conclusion

As we look towards a future where AI's potential is fully realized, it's clear that tools like Taipy will play a pivotal role in bridging the gap between technological capability and practical application. By addressing the critical challenges of siloed development environments and user acceptance, we pave the way for a new era of AI-driven innovation.

Engage with us at Taipy, where we're not just developing technology; we're crafting the future of smart software.

Taipy 3.1: A new era of visualization and data management

Vincent Gosselin — Tue, 05 Mar 2024 10:27:50 +0000

Hello, Dev.to community!

I'm beyond excited to share with you the latest leap forward in our journey - the release of Taipy 3.1. Our team has been hard at work incorporating feedback and pushing the boundaries of what our platform can do. Let's dive into what makes 3.1 a game-changer.

Third-Party Component Integration

Ever found yourself wishing you could integrate your favorite Python libraries seamlessly into your Taipy applications? Wish no more!
With 3.1, we're making it possible to visualize any HTML or Python objects within Taipy's versatile part object.
This means libraries like Folium, Bokeh, Vega-Altair, and Matplotlib are now at your fingertips, ready to enrich your applications with dynamic and engaging visualizations. And the best part? It's all streamlined into a single, cohesive user experience.
Read the release Notes

Native Plotly Integration

Plotly enthusiasts, rejoice! Taipy 3.1 brings native support for Plotly Python, transforming how you integrate sophisticated charts into your applications. Gone are the days of workaround integrations. Now, a single line of code embeds any Plotly chart, maintaining the high performance and interactivity Taipy is known for. This is data visualization at its finest - intuitive, efficient, and visually stunning.
Read the release Notes

Distributed Computing

As datasets grow and computational demands rise, enterprises need scalable solutions. Recognizing this, we've introduced Distributed Computing in Taipy 3.1. This feature allows for distributing computational tasks across multiple machines, enhancing performance for large-scale data projects. It's about doing faster and more efficiently, enabling businesses to tackle complex computations easily.
Read the release Notes

Telemetry

Maintaining the health and performance of your applications is paramount. With Taipy 3.1's Telemetry features, administrators and developers gain critical insights into application performance metrics and health indicators. This proactive monitoring tool is designed to keep operations smooth and identify potential issues before they escalate, ensuring your applications run optimally.
Read the release Notes

Python 3.12 Support

We're also thrilled to announce that Taipy and all its dependencies are now fully compatible with Python 3.12. This update ensures you're working with the latest and greatest that Python offers, further enhancing the robustness and reliability of your data projects.

Whether you're diving into data projects for the first time or leading complex enterprise solutions, this release is designed to elevate your work.

We're excited to see the innovative applications and solutions our community will create with these new features. If you're as eager as we are to explore the possibilities, join our vibrant community of developers and data scientists on Discord. Together, we can shape the future of Python application development.

Thank you for your continuous support and enthusiasm. Here's to making Taipy the go-to framework for your data science needs.

You can star our repo on GitHub

Enhancing Python GUIs with Augmented Markdown

Vincent Gosselin — Tue, 23 Jan 2024 07:57:10 +0000

𝗛𝗲𝗹𝗹𝗼 𝗗𝗲𝘃 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆,

Today, I'd like to share with you an innovative approach to building web-based interfaces in Python. This methodology hinges on 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗠𝗮𝗿𝗸𝗱𝗼𝘄𝗻, a widely-used language for generating HTML pages and seamlessly integrating it with Python for GUI development.

The Fusion of Markdown and Python

At the core of this innovation 🧐 is the idea of augmenting Markdown, a language traditionally used for creating simple HTML content, 𝘁𝗼 𝗶𝗻𝗰𝗹𝘂𝗱𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆. This method allows for seamless integration of dynamic data and interactive elements into a text-based interface.

The concept draws inspiration from research into Markdown based on the work of Dr. Neil Bruce [1] 🗎 for prototyping and dynamic data-driven user interfaces. It builds on the idea of directly linking GUI components to underlying data models, simplifying the process of GUI development in Python.💪🏻

How It Works: Embedding Python in Markdown

Developers can embed Python variables and interactive elements, like sliders or charts, 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗱𝗼𝘄𝗻 𝘁𝗲𝘅𝘁. For instance, a Python variable can be displayed in the GUI by simply adding a tag like <|{variable_name}|> in the Markdown. Editable elements can be created using a syntax like
<|{variable_name}|text_input|>.

Making GUI Development Accessible

One of the key advantages of this approach is its simplification of the GUI design process. The layout of GUI elements is handled automatically, integrating them within the Markdown flow. This significantly reduces the complexity and time involved in GUI layout design.

The augmented Markdown approach is particularly appealing to Python developers and data scientists who may not have extensive experience in GUI development. It allows for the creation of text-based pages with integrated GUI elements without the need to learn new tools or languages.

Underlying Technical Foundations

The technology uses a client-server model, with the Python application running on the server and the GUI displayed in a web browser. It involves transforming Python data into a format suitable for web-based GUIs. Challenges like efficient data transformation, server-client communication, and handling complex data types are addressed using modern protocols and optimization techniques.

