I DUMPED ChatGPT for This AI NEW Model (It’s So MUCH Better)
TLDRThe video presents a comparison between Chad GPT and a new AI model, CL 3.5, highlighting the latter's superior performance in coding tasks. The creator shares his dissatisfaction with Chad GPT's verbosity and lack of precision, contrasting it with CL 3.5's ability to generate precise code snippets and execute them in real-time artifacts. He demonstrates the model's capabilities through various coding examples and introduces a proof-of-concept Flutter app that integrates the new AI, showcasing its potential for enhancing no-code app development.
Takeaways
- 😀 The speaker has transitioned from using Chat GPT to a new LLM (Large Language Model) that they find superior for their specific needs.
- 🔧 The new LLM is praised for its effectiveness in generating code snippets and solving programming-related issues, which was a concern with Chat GPT 4's verbosity.
- 📈 The new LLM, CLA 3.5, is said to outperform Chat GPT 4 across various metrics, as shown in a comparison table provided by the speaker.
- 🛠️ The speaker demonstrates the use of the new LLM's front-end chat interface, which allows for real-time code execution previews through artifacts.
- 🎨 A unique feature highlighted is the ability to generate and modify visual elements like SVG images and animations, viewing them instantly within the chat.
- 🤖 The backend interface of the new LLM is versatile, offering API keys and a more technical interaction for programmatic use in applications.
- 📝 Custom prompts can be created for generating specific code, such as widgets, functions, and actions, which is a feature not commonly found in other LLMs.
- 📱 The speaker showcases a proof-of-concept Flutter Flow app that uses the new LLM as its backend to provide a chat interface for real-time interactions.
- 🌐 The app demonstrates context-aware conversations, remembering previous topics and providing relevant, continuous responses.
- 👥 The speaker invites viewers to join their Patreon community for access to the app, additional resources, and a supportive environment for no-code app development.
- 🔑 API keys are essential for integrating the new LLM into third-party applications, and the process for obtaining them is outlined in the dashboard of the LLM's website.
Q & A
What was the main issue the speaker had with Chad GPT 4?
-The main issue the speaker had with Chad GPT 4 was its verbosity. Even when asked for small modifications or code snippets, it would provide the entire code or widget, which was not what the speaker was looking for.
What new LLM model did the speaker switch to and why?
-The speaker switched to an LLM model from Entropic because it claimed to be better than Chad GPT 4 in various metrics and had features that were more suitable for the speaker's specific use cases, especially in programming.
What are some unique features of the Entropic model that the speaker found beneficial?
-Unique features of the Entropic model include the ability to create artifacts that show real-time execution of code, a front-end chat interface with conversational capabilities, and a back-end interface for more technical interactions and API key access.
How does the speaker plan to demonstrate the capabilities of the new LLM model?
-The speaker plans to demonstrate the capabilities of the new LLM model by showing a proof of concept app built using Flutter Flow that uses the model as its backend for a chat interface.
What is the significance of the 'artifact' feature in the Entropic model?
-The 'artifact' feature in the Entropic model is significant because it allows users to see the output or result of the code immediately after it is generated, without having to run it on their own machine or in the cloud.
How can users access the Entropic model's backend for more technical interactions?
-Users can access the backend of the Entropic model by visiting console.entropic.com, where they can find options to prompt with CLA, generate a prompt, invite collaborators, get API keys, and explore documentation.
What is the speaker's experience with using the new LLM model for generating code?
-The speaker's experience with using the new LLM model for generating code has been positive, stating that the code usually works on the first try and that the model is very good at writing code snippets, generating code, and building different apps.
How does the speaker use the Entropic model in their Flutter Flow apps?
-The speaker uses the Entropic model in their Flutter Flow apps by creating custom prompts for generating custom widgets, functions, and actions, and then integrating the generated code directly into the Flutter Flow platform.
What is the purpose of the custom prompts that the speaker created for Flutter Flow?
