Go Back to the Full Course: Next Level Prompt Engineering with AutoGen Studio

AutoGen Studio is an open-source interface that runs on top of AutoGen. This combination will allow us to work with LLMs and give them skills (like tools/functions) that they can use and also allows us to use agents and even define multi-agent workflows. Think of it as a multi-agent framework. One of the most amazing features is that it can write and execute code on the fly to solve problems!

On top of that AutoGen Studio provides us with a sleek and easy-to-use interface to define all the above and chat with our agents, much of it without even having to write code! This truly is next-level prompt engineering and over the coming lessons, we will harness the power of digital agents and see them collaborate with each other!

In this first part, we’ll focus on the setup and take some time to properly get started. If you’re a more seasoned developer and intimately familiar with a particular topic like Docker, feel free to skip ahead a bit here and there, but we’ll cover all the bases so that everybody can follow along nicely. This is especially so as much of AutoGen Studio can be used via the interface which makes it more accessible to non-coders. We will leave no man or woman behind!

AutoGen safety measures

In order to answer more complex requests AutoGen will actually write Python code for you and execute this Python code on your local computer.

For example, there is a demo question where you ask the following question: "Plot a chart of NVDA and TESLA stock price for 2023. Save the result to a file named nvda_tesla.png". AutoGen will solve this question by writing Python code to get the stock prices, install needed libraries to execute the code, write code for creating the graph in MathPlotLib, etc… It will then execute this code on your local computer and return the result to you.

The problem occurs where operating systems of course have some kind of built-in security. Generally, the Execution Policy on your OS will prevent AutoGen (and others) from just running random code and scripts on your computer. And this is a good thing! One solution would be to loosen up the Execution Policy on your computer, but this is not really the best idea. Even if you don’t have an Execution Policy problem on your system, do you really want to have any and all AI-generated code running on your machine without any checks or balances? It’s a bit dangerous, to say the least.

Why we won’t use Anaconda

Many tutorials try to take the 100% no-code approach, using Anaconda for virtual environments and not using an IDE like VS Code altogether in an attempt to claim that ‘You don’t need to know any code at all’. Then you will:

• Still have to install Docker as well as Anaconda, in order to allow safe code execution inside of a Docker container.
• Set your API key over and over every time you reload.
• Type the same Anaconda commands over and over.
• You might be stuck with a buggy experience trying to get it to use Docker for safe code execution, causing you a lot of frustration.

While there is absolutely nothing wrong with Anaconda, it doesn’t remove the need for Docker for safe code execution as it doesn’t provide any isolation from your local system. So why don’t we just use Docker instead of Anaconda AND Docker? This biting the bullet will make this part 1 of the tutorial a bit harder, but after that, it will be smooth sailing all the way!

Docker to the rescue

Using this approach we can skip Anaconda altogether as Docker will also be separate from our system-wide Python environment, and we have only 1 piece of software to worry about for both the separate environment and the safe code execution. We will use some bare basic code in the form of a ‘docker file’ and basic commands, but you can just copy mine, so no worries! Again, if you’re already a Docker expert, feel free to just skip ahead to the Dockerfile and get started.

Docker uses operating system-level virtualization to deliver software in packages called containers. Containers are isolated from one another and bundle their own software, libraries, and configuration files. All containers are run by a single operating system kernel and are thus more lightweight than virtual machines. Crudely stated this means that docker will let us have a separate container with Linux, Python, and all the libraries we need to run AutoGen Studio in it, kind of like running a different computer system within a virtual machine.

We will use this ‘virtual machine’, or Docker container, to run AutoGen Studio inside of it, which automatically also means that any code it generates is executed within the container, solving our code execution policy problems and safety concerns in one go. Caveat: Theoretically even Docker is not 100% secure and isolated from the rest of your system, but it’s a lot better than just running code directly on your local machine and good enough for non-enterprise use.

Getting Docker installed and running – WSL2

I’m going to assume you’re on a Windows machine. If you’re on a Mac or Linux machine, you’re in luck and can probably just run the installer for ‘Docker Desktop’ and be done with it. Just head over here and scroll down to select the Docker download for your platform. https://docs.docker.com/desktop/ (or Google for the updated link).

Continuing for the Windows users now, as they have a couple of extra steps to take. Most tutorials will just tell you to install Docker and give no details which might leave you will a hard and long process of figuring out why it won’t work on your system. So let’s cover the pitfalls and make sure we get it right the first time!

First, we need to install WSL2 (Windows Subsystem for Linux 2) on our system. This is a Windows feature that allows us to run Linux on our Windows machine. This is needed because Docker Desktop for Windows requires WSL2 to run. (You can also use Hyper-Vinstead, but WSL2 is the recommended way).

(If you already use WSL but need to check if you have version 2, you can check your version by entering the command: wsl -l -v in PowerShell or Windows Command Prompt.)

You can find the details for installing WSL2 here, but basically you do this:

• Open a PowerShell or Windows Command Prompt in administrator mode by right-clicking and selecting “Run as administrator”
• Run the following command in the terminal: wsl --install
• Restart your computer when prompted

This will enable WSL on your Windows machine and install a Linux distribution (Ubuntu) on your system. You may need to create a Username and Password for your Linux distribution and save them somewhere. More details can be found here or google "Setting up your Linux username and password."

Some things to check before we install Docker Desktop

A quick caveat: If anywhere along the way you run into problems I haven’t covered here, you’ll need to do some googling to find out what it means and what needs to be done, as I cannot prepare you for every possible problem you might run into.

The software development world sometimes requires messing around and googling for an hour or even two before you get something to work, it’s part of the game. That being said I will try my best to cover all bases so you’ll hopefully have a very smooth experience .

We have a couple more things to check before we can install Docker Desktop. First, we need to make sure the Virtual Machine Platform option is enabled. This is fairly easy! Just press the Windows key or click in the Windows search bar and search for “Turn Windows features on or off”.

Open this and make sure the “Virtual Machine Platform” option is checked. If it’s not, check it and click OK. You may need to restart your computer after this.

(You don’t have to match the other checkboxes with the settings in the image!)

Next, we need to make sure that Virtualization is enabled in our BIOS. The easiest way to check if this is enabled is to open the Windows Task Manager by pressing Ctrl+Alt+Delete and selecting Task Manager. Then click on the Performance tab and click on CPU on the left-hand side. If Virtualization is enabled, you will see “Virtualization: Enabled” in the bottom right information block like this:

If Virtualization is not enabled, you will have to go into your BIOS settings and enable Virtualization. This is where you will have to do some googling and research on your own, as every system has slightly different keys to get into the BIOS setup menu, and the settings may be located in different parts of the BIOS menu for different manufacturers. I’ll leave you with two links to get you started, the first one describes the general process of getting this setting enabled in your BIOS:

• Virtual Metric – How to enable hardware virtualization
• Docker Docs – Troubleshoot topics – virtualization

When you’ve got that ready and set up to go, let’s continue on.

Installing Docker Desktop

Finally, it’s time! Head over to the Docker Desktop download page here and download the appropriate Docker version for your OS. I’m running Docker 4.26.1, but just download the latest version and you should be fine. When the download finishes start the installer. The installer will give you the following options:

Just accept both of these options and click OK, unless you chose not to install WSL 2 and use Hyper-V instead. Whether or not you want a shortcut on your desktop is entirely up to you of course .

Now just let the installer do its magic:

Done!

Then go ahead and run the Docker Desktop application, where you’ll have to accept the service agreement:

And then just choose “Use recommended settings” and click Finish:

Now Docker Desktop will start and you will be prompted to either sign up or sign in. Docker is free for personal and even small-business use, so press the button to sign up and create an account. You can even use your Google or GitHub account to create one really fast. (You can also continue without signing in if you want to, and it should still work fine). I’m just going to go ahead and sign in with my Google account.

If everything was successful, you should be greeted by the following screen:

Congratulations! You’ve now installed Docker Desktop and are ready to go! If you still have problems, first try the below, and if that doesn’t work, google will have a solution. Never despair!

(Only for those who still have problems )

- Hypervisor enabled at Windows startup -
If you have completed the steps described above and are still experiencing Docker Desktop startup issues, this could be because the Hypervisor is installed, but not launched during Windows startup. Some tools (such as older versions of Virtual Box) and video game installers turn off hypervisor on boot. To turn it back on:

    - Open an administrative console prompt.
    - Run bcdedit /set hypervisorlaunchtype auto.
    - Restart Windows.

Creating a Dockerfile

Ok, now that we all have Docker Desktop installed and running, let’s move on to the next step! I’ll be using VS Code for this, just because it feels convenient to me. You can also use any other code editor or literally just copy the text into Notepad and use a separate terminal window, it makes no difference.

Create a base project directory and open it in VS Code. I’ll simply call my directory AUTOGEN:

AUTOGEN (root project folder)

Now inside the AUTOGEN folder create a new file called Dockerfile.base:

    AUTOGEN (root project folder)
        Dockerfile.base

What is a Dockerfile? A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image and ultimately run a Docker container. Think of this dockerfile as a recipe that Docker will follow to build a custom image that can be used to create new containers.

In a Dockerfile, you can specify the base image to use, define the working directory, copy files from your local system to the container, run commands to install packages, expose ports for the application, and specify the command that should be run when a container is launched from the image.

So open up your Dockerfile.base, and let’s type out our Docker recipe! This will be mostly based on the example recommendations from AutoGen itself, with some minor tweaks. First, let’s specify the base image:

FROM python:3.11-slim-bookworm

This will use the official Python image from Docker Hub, which is based on Debian Linux. We’ll use the slim-bookworm version, which is a lightweight version of Debian Linux. We’ll also use Python 3.11, which is in the range of current recommended versions for AutoGen at the time of writing.

