AI-Powered Engagement: Crafting Comments on LinkedIn and YouTube with a Smart Chrome Extension!

**Building a Chrome Extension to Generate Comments on LinkedIn and YouTube Posts Using AI**

In today’s digital age, leveraging AI to enhance productivity and streamline tasks has become increasingly crucial. One such innovative application of AI is in generating comments on social media platforms like LinkedIn and YouTube. In this article, we will explore how I built a Chrome extension that analyzes posts on these platforms, sends the content to AI, and returns a relevant comment that can be easily pasted into the comment box.

### Introduction

The ability to generate comments using AI can significantly reduce the time and effort required to engage with content on social media platforms. By automating the process of crafting comments, users can focus on other aspects of their online presence. This article will guide you through the steps I took to create a Chrome extension that integrates AI to generate comments on LinkedIn and YouTube posts.

### Steps to Build the Chrome Extension

#### Step 1: Setting Up the Project

To begin, I set up a new project in Chrome using the Chrome Extension template. This template provides a basic structure for building a Chrome extension.

#### Step 2: Extracting Post Content

To extract the post content from LinkedIn and YouTube, I used JavaScript and basic web scraping techniques. For LinkedIn, I focused on the post text and identified the comment section to insert custom buttons. For YouTube, I analyzed the page structure to find the relevant post content.

#### Step 3: Integrating AI

To generate comments, I utilized GPT-3, a powerful AI model. I set up a ready-to-use endpoint as a black box, which handled the backend part and prompt engineering. The extension would send the extracted post content to this endpoint, which would then generate a comment.

#### Step 4: Handling Events and Data Flow

To ensure seamless communication between the different parts of the extension, I implemented a messaging system. The data flow was designed as follows:

1. **Listen to Events**: The extension listens for the `focusin` event on the page. If it is a comment section, it adds three buttons with event `onClick` handlers.
2. **Generate Request Body**: When a button is clicked, the extension collects the text data from the post and generates a request body.
3. **Send Request to Background Worker**: The extension sends the request body as a `generate-comment` event to the background worker using message passing.
4. **Generate Comment**: The background worker listens for the `generate-comment` event and makes an API call with the provided body. It then sends a response as a `generate-comment-response` event to the content.
5. **Display Comment**: The extension listens for the `generate-comment-response` event and triggers the text of the comment in the comment section.

### LinkedIn Page Analysis

To extract post text and identify the comment section, I used the elements tab from Chrome Dev Tools. I discovered that all feed pages are split into similar blocks, and comment sections are implemented as custom divs with a class of `ql-editor`. To extract the relevant post section, I found the parent container of the whole post, which is a div with the class `feed-shared-update-v2`, and extracted the text from the div with the class `feed-shared-update-v2__description-wrapper`.

### YouTube Page Analysis

For YouTube, I analyzed the page structure to find the relevant post content. I used similar techniques to extract the text and identify the comment section.

### Conclusion

The Chrome extension I built is designed to simplify the process of generating comments on LinkedIn and YouTube posts. By leveraging AI, it automates the task of crafting comments, allowing users to focus on other aspects of their online presence. The extension is easy to use; users simply need to click the comment button, and the generated comment will be pasted into the comment box.

### Future Enhancements

In the future, I plan to enhance the extension by adding more advanced features, such as personalized processing of different social media platforms, summaries of articles, and generation of other types of messages. The quality of the responses generated by the AI can be good enough for drafts, but they still need to be checked by humans before posting.

### Conclusion

In conclusion, building a Chrome extension that integrates AI to generate comments on LinkedIn and YouTube posts is a straightforward process. By following the steps outlined in this article, you can create a powerful tool that enhances your online engagement and saves time. Happy coding

Crafting Your Creative Compass: Tailoring GPT for Personalized Idea Generation

To develop a custom GPT that generates creative ideas tailored to an individual’s personality, goals, and preferences, you can follow these steps:

1. **Define the GPT’s Role**:
– **Act as a Creative Coach**: This role will help the GPT generate ideas that align with the user’s personality and goals.

2. **Gather User Information**:
– **Tonal Voice**: Ask the user about their preferred tone for the ideas generated.
– **Personality Style**: Understand their personality traits and how they approach creative tasks.
– **Past Successes**: Identify what has worked well for the user in the past.
– **Goals**: Determine their short-term and long-term goals.

