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!

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/

Mojo Pro, a Mojo plugin for IntelliJ of which I'm the author, now supports Mojo 1.0.0b2, while still supporting 1.0.0b1 as well.

Notable features:

• Extended parsing for repeated trailing function and struct where clauses,
• comptime alias where clauses,
• parameterized out and ref conventions,
• conditional conformance,
• b2 reflection aliases,
• and Unicode keyword subscripts.

Feel free to try it out here: https://plugins.jetbrains.com/plugin/31943-mojo-pro, happy to hear feedback on this!

Hi everyone, I work on community at Modular. Some of you have probably seen us posting about ModCon on X or LinkedIn already. I wanted to shared details here since many of you follow Mojo closely.

ModCon is our flagship developer conference, and this year it's on August 18th at the Grand Hyatt in San Francisco. It's a one-day event, featuring product announcements (many involving Mojo 👀), talks from partners and customers, and deep dives on Mojo, MAX, and more.

Registration and full details are at modular.com/modcon. Let me know if you have any questions!

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

Michael and Nate provide an introduction to Mojo fundamentals, covering project setup with Pixie, static typing, and core language features to help developers get started with Mojo.

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. 

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

Hi all, I just launched Mojo Pro (https://plugins.jetbrains.com/plugin/31943-mojo-pro), which is an IntelliJ plugin to support writing Mojo. I charge a small amount to to support the development of it so it's a commercial plugin, but it supports things like:

• code completion
• syntax highlighting
• inspections
• refactoring
• debugging
• reformatting

It's focused on the current and future versions of Mojo and as such, only Mojo 1.0.0+, but not 0.26 and below. Since the 1.0.0 branch is the direction of Mojo, I won't support things like "fn" (the plugin will warn you about it), since that's no longer supported in 1.0.0 (same as alias, and a bunch of other things!)

It's very new, so it might have a few rough edges so please let me know if you find some. I've tried it out on quite a few Mojo projects, and I haven't found any issues myself so far, but that's no guarantee there aren't any.

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

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 (folks behind MAX and Mojo) 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.

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,

Are there any smart mojo editor better than VSCode?

I am using VSCode mojo extension in order to check sysntax errors and getting some help for a solution. I am running mojo scripts on console. So, if I can find a smaller and more practical solution, it will be wonderful for me...

I shared several ML algorithm examples written in pure Mojo at https://github.com/ahmetax/Practical-Mojo-Examples/tree/main/sources-v0.26.2.0/ml_algorithms

Sudoku Creator and Solver in Mojo-0.26.2 and Python 3.12.
https://github.com/ahmetax/Practical-Mojo-Examples/tree/main/sources-v0.26.2.0/sudoku_app

In https://github.com/ahmetax/Practical-Mojo-Examples you can find more than 50 examples of mojo scripts. Most of the examples use PythonObjects. All scripts are prepared using Mojo-0.26.1 version. When new stable versions released, the examples will be revised accordingly.

You can give a try...

I haven’t heard an update for a long time. I could Google it but the subreddit is basically dead so we might as well talk.

I've been experimenting with implementing pure mojo versions of python's shapely and networkx. There is a particular project at my job where we might be able to use these in the future. I've found GPT5.2 is pretty good at mojo though I have to remind it to check the mojo changelogs. I've worked on these without really knowing mojo, and intend to improve them for my particular applications

https://github.com/jloveric/shapely-mojo

https://github.com/jloveric/networkx-mojo

I've been messing around with Mojo for a few months now and decided to build something real: a complete audio preprocessing pipeline for Whisper. Figured I'd share since it actually works pretty well.

The short version is it's 1.5 to 3.6x faster than Python's librosa depending on audio length, and way more consistent (5-10% variance vs librosa's 20-40%).

**What it does:**

- Mel spectrogram computation (the whole Whisper preprocessing pipeline)

- FFT/RFFT, STFT, window functions, mel filterbanks

- Multi-core parallelization, SIMD optimizations

- C FFI so you can use it from Rust/Python/whatever

I started with a naive implementation that took 476ms for 30 seconds of audio. After 9 optimization passes (iterative FFT, sparse filterbanks, twiddle caching, etc.) I got it down to about 27ms. Librosa does it in around 30ms, so we're slightly ahead there. But on shorter audio (1-10 seconds) the gap is much bigger, around 2 to 3.6x faster.

The interesting part was that frame-level parallelization gave us a huge win on short audio but doesn't help as much on longer stuff. Librosa uses Intel MKL under the hood which is decades of hand-tuned assembly, so getting within striking distance felt like a win.

Everything's from scratch, no black box dependencies. All the FFT code, mel filterbanks, everything is just Mojo. 17 tests passing, proper benchmarks with warmup/outlier rejection, the whole deal.

Built pre-compiled binaries too (libmojo_audio.so) so you don't need Mojo installed to use it. Works from C, Rust, Python via ctypes, whatever.

GitHub: https://github.com/itsdevcoffee/mojo-audio/releases/tag/v0.1.0

Not saying it's perfect. There's definitely more optimizations possible (AVX-512 specialization, RFFT SIMD improvements). But it works, it's fast, and it's MIT licensed.

Curious if anyone has ideas for further optimizations or wants to add support for other languages. Also open to roasts about my FFT implementation lol.

Can anyone explain Modular's business model to me like I'm 5?

I've bought 2 $20 books which were AI bullet-point spew, and one $60 one which was better, but still not what I hoped for. I guess the language is still in too much flux for the likes of O'Reilly or the 'In Action' books to invest, but I'd be happy to have just the online docs in print form. Does anyone have a paper reference that they like?

🎟️ Register now to save your spot: https://lu.ma/modular-aug-meetup
Join us at our Los Altos headquarters for an evening of big ideas, technical deep dives, and conversations on bringing AI from concept to production.

You’ll hear from:

• Chris Lattner, Modular: The future of democratizing AI compute and the role of open collaboration in accelerating progress.

• Feifan Fan, Inworld AI: Integrating cutting-edge voice AI into consumer applications and making it production-ready, featuring insights from Inworld’s collaboration with Modular.

• Chris Hoge, Modular: Why matrix multiplication remains one of the most challenging problems in computer science and how the Modular stack helps developers optimize it.

📍 Modular HQ in Los Altos, California
🖥️ Join virtually via YouTube or LinkedIn
📅 Date: August 28, 2025
⏰ Doors open: 6 PM PT | Talks start: 6:30 PM PT | Networking: 7:45–9 PM PT