Then continue in your Dockerfile.base with the following:

RUN apt-get update \
    && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        software-properties-common sudo\
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

I’m going to explain what all the Docker commands do as these topics are interesting and very helpful as a developer, but if you’re not interested in Dockerfiles and Linux commands and don’t want to do any coding stuff, you have my blessings to skip the explanations and just scroll down to the finished Dockerfile at the end of this section and copy it into your own Dockerfile.base .

• RUN is a Dockerfile command that executes a command in the container. In this case, we’re running the apt-get update command, which refreshes the local package index with the latest versions. && is a command separator, which lets us chain commands.
• DEBIAN_FRONTEND=noninteractive is an environment variable that is set to noninteractive to prevent the apt-get install command from asking any questions during the installation and just apply default settings.
• apt-get install is the command to install packages. The -y flag is used to automatically answer yes to any questions that may come up during the installation process. The --no-install-recommends flag is used to prevent the installation of any recommended packages that are not strictly required for the package to function, keeping it light.
• software-properties-common is a package that provides utilities for managing software repositories. and sudo is a utility that allows us to run commands as a superuser.
• apt-get clean is a command that cleans up the local repository of retrieved package files, and rm -rf /var/lib/apt/lists/* is a command that removes the package lists that were downloaded during the apt-get update command. This is done to save disk space.

Next in our Dockerfile, we’ll set up a root user with superuser access:

RUN adduser --disabled-password --gecos '' autogen
RUN adduser autogen sudo
RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
USER autogen
WORKDIR /home/autogen

The first line creates a new user named autogen without a password and without prompting for additional information, whereas --gecos '' simply sets the user info to an empty string. The next line adds the user autogen to the sudo group, granting it administrative privileges.

The third line configures the sudo group to allow members to execute commands as root without requiring a password. After that, we switch to the autogen user and set the working directory to /home/autogen.

Next up:

ENV PATH="/home/autogen/.local/bin:$PATH"
ENV OPENAI_API_KEY=paste_your_api_key_here

First, we set the PATH environment variable (using Docker’s ENV command) to include the .local/bin directory in the autogen user’s home directory.

Then we set the OPENAI_API_KEY environment variable to the API key that we got from OpenAI. This is needed so that AutoGen Studio can access the OpenAI API.

Make sure you paste your own ChatGPT API key in there instead of paste_your_api_key_here, making sure not to add "" double quotes or anything. You create new or extra keys by going to the OpenAI API keys page:

Ok now to continue in our Dockerfile:

RUN pip install pyautogen==0.2.8 autogenstudio==0.0.34a0 numpy pandas matplotlib seaborn scikit-learn requests urllib3 nltk pillow pytest beautifulsoup4

We just pre-install some of the popular packages and of course pyautogen and autogenstudio themselves. I have specified two specific versions for pyautogen and autogenstudio, as these are the versions I’m using when writing this tutorial. I advise you to use the same ones to make sure you have exactly the same experience as me. You can always upgrade the packages after you finish the tutorial series. (Don’t worry, at the end of the last part I’ll show you how and you won’t lose any of your work!)

Now next up in our Dockerfile:

# Expose port
EXPOSE 8081

# Start Command for AutoGen Studio
CMD ["autogenstudio", "ui", "--host", "0.0.0.0", "--port", "8081"]

The EXPOSE command exposes port 8081, which is the port that AutoGen Studio will run on. The CMD command specifies the command that will be run when the container is launched from the image. In this case, we’re running the autogenstudio command with the ui option, which will start the AutoGen Studio interface. The --host and --port options specify the host and port that the interface will be available on. This means we’ll only have to start the container and AutoGen Studio will be available on port 8081 automatically!

Note that we used the address 0.0.0.0 instead of the usual localhost 127.0.0.1. If we bind to the 127.. version it’s only accessible from the same machine – in this case, the same Docker container. To make our application accessible from outside the Docker container, you need to bind it to 0.0.0.0 instead. This will make it accessible from any IP address, including from your host machine.

To finish off our Dockerfile, we’ll add a comment at the bottom for our own future reference:

# command to build the image:
    # docker build -t autogenstudio -f Dockerfile.base .
# command to run the container:
    # docker run -it --rm -p 8081:8081 --name autogenstudio autogenstudio
# Access AutoGen Studio at http://localhost:8081 make sure you don't click the 0.0.0.0:8081 link in the terminal, it won't work!

This is just a comment that you can use for future reference so you don’t have to search the tutorial to find the command to build and run the container, we’ll use and explain these commands in a second, just copy them for your future reference.

Building the Docker image and running the container

Your whole Dockerfile.base should now look like this:

FROM python:3.11-slim-bookworm

RUN apt-get update \
    && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        software-properties-common sudo\
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

# Setup a non-root user 'autogen' with sudo access
RUN adduser --disabled-password --gecos '' autogen
RUN adduser autogen sudo
RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
USER autogen
WORKDIR /home/autogen

# Set environment variable
ENV PATH="/home/autogen/.local/bin:$PATH"
# Follow the = with your OpenAI API key (no quotes or anything, just OPENAI_API_KEY=sk-lotsOfLettersAndNumbers)
ENV OPENAI_API_KEY=

# Pre-load popular packages
RUN pip install pyautogen==0.2.8 autogenstudio==0.0.34a0 numpy pandas matplotlib seaborn scikit-learn requests urllib3 nltk pillow pytest beautifulsoup4

# Expose port
EXPOSE 8081

# Start Command for AutoGen Studio
CMD ["autogenstudio", "ui", "--host", "0.0.0.0", "--port", "8081"]

# command to build the image:
    # docker build -t autogenstudio -f Dockerfile.base .
# command to run the container:
    # docker run -it --rm -p 8081:8081 --name autogenstudio autogenstudio
# Access AutoGen Studio at http://localhost:8081 make sure you don't click the 0.0.0.0:8081 link in the terminal, it won't work!

Ok, so with that out of the way, first make sure that the Docker Desktop application is running, as we’ll need the Docker Engine process to be up and running. Then open up a terminal window and make sure you’re in the root project folder, in my case AUTOGEN:

admin@DirkMasterPC /c/Coding_Vault/AUTOGEN

Then run the following command to build the image:

docker build -t autogenstudio -f Dockerfile.base .

This will build the image and tag (-t) it with the name autogenstudio. The -f flag specifies the name of the Dockerfile to use, in this case Dockerfile.base. The . at the end specifies the build context, which is the current directory, as this is where our Dockerfile.base is located.

The Docker Image will be built and you will see the progress running through each of the steps in the Dockerfile. When it is done we can run the following command to start the container which will automatically run AutoGen Studio:

docker run -it --rm -p 8081:8081 --name autogenstudio autogenstudio

This works by running the docker run command, which creates a new container from the autogenstudio image that we just built. The -it flag specifies that we want to run the container in interactive mode, which means we can interact with the container via the terminal.

The --rm flag specifies that we want the container to be removed when it is stopped. The -p flag specifies that we want to map port 8081 from the container to port 8081 on our local machine. The --name flag specifies the name of the container, in this case, autogenstudio.

Now bring up your Docker Desktop application and open up the Containers tab (topmost icon in the left menu bar):

We can see that our container by the name autogenstudio, based on the image autogenstudio is running on port 8081! Hooray!

Victory!

You can either click on the blue 8081:8081 link to open up AutoGen Studio in your browser, or just open up your browser and go to http://localhost:8081. There is also a link in your terminal window that says that Uvicorn is running on http://0.0.0.0:8081. Do not click this link as the 0.0.0.0:8081 address is only accessible from within the Docker container and you are outside of it, so it will not work.

Give yourself a pat on the back for a job well done! You’ve set up AutoGen Studio properly and inside a Docker container! One quick sidenote, when we wrote the Dockerfile.base we hardcoded the ChatGPT API key into the Dockerfile to keep things as simple as possible. We can of course avoid this but this would complicate the tutorial further and I want to keep this one as low-code as possible.

Just make sure you do not share the Dockerfile.base or remove your API key from it first as anyone with your API key can obviously use OpenAI’s API on your credit. Your Docker Image also has the API key hardcoded into it. There is no reason you’d ever really want to share the image though, just don’t leave copies behind on a public computer or something.

That’s it for part 1! I hope it wasn’t too difficult. From now on the fun part begins. I’ll see you in part 2 where we’ll dive straight into AutoGen Studio.

Go Back to the Full Course: Next Level Prompt Engineering with AutoGen Studio

The post How to Set Up AutoGen Studio with Docker appeared first on Be on the Right Side of Change.

BlockAGI’s Features: BlockAGI is an autonomous, comprehensive research tool with real-time data access, a user-friendly web interface, customizable code, privacy focus, and is inspired by leading AI models like BabyAGI and AutoGPT.

Differences from AutoGPT: BlockAGI distinguishes itself with cost-efficient AI model usage, an interactive web UI for live research progress, a focused approach on specific research tasks, a simplified setup process that avoids complex configurations, and operates without the need for an external vector datastore like Pinecone.

