# Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026

DevFeed: [Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026](<https://devfeed.tech/articles/plan-for-clojure-ai-ml-and-high-performance-uncomplicate-ecosystem-in-2026-20726.md>)

Original publisher: [Read original article](<http://dragan.rocks/articles/25/Clojure-AI-ML-high-performance-Uncomplicate>)

Published: 2025-11-29T00:41:00Z

Content type: opinion

Language: en

Sources: [Dragan Djuric](<https://devfeed.tech/sources/dragan-djuric.md>)

Topics: [Clojure](<https://devfeed.tech/topics/clojure.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [OpenCL](<https://devfeed.tech/topics/opencl.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [NumPy](<https://devfeed.tech/topics/numpy.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [algebra](<https://devfeed.tech/tags/algebra.md>), [apple](<https://devfeed.tech/tags/apple.md>), [clojure](<https://devfeed.tech/tags/clojure.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [linear](<https://devfeed.tech/tags/linear.md>), [matrices](<https://devfeed.tech/tags/matrices.md>), [neanderthal](<https://devfeed.tech/tags/neanderthal.md>), [opencl](<https://devfeed.tech/tags/opencl.md>), [programming](<https://devfeed.tech/tags/programming.md>), [vectors](<https://devfeed.tech/tags/vectors.md>)

## AI overview

The article outlines a 2026 development and funding plan for the Uncomplicate ecosystem of Clojure libraries for AI, machine learning, and high-performance computing. It describes support for Nvidia GPUs, Apple Silicon, CPUs, CUDA, OpenCL, and several planned library improvements.

## Source excerpt

I've applied for Clojurists Together yearly funding in 2026. Here's my application. If you are a Clojurists Together member, and would like to see continued development in this area, your vote can help me keep working on this :) My goal with this funding in 2026 is to continuously develop Clojure AI, ML, and high-performance ecosystem of Uncomplicate libraries (Neanderhal and many more), on Nvidia GPUs, Apple Silicon, and traditional PC. In this year, I will also focus on writing tutorals on my blog and creating websites for the projects involved, which is something that I wanted for years, but didn't have time to do because I spent all time on programming. How that work will benefit the Clojure community This will highly benefit the Clojure community as this is THE AI ecosystem for Clojure, and supporting AI is arguably the main focus on probably all software platforms. Clojure has something to offer on that front, beyond just calling OpenAI API as a web service! Uncomplicate grew to quite a few libraries (of which some are quite big; just Neanderthal is 28,000 lines of highly-condensed, aggresively macroized, and reusable code): Diamond ONNX Runtime, Neanderthal, Deep Diamond, ClojureCUDA, ClojureCPP, Apple Presets, ClojureCL, Fluokitten, Bayadera, Clojure Sound, and Commons. Here's a word or two of how I hope to improve each of these libraries with Clojurists Together funding in 2026. Neanderthal (Clojure's alternative to NumPy, on steroids) In 2025, Neanderthal celebrated its 10th birthday. It started as a humble but fast matrix and vector library for Clojure, but after 10 years of relentless improvements, now it boasts a general matrix/vector/linear algebra API implemented by no less than 5(!) engines for CPUs, GPU (Nvidia CUDA), GPU (OpenCL: AMD, Intel, Nvidia), Apple Silicon (Accelerate), and general CPU (OpenBLAS). And this is not a superficial support for the sake of ticking a check box; each of these engines support much more operations on exotic structure