3. **Generate Ideas**:
– **Divergent Thinking**: Use the GPT’s ability to generate a wide range of ideas.
– **Convergent Thinking**: Focus on merging these ideas to create a cohesive plan.

4. **Evaluate and Refine Ideas**:
– **User Feedback**: Allow the user to select and refine the ideas that resonate with them.
– **Iterative Process**: Continue to generate and refine ideas until the user is satisfied.

5. **Plan Implementation**:
– **Actionable Steps**: Outline the steps the user needs to take to achieve their goals.

### Example Interaction

**User Input**:
“`plaintext
I want to act as a creative coach. Please help me generate ideas for a new project. I am looking for ideas that align with my personality style and will help me achieve my goals. My personality style is creative and innovative. I have had success in the past with projects that involve storytelling and collaboration. My goal is to create a new product that can be marketed globally.
“`

**GPT Response**:
“`plaintext
Based on your personality style and past successes, I recommend exploring ideas that combine storytelling with innovative technology. Here are some potential ideas:

1. **Storytelling App**: Develop an app that allows users to create interactive stories with augmented reality elements.
2. **Collaborative Novel**: Write a novel where users can contribute to the story through a collaborative platform.
3. **Innovative Product Launch**: Launch a new product with a unique storytelling campaign that involves user-generated content.

To achieve your goal, I suggest the following steps:

1. **Research Market Trends**: Study the current market trends in storytelling and innovative technology.
2. **Develop a Prototype**: Create a prototype of the storytelling app or novel platform.
3. **Test and Refine**: Gather feedback from users and refine the product based on their input.
4. **Marketing Strategy**: Develop a comprehensive marketing strategy that highlights the unique storytelling elements of your product.

These ideas and steps should help you align your creative project with your personality style and goals. Feel free to refine or explore new ideas further.“`

Unlocking Creativity: How Make.com and Perplexity AI are Transforming YouTube Content Creation

**Revolutionizing Content Creation with Make.com and Perplexity AI**

In a significant advancement for content creators, Make.com has introduced a new scenario that leverages the power of Perplexity AI and ChatGPT to streamline the process of creating YouTube content. This innovative solution allows users to take any message from a specific Slack channel, process it through Perplexity AI, and then use ChatGPT to generate a YouTube title, description, and voice script. Additionally, it creates an image and puts all the content into a Trello card, making it ready for publication. This integration offers numerous benefits, including increased efficiency, enhanced creativity, and improved content quality.

### Key Features and Benefits

1. **Automated Content Generation**:
– **Perplexity AI**: The scenario uses Perplexity AI to process the Slack message and extract relevant information. This AI tool can understand and analyze the message, making it easier to generate high-quality content.
– **ChatGPT**: ChatGPT is then used to create a YouTube title, description, and voice script. This ensures that the content is well-structured and engaging.
– **Image Creation**: The scenario also generates an image related to the content, which can be used as a thumbnail or for other visual elements.

2. **Efficiency and Time-Saving**:
– By automating the content creation process, users can save significant time and effort. This is particularly useful for content creators who need to manage multiple projects simultaneously.
– The scenario can run in the background, allowing users to focus on other tasks while the content is being generated.

3. **Enhanced Creativity**:
– The integration of Perplexity AI and ChatGPT enables users to generate content that is both informative and engaging. The AI tools can suggest ideas and phrases that might not have been considered otherwise.
– The scenario can be set up to run automatically, ensuring that content is consistently created and published without manual intervention.

4. **Improved Content Quality**:
– The use of AI tools ensures that the content is well-researched and accurate. The AI can analyze the message and provide relevant information, which is then used to generate high-quality content.
– The scenario can be customized to meet specific content requirements, such as tone, style, and audience.

5. **Integration with Trello**:
– The generated content is placed into a Trello card, making it easy to manage and organize the content workflow.
– Trello cards can be used to track the status of the content, assign tasks, and collaborate with team members.

### How to Set Up the Scenario

To set up this scenario, users need to follow these steps:

1. **Create a Slack Channel**: Establish a Slack channel where users can post messages that will be processed by the scenario.
2. **Set Up Make.com**: Sign up for Make.com and create a new scenario.
3. **Connect Slack and Perplexity AI**: Connect the Slack channel to Perplexity AI using Make.com. This will allow Perplexity AI to process the messages from the Slack channel.
4. **Configure ChatGPT**: Configure ChatGPT to generate YouTube titles, descriptions, and voice scripts based on the processed messages.
5. **Integrate with Trello**: Integrate the scenario with Trello to create a card for each generated content piece.