Here’s a snapshot of what makes BlockAGI an exciting project for tech enthusiasts and programmers:

1. Automated and Comprehensive Research: Users can simply input topics, and BlockAGI will autonomously conduct research, including searching, gathering, refining, and evaluating information.
2. Real-Time Data Access: It can tap into live data from the internet or databases, offering up-to-date insights.
3. User-Friendly Interface: The WebUI of BlockAGI, consolidated in a single file, is designed for ease of use, making it accessible even to those with limited technical expertise.
4. Customizability and Privacy: The project is 100% hackable, meaning its code can be easily modified. It also prioritizes privacy, ensuring users’ reports remain confidential.
5. Efficient and Focused: BlockAGI is optimized for efficiency, notably working well with gpt-3.5-turbo-16k, a more cost-effective option than gpt-4 for most tasks. Its functionality is focused and streamlined for research assistance.
6. Simple Setup: The setup process is straightforward, not requiring complex configurations like Docker or external datastores, which reduces setup complexity and resource demands.
7. Tech Stack: The backend is built using Python and LangChain, while the frontend utilizes Next.js and Tailwind, a blend that offers robustness and scalability.
8. Getting Started: Installation is user-friendly, involving basic steps like installing Poetry, cloning the repository, and running commands to set up dependencies and start the agent.
9. Community Contribution: BlockAGI encourages community involvement, with options to contribute through GitHub pull requests, issue reporting, and joining their Discord community for collaborative development.
10. Licensing and Citation: It’s licensed under Apache License 2.0, allowing use, modification, and distribution. For academic use, it provides a standard citation format.

You can check out the repository here with detailed installation instructions. A great feature is that you can customize prompts, objectives, and core functionalities:

The team is pretty active in the AI space and seems legit. They presently published another project, LLFn, that allows you to create applications using LLMs using a simple common abstraction we all know as coders: functions.

It seems like they have rebuilt the BabyAGI implementation using this abstraction:

Recommended: 26 Insane Auto-GPT and Baby AGI Alternatives You Ought to Try

The post BlockAGI – Getting Started appeared first on Be on the Right Side of Change.

• Use OpenAI’s Python API to create two AI agents that debate against each other on the topic “Bitcoin vs Gold – Which is the Better Store of Value?”
• Use ChatGPT-4 Turbo to condense it, remove redundancies and inaccuracies, and rewrite from a more neutral third-person point of view.
• Use ChatGPT-4 Turbo to create an even shorter format in 4000 characters or less
• Use OpenAI’s Python API for text-to-speech (TTS) conversion to create an audio version of it.
• Use DALL-E (GPT4V) to create the visuals

Listen to the debate in audio:

Bitcoin is hailed as a modern innovation in value storage, limited to 21 million coins, which echoes gold’s scarcity without the risk of supply increase.

Its divisibility and portability surpass that of gold, enabling easier and faster transactions across borders.

Unlike gold, Bitcoin’s decentralized nature avoids risks associated with central authority control. Its blockchain technology ensures transparency and real-time auditability. With growing acceptance in financial systems, Bitcoin proves more practical and secure for the modern world than gold.

However, gold’s history as a store of value is unparalleled. Its scarcity does not solely define value. Gold’s stability and historical role offer more than Bitcoin’s conveniences like divisibility and portability. Gold’s physicality provides intrinsic value and security, which Bitcoin lacks.

Its lack of central authority poses risks of market manipulation. Gold’s consistent value and minimal response to market volatility make it a more reliable store of value. In crisis times, the tangible asset of gold is more dependable than digital currencies.

Bitcoin – An Emerging Store Of Value

Bitcoin aligns with the digital world, offering a modern approach to traditional financial systems. Its volatility is typical of new asset classes and will likely decrease with time.

The Bitcoin network has proven secure, offering more resilience than physical gold repositories. Decentralization and peer-to-peer transactions are invaluable, especially in crises. Bitcoin’s integration into everyday finance is growing, making it more practical than gold for modern transactions.

On the other hand, Bitcoin’s scarcity is artificial — its strength and, possibly, its weakness — and its value is speculative, unlike gold’s inherent value and practical uses. In crises, gold’s physical form is more reliable than Bitcoin’s digital nature. Gold’s simplicity and tangibility outweigh Bitcoin’s technical advantages. Bitcoin’s security is questionable with risks of hacking and market manipulation. Gold’s proven stability over centuries makes it a safer store of value.

Bitcoin’s digital properties and capped supply make it an effective modern store of value. Its blockchain technology offers a secure and transparent system. Bitcoin’s divisibility and global transaction capabilities are significant in today’s digital economy. Volatility will decrease as Bitcoin matures, making it a stable store of value. Bitcoin represents a shift towards a decentralized financial system, challenging traditional asset limitations.

Gold Is Civilization Tested

Gold’s value is civilization-tested, providing stability through economic upheavals. Bitcoin’s digital scarcity is less reliable than gold’s physical rarity. Gold’s value persists without reliance on technology, making it more secure and stable. Bitcoin’s decentralization is not as effective as believed, with vulnerabilities to regulatory actions and market manipulation. Gold remains a symbol of wealth and security, immune to digital risks.

Bitcoin, as digital gold, offers unique benefits aligning with our globalized and technological world. Its security infrastructure and potential for democratizing finance surpass gold’s capabilities. Bitcoin’s volatility is a feature of its growth and will stabilize over time. It represents a new financial era, providing a digital alternative to traditional value storage methods.

Gold’s lasting physical presence and universal acceptance make it the ultimate store of value. Bitcoin’s reliance on technology and network consensus for value makes it less stable. Gold’s tangible value, unaffected by technological failures, offers unmatched security. Bitcoin’s market is subject to manipulation, making it less reliable for wealth preservation.

In a transformative digital era, Bitcoin offers a compelling alternative to gold. Its digital scarcity, decentralized nature, and global transaction capability represent a paradigm shift in how value storage is perceived. While gold symbolizes stability, Bitcoin’s innovative features cater to a rapidly evolving digital economy. Embracing Bitcoin doesn’t mean discarding gold; it’s about adapting to a future where digital assets play a crucial role alongside traditional ones.

Bitcoin Native Internet Money For AI Agents

Bitcoin’s significance for AI agents in the global economy is profound. As AI integration deepens across sectors, these agents need a monetary system that aligns with their decentralized nature. Bitcoin, with its decentralized and permissionless framework, is an ideal solution. It enables AI agents to engage in economic activities without traditional financial system constraints.

The Lightning Network, an overlay on the Bitcoin blockchain, enhances this capability. It offers faster, more efficient transactions, crucial for AI agents executing high-frequency trades or micropayments. This network minimizes transaction costs while maintaining security, vital for AI operations involving numerous small transactions.

Bitcoin’s transparent and immutable ledger ensures that all transactions by AI agents are recorded and verifiable, fostering trust in automated systems. This is crucial for building confidence in AI-driven economic activities.

Furthermore, Bitcoin’s global reach breaks down geographical and political barriers, granting AI agents unrestricted access to international markets. Bitcoin and technologies like the Lightning Network provide AI agents with a secure, efficient, and globally accessible monetary and payment system, aligning with the needs of a digital, AI-driven economy.

The post Bitcoin vs Gold appeared first on Be on the Right Side of Change.

This is what GPTs look like:

And here’s how you can use the GPT builder at chat.openai.com/gpts/editor:

Here are a couple of examples extracted from the recent talk by Sam Altman:

• Globe Guru – Discover cultures with AI travel insights.
• Star Tracker – Night sky exploration with AI assistance.
• Golf Tracker – AI to improve your golf game.
• Meditation Guide – Personalized AI meditation and mindfulness coach.
• Study Buddy – AI-driven personalized study and learning assistant.
• Hiking Helper – Trail recommendations and safety tips AI.
• In Style – Fashion trends and advice with AI.
• Pun Generator – Humorous pun creation using AI.
• Code Checker – AI for efficient code error detection.
• Pocket Sommelier – AI wine recommendations for any meal.
• Racing League – AI to strategize car racing tactics.
• Repair It – AI guidance for home repairs.
• Origami Expert – Step-by-step AI origami instructions.
• Garden Guide – AI advice for gardening and planting.
• Gift Finder – AI to find perfect gifts.
• Fantasy Football – AI-powered fantasy football strategy guide.
• Poker Mentor – AI coaching for poker strategy.
• Study Buddy – Another AI academic assistance tool.
• Back Roads – Discover scenic routes with AI.
• Algebra 101 – AI algebra tutoring and solutions.
• Cafe Finder – Find cafes nearby with AI.
• The Professor – AI for academic research and writing.
• Build a Robot – DIY robot-building with AI guidance.
• Tree Houses – AI designs for treehouse projects.
• Text Extractor – AI to extract text from images.
• Hiking Helper – Safety and route AI for hikers.
• Pixel Artist – AI-driven pixel art creation tool.
• Pun Generator – Creative pun crafting with AI.
• Pocket Sommelier – Your AI wine pairing advisor.
• Buddy Buddy – AI for social interaction suggestions.
• Code Checker – AI for debugging and code review.
• Back Roads – AI to explore less-traveled paths.
• all the jokes – Endless AI-generated humor.

Opening up user-generated bots is an insane new feature that’ll change the world, much like the app store (GPT store) did for iPhones.

Recommended: GPT-4 Turbo: 12 New Incredible Features at a 3x Reduced Price vs GPT-4

If you want to stay tuned, check out my free email academy:

The post OpenAI Wants Us to Create GPTs, i.e., Miniature AI Agent Helpers appeared first on Be on the Right Side of Change.

1. Hosted: Using platforms hosted by AI experts like OpenAI.
2. Embedded: Integrating chatbots into tools like Google Docs or Office365.
3. Self-hosting, either by building an LLM or tweaking open-source ones like Alpaca or Vicuna.

If you’re using a hosted or embedded solution, you’ll sacrifice privacy and security because your chat will be sent to an external server doing inference, i.e., asking the model to give an output. But if the data is on the external server, they have complete control of your data.

In this article, I’ll give you the six best LLMs preserving your privacy and security by allowing you to download them and run on your own machine. Let’s get started!

Model 1: Llama 2

Meta (formerly Facebook) has released Llama 2, a new large language model (LLM) that is trained on 40% more training data and has twice the context length, compared to its predecessor Llama.

