My Experience Building a Personal AI Assistant

My Experience Building a Personal AI Assistant

My Experience Building a Personal AI Assistant

My Experience Building a Personal AI Assistant

For the past three weeks, I've been working on building my own version of "Samantha" from the movie "HER". This project involved leveraging multiple AI and machine learning tools to create a highly advanced and interconnected assistant that could help me with various tasks throughout the day. In this post, I'll be sharing my journey, challenges, and the result of this project.

The Journey

Overview of the Development Process

I started working on this project about 3-4 weeks ago with the idea that I could slowly build out an interconnected model which leverages Whisper, Assembly, GPT-3, Azure’s Vision API, ElevenLabs, n8n, Discord, LangChain, Stable Diffusion, and eventually also Midjourney. The project was monumental in size and something that I knew I could tackle but would need to research more.

The idea was relatively simple: I would use GPT-3 (which then I swapped to ChatGPT’s API an hour after release) as the primary “brain” which is then connected to all the other tools in the available open repertory. Whisper would transcribe my voice to text. Assembly would classify the tone of my voice. Azure would turn what I’m seeing into text. ChatGPT + LangChain would intake the pieces and then using tools, agents, and indices, could provide 2 outputs which would funnel through Eleven and result in an incredibly powerful, albeit slow, "assistant."

Challenges Faced During the Process

One of the biggest challenges I faced during this process was connecting all the different tools and APIs together. Each tool had its own unique interface and required specific inputs and outputs, which made it difficult to integrate them all together. Additionally, some of the tools had limitations on the number of requests that could be made, which made it difficult to scale the assistant.

Another challenge I faced was tuning the model to accurately understand my voice commands, tone of voice, and respond appropriately. It took a lot of trial and error to fine-tune the model to the point where it was accurately recognizing my voice and responding to my commands with correct intentions.

Milestones Reached During the Process

Despite the challenges, there were several key milestones that I reached during the process. One of the biggest milestones was successfully integrating all the different tools and APIs together to create a functional assistant. Another milestone was fine-tuning the model to accurately recognize my voice’s tones and respond appropriately. Think about it this way: I wanted to make sure that the model recognized my mood and tone - so, for example, say I’m having a bad day, I wanted the model to recognize from the tone of my voice that I was not content or that something is wrong. The model would pick up the tone, convert that to a textual prompt injection, and then pass that along with my transcribed command. The “brain” would then understand that my command was given with a degree of sadness and would provide an output that reflected my mood. Currently, it is able to determine 5 basic emotions and respond accordingly.

The Result

Description of the Final Product

The final product is a highly advanced AI assistant that can perform a wide range of tasks, including sending emails, scheduling meetings, responding to Slack messages, providing recommended recipes (based on trends, time/location, and mood), playing music, and answering general knowledge questions. The assistant is powered by a combination of ChatGPT’s API, GPT-3, Whisper, Assembly, Azure’s Vision API, ElevenLabs, n8n, Discord, LangChain, Stable Diffusion, and Midjourney, which enables it to process and respond to a wide range of inputs.

Capabilities of the Final Product

The final product has a wide range of capabilities, including:

  • Transcribing voice commands to text

  • Analyzing the tone of my voice

  • Recognizing objects in real-time using Azure’s Vision API

  • Generating natural language responses using GPT-3 & ChatGPT (why limit yourself to one, right?)

  • Connecting to various web services, such as Google Calendar and Spotify

  • Responding to voice commands with appropriate actions

Success Rate of the Final Product

The success rate of the final product is quite high, with the assistant accurately recognizing and responding to voice commands about 90% of the time. However, there are still some areas for improvement, such as improving the assistant's ability to recognize more nuanced voice commands and improving the speed of the assistant's response times. This is largely due to the infrastructure I am using which is mostly reliant on intermediaries.

Future Possibilities

Potential for future development

There is significant potential for future development of the assistant, including the integration of additional tools and APIs, as well as the implementation of new features and capabilities. With the rapid pace of technological development, the possibilities for future enhancements are virtually limitless.

Areas of improvement

While the assistant was highly successful, there were still some areas for improvement, including increasing the speed and efficiency of certain processes, as well as improving the accuracy of certain responses. These areas will be the focus of future development efforts.

Speculation on future possibilities

Looking ahead, there are many exciting possibilities for the future of AI-powered assistants, including the potential for even more advanced natural language processing, enhanced image and speech recognition, and the ability to integrate with even more advanced tools and systems. It is possible that AI assistants will become more human-like in their interactions, with the ability to understand context, emotions, and nonverbal cues. Additionally, as AI systems continue to advance, it is possible that they may become more self-aware and able to adapt to new situations in real-time.

As AI assistants become more advanced, it is also possible that they will become more integrated into our daily lives, with the ability to assist us in everything from managing our schedules and tasks to providing emotional support and companionship. There is also potential for AI assistants to revolutionize industries such as healthcare, education, and customer service.

Overall, the future of AI-powered assistants is incredibly exciting, and there are countless possibilities for future development and innovation. As the technology continues to advance, it is likely that AI assistants will become an increasingly important part of our daily lives, helping us to achieve our goals, simplify our tasks, and improve our overall quality of life.