### Conclusion

The integration of Make.com, Perplexity AI, and ChatGPT offers a powerful solution for content creators. By automating the content creation process, users can save time, enhance creativity, and improve content quality. The scenario’s ability to generate high-quality content and place it into a Trello card makes it an essential tool for anyone looking to streamline their content creation workflow.

From Concept to Creation: Building an AI-Powered No-Code App for Video Shorts in Just Three Days

**Creating a No-Code SaaS App in Three Days: A Journey**

Here’s my recent experience of building a no-code app in just three days. The app, available at [videothinktank.com](http://videothinktank.com “‌”), uses AI to generate video shorts ideas from longform video content. All you need to do is input a YouTube URL, and the app does the rest!

### The Idea

The idea for this app came to me when I realised how tedious it can be to repurpose long-form videos into multiple YouTube shorts. I wanted to create a tool that could automate this process, saving time and effort. With the help of AI, I set out to build an app that could transform a single YouTube video into multiple short clip ideas, ready for recording and posting to social media.

### The Building Process

I started by researching existing tools and platforms that could help me achieve this. I found that many of these tools were either too complex or too basic. I needed something that could handle the complexity of video content while being user-friendly.

### How It Works

After some trial and error, I worked out how to retrieve captions on any Youtube video through the HTTP module in [make.com](http://make.com “‌”) , turn that into JSON and then use a combination of chatGPT prompts to analyse the 5 most important points of the video and create new short form content ideas.

### The App’s Features

The app, [videothinktank.com](http://videothinktank.com “‌”), allows users to input a YouTube URL and generate multiple video shorts ideas. Here are some key features:

1. **AI-Powered Video Generation**: The app uses AI to analyze the long-form video content and generate multiple short clip ideas with scripts and descriptions. This ensures that the content is engaging and relevant for social media platforms.
2. **Payment Options**: The platform used allows for easy payment through Stripe. A user can Sign Up with either Google or manually with name and email and then pay through Stripe which gives them access to the /create-content page.
3. **Ease of Use**: The app is designed to be user-friendly, making it accessible to anyone who wants to repurpose their video content.

### The Results

After building the app, I tested it with various types of video content, including educational, motivational, and product review videos. The results were impressive. The AI-generated shorts ideas were engaging, well-structured, and ready for social media platforms.

### Conclusion

Building this app was a fun and challenging experience. The main benefit is that I didn’t have to code user management and payment integration. The data backend is simply a Google spreadsheet.

If you’re interested in seeing how the app works, I’d be happy to provide a demo or share more details about the process. Let me know in the comments below!

"Unlocking Success: 7 Compelling Reasons to Embrace Short-Form Content"

Here are seven different points and benefits for why you should create short-form content:

1. **Higher Engagement**: Short-form videos are 2.5 times more engaging than long-form videos, ensuring that audiences consume your entire message instead of tuning out halfway through a long video.

2. **Increased Shareability**: Short videos are universally appealing because they require minimal time investment from viewers. This brevity contributes to their potential for virality, making them more likely to be shared repeatedly across various platforms.

3. **Cost-Effective**: Short-form content is less expensive to produce compared to long-form content. It doesn’t take as long to produce, freeing up time for other marketing efforts and allowing businesses to publish a greater volume of content.

4. **Ideal Length**: Short-form content is the ideal length for the modern attention span, which is around 2.7 minutes on average. This makes it perfect for social media platforms where users scroll quickly through their feeds.

5. **Quick Conversions**: Short-form content can be consumed quickly, so consumers will reach your call-to-action and internal links faster, leading to quicker conversions.

6. **Easy to Absorb**: Shorter pieces lower commitment levels and encourage consumers to stick around till the end. This makes it easier for readers to absorb the information and stay engaged.

7. **Flexibility and Versatility**: Short-form content can be adapted or repurposed across multiple platforms without losing its effectiveness. This versatility maximizes its reach and impact, making it a powerful tool for content marketing.

"Exploring the Impact of Turing Natural Language Generation and RoBERTa in Natural Language Processing"

Microsoft’s Turing Natural Language Generation (T-NLG) and Facebook’s RoBERTa are both large language models that have made significant contributions to the field of natural language processing (NLP). Here are some key points about each model:

### Microsoft’s Turing Natural Language Generation (T-NLG)

– **Parameters**: T-NLG is a 17-billion-parameter language model, making it one of the largest models at the time of its release.
– **Applications**: It excels in various practical tasks, including summarization and question answering. It is also used for direct question answering and zero-shot question capabilities.
– **Development**: T-NLG was developed using the DeepSpeed library and ZeRO optimizer, which allowed for efficient training of large models.