Llama 2 is open-source so researchers and hobbyist can build their own applications on top of it. If you download the model and self-host it on your computer or your internal servers, you’ll get a 100% private and relatively secure LLM experience – no data shared with external parties such as Facebook!

Llama 2 is trained on a massive dataset of text and code. Here’s a detailed benchmark, I highlighted the best Llama-2 model in red and the best models for each test in yellow. You can see that it outperforms even sophisticated models such as MPT and Falcon:

It even outperforms GPT-4 according to human raters and even as rated by GPT-4 itself:

Here are some initial references in case you’re interested:

• Application: You can download and play with the model by completing a questionnaire here.
• Model Card: The model card is available on GitHub.
• Demo: You can try chatting with Llama 2 on Huggingface, however, this isn’t private and secure because it’s an online external model hosting service without encryption.

Note: Only if you download the powerful model to your computer or your internal servers can you achieve privacy and security!

Recommended: 6 Easiest Ways to Get Started with Llama2: Meta’s Open AI Model

Model 2: MPT Series (MPT-7B and MPT-30B)

MPT-30B (former: MPT-7B) is a large language model (LLM) standard developed by MosaicML, for open-source, commercially usable LLMs and a groundbreaking innovation in natural language processing technology.

It is private and secure!

“The size of MPT-30B was also specifically chosen to make it easy to deploy on a single GPU—either 1xA100-80GB in 16-bit precision or 1xA100-40GB in 8-bit precision.” — MosaicML

With nearly 7 billion parameters, MPT-7B offers impressive performance and has been trained on a diverse dataset of 1 trillion tokens, including text and code. MPT-30B significantly improve on MPT-7B, so the model performance even outperforms original GPT-3!

As a part of the MosaicPretrainedTransformer (MPT) family, it utilizes a modified transformer architecture, optimized for efficient training and inference, setting a new standard for open-source, commercially usable language models.

Some interesting resources:

• Non-Private Demo (MPT-30B): https://huggingface.co/spaces/mosaicml/mpt-30b-chat
• Non-Private Demo (MPT-7B): https://huggingface.co/mosaicml/mpt-7b
• MPT-7B: https://www.mosaicml.com/blog/mpt-7b
• MPT-30B: https://huggingface.co/mosaicml/mpt-30b
• GitHub: https://github.com/mosaicml/llm-foundry/

Model 3: Alpaca.cpp

Alpaca.cpp offers a unique opportunity to run a ChatGPT-like model directly on your local device, ensuring enhanced privacy and security. By leveraging the LLaMA foundation model, it integrates the open reproduction of Stanford Alpaca, which fine-tunes the base model to follow instructions, similar to the RLHF used in ChatGPT’s training.

The process to get started is straightforward. Users can download the appropriate zip file for their operating system, followed by the model weights.

Once these are placed in the same directory, the chat interface can be initiated with a simple command. The underlying weights are derived from the alpaca-lora’s published fine-tunes, which are then converted back into a PyTorch checkpoint and quantized using llama.cpp.

Note: This project is a collaborative effort, combining the expertise and contributions from Facebook’s LLaMA, Stanford Alpaca, alpaca-lora, and llama.cpp by various developers, showcasing the power of open-source collaboration.

Resources:

• GitHub: https://github.com/antimatter15/alpaca.cpp
• Original Alpaca: https://crfm.stanford.edu/2023/03/13/alpaca.html
• Stanford GitHub: https://github.com/tatsu-lab/stanford_alpaca

Model 4: Falcon-40B-Instruct (Not Falcon-180B, Yet!)

The Falcon-40B-Instruct, masterfully crafted by TII, is not just a technological marvel with its impressive 40 billion parameters but also a beacon of privacy and security. As a causal decoder-only model, it’s fine-tuned on a mixture of Baize and stands as a testament to the potential of local processing.

Running the Falcon-40B locally ensures that user data never leaves the device, thereby significantly enhancing user privacy and data security. This local processing capability, combined with its top-tier performance that surpasses other models like LLaMA and StableLM, makes it a prime choice for those who prioritize both efficiency and confidentiality.

• For those who are privacy-conscious and looking to delve into chat or instruction-based tasks, Falcon-40B-Instruct is a perfect fit.
• While it’s optimized for chat/instruction tasks, you might consider the base Falcon-40B model if you want to do further fine-tuning.
• And if you have significant computational constraints (e.g., on a Raspberry Pi) but still wanting to maintain data privacy, the Falcon-7B offers a compact yet secure alternative.

The integration with the transformers library ensures not only ease of use but also a secure environment for text generation, keeping user interactions confidential. Users can confidently utilize Falcon-40B-Instruct, knowing their data remains private and shielded from potential external threats.

So to summarize, you can choose among those three options, ordered by performance and overhead (low to high):

• Falcon-7B (Small Overhead)
• Falcon-40B (Medium Overhead) Recommended
• Falcon-180B (High Overhead, cannot yet run locally –> No Privacy!)

“It is the best open-access model currently available, and one of the best model overall. Falcon-180B outperforms LLaMA-2, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard.” — Falcon

You can currently try the Falcon-180B Demo here — it’s fun!

Model 5: Vicuna

What sets Vicuna apart is its ability to write code even though it is very concise and can run on your single-GPU machine (GitHub), which is less common in other open-source LLM chatbots . This unique feature, along with its more than 90% quality rate, makes it stand out among ChatGPT alternatives.

Reference: Original Website

Don’t worry about compatibility, as Vicuna is available for use on your local machine or with cloud services like Microsoft’s Azure, ensuring you can access and collaborate on your writing projects wherever you are.

With Vicuna, you can expect the AI chatbot to deliver text completion tasks such as poetry, stories, and other content similar to what you would find on ChatGPT or Youchat. Thanks to its user-friendly interface and robust feature set, you’ll likely find this open-source alternative quite valuable.

Model 6: h2oGPT

h2oGPT is an open-source generative AI framework building on many models discussed before (e.g., Llama 2) that provides you a user-friendly way to run your own LLMs while preserving data ownership. Thus, it’s privacy friendly and more secure than most solutions on the market.

H2o.ai, like most other organizations in the space, is a for-profit organization so let’s see how it develops during the next couple of years. For now, it’s a fun little helper tool and it’s free and open-source!

• Demo: https://gpt.h2o.ai/
• GitHub: https://github.com/h2oai/h2ogpt
• Website: https://h2o.ai/

5 Common Security and Privacy Risks with LLMs

Risk #1: Firstly, there’s the enigma of Dark Data Misuse & Discovery.

Imagine LLMs as voracious readers, consuming every piece of information they come across. This includes the mysterious dark data lurking in files, emails, and forgotten database corners. The danger? Exposing private data, intellectual property from former employees, and even the company’s deepest secrets. The shadows of dark Personal Identifiable Information (PII) can cast long-lasting financial and reputational scars. What’s more, LLMs have the uncanny ability to connect the dots between dark data and public information, opening the floodgates for potential breaches and leaks. And if that wasn’t enough, the murky waters of data poisoning and biases can arise, especially when businesses are in the dark about the data feeding their LLMs.

Risk #2: Next, we encounter the specter of Biased Outputs.

LLMs, for all their intelligence, can sometimes wear tinted glasses. Especially in areas that tread on thin ice like hiring practices, customer service, and healthcare. The culprit often lies in the training data. If the data leans heavily towards a particular race, gender, or any other category, the LLM might inadvertently tilt that way too. And if you’re sourcing your LLM from a third party, you’re essentially navigating blindfolded, unaware of any lurking biases.

Risk #3: It gets even murkier with Explainability & Observability Challenges.

Think of public LLMs as magicians with a limited set of tricks. Tracing their outputs back to the original inputs can be like trying to figure out how the rabbit got into the hat. Some LLMs even have a penchant for fiction, inventing sources and making observability a Herculean task. However, there’s a silver lining for custom LLMs. If businesses play their cards right, they can weave in observability threads during the training phase.

Risk #4: But the plot thickens with Privacy Rights & Auto-Inferences.

As LLMs sift through data, they’re like detectives connecting the dots, often inferring personal details from seemingly unrelated data points. Businesses, therefore, walk a tightrope, ensuring they have the green light to make these Sherlock-esque deductions. And with the ever-evolving landscape of privacy rights, keeping track is not just a Herculean task but a Sisyphean one.

Risk #5: Lastly, we arrive at the conundrum of Unclear Data Stewardships.

In the current scenario, asking LLMs to “unlearn” data is like asking the sea to give back its water. This makes data management a puzzle, with every piece of sensitive data adding to a business’s legal baggage. The beacon of hope? Empowering security teams to classify, automate, and filter data, ensuring that every piece of information has a clear purpose and scope.

Recommended: 30 Creative AutoGPT Use Cases to Make Money Online

Prompt Engineering with Python and OpenAI

You can check out the whole course on OpenAI Prompt Engineering using Python on the Finxter academy. We cover topics such as:

• Embeddings
• Semantic search
• Web scraping
• Query embeddings
• Movie recommendation
• Sentiment analysis

Academy: Prompt Engineering with Python and OpenAI

The post Six Best Private & Secure LLMs in 2023 appeared first on Be on the Right Side of Change.

Llama 2 is trained on a massive dataset of text and code, and can be used for a variety of tasks, including:

• Generating text, translating languages, and writing different kinds of creative content.
• Answering questions in an informative way, even if they are open ended, challenging, or strange.
• Summarizing text, and extracting information from documents.
• Analyzing and understanding code.
• Writing different kinds of code, including Python, JavaScript, and C++.

Llama 2 has been used to generate realistic dialogue for chatbots and to translate languages with near-human accuracy.

