The Rise and Decline of Text Input and Chatbot Functionality in App Development

In spite of the decline of text input and chatbot functionality, these features are still prominent in many apps. Interestingly, the text input is approximately almost two feet higher than the chatbot functionality. This disparity highlights the ongoing evolution of app development, where certain features may see a decline in usage while others remain popular.

The state of LM (Large Language Model) Apps and their Trend Analysis

Recently, a deep dive into the world of LM apps was conducted, showcasing the most popular apps in this niche. The results revealed that there are 46 public apps built using Web8 in the form of Signal Text Input, while chatbot functionality stands at 41 public apps. Furthermore, the top five most popular apps were displayed, which were pulled dynamically from the Community Cloud Server.

A report on LM Trends was created in pure Python using Shamlet, providing an overview of the app's architecture and components. The major components include large language models orchestration and vector retrieval. This report is a testament to the growing interest in LM technology and its applications in app development.

App Development with Alms

When building with Alms, concerns arise regarding performance issues that may arise due to high traffic. A prior video discussed the fundamental LM app architecture, which comprises large language models orchestration, vector retrieval, and methodology used for creation. To address these concerns, an interactive table displaying all apps in the gallery was created. This table allows users to sort apps by URL, app name, cumulative views, GitHub URL, and app type.

App Performance Optimization

After launching the app, performance issues were experienced due to high traffic. The code was factored to optimize data processing, calculation of plots, and retrieval of variables. These optimizations resulted in the app loading about 70% faster than before, showcasing the importance of addressing performance concerns in app development.

Conclusion

Creating a viral app or one that scales with a larger community user base requires careful consideration of performance issues. By optimizing code and addressing potential bottlenecks, developers can create apps that are both functional and efficient. The creation of the LM Trends report serves as an example of this approach, highlighting the importance of effective app development strategies.

The Journey to Creating Interactive Data Apps

As data scientists, building interactive data apps is essential for understanding complex concepts and trends. By creating apps like the one discussed in this article, developers can engage with their audience and provide valuable insights into the world of LM technology.

Best Practices and Tips for Creating Street Apps

If you're interested in learning more about best practices for creating street apps, drop your comments below. The team behind the LM Trends report shared valuable insights on how to optimize app performance and create scalable apps that can handle high traffic. By following these tips, developers can create apps that are both functional and efficient.

The Future of App Development

As technology continues to evolve, it's essential for developers to stay up-to-date with the latest trends and best practices. The world of LM apps is rapidly changing, and creating interactive data apps will remain a crucial part of this evolution. By embracing new technologies and strategies, developers can create apps that are both innovative and user-friendly.

The Importance of Community Engagement

Engaging with your community is vital for app development success. By sharing knowledge and best practices, developers can learn from each other and create better apps. The LM Trends report serves as an example of this approach, highlighting the importance of collaboration and open communication in the world of app development.

Stay Tuned for More Content

The best way to learn data science is by building interactive data apps. Stay tuned for more content on this topic, where we'll explore new technologies and strategies for creating scalable and efficient apps. By embracing these trends, developers can create apps that are both functional and engaging.

