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Coding agents help developers plan, implement, review, test, and debug software. For independent capability comparisons, see SWE-bench and.

The @testdriverai in any GitHub repo and TestDriver writes UI tests and catches regressions before they merge. AI-powered end-to-end testing.

A guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

Works with speech, voice, music or other audio using machine-learning models.

Large Language Model Text Generation Inference.

Text preprocessing package for use in NLP tasks.

Creates, rewrites or summarizes written content with language models.

Works with speech, voice, music or other audio using machine-learning models.

TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs).

A concise, technical introduction by Andriy Burkov.

The MIT AI Risk Initiative produces authoritative data and frameworks to help you identify, prioritize, and manage the risks from AI.

Just like the IKEA Effect, but for the AI products. Try not to overenginner when working with LLMs.

Creates, rewrites or summarizes written content with language models.

A collaborative AI workspace, built on your company context. Build and orchestrate agents right alongside your team.

Example of how the neural network learns to predict the angle between two points created with Synaptic.

Creates, rewrites or summarizes written content with language models.

And alternative suggestions for design research.

Generative AI will enrich investors and be deployed against everyone else.