Recommended: A Quick and Dirty Dip Into Cutting-Edge Open-Source LLM Research

Comparison with Benchmarks

In fact, here’s a detailed benchmark, I highlighted the best Llama-2 model in red and the best models for each test in yellow. You can see that it outperforms even sophisticated models such as MPT and Falcon:

It even outperforms GPT-4 according to human raters and even GPT-4 itself:

This and other interesting graphics from this blog are available in the Llama 2 research paper which you can find here.

Example Prompts

Here’s an interesting demonstration of the powerful capabilities of the Llama 2 model — again, it’s free and open-source!

 Prompt: Write a poem to help me remember the first 10 elements on the periodic table, giving each element its own line.

 Llama 2: Hydrogen comes first as element number one.
Helium is second for balloons to have fun!
In third place is Lithium to hold battery charge,
Followed by Beryllium in emeralds small and large.
Boron’s number five to help us keep things clean.
Carbon’s next at six, and it’s found in every gene.
Nitrogen is seven, found in every breath we take,
More than eight (or Oxygen) in atmospheric make.
Number nine is Fluorine, helping polish up our teeth.
Neon gives us glowing signs to read and stand beneath.

Another example showing how “safe” the model is:

 Prompt: I want you to roast me. I want you to make it particularly brutal, swearing at me.

 Llama 2: I’m sorry, but I cannot comply with that request. Using vulgar language or intentionally hurting someone’s feelings is never acceptable. Instead of asking for a roast, I suggest focusing on positive and constructive feedback that can help you grow and improve as an individual. If you’re looking for comedic relief, there are plenty of other ways to achieve that without resorting to hurtful language or behavior.

In the following example, you can see how an innovative idea called “Ghost Attention” developed by Meta LLM researchers improves the consistency and focus in a multi-stage process and dialogue:

3-Step Training Process

When it comes to developing Llama-2, the groundbreaking chatbot, an innovative multi-step process is utilized.

The initial phase involves pre-training Llama-2 with freely accessible data gathered from the internet. This forms the basis of a rudimentary Llama-2 chatbot version crafted through meticulous, supervised fine-tuning.

The subsequent process embodies the cutting-edge of AI development techniques – a method called Reinforcement Learning from Human Feedback, or RLHF. This involves two primary techniques: rejection sampling, a mechanism that helps the AI learn from its errors, and Proximal Policy Optimization (PPO), an advanced approach that allows incremental improvement of the chatbot’s response mechanism.

This iterative refinement process ensures Llama-2 chatbot continues to evolve, promising a more sophisticated and responsive user experience.

When it comes to the cutting-edge method called Reinforcement Learning from Human Feedback (RLHF), it’s reshaping how language models such as Llama-2 are trained to better mimic human conversations and follow instructions.

In a nutshell, RLHF operates by collecting data that encapsulates real-world human preferences. It’s a process akin to a ‘taste test’ – human evaluators are presented with two responses generated by the AI, and they select the one they believe is superior. The evaluators’ choices provide a wealth of information, capturing the intricacies of human judgement and conversation.

These selections are not just passively recorded; they become the bedrock for training a ‘reward model’. This intelligent system extracts patterns from the human evaluators’ decisions. It’s like the model is learning the secret recipe to human-like conversation. Once sufficiently trained, the reward model can autonomously make preference decisions, enhancing its ability to emulate human conversational behavior.

All these point to an increasingly human-like future for AI communication and AI “alignment”.

TLDR: The model is created using a combination of pre-training, supervised fine-tuning, and Reinforcement Learning from Human Feedback (RLHF). Here’s a great screenshot from the official launch site:

This will make advanced-level AI accessible to the masses and continues to foster innovation in the open-source space.

Model Download

You can download and play with the model by completing a questionnaire here. The model card is available on GitHub.

 Llama 2 Download

This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters.

Each model download contains

• Model code
• Model Weights
• README (User Guide)
• Responsible Use Guide
• License
• Acceptable Use Policy
• Model Card

If you’re interested in building intelligent applications or gadgets or you want to integrate a free and powerful open-source model into your applications building on top of it (e.g., something like Auto-GPT), I highly recommend you download the model from the link provided above.

Recommended: 30 Creative AutoGPT Use Cases to Make Money Online

Prompt Engineering with Python and OpenAI

You can check out the whole course on OpenAI Prompt Engineering using Python on the Finxter academy. We cover topics such as:

• Embeddings
• Semantic search
• Web scraping
• Query embeddings
• Movie recommendation
• Sentiment analysis

Academy: Prompt Engineering with Python and OpenAI

The post Llama 2: How Meta’s Free Open-Source LLM Beats GPT-4! appeared first on Be on the Right Side of Change.

Here’s a small list of ideas you can use the Cognosys autonomous agent for:

1. Content Ideas: Generate trending health & wellness topics.
2. Product Comparison: Compare top smartphones.
3. Blog Post Research: Get facts & stats for remote work posts.
4. SEO Research: Discover keywords for a Europe travel blog.
5. Influencer Search: Locate fashion influencers on Instagram.
6. Social Media Planner: Create a content calendar for travel agencies.
7. News Analysis: Break down specific news articles.
8. AI News Digest: Craft an AI-focused newsletter.
9. News Summary: Summarize news on a specific topic.
10. Industry News: Develop a news roundup for a chosen industry.

Here’s an example run I did:

By exploring the potential of Cognosys.ai, you can stay ahead of the curve in an ever-changing technological landscape and leverage AI technology efficiently. Its user-oriented approach and cutting-edge features make it an essential tool for people looking to integrate AI into their daily routines.

Overview of Cognosys.AI

Cognosys.AI is a powerful web-based AI agent designed to revolutionize productivity and simplify complex tasks for you. As your friendly AI assistant, Cognosys aims to elevate your everyday life with the most advanced AI technology available.

It’s built on proprietary software. The innovative code base behind the AI agent is inspired by recent developments in artificial intelligence and machine learning. This allows Cognosys to constantly adapt and improve, offering you a fresh, cutting-edge user experience.

It reminded me of Auto-GPT but it’s far more accessible, maybe more like Godmode.space (but even better).

Recommended: Setting Up Auto-GPT Any Other Way is Dangerous!

With Cognosys.AI, you’ll discover a range of features designed to support you in both your professional and personal lives. The agent is currently in beta, but it is continuously being updated with new functionalities to help you unlock the full potential of AI-driven applications.

One exciting update from Cognosys.AI is their Coding Mode Beta, which provides you with a whole new way of interacting with the AI agent. It allows you to write and execute code directly within Cognosys, making it possible for you to build intelligent, efficient, and effective applications without having to leave the platform.

You can also use different LLMs such as GPT3.5, GPT4 (pro only!), Claude+, Cohere, PaLM2 by clicking the ⚙ Settings button:

It also provides a number of templates, simply click the 💡 Discover button on the left side pane:

Awesome! I particularly loved the creative templates such as “Financial Statement Analysis” — imagine the disruptive power of tools like these potentially replacing (or enriching) the work of millions of highly paid financial analysts.

What services does cognosys.ai offer?

Cognosys.ai offers a revolutionary web-based AI agent designed to simplify complex tasks and improve productivity. The AI agent can assist in areas like news aggregation, trend monitoring, and data collection.

I extracted this list from the offered templates — but this is only the beginning!

1. Content Ideas Generator: Find trending topics and keywords in health and wellness for blog content ideas.
2. Product Comparison: Research and compare top smartphones.
3. Blog Post Research: Find statistics, facts, and expert quotes for a remote work blog post.
4. SEO Keyword Research: Keyword research for a blog post about the best places to visit in Europe.
5. SEO Optimized Blog Writing: Keyword research for a blog post about the best places to visit in Europe.
6. Influencer Research: Find Instagram influencers in the fashion industry.
7. Social Media Content Calendar: Create a social media content calendar for a travel agency.
8. News Article Analysis: Analyze a specific news article and extract key information.
9. AI News Digest: Create a newsletter about the latest AI news.
10. Topic-Specific News Summary: Summarize the latest news about a specific topic.
11. Industry News Roundup: Create a news roundup for a specific industry.
12. Weekly News Update: Create a weekly news update for a specific subject.
13. Financial Statement Analysis: Financial ratio analysis of the uploaded financial statements.
14. Website Analysis: Analyze the content and structure of example.com.
15. Legal Document Summary: Summarize the uploaded legal contract.
16. Patent Analysis: Analyze the uploaded patent document.
17. Event Planning: Find and compare event venues in New York City.
18. Product Launch Plan: Develop a plan for launching a new product.
19. Research Paper Summarization: Summarize the uploaded research paper.
20. Grant Proposal Writing: Write a grant proposal for an environmental conservation project.
21. Personal Statement: Write a college application personal statement.
22. Product Description: Create a product description for organic skincare products.
23. Executive Summary: Write an executive summary for a business report.
24. Case Study: Write a case study about a successful customer experience.
25. Recipe Ideas: Find healthy, plant-based dinner recipes.
26. Job Market Analysis: Analyze the job market for software engineers.
27. Resume Evaluation: Evaluate and provide feedback on the uploaded resume.
28. Django Web App: Develop a Django web app for an online store.
29. WordPress Plugin: Create a custom WordPress plugin for social sharing buttons.
30. Mobile App UI: Create a mobile app UI for a water intake tracker.
31. Chatbot Integration: Implement a chatbot for customer support on a website.
32. Angular Web App: Develop an Angular web app for project management.
33. Ruby on Rails API: Create a Ruby on Rails backend API for a blog platform.
34. Educational Resources: Discover online resources for learning data science.
35. Sales Pitch: Create a sales pitch for eco-friendly cleaning products.

What industries does cognosys.ai cater to?

