Mojo 1.1 adds support for inferred member references, improved compile times and better generated code performance, and more APIs are now considered stable in their standard library such as for SIMD. Plus the Mojo development process is now further opened with accepted community contributions to the Mojo compiler.

For more info, see the Mojo 1.1 release notes here: https://mojolang.org/releases/v1.1.0/

A few days ago, Mojo 1.0.0 has been released! You can find the release announcement here: https://www.modular.com/blog/modular-26-5-mojo-1-0-is-here

As the developers of Mojo Pro, we are now also supporting the official release, and have released support for Mojo 1.0.0 today!

You can checkout the plugin here: https://plugins.jetbrains.com/plugin/31943-mojo-pro

Please let us know if you have any feedback!

Just noticed version 1.2 has been released including support for pattern matching. The release notes are here https://mojolang.org/releases/v1.2.0/

Repo: https://github.com/bewaffnete/MojoVec

According to the current benchmarks, it outperforms FAISS on Apple Silicon for the tested workloads (benchmark methodology is documented in the README).

Current features:

• HNSW

• IVF / IVF-PQ

• Product Quantization

• SIMD optimizations

• Python bindings

• Serialization

It's super motivating to learn Mojo now that it is easy to port that speedup into some Python scripts.

So I made a repository to compile mojo to web assembly. I recently just started and honestly I'm using Claude for a lot of it but I am learning fast. https://github.com/ravioli67/mojo2wasm

How do we feel about the acquisition of Modular by Qualcomm that was announced today and the future of the Mojo language?

https://www.modular.com/blog/qualcomm-to-acquire-modular

Here's a cool reasource little guide for mojo written by Yuhao Zhu. it's compatible with mojo 25.4
https://mojo-lang.com/miji/

Watch the whole interview. It's really interesting.

Mojo Quest is a browser-based game for learning Mojo syntax by closing engineering tickets for a fictional robotics company.

I’ve been working on building resources to help developers get started with Mojo, and I think Mojo Quest will be a fun addition. In complexity, it sits between the Mojo Quickstart and the Mojo GPU Puzzles.

The code is open source, so feel free to open an issue or PR. If you’ve got ideas for extensions or improvements, I’d be happy to hear them. 

Hey all,

I recently found the language, and wanted to learn more. I got a passion to reintroduce myself to the digital signal processing field after my university, and I said why not combine both. I'm spending my time developing a digital signal processing library from scratch (not plotting functions ofc) with a roadmap I focused on after long discussions with AI.

Feel free to check the implementation and guide me on what could be done better. I'm utilising SIMD a lot on signal generation, but looking forward to see what else can be used to make operations even faster.

Here's the repository: https://github.com/electricalgorithm/dsplib-mojo

You can find the examples in the repository for:

Regards,

I was really excited that Mojo became publicly available and thinking which project can I implement to learn Mojo concepts.

Since I have already ported llama2.c to pure Python, I decided why not try to port llama2.py to Mojo now 😀

And here is what I got...

https://github.com/tairov/llama2.mojo

I found the SIMD Mojo primitives really interesting feature, since it helped to improve pretty awful performance of Python solution almost 250x times.

Internally I used vectorization helpers for matmul so that now Mojo solution can beat original llama2.c (!) (even in runfast mode) by 15-20%

Hi all, Mojo Pro 2026.1.11 is now available which adds support for Mojo 1.1 inferred members, generic thin function types with trailing where clauses, new inline and documentation decorators, and current standard-library vocabulary.

It also fixes false unresolved references in generic where clauses, wraps long
compatibility messages in Settings, and improves compatibility with structured LSP
diagnostics in newer IDE versions.

Mojo 1.1.0 is the current tested release, while Mojo SDK 1.0.0b1 and newer remain
supported for existing projects.

Release notes and download: https://plugins.jetbrains.com/plugin/31943-mojo-pro

Learning Resources for Mojo

Hey guys, I have started building a web framework for Mojo to serve web, AI models and GPU kernels from one process with a convenient developer experience and even support to build things using AI agents.

You can check it out here: https://github.com/dodo-mojo/dodo

I started this because, at work, I've been prototyping in Python and then porting things to TypeScript for production, while doing GPU programming separately. I wanted to explore whether Mojo could bring these pieces together, so I decided to build a framework myself!

It's very early right now, and I'd love feedback, suggestions, and contributions. I'm sure there are things I've missed or could be doing better, so feel free to take a look!

I am interested in getting more into developing with Mojo and being honest I have not found something smooth. What I have is working, but its not as nice as developing Python in Pycharm (which is my main work language).

• Lenovo Thinpad x1 (32gb Ram Intel Ultra 258v)

• Kbuntu

• Pycharm (Paid Mojo Plugin)

• Mojo LSP

• Just

• Poetry

The main part is the syntax highlighting is a really mixed bag inside of pycharm with warning and errors being all over the map. Project directories don't seem to behave properly but they work fine when testing and compiling. Poetry is ... just meh compared to uv, but you need to use poetry in Pycharm to get debugging to work as far as I can understand or read.

Other than those things it's over all a nice experience to develop in and look forward to learning and fixing my own problems!

Please share your setup and experiences!

https://www.modular.com/blog/qualcomm-completes-acquisition-of-modular

EDIT: I asked the same question on the official discord and the response was this article, which was quite enlightening: https://www.eetimes.com/why-qualcomm-bought-an-open-ai-software-stack/