Grab a cup of coffee, your laptop, and a great mood! Otherwise, it’s not gonna happen :)

Gensyn is a decentralized AI computing network for training models… blah-blah-blah… let’s keep it simple!

Imagine you’ve got a smart neural network (AI). To “train” it, you need massive computing power—like a whole room full of servers. The problem? Most people and companies don’t have that kind of firepower.

Enter Gensyn, which figured out how to connect people and companies worldwide into one network where anyone can plug in their computer or server and “rent it out” for AI training.

The process is straightforward: Got a powerful GPU? Join Gensyn → the system sends you a task like “train a model for speech recognition” → your computer crunches the numbers → the blockchain verifies it → you earn tokens .

Think of Gensyn as the Uber of AI. Instead of transporting people, it’s about training neural networks, and instead of cars, it’s computers worldwide.

Got it? I think so—I mean, even I understood it :)

Investments and Team:

• $50.6M from heavyweights like a16z and Galaxy

• The team’s standout is Jeff Amico, the current COO and former a16z partner, who likely played a key role in securing that capital

BRIEF SUMMARY:

+ AI project (the trend of this decade)
+ Solid team
+ a16z on board
- High entry barrier with a risk of not breaking even
-/+ Weak community hype due to the same reason… could impact TGE valuation, but marketing before token launch can fix this

IN SIMPLE TERMS: a16z backing, major investment, AI tech… If you’re in crypto projects, add this to your to-do list and get it done. You’re not just working on one project, right? Diversification is key—some will hit, some won’t.

PROJECT RATING: 7.5/10

Now let’s get to the main event… the activities!

1 - Rent a server via Vast.ai: https://cloud.vast.ai/?ref_id=256559

You can pay with stablecoins on Base or with a card (you can also use a Solayer card if you’ve got one—two activities in one!)

If you run into issues with server setup or node installation, try switching the server’s country. I had problems with Poland, but Quebec, Canada worked perfectly.

2 - After purchasing the server, open the terminal.

3 - SETTING UP THE NODE

Installing the necessary packages:

cd $HOME
sudo apt-get update && sudo apt-get upgrade -y
sudo apt install screen curl iptables build-essential git wget lz4 jq make gcc nano automake autoconf tmux htop nvme-cli libgbm1 pkg-config libssl-dev libleveldb-dev tar clang bsdmainutils ncdu unzip -y

Installing Python and Node.js

sudo apt install -y python3 python3-pip python3.10-venv

curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
sudo apt install -y nodejs
sudo npm install -g yarn

Clone and update the repository:

git clone https://github.com/gensyn-ai/rl-swarm.git && cd rl-swarm

Installing the RL Swarm Node

screen -S swarm

python3 -m venv .venv
source .venv/bin/activate

cd
cd rl-swarm

./run_rl_swarm.sh

Next, detach from the screen session: Press Ctrl + A, then D

Installing and Authenticating Ngrok

wget https://bin.equinox.io/c/bNyj1mQVY4c/ngrok-v3-stable-linux-amd64.tgz
tar -xvzf ngrok-v3-stable-linux-amd64.tgz
mv ngrok /usr/local/bin/

Then visit https://ngrok.com/, create an account, copy the Authtoken, and enter it in the terminal:

ngrok config add-authtoken YOUR_TOKEN

Login

ngrok http 3000

Follow the link and sign in with your Google account. After successful login, close the window.

Starting the Node

screen -r swarm

After the download completes, respond to the prompt by pressing N. Press Enter to skip the second question.

If the node starts successfully, it will begin its first training task. The system will automatically generate a node ID and username for you. Copy and save them for tracking purposes.

Dashboard

Here, we check if our node is working: https://dashboard.gensyn.ai/

This role confirms that you’ve successfully run your node at least once. To obtain this role, you’ll need Telegram (the role’s purpose is to send node activity updates to your Telegram).

There’s an official guide: https://gswarm.dev/docs

Open a new terminal window in Jupyter.

cd ~wget https://go.dev/dl/go1.24.0.linux-amd64.tar.gzsudo rm -rf /usr/local/gosudo tar -C /usr/local -xzf go1.24.0.linux-amd64.tar.gz # Set environment variablesecho 'export PATH=$PATH:/usr/local/go/bin' >> ~/.bashrcecho 'export GOPATH=$HOME/go' >> ~/.bashrcecho 'export PATH=$PATH:$GOPATH/bin' >> ~/.bashrcsource ~/.bashrc # Verify Go installationgo version

go install github.com/Deep-Commit/gswarm/cmd/gswarm@latest

gswarm --version

We’ve started the monitoring service. Now, enter the required data: first, the token, then your Telegram bot’s ID.

1. Go to Telegram and create a bot via @BotFather (to get the token and ID).

2. Send /newbot and follow the instructions.

3. Save the provided bot token.

4. Start a chat with your new bot and send any message.

5. Visit https://api.telegram.org/botYOUR_BOT_TOKEN/getUpdates (replace YOUR_BOT_TOKEN with your token).

6. Find your chat ID (if you see {"ok":true,"result":[]}, send another message to the bot and refresh the page).

The latest activity… training a model based on our actions.

Official guide: https://github.com/gensyn-ai/blockassist (works on Mac and Linux)

Quick note: I went through this process on macOS, so if you hit errors on Linux, we’ll troubleshoot together. For Mac users, you’re in luck—I’ve encountered and resolved every possible issue, so my help will be especially useful.

ENTER THE CODE

Head to the official guide and follow the code input steps (everything is clearly explained).

Starting BlockAssist

On macOS:

pyenv exec python run.py

On Linux:

python run.py

Hugging Face Token

You’ll be prompted to enter a Hugging Face API token. Go to https://huggingface.co/docs/hub/en/security-tokens, create an account, generate a token, and enter it in the terminal.

Login

Next, you’ll be asked to log in via a browser at http://localhost:3000. If you’ve already authenticated, this step will be skipped. Otherwise, use the browser window that opens to log in.

Many users, including me, faced issues at this step because the program was running on different ports. I solved it using the Cursor app (an AI code editor). I asked Cursor to run the program (shared the GitHub guide link) on port 3000. It worked some magic and got it done

Playing Minecraft

After entering the token and logging in, two game windows will open. Once both windows appear, press Enter in the terminal to start the game and begin recording gameplay.

To start moving, press Enter in the game and begin building a house until it reaches 100% (though many say this doesn’t greatly impact your leaderboard points).

Either way, after playing for a while or reaching 100%, go back to the terminal and press Enter a few times until you see a response.

Then, wait for the model to train. Once the process is complete, you’ll receive a Hugging Face link containing the files needed to claim the BLOCK role.

Is playing once enough? No. Check the BlockAssist section on the dashboard to see your points. The more you play and train, the more points you earn, and the bigger the drop!

In the link-for-access branch, you’ll find a guide with the commands to enter to get the BLOCK role.

X - https://x.com/wasabu0x Youtube - https://www.youtube.com/@wasabu0x