Alternatives
Products that do what I'm building a simple way to package, collaborate, and deploy ML models does
Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!? To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits: 1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant…
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- 3SP
Hi HN, Over the past 6 months I've been working on a technical book focused on helping aspiring data scientists to get hands-on experience with cloud computing environments using the Python ecosystem. The book is targeted at readers already familiar with libraries such as Pandas and scikit-learn that are looking to build out a portfolio of applied projects. To author the book, I used the Leanpub platform to provide drafts of the text as I completed each chapter. To typeset the book, I used the R bookdown package by Yihui Xie to translate my markdown into a PDF format. I also used Google docs…
2020
- 4CM
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…
2023 · waveworks.dk
- 5NA
I've been tired with the current options on the market for awhile and decided to do something about it after the running into the disaster that is MLOps at my last two startups and having to manage a multiple operation platforms both for my fellow ML engineers, the general application CI/CD and orchestration layers while simultaneously building the application itself. Its still extremely early for the product but its functioning and is well on its way. I'd love feedback on the approach and peoples thoughts on the problem space. Personally my irritations have been in the poor tooling,…
2025 · egdaemon.com
- 6DM
2021 · github.com
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Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.com
- 9TS
Hello Hacker News community! I'm currently working in financial risk management within the banking sector, and I began my career as a Data Science specialist. For quite some time, my friend and I have been developing a small pet project just for fun. This tool has repeatedly helped us save time when testing various hypotheses and machine learning models. The core idea is to combine different scripts—created in various programming languages and virtual environments—within a minimalist graphical interface. Whether you're building models, running a local neural network, or sending requests to…
2024
- 10MO
Hey HN! We built mlop (https://mlop.ai), a fully open source (https://github.com/mlop-ai) ML experiment tracking platform, much like Weights & Biases. Unlike the existing competitors we focus heavily on performance (yes our ingestion backend is in Rust), and top tier user experience, and we are fully open sourced with easy self hosting using docker. Why did we build this? WandB was misleading about their performance, they say they are non blocking, but in fact, they block user code (see video https://docs.mlop.ai/docs/demo), our logger tries to be…
2025 · github.com
- 11CM
Hey HN, I'm Jinen. I’m a PhD student working on DL interpretability & Optimization Theory. Before beginning my PhD, I worked at DagsHub fine tuning vision models for domain specific deployments. I wanted to use ML models to help label the data, based on Label Studio's ML Backends. The goal was to use a model registered and tracked on MLflow. I found it tedious and involved a lot of boilerplate code to pipeline an MLFlow registered model into Label Studio's ML backend . Part of the challenges was setting up the web server, adapting the model outputs and reading through a lot of documentation…
2024
- 12DA
Hi. :) I’m Andrey, the creator of dstack. I started this project while I was working at JetBrains where I helped the PyCharm team to improve support for Jupyter notebooks. As I was in close contact with many ML devs (who used PyCharm) I was able to see their struggle with running ML workflows. Unlike traditional dev workflows, ML workflows are difficult to run on a local machine (due to the lack of memory, more CPUs/GPUs, etc). This is why people often have to use remote machines (e.g. via SSH), or adopt one of the end-to-end MLOps platforms. Using remote machines is not difficult but…
2022 · github.com
- 13KO
2025 · kit.cased.com
- 14OS
2023 · github.com
- 15IB
Hi, I have been building small AI Agents for quite some time now using various frameworks and one thing that always bugged me was that iterating on small things like prompts, flows, tools etc always took a code change + deployment of the app. While the prompt part can be solved with Langfuse I haven't found a good way to keep the flow management remote (and open source). Lately I have been working with Genkit (https://github.com/firebase/genkit) and love how modular it is. So I thought why not build a UI builder on top of that that can handle simple flows, prompts and…