"WEBVTTKind: captionsLanguage: enin early November streamlet released the state of Alm apps for showcasing insights on the L trends of more than 20,000 apps built by the community of over 13,000 developers and several insights were gained from this interactive report including the top models orchestration Vector retrievals insights on weather chatbot functionality is the future for LMS and also a searchable app gallery of LM powered apps so I've actually created a video on the room YouTube channel providing an overview of this and I'll provide you a brief overview also but then I'm going to provide you a behind the scenes look at the creation of this app so this state of LOM apps 2023 is reporting the general emerging trends of tools and use cases in the development of LM apps from over 21,000 and also built by 13,000 developers and it should be noted that all of the apps and insights and Analysis are are based on apps deployed on the tret community cloud and so in this app we're going to have the sidebar which provides a table of content like navigation and if you click on one of them you could hop over to the various sections and hop back up or down as you like here so let's have a look at the key takeaways which has been summarized accordingly here and then if You' like to have a look at the respective data you could click on the buttons here at the bottom so this are the four major components of the LM apps mentioned in this report and we have the large language models we have the LM orchestration we have the vector retrievals and also the chatbot so if you click here you're going to see the interactive charts and if you modify the selection here you're going to see the data being recalculated so why don't I do that let me say if I want to have a look at llama index and then the data will be displayed only for llama index if I click here bring Lang chain back and then what if we have a look at the percent usage and you're going to see that the plot here has been regenerated on the fly as we make the selection is and if you over your mouse you're going to see a value that are displayed on top of the lines so all of these are applicable to all of the plots mentioned in the report and they're interactive and they're fun to have a look at especially when you're playing around with the various functions here and it will be regenerated and here we have a look at the Bots or data future and then you're going to see that there's a gradual decline of the simple text input whereby user provides just a simple text prompt like a single prompt and then the app will generate the response for that single prompt and then we have the chatbot functionality where users are able to interact with the chatbot app where they provide their first question and then the chatbot will provide the answer and then the user will provide their second question and then the chatbot will iterate and provide the subsequent LM generated response Let's see we can see that l chatbot Trends are on the rise and the single text input are on the decline in spite of that the text input are approximately almost two fots higher than the chatbot functionality and if you would like to take a deeper look into the actual apps here in the gallery the great thing here is that all of the apps are displayed in this interactive table you could sort them by the URL by the app Name by the cumulative views for the GitHub URL by the app type and then here you can make your selection if you want to have a look at wv8 you want to have a look at single text input using wv8 then the interactive data frame here will be regenerated and so it's recalculating so here we can see that there are 46 public apps built using web8 in the form of a signal text input and if we have a look at the chatbot functionality and we're going to see that it is at 41 public apps and at the most popular apps the top five are displayed here in the screenshot and they are pulled dynamically from the community Cloud Server and this is the concerns for when building with alms so you could have a look at the prior video that I've mentioned before and then the fundamental LM app architecture that the major components here are comprising of large language models orchestration and also the vector retrieval about stret and the methodology used for the creation of this particular report so the report for the LM Trends was mentioned earlier that it was created in pure python using shamlet and we're just going to take a overview look here that the entirety of the app was created and they are inside the app.py and they are inside the app.py and so app.py will provide the basic functionality of the sidebar and also the various contents that are displayed here such as the text The Columns that are used use here however all of the graph functionality these are created using out a the underlying data and the processing of the data are actually in the utilities function and so instead of reusing the code we just packaged it up into a class and then we reuse it by importing the necessary functions from the utilities python file here and then we provided pretty heavy CSS customization so you're going to notice that sidebar here was also created and stylized using custom CSS several of the layouts here even the creation of this blue box was generated using CSS styling and also precise pixel Arrangement and placement of various text elements centering the page you know like creating this hoverable icons of social media platforms was generated in CSS and and they are in the CSS file here and so going to and the images generated at the bottom of the app was stored in this static folder here the image of the fundamental LM app architecture and so we're in the works of deciding whether to write a blog about the creation of this particular state of LM apps and whether you'd be interested in accessing the underlying code so drop your comments Down Below in the comment section let me know if You' like to read a blog about this the thought process behind creating this and also one important thing that I've learned as a part of the team creating this state of LM report was that creating a St app and also creating an app that scales with a larger Community user base because the app was accessed by thousands of users concurrently and if you think of that as like a viral app it is right and one of the downside of that is that when so many people are are accessing the app at the same time there might be some performance issue the app might be a bit slower than it is if only a few users are using it casually so then after a few hours of the launch we've experienced that the app was a bit slow and then we we factored the code of the app so you might see that the app looks almost exactly the same but then the underlying processing of the data underlying calculation of the plots that you see here and the retrieval of respective variables that are used and reused in the app was reconceptualized and also optimize so that it loads about twice faster or or at least 70% faster than it was when it was launched and let me know in the comments down below if you like to read about such a Blog that shares the thought process that is involved in creating a viral app or a highly trafficked app because surely a lot has been happening in the back end in order to make an app much faster optimizing its loading and app performance and so I hope that you've liked this thought process on the creation of this state of LM apps 2023 and I'd love to hear from you all of your best practices tips and tricks for creating Street apps and I hope that you found this helpful and please don't forget to smash the like button share it with your peers stay tuned for the next video and as always the best way to learn data science is to build an interactive data app and please