My Experience Building a Personal AI Assistant

For the past three weeks, I've been working on building my own version of "Samantha" from the movie "HER". This project involved leveraging multiple AI and machine learning tools to create a highly advanced and interconnected assistant that could help me with various tasks throughout the day. In this post, I'll be sharing my journey, challenges, and the result of this project.

The Journey

Overview of the Development Process

I started working on this project about 3-4 weeks ago with the idea that I could slowly build out an interconnected model which leverages Whisper, Assembly, GPT-3, Azure’s Vision API, ElevenLabs, n8n, Discord, LangChain, Stable Diffusion, and eventually also Midjourney. The project was monumental in size and something that I knew I could tackle but would need to research more.

The idea was relatively simple: I would use GPT-3 (which then I swapped to ChatGPT’s API an hour after release) as the primary “brain” which is then connected to all the other tools in the available open repertory. Whisper would transcribe my voice to text. Assembly would classify the tone of my voice. Azure would turn what I’m seeing into text. ChatGPT + LangChain would intake the pieces and then using tools, agents, and indices, could provide 2 outputs which would funnel through Eleven and result in an incredibly powerful, albeit slow, "assistant."

Challenges Faced During the Process

One of the biggest challenges I faced during this process was connecting all the different tools and APIs together. Each tool had its own unique interface and required specific inputs and outputs, which made it difficult to integrate them all together. Additionally, some of the tools had limitations on the number of requests that could be made, which made it difficult to scale the assistant.

Another challenge I faced was tuning the model to accurately understand my voice commands, tone of voice, and respond appropriately. It took a lot of trial and error to fine-tune the model to the point where it was accurately recognizing my voice and responding to my commands with correct intentions.

Milestones Reached During the Process

Despite the challenges, there were several key milestones that I reached during the process. One of the biggest milestones was successfully integrating all the different tools and APIs together to create a functional assistant. Another milestone was fine-tuning the model to accurately recognize my voice’s tones and respond appropriately. Think about it this way: I wanted to make sure that the model recognized my mood and tone - so, for example, say I’m having a bad day, I wanted the model to recognize from the tone of my voice that I was not content or that something is wrong. The model would pick up the tone, convert that to a textual prompt injection, and then pass that along with my transcribed command. The “brain” would then understand that my command was given with a degree of sadness and would provide an output that reflected my mood. Currently, it is able to determine 5 basic emotions and respond accordingly.

The Result

Description of the Final Product

The final product is a highly advanced AI assistant that can perform a wide range of tasks, including sending emails, scheduling meetings, responding to Slack messages, providing recommended recipes (based on trends, time/location, and mood), playing music, and answering general knowledge questions. The assistant is powered by a combination of ChatGPT’s API, GPT-3, Whisper, Assembly, Azure’s Vision API, ElevenLabs, n8n, Discord, LangChain, Stable Diffusion, and Midjourney, which enables it to process and respond to a wide range of inputs.

Capabilities of the Final Product

The final product has a wide range of capabilities, including:

  • Transcribing voice commands to text

  • Analyzing the tone of my voice

  • Recognizing objects in real-time using Azure’s Vision API

  • Generating natural language responses using GPT-3 & ChatGPT (why limit yourself to one, right?)

  • Connecting to various web services, such as Google Calendar and Spotify

  • Responding to voice commands with appropriate actions

Success Rate of the Final Product

The success rate of the final product is quite high, with the assistant accurately recognizing and responding to voice commands about 90% of the time. However, there are still some areas for improvement, such as improving the assistant's ability to recognize more nuanced voice commands and improving the speed of the assistant's response times. This is largely due to the infrastructure I am using which is mostly reliant on intermediaries.

Future Possibilities

Potential for future development

There is significant potential for future development of the assistant, including the integration of additional tools and APIs, as well as the implementation of new features and capabilities. With the rapid pace of technological development, the possibilities for future enhancements are virtually limitless.

Areas of improvement

While the assistant was highly successful, there were still some areas for improvement, including increasing the speed and efficiency of certain processes, as well as improving the accuracy of certain responses. These areas will be the focus of future development efforts.

Speculation on future possibilities

Looking ahead, there are many exciting possibilities for the future of AI-powered assistants, including the potential for even more advanced natural language processing, enhanced image and speech recognition, and the ability to integrate with even more advanced tools and systems. It is possible that AI assistants will become more human-like in their interactions, with the ability to understand context, emotions, and nonverbal cues. Additionally, as AI systems continue to advance, it is possible that they may become more self-aware and able to adapt to new situations in real-time.

As AI assistants become more advanced, it is also possible that they will become more integrated into our daily lives, with the ability to assist us in everything from managing our schedules and tasks to providing emotional support and companionship. There is also potential for AI assistants to revolutionize industries such as healthcare, education, and customer service.

Overall, the future of AI-powered assistants is incredibly exciting, and there are countless possibilities for future development and innovation. As the technology continues to advance, it is likely that AI assistants will become an increasingly important part of our daily lives, helping us to achieve our goals, simplify our tasks, and improve our overall quality of life.