Sep 2025
- 16IB
I had 14,000 photos sitting on a drive and wanted an excuse to play with local vision models and Elixir/Phoenix. I originally tried to get LLaVA to tell me if a photo was 'good' or matched my style, but quickly learned that LLMs have terrible taste. I ended up demoting the LLM to just extract metadata, and built a custom CLIP/Ridge Regression pipeline to actually learn my preferences based on how I rate things. The stack is Phoenix/Oban on the orchestrator side, and Python/FastAPI/Instructor for the AI workers. Happy to answer any questions about the architecture,…
Apr 2026 · qwelian.com
- 17LM
We are the developers of an open-source package Metaflow that we started at Netflix. Metaflow provides a human-friendly interface to the full stack of ML infrastructure, including data access, compute, workflow orchestration, and versioning. It is used by hundreds of companies across industries. Over the past years, we have seen that there are two major stumbling blocks for folks who want to learn to build real-world ML applications: 1) Setting up the full infrastructure stack in the cloud costs time and money. The investment is worth it once you know what you want to do, but that's not…
2023 · outerbounds.com
- 18WB
Hey HN: Kaveh here, founder of https://www.usage.ai/ We help companies drive down AWS, GCP, and Azure spend. Why? Because the way it's done now is a pain. DevOps and Software Engineers end up spending time managing costs rather than focusing on business problems. I have been building Usage AI for almost 4 years now (4 year anniversary in 1 month from now!) with an incredible group of founding people. We started as a product just to help lower AWS EC2 costs, and now we do all major AWS services (such as RDS, OpenSearch, ElastiCache, and Redshift with more on the way) and other…
2024
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machine learning, python, programming, beginner, software
Jul 2026 · mirthi.gumroad.com
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Hey HN! I wanted to practice "vibe coding" and see how far and fast I can go by only prompting, without actual coding. I decided to make a simple CLI app that scrapes web docs into a single md file (I was annoyed that LLM keeps writing Tailwind 3 code for a Tailwind 4 project). In just a couple of hours, the CLI app was ready! Then iterated on arguments for another couple of hours. Result: https://github.com/vladstudio/web2llm Then I decided to go further and "productize" the CLI by making a web app for it. Another half-day, and the web app is ready!…
2025 · web2llm.dev
- 21IM
I spent the past few weeks making an open source cloud code editing environment with an AI copilot and multiplayer collaboration! It's fully self-hostable in 5-10 minutes. There's a lot of minor improvements to be made, and some are already listed in the Github issues. Let me know what you think and feel free to try it out.
2024 · github.com
- 22BA
I built CodinIT because I wanted that "Bolt-like" experience, but on my own terms. 100% Open Source The core idea: You should be able to prompt a full-stack application into existence, but the environment should be local, the models should be swappable (Ollama/LM Studio support was a priority), and the output should be standard code you actually own. A few things I focused on: Context Management: One of the hardest parts was figuring out how to feed the right file context back to the LLM without blowing out the token limit. I’ve implemented a custom indexing approach to keep the "vibe…
Dec 2025 · github.com
- 23HA
Demo starts at 50m into the video. This was a bit terrifying to record because 2am the previous night everything was totally broken after a major refactor (so that we could add external LLM support as well as local GPUs). But pressure can be a useful force :-D We start with a stack deployed on my laptop without a GPU, pointing to together.ai so we can run open source LLMs easily without having to have access to a GPU. We show simple inference through the ChatGPT-like web interface (with users, sessions etc) and then simple drag'n'drop RAG. Then we show some helix apps defined as yaml: Marvin…
2024 · youtube.com
- 24NT
I built a CLI tool that turns codebases and PRs into diagrams so you can quickly understand how things fit together. Originally made it because I couldn't follow my own AI-generated repos. Just shipped a big update: - Switched from D2 to Mermaid for rendering - Tree-sitter AST parsing + agentic flow instead of raw LLM calls. ~50x faster. - Works on any GitHub repo or PR, not just local - Dropped the web frontend, it's just a CLI now - Published as a pip package Still a ton to improve and I'm building fast. Feedback, issues, PRs all welcome.
Feb 2026 · github.com
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