-The purpose of the custom prompts is to generate specific code snippets for Flutter Flow apps, such as custom widgets, functions, and actions, which can then be easily integrated into the apps without having to write the code from scratch.
How does the speaker plan to share the custom prompts and apps with their audience?
-The speaker plans to share the custom prompts and apps with their audience through their Patreon community, where members can access the apps, resources, and additional content created by the speaker.
Outlines
🤖 Transition from Chad GPT to a New LLM
The speaker has been a long-term user of Chad GPT but has recently encountered issues with its performance, leading to a switch to a new language model that has proven to be more effective for specific use cases. The video will introduce this new model, compare its features with Chad GPT, and showcase a proof of concept app built with Flutter Flow that utilizes the new model as its backend. The speaker also invites viewers to join their Patreon community for access to apps and resources discussed in the video.
🔍 Exploring Features of the New LLM
The speaker demonstrates the front-end experience of the new LLM, highlighting its ability to generate artifacts that allow users to see the execution of code in real-time. Examples include creating an 8-bit style SVG crab, animations of red crabs, and a JavaScript clock animation. The model's capability to update artifacts with new code on-the-fly is also showcased, emphasizing its powerful features for coding and app development.
🛠 Backend Experience and Customization with the New LLM
The video shifts focus to the backend experience, where the speaker shows how to use the model via APIs for more technical interactions. Custom prompts for generating Flutter widgets, functions, and actions are discussed, along with the process of creating and executing these prompts to generate custom code snippets that can be directly implemented in Flutter Flow apps.
📱 Building a Flutter Flow App with the New LLM Backend
The speaker describes the process of building a Flutter Flow app that integrates the new LLM as its backend, allowing for real-time chat interactions within the app. The app is demonstrated to handle context-aware conversations, such as discussing the capital and population of Paris, and the number of countries France borders. The backend setup for the chat client is also briefly explained.
🌐 Utilizing the New LLM for Enhanced Coding and App Development
The video emphasizes the new LLM's solid performance in generating code that typically works on the first try, facilitating complex tasks in Flutter Flow apps. The speaker shares custom prompts for creating functions and actions, and plans to make these available to Patreon subscribers. The ease of adding new features and the model's ability to handle multiple variables for generating various custom widgets are highlighted.
🎉 Conclusion and Invitation to the Patreon Community
In conclusion, the speaker expresses satisfaction with the new LLM for its superior performance in coding tasks compared to Chad GPT and its unique features. The speaker invites viewers to join their Patreon community to access the demonstrated app, future apps, and additional learning resources. The Patreon community is portrayed as a supportive environment for enhancing no-code skills and receiving help from fellow members.
Mindmap
Keywords
💡LLM (Large Language Model)
💡Flutter
💡API Keys
💡Artifact
💡Entropic
💡Code Snippet
💡Patreon
💡No-code
💡Custom Widget
💡Proof of Concept
💡Evaluation
Highlights
Switched from Chad GPT to a new LLM for better results in specific use cases.
Introduction of an LLM with features not available in Chat GPT.
Demonstration of a proof of concept app built using Flutter Flow with the new LLM backend.
Issues with Chad GPT 4's verbosity and lack of concise responses.
Entropic's new model claimed to outperform GPT 4 in various metrics.
Positive community feedback on the new model for programming tasks.
Features of the new model including an interactive frontend chat interface.
Artifacts in the chat interface allow for real-time code execution previews.
The ability to generate and modify code snippets directly within the chat.
Creating custom widgets, functions, and actions with the new model for Flutter Flow apps.
Using the model's backend for programmatic interactions via API keys.
Efficiency of generating custom code with the new model compared to other LLMs.
Integration of the new model into no-code platforms like Flutter Flow.
Creating a chat client in Flutter Flow using the new model's backend.
Customization and flexibility of the new model for various applications.
Potential future developments and integrations of the new model.
Invitation to join the Patreon community for access to apps, resources, and support.
Emphasis on the community aspect and support for learning and building no-code apps.