Compile ML models into dependency-free source code for easy deployment
I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…
Compile ML models into dependency-free source code for easy deployment launched on April 5, 2023 with 37 votes, #1,485 of 4,853 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
This tool converts machine learning models into standalone source code without external dependencies, making it easier to deploy models on mobile apps, smartwatches, and microcontroller firmware. Users can submit Keras or ONNX model files, and the compiler generates pure code that eliminates library overhead and compatibility issues. It's designed for developers and ML engineers who need lightweight model deployment without runtime dependencies.
written from the facts on this page · September 2026
From the sources
Flow Coder takes the pain out of integrating a machine learning model into your software or firmware project. It converts trained machine learning models straight into dependency-free source code for many common programming languages, thereby eliminating the complexity and overhead of inference libraries in deployment projects. The compiler can be invoked online, using a desktop tool or as a single executable command-line tool, so it’s easy to integrate as a pre-build step in a build pipeline. The compiler supports all layers and operations including quantizedfrom waveworks.dk
In the maker’s words, at launch
I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a…
Does a similar job
all alternatives →
Deploy ML Models on a Budget2021 · github.com · ▲117Deploy a ML inference service on a budget in less than 10 lines of code. - ebhy/budgetml
- HPHigh-performance ahead-of-time compiler for Machine Learning2018 · scss.tcd.ie · ▲83
Fullstack ML – From Notebooks to Deployment2019 · github.com · ▲198Contribute to xadrianzetx/fullstack.ai development by creating an account on GitHub.
Machine learning automation from creating to using models in production2021 · github.com · ▲72a delightful machine learning tool that allows you to train, test, and use models without writing code - GitHub - nidhaloff/igel at v0.4.0
More ai this month
the category →
Paintings by AI models: programs run through a simulation of oil paint.
AI · 20d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 28d ago · world.pixverse.video
Source: Show HN post ↗ · launch text quoted in part
Launched alongside, April 2023
the whole month →
- G4
Hi HN, Today we’re launching GPT-4 answers on Phind.com, a developer-focused search engine that uses generative AI to browse the web and answer technical questions, complete with code examples and detailed explanations. Unlike vanilla GPT-4, Phind feeds in relevant websites and technical documentation, reducing the model’s hallucination and keeping it up-to-date. To use it, simply enable the “Expert” toggle before doing a search. GPT-4 is making a night-and-day difference in terms of answer quality. For a question like “How can I RLHF a LLaMa model”, Phind in Expert mode delivers a…
AI · 2023 · phind.com