Taipy: Inspired by Augmented Markdown Technology

It's noteworthy to mention that Taipy has drawn inspiration from this innovative concept of augmenting Markdown for GUI development. Leveraging the idea of seamlessly integrating Python functionality within Markdown, Taipy has built its framework around this approach. This adaptation exemplifies how theoretical concepts and technological innovations can inspire practical tools, providing Python developers with new, efficient ways to create interactive and dynamic GUIs.

You can check our GitHub repo here and drop a ⭐ to support the project

Conclusion

This augmented Markdown approach presents a novel and efficient way to develop web-based GUIs in Python. It leverages the simplicity of Markdown and the power of Python, providing an accessible and streamlined method for GUI development in the Python ecosystem.

References:
[1]: Prototyping with Markdown. Dr. Neil Bruce, 2017.

Our open-source project for building AI / Data full-stack apps got funded! 🎉 🎉

Vincent Gosselin — Mon, 15 Jan 2024 12:23:35 +0000

𝗛𝗶 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆,
We are excited to share with you this wonderful news: we completed our $5m Seed Funding round last month to help developers build AI / Data full-stack apps.

𝗧𝗵𝗲 𝗧𝗮𝗶𝗽𝘆 𝗦𝘁𝗼𝗿𝘆
A few years back, Albert and I after years of heading AI projects for large organizations, decided that it was time to transition to full Python development and to stop using traditional Java, JS, .Net stacks, etc.

We had a pretty clear idea of what features we were looking for, but to our surprise, we couldn’t find them amongst the flurry of existing Python packages.

Our mission was straightforward yet ambitious: to provide the missing bricks that prevent so many AI/Data pilots from making it into a successful deployed project.

In particular, we wanted to bring the end-user back into the “AI/Data” picture. I am still amazed today to see how little is mentioned about the end-user: from Data Scientists to Data Engineers, it is all about data flows, exposing algorithms, etc? No mention about how a human being is going to interact with AI/Data models… We wanted to change all that!

So we decided to build Taipy…

𝗧𝗮𝗶𝗽𝘆 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀:

A powerful Interactive front-end Application builder yet very simple to learn/use.
“Scenarios” which is the possibility for the end-user (and the data scientist as well) to interact with data and algorithms easily.

In 2022, we first launched Taipy as an open-source project (do check out our GitHub Page), followed by the Enterprise version later that year.

Thanks to the overwhelming support and interest from this amazing community, our GitHub project not only trending but also saw a phenomenal rise in popularity, growing from 100 to over 3,000 stars in a few weeks! We are beyond grateful for your support and enthusiasm.

Feel free to ⭐ the Taipy repository

I would also like to thank our early corporate adopters, who are so important in validating and testing a new technology. Specials thanks to McDonald’s, KnowledgeTouch, Groupe Les Mousquetaires, Total Energies, Textil Apparel Limited, IFP-EN, etc.

𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝘁𝗮𝘁𝘂𝘀
We recently released:

Taipy Cloud to allow community users to deploy, host, and share their apps with the rest of the world
Important Backend features: Taipy Studio, Application Versioning, Tasks Scheduling, Python API, Visual Component for Scenario Management, a new CLI, and more...
On the Front-end side: a new Style Kit, a default set of stylesheet templates for instant application creation, new charts...
TalkToTaipy, the LLM-based application to explore datasets using only natural languages

𝗧𝗮𝗶𝗽𝘆’𝘀 𝗳𝘂𝘁𝘂𝗿𝗲
This significant investment allows us to continue our full-time commitment to improving Taipy. This funding is also a crucial step towards realizing our vision, positioning Taipy as the leading platform for Python AI/Data projects.

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲 (𝗽𝗹𝗮𝗻𝗻𝗲𝗱 𝗳𝗼𝗿 𝗤𝟭 𝟮𝟬𝟮𝟰)
A fantastic release coming up soon (this quarter) with major new functionalities:

A brand new No-Code GUI Designer: you will be able to build your GUI page with no coding! Sorry for the spoiler but this new Designer is a killer!
Distributed Computing: Execution on remote clusters for parallel scenario/task execution.
Integration with major Platforms: like Databricks, Dataiku, etc.

And all this, while staying true to our open-source roots!

𝗢𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘀𝗶𝗱𝗲:

We’re now present on several community platforms: Discord, LinkedIn, X, and YouTube.
We sponsored several events: conferences (PyData, Pycon, ODSC...), hackathons, meetups, webinars…
We also plan to start regular Taipy Tech-Talks soon. .

And yes, all of this in open source!

We're always eager to hear from you, so if there's something you think Taipy could improve on or add, let us know. Your input is invaluable in shaping our roadmap.

Thank you, everyone, for your support. We couldn't have reached this milestone without you!
Vincent Gosselin & Albert Antoine
Co-founders of Taipy

Haven't checked out Taipy yet? Feel free to visit our GitHub page.