### Facebook’s RoBERTa

– **Parameters**: RoBERTa is a transformer-based model that was trained on a massive amount of text data.
– **Improvements**: It improves on BERT’s language masking strategy by removing the next-sentence pretraining objective and using larger mini-batches and learning rates.
– **Performance**: RoBERTa achieved state-of-the-art results on several NLP benchmarks, including the General Language Understanding Evaluation (GLUE) benchmark, and matched the performance of XLNet-Large.

### Popularity

Both models are part of the larger family of transformer-based language models that have revolutionized NLP. They are widely used and cited in research and industry applications. The popularity of these models is evident from their extensive use in various tasks, including sentiment analysis, question-answering, text classification, and machine translation.

In summary, both T-NLG and RoBERTa are highly popular and influential models in the field of NLP, known for their large scale and advanced capabilities.

"Perplexity AI: Revolutionizing Conversational Search with Innovative Features and Ethical Challenges"

Perplexity AI, a cutting-edge AI-powered conversational search engine, has been making significant strides in the field of information discovery and retrieval. The company, founded in 2022 by a team of engineers with backgrounds in AI, machine learning, and back-end systems, has been rapidly expanding its capabilities and user base. As of Q1 2024, Perplexity AI had reached 15 million monthly users, a testament to its growing popularity and effectiveness in providing accurate, real-time answers to user queries.

### New Features and Models

Perplexity AI has introduced several new features and models in recent months, enhancing its capabilities and user experience. One of the most notable additions is the “Pages” feature, launched in May 2024. This feature allows users to generate customizable web pages based on user prompts, utilizing Perplexity’s AI search models to gather information and create research presentations that can be published and shared with others.

Additionally, Perplexity AI has launched a new enterprise version of its product in April 2024, catering to the needs of businesses and organizations looking to leverage advanced AI-driven research tools. The company has also introduced a new feature called “Focus,” which allows users to restrict their search to specific sources such as Reddit, YouTube, WolframAlpha, or academic research papers.

### Pro Search and Copilot

The Pro Search feature, available through the Pro subscription plan, engages users in a conversational search experience. It asks clarifying questions to refine queries and provides more detailed and context-aware results. This feature is particularly useful for users who need to delve deeper into specific topics, as it ensures that the search results are tailored to their needs.

Perplexity AI’s Copilot feature, part of the Pro Search experience, serves as a guided AI search assistant. It enhances the search experience by providing a personalized, real-time search experience with inline citations. This feature is available on both web browsers and mobile apps, making it accessible to a wide range of users.

### API and Integration

Perplexity Labs offers an API that allows developers to integrate the company’s AI capabilities into their own products and services. This API supports features such as natural language processing, web search, and content generation. Developers can use various programming languages and tools to interact with the API, making it a versatile tool for integrating AI functionalities into different applications.

### Ethical Concerns and Controversies

Despite its innovative features and growing user base, Perplexity AI has faced some ethical controversies. In June 2024, Forbes publicly criticized the company for using content from their articles without proper attribution. Forbes accused Perplexity of plagiarizing their content, citing a story that was largely copied from a proprietary Forbes article without mentioning or prominently citing Forbes. Similarly, Wired reported that Perplexity had ignored the Robots Exclusion Protocol to surreptitiously scrape areas of websites that publishers did not want bots to access.

### Future Outlook

As Perplexity AI continues to evolve and expand its capabilities, it remains a significant player in the AI-driven search engine landscape. With its focus on providing accurate, real-time answers and its commitment to innovation, the company is poised to maintain its position as a leader in the field. The introduction of new features like Pages and the enterprise version of its product underscores its dedication to meeting the diverse needs of users and businesses alike.

In conclusion, Perplexity AI’s latest models and features demonstrate its commitment to advancing the field of AI-powered search engines. While it faces some ethical challenges, its innovative approach and growing user base indicate a promising future for the company.

"Unveiling Llama 3.1 Sonar: A Major Leap in AI Search and Language Processing"

The Perplexity AI platform has recently introduced new models, specifically the “sonar-small-chat” and “sonar-medium-chat” models, along with their search-enhanced versions. These models are designed to improve the search functionality and enhance the user experience. In this article, we will delve into the details of the new “llama 3.1 Sonar” model and compare it with the older models.