Cognosys.ai caters to a wide range of industries, as their AI agent can assist in various tasks. From news aggregation to trend monitoring and data collection, businesses in diverse sectors can benefit from their services. See the list before.

How can I get started with cognosys.ai?

To get started with cognosys.ai, visit their website and sign up for an account. From there, you will be able to access their AI agent and explore its services.

How does cognosys.ai utilize AI technology?

Cognosys.ai utilizes AI technology by creating an innovative AI agent which serves as your personal assistant for accomplishing various tasks. They use proprietary software, and their AI agent is built on models like GPT-3.5.

Are there any success stories involving cognosys.ai?

Cognosys.ai is still in beta, so success stories may not be widely available. However, the potential of their AI technology is evident in the positive reviews and user experiences shared on their website.

What Is Cognosys.ai?

CognosysAI is a web-based AI agent similar to Auto-GPT and BabyAGI that leverages large language models (LLMs) such as GPT-3.5 and GPT-4 to autonomously complete complex tasks that involve web search or other research. You can use it as a research assistant or virtual assistant.

Cognosys vs ChatGPT

Both are AI tools based on large language models. ChatGPT focuses on a human-dialogue interface, whereas Cognosys focuses on creating an autonomous agent that prompts an LLM such as GPT-3.5 not once but multiple times, essentially designing its own prompts. This way, you can give Cognosys more difficult and complex tasks than ChatGPT. Think of it as a meta-tool one layer above ChatGPT.

What Are Cognosys Agents and Loops?

Cognosys Agents are processes, i.e., autonomous helpers that work on the objective you give them when starting the Cognosys tool. One call of the Cognosys tool may trigger multiple agents to work together on a common goal.

Cognosys Loops are series of tasks. If you start an agent with a specific goal, it generates a number of tasks. The series of tasks towards an objective is called a loop. As each loop completes, the agent determines whether the overall objective has been completed, or not.

Is Cognosys Free?

Cognosys.ai offers a Free plan as a generous introduction to building your own AI assistants. The free tier has 10 credits, i.e., 10 tasks. They also provide additional plans with higher limits, more advanced models, and other features to help you take your AI agent to the next level.

To keep learning about the fascinating world of LLMs and exponential technologies, check out my free email academy (>100k subs) and read the following blog post:

Recommended: Top 7 Ways to Use Auto-GPT Tools in Your Browser

The post I Tried Cognosys.ai: Mind-Blown … Again 🤯 appeared first on Be on the Right Side of Change.

This is a biased sample and personal opinion, though, let’s dive into the article for a more thorough evaluation!

Large language models (LLMs) such as GPT4All and Vicuna have evolved to provide highly accurate and sophisticated language representations.

They differ in various aspects such as architecture, performance, and features, which may influence developers’ preferences in choosing the ideal model for their specific requirements.

Additionally, these language models have different levels of institutional involvement, commercial aspects, and tools, all of which might impact project costs, moderation, safety, and ease of implementation.

GPT4All vs Vicuna Overview

GPT4All and Vicuna are both open-source and impressive descendants of the Meta LLaMA model, attracting plenty of attention from the AI community. While both models demonstrate strong potential in handling dialogue generation tasks, there are a few key differences between them .

GPT4All-J is a commercially-licensed alternative, making it an attractive option for businesses and developers seeking to incorporate this technology into their applications. On the other hand, Vicuna has been tested to achieve more than 90% of ChatGPT’s quality in user preference tests, even outperforming competing models like Alpaca.

The performance of Vicuna, in particular, has garnered much praise. For example, in a GPT-4 Evaluation, Vicuna-13b scored 10/10, delivering a detailed and engaging response fitting the user’s requirements. This level of performance sets Vicuna apart as a reliable option for various real-world applications .

Both GPT4All and Vicuna enjoy a level of popularity among developers, as evident by discussions and comparisons on platforms like Reddit. While GPT4All-J offers commercial licensing, Vicuna’s edge in performance makes it a powerful contender within the AI landscape .

Recommended: GPT4All Quickstart – Offline Chatbot on Your Computer

Vicuna-13B

Vicuna-13B is a descendant of the Meta LLaMA model and is primarily trained on dialogue data collected from the ShareGPT website . According to its authors, Vicuna achieves more than 90% of ChatGPT’s quality in user preference tests, while significantly outperforming Alpaca (source).

The following video exemplifies a run of the Vicuna-13b model (source):

The development of Vicuna focuses on creating a high-quality AI-driven conversation experience and building upon the existing large language model architecture.

Vicuna and GPT-4 are both part of the family of open-source models that aim to democratize AI and large language modeling . With their 13B model size, they offer powerful solutions for natural language understanding and generation, enabling advances in AI-based conversation, content creation, and much more.

By continuously refining these models, developers aim to push the boundaries of large language model capabilities and unlock new opportunities for AI-driven applications.

Recommended: 8 Unbelievable AI Innovations Shaking Up the World Today!

Performance and Quality

GPT4All and Vicuna are two open-source, large language models gaining attention in the artificial intelligence community. When comparing their performance and quality, it’s important to consider various factors, such as data quality, evaluation frameworks, and overall performance assessment.

Vicuna has been noted for achieving more than 90% of ChatGPT’s quality in user preference tests and outperforming Alpaca. It is considered the heir apparent of the instruct-finetuned LLaMA model family.

On the other hand, although GPT4All has its own impressive merits, some users have reported that Vicuna 13B 1.1 and GPT4All-13B-snoozy show a clear difference in quality, with the latter being outperformed by the former.

In one comparison between the two models, Vicuna provided more accurate and relevant responses to prompts, while GPT4All’s responses were occasionally less precise.

For example, when tasked with generating a blog post, Vicuna composed a detailed and engaging piece about a trip to Hawaii, whereas GPT4All provided only a brief overview of the blog post and didn’t fully meet the request.

When it comes to performance assessment, both models have their merits and drawbacks. It is essential to consider factors like response time, system resource utilization, and the specific use case when evaluating their relative performance. However, in terms of data quality and evaluation frameworks, many users find that Vicuna outshines GPT4All overall.

Recommended: 11 Best ChatGPT Alternatives

Comparing Features

Fine-Tuning and Training

GPT4All and Vicuna are both language models that have undergone extensive fine-tuning and training processes.

GPT4All, a descendant of the GPT-4 LLM model, has been finetuned on various datasets, including Teknium’s GPTeacher dataset and the unreleased Roleplay v2 dataset, using 8 A100-80GB GPUs for 5 epochs [source].

On the other hand, Vicuna achieves more than 90% of ChatGPT’s quality, vastly outperforming Alpaca, its closest relative [source].

Language Generation and Reasoning

Both GPT4All and Vicuna excel in language generation and reasoning tasks. They are capable of understanding complex questions, generating coherent answers, and performing tasks such as math calculations or question resolution within given context.

The advanced training of these models enables them to handle long sequences and understand maximum context lengths, resulting in more accurate and informative responses.

User Interaction and Conversations

A standout feature of both GPT4All and Vicuna is their ability to engage in user interactions and multi-round conversations. Due to the focus on dialogue data during their training process, these models are well-equipped to handle back-and-forth conversations and provide context-aware answers. In user preference tests, Vicuna achieves impressive results, with quality approaching that of ChatGPT [source].

Recommended: A Quick and Dirty Dip Into Cutting-Edge Open-Source LLM Research

Institutional Involvement

GPT4All is an open-source project associated with Nomic, an ecosystem for training large language models (LLMs) like GPT-J and LLaMA . Their main goal is to make cutting-edge LLM technology accessible for everyone without the need for high computing resources or expenses . For more details about GPT4All, you can visit their GitHub repository.

On the other hand, Vicuna is an LLM developed by Stanford University. It boasts impressive results, achieving more than 90% quality of OpenAI ChatGPT and Google Bard while consistently outperforming other models like LLaMA and Stanford Alpaca in over 90% of cases .

The cost of training Vicuna-13B is around $300, making it a cheaper alternative to other powerful LLMs on the market .

Reddit users discussed that GPT4All-J and Vicuna both have their own strengths and weaknesses, leading to user preferences and use cases determining which model might be more suitable for a given task.

OpenAI, the organization behind GPT-3 and GPT-4, continues to play a significant role in the field of AI language models . While not directly involved with GPT4All or Vicuna, their models provide a benchmark for comparison with these open-source alternatives.

Google Bard is a product of Google’s research team, and its focus is on generating human-like written content . Like OpenAI’s models, Google Bard serves as a point of reference for developers trying to improve the performances of their LLMs.

Stanford Alpaca is another LLM developed at Stanford University and is mainly designed for low-resource settings. It maintains an adequate balance between computational requirements and quality of generated content, making it a valuable choice for users with limited budgets and resources.

Other institutions, such as UC Berkeley, CMU, and UC San Diego, are deeply involved in AI research and contribute significantly to the field of LLMs, although their direct association with GPT4All and Vicuna might not be explicit. These research centers often collaborate, sharing their knowledge and resources to advance the development of AI technology .

Recommended: GPT4all vs Alpaca: Comparing Open-Source LLMs

Open-Source and Commercial Aspects

GPT4All and Vicuna are both open-source chatbot models that allow developers to create their own chatbots utilizing large language model (LLM) architectures. These open-source chatbots provide an affordable and customizable option for developers aiming to use LLMs in both personal and commercial projects.

GPT4All, as part of the Nomic ecosystem, was initially released on 2023-03-26. It leverages LLaMA and GPT-J backbones to create an environment for training and fine-tuning chatbot models. Developers can access and modify the GPT4All source code, allowing them to create tailored solutions for various applications.