My Experience Building a Personal AI Assistant

For the past three weeks, I've been working on building my own version of "Samantha" from the movie "HER". This project involved leveraging multiple AI and machine learning tools to create a highly advanced and interconnected assistant that could help me with various tasks throughout the day. In this post, I'll be sharing my journey, challenges, and the result of this project.

The Journey

Overview of the Development Process

I started working on this project about 3-4 weeks ago with the idea that I could slowly build out an interconnected model which leverages Whisper, Assembly, GPT-3, Azure’s Vision API, ElevenLabs, n8n, Discord, LangChain, Stable Diffusion, and eventually also Midjourney. The project was monumental in size and something that I knew I could tackle but would need to research more.

The idea was relatively simple: I would use GPT-3 (which then I swapped to ChatGPT’s API an hour after release) as the primary “brain” which is then connected to all the other tools in the available open repertory. Whisper would transcribe my voice to text. Assembly would classify the tone of my voice. Azure would turn what I’m seeing into text. ChatGPT + LangChain would intake the pieces and then using tools, agents, and indices, could provide 2 outputs which would funnel through Eleven and result in an incredibly powerful, albeit slow, "assistant."

Challenges Faced During the Process

One of the biggest challenges I faced during this process was connecting all the different tools and APIs together. Each tool had its own unique interface and required specific inputs and outputs, which made it difficult to integrate them all together. Additionally, some of the tools had limitations on the number of requests that could be made, which made it difficult to scale the assistant.

Another challenge I faced was tuning the model to accurately understand my voice commands, tone of voice, and respond appropriately. It took a lot of trial and error to fine-tune the model to the point where it was accurately recognizing my voice and responding to my commands with correct intentions.

Milestones Reached During the Process

Despite the challenges, there were several key milestones that I reached during the process. One of the biggest milestones was successfully integrating all the different tools and APIs together to create a functional assistant. Another milestone was fine-tuning the model to accurately recognize my voice’s tones and respond appropriately. Think about it this way: I wanted to make sure that the model recognized my mood and tone - so, for example, say I’m having a bad day, I wanted the model to recognize from the tone of my voice that I was not content or that something is wrong. The model would pick up the tone, convert that to a textual prompt injection, and then pass that along with my transcribed command. The “brain” would then understand that my command was given with a degree of sadness and would provide an output that reflected my mood. Currently, it is able to determine 5 basic emotions and respond accordingly.

The Result

Description of the Final Product

The final product is a highly advanced AI assistant that can perform a wide range of tasks, including sending emails, scheduling meetings, responding to Slack messages, providing recommended recipes (based on trends, time/location, and mood), playing music, and answering general knowledge questions. The assistant is powered by a combination of ChatGPT’s API, GPT-3, Whisper, Assembly, Azure’s Vision API, ElevenLabs, n8n, Discord, LangChain, Stable Diffusion, and Midjourney, which enables it to process and respond to a wide range of inputs.

Capabilities of the Final Product

The final product has a wide range of capabilities, including:

  • Transcribing voice commands to text

  • Analyzing the tone of my voice

  • Recognizing objects in real-time using Azure’s Vision API

  • Generating natural language responses using GPT-3 & ChatGPT (why limit yourself to one, right?)

  • Connecting to various web services, such as Google Calendar and Spotify

  • Responding to voice commands with appropriate actions

Success Rate of the Final Product

The success rate of the final product is quite high, with the assistant accurately recognizing and responding to voice commands about 90% of the time. However, there are still some areas for improvement, such as improving the assistant's ability to recognize more nuanced voice commands and improving the speed of the assistant's response times. This is largely due to the infrastructure I am using which is mostly reliant on intermediaries.

Future Possibilities

Potential for future development

There is significant potential for future development of the assistant, including the integration of additional tools and APIs, as well as the implementation of new features and capabilities. With the rapid pace of technological development, the possibilities for future enhancements are virtually limitless.

Areas of improvement

While the assistant was highly successful, there were still some areas for improvement, including increasing the speed and efficiency of certain processes, as well as improving the accuracy of certain responses. These areas will be the focus of future development efforts.

Speculation on future possibilities

Looking ahead, there are many exciting possibilities for the future of AI-powered assistants, including the potential for even more advanced natural language processing, enhanced image and speech recognition, and the ability to integrate with even more advanced tools and systems. It is possible that AI assistants will become more human-like in their interactions, with the ability to understand context, emotions, and nonverbal cues. Additionally, as AI systems continue to advance, it is possible that they may become more self-aware and able to adapt to new situations in real-time.

As AI assistants become more advanced, it is also possible that they will become more integrated into our daily lives, with the ability to assist us in everything from managing our schedules and tasks to providing emotional support and companionship. There is also potential for AI assistants to revolutionize industries such as healthcare, education, and customer service.

Overall, the future of AI-powered assistants is incredibly exciting, and there are countless possibilities for future development and innovation. As the technology continues to advance, it is likely that AI assistants will become an increasingly important part of our daily lives, helping us to achieve our goals, simplify our tasks, and improve our overall quality of life.