A 2023 LLVM Developers' Meeting keynote by Abdul Dakkak, Chris Lattner and Jeff Niu introducing Mojo, a Python-family systems language built on MLIR and LLVM for heterogeneous computing.
Why Hacker News points people here: HN points to it as the best deep dive into Mojo's design, including MLIR use and SIMD-first scalars.
Convincing talk on Mojo's design, templated IR and why the project deserves attention.
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When mojo was initially released I didn’t think much of it because: 1. It was announced prior to being available, even in closed source form. 2. Emoji filenames meant to me that it wasn’t taking itself too seriously. 3. Many people have tried to make Python fast and failed, and that was my naive take on it, and despite the fact that LLVM allstars were working on it, it seemed like a lofty goal. 4. Closed source programming languages usually suck. They presented at the LLVM conference in 2023 and this video totally changed my view: this video There are some fascinating ideas here, I’m especially excited about what could come from their templated IR. Recommend others give it a watch if they’re confused on why they should pay attention to this project.
RecommendsMohamedMabroukAug 2026
“I think they had a good dive here”
Explains MLIR's multithreaded design and per-function parallel LLVM codegen.
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I think they had a good dive here this video The gist is that MLIR (unlike LLVM) was designed to be multithreaded so the whole stack above LLVM IR is parallel, and then LLVM is used as a per-function codegen tool in parallel, so although LLVM itself is single threaded there are multiple seperate contexts of it. also I think they wrote their own linker.
Cites itzoogenyNov 2023
“There is a section on SIMD and how they treat scalar values as a special case of vector operations”
Talk covers Mojo treating scalars as special case of SIMD vector operations.
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I was watching a recent talk about the new Mojo language [1]. There is a section on SIMD and how they treat scalar values as a special case of vector operations (around the 33 min time). It does seem that tensors are one of the core abstractions for modern ML systems. I've heard people joke that AI is just matrix multiplication. Python is such a flexible language that creating abstractions around its syntax was super easy. I believe that was part of the reason Python has come to dominate this space (not the only reason obviously). I too felt the same as you, but as a distant admirer of Zig. I totally understand that operator overloading can be misused. But I have an intuition that at least providing operators for linear algebra (and probably complex/quaternion) is a really important feature for any languages from this point in history going forward. 1. this video
Cites itmelodyogonnaSep 2025
“They have some fancy tech around compile-time interpreter, mojopkg, and their code generator.”
Covers compile-time interpreter, mojopkg and code generator of Mojo.
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They've given a talk abut this on the LLVM dev meeting: this video They have some fancy tech around compile-time interpreter, mojopkg, and their code generator. They also have a graph compiler (not mentioned in the talk) that can fuse kernels written in Mojo.