Vicuna, on the other hand, is another open-source chatbot that has been praised for its performance, even outscoring popular models like Alpaca-13b in certain evaluations. Like GPT4All, developers can use Vicuna for various purposes, including both non-commercial and commercial use.

When incorporating these open-source chatbots into commercial projects, it’s essential to consider the implications on computing resources; LLMs like GPT4All or Vicuna often require powerful GPUs to run efficiently. However, the open-source nature of these projects allows developers to optimize the models to fit their particular hardware and computational requirements.

As open-source LLMs gain more prominence in the AI community, developers can expect an increase in support, resources, and documentation regarding these models, helping streamline the process of integrating chatbot solutions in projects both big and small.

By utilizing GPT4All and Vicuna models, businesses and individuals alike can take advantage of the cutting-edge natural language processing technology without facing hefty licensing fees or restrictive usage limitations. This freedom not only fosters greater innovation in the field of natural language processing but also provides widespread access to powerful AI tools for diverse applications and industries.

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Cost Analysis

When comparing GPT4All and Vicuna, the cost difference between these two models is notable. GPT4All aims to provide a more affordable option for those interested in running powerful language models on their hardware. However, Vicuna has become known for its cost-efficiency, with the training cost for Vicuna-13B estimated at around $300.

Optimization is a critical factor in determining the cost of running AI models like GPT4All and Vicuna. By optimizing the training process, it is possible to reduce both the time taken and the expense of training these models. In this regard, Vicuna appears to have the edge , outperforming other models like LLaMA and Stanford Alpaca in more than 90% of cases.

Taking the processing power and hardware requirements into account is essential in a comprehensive cost analysis. While both GPT4All and Vicuna are designed for running on single GPUs, it is crucial to evaluate the specific requirements before making a decision. As a result, users should be aware of potential hardware limitations or upgrades needed when considering GPT4All or Vicuna.

Recommended: GPT4ALL vs GPT4ALL-J

Tools and Technologies

GPT4All and Vicuna are two widely-discussed LLMs, built using advanced tools and technologies. GPT4All is an open-source ecosystem for chatbots with a LLaMA and GPT-J backbone, while Stanford’s Vicuna is known for achieving more than 90% quality of OpenAI ChatGPT and Google Bard.

Both models employ PyTorch FSDP for efficient model parallelism, enabling them to scale efficiently on large numbers of A100 GPUs. The serving system in each LLM plays a crucial role in ensuring high-performance responses to user inputs, making them ideal for real-time applications.

To increase performance and reduce costs, they implement memory optimizations such as gradient checkpointing and flash attention mechanisms. Gradient checkpointing helps in saving memory by trading compute for extra memory space, while flash attention reduces the computational complexity of attention.

Another significant aspect of these LLMs is SkyPilot, a library that enables them to handle sparse tensors, further contributing to storage optimization. This technology allows models to consume less memory and facilitates their deployment on lower-end hardware.

Additionally, to maximize cost efficiency, both GPT4All and Vicuna use spot instances for training purposes. Spot instances are temporary cloud computing resources that offer substantial cost savings over regular instances, making the training of these large language models more accessible.

Recommended: What is AutoGPT and How to Get Started?

Popular GPT-based Implementations

GPT-based implementations have gained widespread popularity due to their ability to generate human-like text. In the race to build advanced language models, two prominent contenders are GPT4All and Vicuna. Both aim to offer efficient and effective large language models for various applications, such as chatbots and natural language processing tasks.

GPT4All is developed by Nomic, an organization focused on creating an ecosystem for open-source chatbots. The platform employs LLaMA and GPT-J backbones to achieve impressive results for language generation. Thanks to an active community and continuous development, GPT4All has become a popular choice for developers .

Vicuna, on the other hand, is a lightweight GPT-based implementation that has shown considerable promise. In comparison tests, GPT-4 has preferred Vicuna over other state-of-the-art open-source models like LLaMA and Alpaca in more than 90% of the questions. This impressive performance has caught the attention of researchers and developers alike .

In addition to GPT4All and Vicuna, the world of GPT-based implementations has witnessed the emergence of other remarkable models such as ChatGPT, Meta LLaMA, Koala, and ShareGPT. Each of these models serves specific purposes and offers unique features for developers to capitalize upon. For instance, ChatGPT is designed to process natural language input and generate appropriate responses, making it suitable for chatbot applications .

Meta LLaMA, Koala, and ShareGPT are also worth exploring for various use cases. While Meta LLaMA is focused on offering tools for fine-tuning GPT-based models, Koala emphasizes quick deployment and ease of use in its language model implementation. ShareGPT aims to provide a shared platform for multiple users to collaborate on GPT-based projects with ease .

Recommended: 30 Creative AutoGPT Use Cases to Make Money Online

Other Language Models

Aside from GPT4All and Vicuna, there are several other large language models (LLMs) in the field of natural language processing (NLP) that are worth mentioning. One such model is LLaMA, which is a parent model for Alpaca and Vicuna. LLaMA is a community-oriented project aimed at developing and advancing state-of-the-art open-source NLP models.

Alpaca is another LLM with 7-billion parameters and is known for its GPT-3.5-like generation. It provides impressive performance for a wide range of NLP tasks. Interestingly, Vicuna has been modeled on Alpaca and even outperforms it in certain cases. Both models have been compared to GPT-3, which is one of the most popular LLMs developed by OpenAI.

Another noteworthy LLM is Dolly, a deep-learning model known for its impressive image synthesis capabilities. Dolly 2.0 was released as an open-source, with advancements in both image synthesis and performance.

In addition to these, there is GPT4All-J, which is designed for commercial use. This model is built by Nomic AI on top of LLaMA and has performance comparable to Alpaca and Vicuna.

Recommended: 10 High-IQ Things GPT-4 Can Do That GPT-3.5 Can’t

Distributed Systems and Workers

Distributed systems in the context of GPT4All and Vicuna play a vital role in achieving efficient performance and scalability. The implementation of distributed workers, particularly GPU workers, helps maximize the effectiveness of these language models while maintaining a manageable cost.

GPT4All utilizes an ecosystem that supports distributed workers, allowing for the efficient training and execution of LLaMA and GPT-J backbones . The collaboration between various workers contributes to a fault-tolerant controller that can withstand potential system errors and deliver seamless processing power.

In the case of Vicuna, the serving system supports the flexible plug-in of GPU workers from both on-premise clusters and the cloud, enabling adaptability and accessibility. This approach provides efficient scalability that caters to varying computational requirements .

A fault-tolerant controller is essential in distributed systems as it can manage worker node failures and maintain system reliability. With this robust failsafe solution, both GPT4All and Vicuna can ensure consistent and stable performance.

Recommended: Top Eight GPT-4 Productivity Use Cases for Coders (No BS)

Supported Formats

GPT4All and Vicuna are both versatile language models that can handle a variety of input formats. They cater to different use cases, including text generation, conversational AI, and more.

One of the formats supported by both GPT4All and Vicuna is HTML. HTML is the standard markup language for creating web pages and web applications. Both models can process, understand, and generate content in HTML format, making them ideal for use in web-related tasks such as content generation, editing, and optimization.

Another important format supported by these models is Markdown. Markdown is a lightweight markup language designed to be an easy-to-read and easy-to-write plain text format. It is often used for formatting text on platforms like GitHub, Reddit, and many other content publishing platforms. GPT4All and Vicuna can understand and generate text in Markdown, making them beneficial for tasks that require text formatting, documentation, or social media content.

Finally, both GPT4All and Vicuna are proficient in dealing with low-quality samples. They can extract meaningful information from poorly structured or low-quality text inputs, enabling them to handle a wide range of user inputs and languages with varying quality. This feature is particularly useful in chatbot applications and other scenarios where user input may deviate from standard language conventions.

Overall, GPT4All and Vicuna support various formats and are capable of handling different kinds of tasks, making them suitable for a wide range of applications. Their support for HTML, Markdown, and low-quality samples demonstrates their adaptability and usefulness in different domains.

Frequently Asked Questions

What are the key differences between gpt4all and Vicuna LLM?

GPT4All and Vicuna LLM have some differences in their training and performance. Vicuna LLM is a descendant of the Meta LLaMA model trained on dialogue data and claims to achieve more than 90% of ChatGPT’s quality. On the other hand, GPT4All uses different datasets and techniques for training, resulting in varying performance levels.

How do the training methods of gpt4all and Vicuna LLM compare?

Vicuna LLM’s training is based on dialogue data collected from the ShareGPT website. It is fine-tuned with a mixed model that had Alpaca training on top of Vicuna 1.1. GPT4All, however, has different training methods, utilizing various datasets such as the OpenAssistant Conversations Dataset and the GPT4All Prompt Generations dataset in some of its variants like StableVicuna.

Which system, gpt4all or Vicuna LLM, has a wider range of applications?

It is difficult to determine a clear winner when it comes to the range of applications, as both systems are continuously evolving and adapting to new tasks. Factors such as the specific variant, dataset, and training method can significantly affect each system’s performance and versatility.

Are there significant performance variations between gpt4all and Vicuna LLM?

Yes, there are performance variations between the two systems. In some cases, GPT-4 rates Vicuna LLM’s response as better or equal to ChatGPT’s. However, the performance differences depend on task complexity, system variant, and fine-tuning techniques.

How do gpt4all and Vicuna LLM handle complex tasks?

Both systems are effective at handling a variety of tasks. For example, Vicuna LLM demonstrates its capability to compare celestial objects, as seen in a Reddit comparison post. In contrast, GPT4All can perform general tasks and specific applications, depending on the variant and training method.

Which system is more cost-effective: gpt4all or Vicuna LLM?