## New Models: Llama 3.1 Sonar

The “llama 3.1 Sonar” model is a significant upgrade from the previous models. It is based on the Llama 3.1 70B architecture, which is known for its advanced language processing capabilities. This model is optimized for search and is designed to provide more accurate and relevant responses to user queries.

### Key Features of Llama 3.1 Sonar

1. **Advanced Language Processing**: The Llama 3.1 70B architecture is known for its ability to process complex language patterns and understand nuances in human communication. This allows the model to provide more accurate and contextually relevant responses.

2. **Search Optimization**: The model is specifically designed to enhance search functionality. It is trained to understand and respond to search queries more effectively, providing users with more relevant and accurate results.

3. **Enhanced Contextual Understanding**: The model is capable of understanding the context of a query and providing responses that are tailored to the user’s specific needs. This is particularly useful in scenarios where users need detailed and specific information.

### Differences from Older Models

The “llama 3.1 Sonar” model represents a significant departure from the older models in several key ways:

1. **Architecture**: The Llama 3.1 70B architecture is a major upgrade from the previous models. It is designed to handle more complex queries and provide more accurate responses.

2. **Search Functionality**: The new model is specifically optimized for search, which means it is better equipped to handle search queries and provide more relevant results.

3. **Contextual Understanding**: The model’s ability to understand context is significantly improved, allowing it to provide more tailored and accurate responses.

### User Feedback

User feedback on the new model has been mixed. Some users have reported that the model is able to provide thoughtful and intuitive responses, while others have noted that it can sometimes struggle with complex queries or provide inaccurate information. However, overall, the feedback suggests that the new model is a significant improvement over the older models.

## Conclusion

The introduction of the “llama 3.1 Sonar” model marks a major upgrade in the capabilities of the Perplexity AI platform. The model’s advanced language processing capabilities, search optimization, and enhanced contextual understanding make it a powerful tool for users seeking accurate and relevant information. While there are still some issues to be addressed, the new model represents a significant step forward in the development of AI-powered search and language processing technologies.

"Funding Frenzy: Meet Australia's Exciting New Start-Ups of 2024"

Several new Australian start-ups have emerged in 2024 with significant funding amounts. Here are some examples:

1. **Goterra**
– **Founder**: Olympia Yarger
– **Founded**: 2016
– **Raised**: $10 million
– **Industry**: Robotic insect farming

2. **Blossom**
– **Founders**: Gaby and Ali Rosenberg
– **Founded**: 2021
– **Industry**: Finance Technology
– **Raised**: Not specified

3. **Wander**
– **Founder**: Cassandra Sasso
– **Founded**: 2019
– **Industry**: Hospitality
– **Raised**: Not specified

4. **Cauldron**
– **Founder**: Michele Stansfield
– **Founded**: 2022
– **Raised**: $10.5 million
– **Industry**: Precision fermentation

5. **Silicon Quantum Computing**
– **Founder**: Michelle Simmons
– **Raised**: $50.4 million
– **Industry**: Healthcare, cybersecurity, and finance

6. **Andisor**
– **Founder**: Vandana Chaudhry
– **Raised**: $1 million
– **Industry**: E-commerce supply chain

7. **Apromore**
– **Industry**: Analytics, Business Intelligence
– **Raised**: $15 million
– **Recent Funding Date**: August 06, 2024

8. **TEAMology**
– **Industry**: Education
– **Raised**: $3 million
– **Recent Funding Date**: August 06, 2024

9. **Rich Data Co**
– **Industry**: Analytics, Artificial Intelligence
– **Raised**: $6 million
– **Recent Funding Date**: August 05, 2024

10. **Ohmie GO**
– **Industry**: Automotive, Electric Vehicles
– **Raised**: $1 million
– **Recent Funding Date**: July 31, 2024

11. **InvestorHub**
– **Industry**: FinTech, Information Technology
– **Raised**: $5 million
– **Recent Funding Date**: July 29, 2024

12. **Fundabl**
– **Industry**: FinTech
– **Raised**: $3 million
– **Recent Funding Date**: July 24, 2024

13. **ReciMe**
– **Industry**: Artificial Intelligence
– **Raised**: $997,473
– **Recent Funding Date**: July 2024

14. **Redactive**
– **Industry**: Artificial Intelligence, Data
– **Raised**: $11,500,000
– **Recent Funding Date**: July 2024