enjoy the journeyin early November streamlet released the state of Alm apps for showcasing insights on the L trends of more than 20,000 apps built by the community of over 13,000 developers and several insights were gained from this interactive report including the top models orchestration Vector retrievals insights on weather chatbot functionality is the future for LMS and also a searchable app gallery of LM powered apps so I've actually created a video on the room YouTube channel providing an overview of this and I'll provide you a brief overview also but then I'm going to provide you a behind the scenes look at the creation of this app so this state of LOM apps 2023 is reporting the general emerging trends of tools and use cases in the development of LM apps from over 21,000 and also built by 13,000 developers and it should be noted that all of the apps and insights and Analysis are are based on apps deployed on the tret community cloud and so in this app we're going to have the sidebar which provides a table of content like navigation and if you click on one of them you could hop over to the various sections and hop back up or down as you like here so let's have a look at the key takeaways which has been summarized accordingly here and then if You' like to have a look at the respective data you could click on the buttons here at the bottom so this are the four major components of the LM apps mentioned in this report and we have the large language models we have the LM orchestration we have the vector retrievals and also the chatbot so if you click here you're going to see the interactive charts and if you modify the selection here you're going to see the data being recalculated so why don't I do that let me say if I want to have a look at llama index and then the data will be displayed only for llama index if I click here bring Lang chain back and then what if we have a look at the percent usage and you're going to see that the plot here has been regenerated on the fly as we make the selection is and if you over your mouse you're going to see a value that are displayed on top of the lines so all of these are applicable to all of the plots mentioned in the report and they're interactive and they're fun to have a look at especially when you're playing around with the various functions here and it will be regenerated and here we have a look at the Bots or data future and then you're going to see that there's a gradual decline of the simple text input whereby user provides just a simple text prompt like a single prompt and then the app will generate the response for that single prompt and then we have the chatbot functionality where users are able to interact with the chatbot app where they provide their first question and then the chatbot will provide the answer and then the user will provide their second question and then the chatbot will iterate and provide the subsequent LM generated response Let's see we can see that l chatbot Trends are on the rise and the single text input are on the decline in spite of that the text input are approximately almost two fots higher than the chatbot functionality and if you would like to take a deeper look into the actual apps here in the gallery the great thing here is that all of the apps are displayed in this interactive table you could sort them by the URL by the app Name by the cumulative views for the GitHub URL by the app type and then here you can make your selection if you want to have a look at wv8 you want to have a look at single text input using wv8 then the interactive data frame here will be regenerated and so it's recalculating so here we can see that there are 46 public apps built using web8 in the form of a signal text input and if we have a look at the chatbot functionality and we're going to see that it is at 41 public apps and at the most popular apps the top five are displayed here in the screenshot and they are pulled dynamically from the community Cloud Server and this is the concerns for when building with alms so you could have a look at the prior video that I've mentioned before and then the fundamental LM app architecture that the major components here are comprising of large language models orchestration and also the vector retrieval about stret and the methodology used for the creation of this particular report so the report for the LM Trends was mentioned earlier that it was created in pure python using shamlet and we're just going to take a overview look here that the entirety of the app was created and they are inside the app.py and they are inside the app.py and so app.py will provide the basic functionality of the sidebar and also the various contents that are displayed here such as the text The Columns that are used use here however all of the graph functionality these are created using out a the underlying data and the processing of the data are actually in the utilities function and so instead of reusing the code we just packaged it up into a class and then we reuse it by importing the necessary functions from the utilities python file here and then we provided pretty heavy CSS customization so you're going to notice that sidebar here was also created and stylized using custom CSS several of the layouts here even the creation of this blue box was generated using CSS styling and also precise pixel Arrangement and placement of various text elements centering the page you know like creating this hoverable icons of social media platforms was generated in CSS and and they are in the CSS file here and so going to and the images generated at the bottom of the app was stored in this static folder here the image of the fundamental LM app architecture and so we're in the works of deciding whether to write a blog about the creation of this particular state of LM apps and whether you'd be interested in accessing the underlying code so drop your comments Down Below in the comment section let me know if You' like to read a blog about this the thought process behind creating this and also one important thing that I've learned as a part of the team creating this state of LM report was that creating a St app and also creating an app that scales with a larger Community user base because the app was accessed by thousands of users concurrently and if you think of that as like a viral app it is right and one of the downside of that is that when so many people are are accessing the app at the same time there might be some performance issue the app might be a bit slower than it is if only a few users are using it casually so then after a few hours of the launch we've experienced that the app was a bit slow and then we we factored the code of the app so you might see that the app looks almost exactly the same but then the underlying processing of the data underlying calculation of the plots that you see here and the retrieval of respective variables that are used and reused in the app was reconceptualized and also optimize so that it loads about twice faster or or at least 70% faster than it was when it was launched and let me know in the comments down below if you like to read about such a Blog that shares the thought process that is involved in creating a viral app or a highly trafficked app because surely a lot has been happening in the back end in order to make an app much faster optimizing its loading and app performance and so I hope that you've liked this thought process on the creation of this state of LM apps 2023 and I'd love to hear from you all of your best practices tips and tricks for creating Street apps and I hope that you found this helpful and please don't forget to smash the like button share it with your peers stay tuned for the next video and as always the best way to learn data science is to build an interactive data app and please enjoy the journey\n"