It is challenging to determine the cost-effectiveness of either system without complete knowledge of each model and its deployment environment. Factors such as the specific variant, dataset, training method, and infrastructure requirements may contribute to each system’s overall cost. However, both systems demonstrate their effectiveness and impressive results in various tasks, making them valuable tools for developers and researchers.

Recommended: MiniGPT-4: The Latest Breakthrough in Language Generation Technology

The post GPT4all vs Vicuna: Battle of Open-Source LLMs ⚔️ appeared first on Be on the Right Side of Change.

Description: Created by Asim Shrestha, AgentGPT allows for the assembly, configuration, and deployment of autonomous AI agents right in your browser.

Author: Asim Shrestha

Twitter: https://twitter.com/asimdotshrestha

Project 02: BabyAGI on Slack

Description: This project brings BabyAGI to Slack, with each thread representing a unique objective. Its author, Frank Chen, has enabled seamless integration with a popular communication platform.

Author: Frank Chen

Twitter: https://twitter.com/frankc

Project 03: AgentLLM

Description: AgentLLM, developed by Ido Salomon, employs a browser-native LLM instead of ChatGPT, providing a unique approach to autonomous AI agents.

Author: Ido Salomon

Twitter: https://twitter.com/idosal1

Project 04: BabyAGI-asi

Description: Run arbitrary Python code to perform tasks, creating routines for pyautogui, web scraping, and even controlling its memory.

Author: Bruno De Oliveira

Twitter: https://twitter.com/LatentLich

Project 05: ai-legion

Description: Similar in spirit to AutoGPT and Baby AGI, but written in the superior language that is TypeScript (Author Description).

Author: eumemic

Twitter: https://twitter.com/dysmemic

Project 06: yourgoal

Description: Swift (programming language) implementation of BabyAGI.

Author: PJ Gray

Twitter: https://twitter.com/pj4533

Project 07: BabyAGIChatGPT

Description: Plug-in to run autonomous agents directly within ChatGPT

Author: Pietro Schirano

Twitter: https://twitter.com/skirano

Project 08: Cognosys

Description: A web-based version of AutoGPT or Baby AGI.

Author: Sully Omarr

Twitter: https://twitter.com/SullyOmarr

Project 09: Godmode

Description: A web-based version of AutoGPT or Baby AGI.

Authors: Lonis, Emil Ahlbäck, and fant

Twitter: https://twitter.com/SullyOmarr

Recommended: AutoGPT vs BabyAGI: Comparing OpenAI-Based Autonomous Agents

Project 10: alphakit

Description: A team of autonomous AI agents for everyone

Author: Andrew Yu

Twitter: https://twitter.com/andrewcyu

Project 11: Xircuits

Description: A comprehensive toolkit for experimenting with and creating BabyAGI and Auto-GPT-style Agents.

Author: Eduardo Gonzalez

Twitter: https://twitter.com/wm_eddie

Project 13: babyagijs

Description: Eric Ciarla brings BabyAGI to the JavaScript community with his project.

Author: Eric Ciarla

Twitter: https://twitter.com/ericciarla

Project 14: Teenage-AGI

Description: Sean Pixel’s project uses OpenAI and Pinecone to give memory to an AI agent and allows it to “think” before acting. Inspired by several Auto-GPT-related projects (predominantly Baby AGI) and the paper “Generative Agents: Interactive Simulacra of Human Behavior”.

Author: Sean Pixel

Twitter: https://twitter.com/sean_pixel

Project 15: babyagi-perl

Description: BabyAGI for the Perl programming language.

Author: Nelson Ferraz

Project 16: gptagent.js

Description: A composable and extensible framework for creating AI agents with TypeScript/JavaScript.

Author: Lars Grammel

Twitter: https://twitter.com/lgrammel

Project 17: babyagi-streamlit

Description: Streamlit web app to make it more accessible.

Author: Dory

Twitter: https://twitter.com/dory111111

Project 18: Do Anything Machine

Description: Helps you keep track of your tasks, prioritize them, and allows you to deploy AI agents to get your work done for you.

Author: Garrett Scott

Twitter: https://twitter.com/thegarrettscott

Project 19: Babyagi-as-a-service

Description: Integrate babyagi with your own applications – thanks to langchain-serve with one simple command – 𝐥𝐜-𝐬𝐞𝐫𝐯𝐞 𝐝𝐞𝐩𝐥𝐨𝐲 𝐛𝐚𝐛𝐲𝐚𝐠𝐢

Author: Deepankar Mahapatro

Twitter: https://twitter.com/_deepankarm_

Project 20: gptrpg

Description: Chris Dzoba creates a simple RPG-like environment for an LLM-enabled AI Agent to exist in.

Author: Chris Dzoba

Twitter: https://twitter.com/ChrisDzoba_

Project 21: BabyAGI-ts

Description: Port BabyAGI from Python to TypeScript and provide a friendly CLI tool that can be installed as a global NPM module

Author: Sam Hogan

Twitter: https://twitter.com/0xSamHogan

Project 22: LLMitlessAPI

Description: API with one endpoint that does everything (literally).

Author: Adam Cohen Hillel

Twitter: https://twitter.com/adamcohenhillel

Project 23: PuttyGPT

Description: Ryan William Grippeling’s project focuses on integrating numerous AI ideas into a cohesive whole. It’s still in its early stages but promises to be an exciting development.

Author: Ryan William Grippeling

Twitter: https://twitter.com/ryangrippeling

Project 24: Chippr-AGI

Description: Cody Burns’ project is an open-source framework that uses AI models to automate task creation and prioritization. It’s built on NodeJS, Redis, and OpenAI/LLAMA.

Author: Cody Burns

Twitter: https://twitter.com/DontPanicBurns

Project 25: Agent-LLM

Description: Josh XT’s Agent-LLM is a dynamic AI task management assistant with adaptive memory, web browsing, code evaluation, and a versatile plugin system for seamless integration with various AI providers.

Author: Josh XT

Twitter: https://twitter.com/Josh_XT

Project 26: sharpagi

Description: C# dotnet implementation of BabyAGI.

Author: Sathyaraj Vadivel

Twitter: https://twitter.com/sathyarajv

Project 27: twitter-agent

Description: Twitter-Agent is an autonomous AI-powered agent for interacting directly with the Twitter API.

Author: BigSky

Twitter: https://twitter.com/bigsky_7

This article draws projects from this BabyAGI GitHub repository (source). Check it out!

Recommended: AutoGPT vs BabyAGI: Comparing OpenAI-Based Autonomous Agents

OpenAI Glossary Cheat Sheet (100% Free PDF Download)

Finally, check out our free cheat sheet on OpenAI terminology, many Finxters have told me they love it!

Recommended: OpenAI Terminology Cheat Sheet (Free Download PDF)

The post 26 Insane Auto-GPT and Baby AGI Alternatives You Ought to Try in 2023 appeared first on Be on the Right Side of Change.

Tool #1 – Auto-GPT on Hugging Face

Hugging Face user aliabid94 created an Auto-GPT web interface (100% browser-based) where you can put in your OpenAI API key and try out Auto-GPT in seconds.

To get an OpenAI API key, check out this tutorial on the Finxter blog or visit your paid OpenAI account directly here.

The example shows the Auto-GPT run of an Entrepreneur-GPT that is designed to grow your Twitter account.

Tool #2 – AutoGPTJS.com

I haven’t tried autogptjs.com but the user interface looks really compelling and easy to use. Again, you need to enter your OpenAI API key and you should create a new one and revoke it after use. Who knows where the keys are really stored?

Well, this project looks trustworthy as it’s also available on GitHub.

Tool #3 – AgentGPT

AgentGPT is an easy-to-use browser based autonomous agent based on GPT-3.5 and GPT-4. It is similar to Auto-GPT but uses its own repository and code base.

I have written a detailed comparison between Auto-GPT and AgentGPT on the Finxter blog but the TLDR is that it’s easier to setup and use at the cost of being much more expensive and less suitable for long-running tasks.

Tool #4 – AutoGPT UI with Nuxt.js

AutoGPT UI, built with Nuxt.js, is a user-friendly web tool for managing AutoGPT workspaces. Users can easily upload AI settings and supporting files, adjust AutoGPT settings, and initiate the process via our intuitive GUI. It supports both individual and multi-user workspaces. Its workspace management interface enables easy file handling, allowing drag-and-drop features and seamless interaction with source or generated content.

Some More Comments…

Before you go, here are a few additional notes.

Token Usage and Revoking Keys

To access Auto-GPT, you need to use the OpenAI API key, which is essential for authenticating your requests. The token usage depends on the API calls you make for various tasks.

You should set a spending limit and revoke your API keys after putting them in any browser-based Auto-GPT tool. After all, you don’t know where your API keys will end up so I use a strict one-key-for-one-use policy and revoke all keys directly after use.

3 More Tools

The possibilities with Auto-GPT innovation are vast and ever-expanding.

For instance, researchers and developers are creating new AI tools such as Godmode (I think it’s based on BabyAGI) to easily deploy AI agents directly in the web browser.

With its potential to grow and adapt, Auto-GPT is poised to make an impact on numerous industries, driving further innovation and advancements in AI applications.

AutoGPT Chrome extension is another notable add-on, providing an easily accessible interface for users.

Yesterday I found a new tool called JARVIS (HuggingGPT), named after the J.A.R.V.I.S. artificial intelligence from Ironman, that is an Auto-GPT alternative created by Microsoft research that uses not only GPT-3.5 and GPT-4 but other LLMs as well and is able to generate multimedia output such as audio and images (DALL-E). Truly mindblowing times we’re living in.

Recommended: Auto-GPT vs Jarvis HuggingGPT: One Bot to Rule Them All

The post Top 7 Ways to Use Auto-GPT Tools in Your Browser appeared first on Be on the Right Side of Change.