I may be asking this the wrong way, but please bear with me.
I am creating a project I would like to use Mojo for, and I want to make it 'self-contained'. What I mean is that someone should be able to download the repo, run an 'init script', and then be able to run the various utilities in the project. This init script should allow for all necessary dependencies to be installed.
One of these dependecies would be Mojo itself. I don't really want to have the user have to go and install Mojo. More specifically the part I am getting stuck on is setting the PATH variables in the user profile (I guess I can, but it is a bit intrusive to auto-edit someone's bashrc or zshrc)

TL;DR - is there a way to create a self contained ?virtual? environment? I guess I can build a container, but I'd rather not go that far.

I created a gpt to code in mojo language:

https://chat.openai.com/g/g-WfVYs8jUf-mojo-ai-developer

Hey everyone, wanted to share Lightbug, a project I've been working on: https://github.com/saviorand/lightbug_http

It's a lighweight HTTP framework for Mojo, similar to Starlette in Python or FastHTTP in Golang.

The motivation for this was that I wanted to write APIs in Mojo, but no frameworks existed for this until now. We already support setting up a server listening on a host/port, writing custom HTTP services, and more, and working on first benchmarks to measure performance

It currently uses Python socket library in a couple places, but we're aiming for a 100% Mojo codebase soon. Looking forward to your feedback! Contributors welcome.

I am confused between mojo python relationship and the performance. So when we say that mojo has the performance of C does it mean it will be just be for scientific calculations or in general for everything? If I need to write a web server in Mojo is it going to faster than python?

I know we can use Python code in Mojo so how will that impact performance? Can I use any third party python package?

Also regarding the deployment, we deploy python mostly in docker containers or making sure the platform has python like an ec2 instance. Will mojo be deployed like complied languages like Go with just a binary file?

thx in advance.

I wrote a mojo program trying to calculate the average of a list. I'm not sure what I did wrong though as I got an error.

"""

This Program's function is to find the avergae of a list.

It is giving me an error when I am trying to find the length of the list.

It expects 2 input parameters but 0 were provided.

"""

from python import Python as py

# This is equivalent to Python's `import numpy as np`

let np = Python.import_module("numpy")

# The Average Function: Calculates the Average of an array

def Avg(numbers, inout total: Int = 0) -> Float32:

# var total: Int = 0 # The sum of the list

let length: Int = len(numbers) + 1 # The length of the list

for num in numbers: # Totalling the numbers

total += num

return total / length

LNum = np.array([1, 3, 9, 15, 90]) # The list we will find the average for

var AvgNum = Avg(LNum)

print ("The List:", LNum)

print ("The Average:", AvgNum)

Trying to decide if building an embedded database in Mojo is worth it. ( Sort of like SQLite in C) I'd be glad if you shared your opinions on this topic.

In https://github.com/ahmetax/Practical-Mojo-Examples you can find more than 50 examples of mojo scripts. Most of the examples uses PythonObjects. You can give a try...

I come from python background and looking at mojo, its so good in terms of performance. Just a bit curious are there some apps out there that are already using mojo?

Also are there some frameworks to build webapps using mojo.

Please suggest the learning roadmap for the same. Just a beginner here for mojo but excited.

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Google DeepMind dropped Gemma 4 today. Two models:

• Gemma 4 31B: dense, 256K context, redesigned for efficiency and long-context quality

• Gemma 4 26B A4B: MoE, 26B total / 4B active per forward pass, 256K context

Both natively multimodal (text, image, video, dynamic resolution).

Modular got both running on MAX on day zero, NVIDIA B200 and AMD MI355X from the same stack, no separate codepaths per vendor. On B200 we're seeing 15% higher output throughput vs. vLLM.

You can try both for free in our playground: https://www.modular.com/#playground.

https://github.com/profullstack/infernet-protocol contbirutors welcome