15. **DASH Technology**
– **Industry**: Automotive
– **Raised**: $13,347,490
– **Recent Funding Date**: July 2024

16. **Gelomics**
– **Industry**: Biotechnology
– **Raised**: $2,200,000
– **Recent Funding Date**: July 2024

17. **Marketboomer**
– **Industry**: E-commerce
– **Raised**: $3,258,414
– **Recent Funding Date**: July 2024

18. **Lombard**
– **Industry**: Finance
– **Raised**: $16,000,000
– **Recent Funding Date**: July 2024

19. **JigSpace**
– **Industry**: Augmented Reality
– **Raised**: Not specified
– **Recent Funding Date**: July 2024

20. **Sircel**
– **Industry**: Data, B2B Software
– **Raised**: $5,000,000
– **Recent Funding Date**: July 2024

21. **Fugu Carbon**
– **Industry**: Energy
– **Raised**: Not specified
– **Recent Funding Date**: July 2024

22. **KC8 Capture Technologies**
– **Industry**: Environment
– **Raised**: $6,741,951
– **Recent Funding Date**: July 2024

23. **EVOS Energy**
– **Industry**: EV, Automotive
– **Raised**: $2,698,558
– **Recent Funding Date**: July 2024

24. **Onilia Capital Partners**
– **Industry**: Finance, Investing
– **Raised**: $376,000
– **Recent Funding Date**: July 2024

25. **Consolidated Linen Service**
– **Industry**: Professional Services
– **Raised**: $4,047,837
– **Recent Funding Date**: July 2024

26. **Symphony**
– **Industry**: Augmented Reality
– **Raised**: $202,245,191
– **Recent Funding Date**: July 2024

27. **HammerTech Global**
– **Industry**: Construction
– **Raised**: $70,000,000
– **Recent Funding Date**: July 2024

28. **ExoFlare**
– **Industry**: Data, Analytics
– **Raised**: $3 million
– **Recent Funding Date**: July 2024

"Exploring the Evolution of Large Language Models: Key Players Shaping the Future of AI"

The world of large language models (LLMs) has seen significant advancements in recent years, driven by the continuous improvement in computer memory, dataset size, and processing power. Here are some of the latest and most influential LLM models:

## BERT
Introduced by Google in 2018, BERT is a transformer-based model that can convert sequences of data to other sequences of data. It features 342 million parameters and was pre-trained on a large corpus of data, then fine-tuned to perform specific tasks such as natural language inference and sentence text similarity. BERT was used to improve query understanding in the 2019 iteration of Google search.

## Claude
Claude is an LLM created by Anthropic, focusing on constitutional AI. It shapes AI outputs guided by principles to ensure the AI assistant is helpful, harmless, and accurate. The latest iteration is Claude 3.0.

## Cohere
Cohere is an enterprise AI platform that provides several LLMs, including Command, Rerank, and Embed. These models can be custom-trained and fine-tuned to a specific company’s use case. Cohere is not tied to a single cloud, unlike OpenAI, which is bound to Microsoft Azure.

## Ernie
Ernie is Baidu’s large language model, powering the Ernie 4.0 chatbot. Released in August 2023, it has garnered more than 45 million users and is rumored to have 10 trillion parameters. It works best in Mandarin but is capable in other languages.

## Falcon 40B
Developed by the Technology Innovation Institute, Falcon 40B is a transformer-based, causal decoder-only model trained on English data. It is available in two smaller variants: Falcon 1B and Falcon 7B (1 billion and 7 billion parameters). Amazon has made Falcon 40B available on Amazon SageMaker, and it is also available for free on GitHub.

## Llama
Llama is Meta’s LLM, released in 2023. The largest version is 65 billion parameters in size. Llama was originally released to approved researchers and developers but is now open source. It comes in smaller sizes that require less computational power.

## Semantic Kernel
The Microsoft Semantic Kernel is a tool that chains several LLM actions together. It can generate titles, fix grammar, create images, and convert text into a Quarto Markdown file. It has been used to improve the efficiency and organization of blog posts.

## ChatGPT
ChatGPT, which runs on a set of language models from OpenAI, attracted more than 100 million users just two months after its release in 2022. It is one of the most well-known language models today, known for its natural language processing capabilities.

These models have significantly advanced the field of natural language processing and are driving the generative AI boom. They are being used in a variety of applications, from generating text to creating image captions and even solving math problems and writing code.