Alternatives
Products that do what Cellulose – a tool to improve inference performance of ML models does
Hey HN! It’s Zheng here. I’m the founder of Cellulose (https://www.cellulose.ai). Cellulose is a tool that helps ML engineers understand, fine tune, and improve the inference performance of their ONNX models. With Cellulose, they can eventually resolve these issues in just hours, not weeks. Preparing ML models for production is a very manual and time consuming process. Unfortunately, it is also a necessary step for ML inference cost savings, sometimes even a hard requirement for certain applications like robotics and space tech. Today’s ML visualization tools are over 6 years old…
- 1
- 2
- 3
- 4
Working on Mac, Linux, and Windows now. I include a simple GUI to find new models and get things built and set up. It is working quite well across a few models for me. The GitHub README and DESIGN.md files go into detail of the how/why and it's working remarkably well so far. https://github.com/notactuallytreyanastasio/shoehorn
19d ago · notactuallytreyanastasio.github.io
- 5
- 6
- 7MS
Hi HN, I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio. The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun. What MLJAR Studio does: - Sets up a local Python environment automatically, runs on Mac, Windows, and Linux - Installs missing packages during the…
May 2026 · mljar.com
- 8
- 9
- 10OA
Hi HN, we built world-model-optimizer, an open source tool to continually improve a specialized model for an agent. It does this by simulating production tool responses through text world modeling (similar to QwenAgentWorld, summary here https://x.com/silennai/status/2073887455884058814). We can then use this to train a router for frontier, OS, and local models (use defaults or pick which ones to optimize against). wmo ingests agent traces, builds the simulation, embeds the traces, runs different models you choose against the simulation scenarios, and then uses a KNN…
Jul 2026 · github.com
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 183I
Hello HackerNews, I am Paul, and I would like to get some feedback on the tool we are releasing as beta today. 3LC is an ML tool that gives detailed insights, real-time data-centric iterative workflows for training/finetuning, and data quality improvements for your Machine Learning datasets and models. 3LC serves as a visualizer, editor, and debugger, focusing on how models learn from the training data. Key Features of 3LC: • Detailed Data Analysis: 3LC enables users to dive into model performance beyond typical labeling errors. It offers the capability to analyze intricate false…
2024 · pypi.org
- 19
- 20
- 21
- 22
- 23MF
Jun 2026 · modeloop.app
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →