Lefts
Lefts is a very simple domain specific language for building complex machine learning workflows from simple ones.
Quick answer
What is Lefts?
At a glance
- Category
- Indie AI Tools
- Pricing
- Not disclosed
- Starting price
- Not disclosed
- Free tier
- No
- Platforms
- Not disclosed
- Launched
- Not disclosed
- Company
- Not disclosed
About Lefts
Lefts is a very simple domain specific language for building complex machine learning workflows from simple ones. Starting with your favourite machine learning models, you can use Lefts operations to:. Without making subsequent model fitting, evaluation or experimentation any more complex than it was with the original model. This implementation is built on top of the excellent Polars DataFrame library. E nsemble: Takes a set of models and makes them evaluate as one. F eed: Allows the output of one model to be used as a feature or target by another.
From the community
User reviews
No user reviews yet. Be the first to share your experience with Lefts.
Alternatives
Similar to Lefts
Jørnal
Yesterday's entry is on the page above, read it whenever; it can't be rewritten.
Trace
A macOS menu-bar app that turns any conversation into a clean markdown transcript, with an optional on-device summary. Runs a local speech model entirely on your Mac, in...
BrandLM.AI
In each answer, tracked brand names are highlighted as they appear and tallied below, together with the pages the AI cited.
Ilya Sutskever's 30 Papers
Explore the deep learning reading list Ilya Sutskever gave John Carmack, with free audio explainers for AlexNet, ResNet, Transformers, scaling laws, and more.
Tail Panic
Tail Panic: command animals on a 25×25 grid to chase and escape. Write onFrame scripts, join bot matches and watch 3D replays.
JobTrue
One place for your entire job search. Know your fit before you apply, keep your resume in one master document, track every opportunity. Apply less. Land more.
Techavy is reader supported. When you buy through links on our site we may earn an affiliate commission. This never changes our ratings or what we recommend. See our methodology.