Nishith Jain's picture

Nishith Jain

KingNish

AI & ML interests

AI is fun actually. Busy till June 2025.

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liked a dataset 1 day ago
mozilla-foundation/common_voice_17_0
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KingNish's activity

reacted to abidlabs's post with ❤️ 1 day ago
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1574
JOURNEY TO 1 MILLION DEVELOPERS

5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.

Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobabooga’s Text WebUI, Dall-E Mini, and LLaMA-Factory.

How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:

1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs

1. Invest in good primitives, not high-level abstractions

When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:

Read the rest here: https://x.com/abidlabs/status/1907886
reacted to hexgrad's post with 👀 1 day ago
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2224
To Meta AI Research: I would like to fold ylacombe/expresso into the training mix of an Apache TTS model series. Can you relax the Expresso dataset license to CC-BY or more permissive?

Barring that, can I have an individual exception to train on the materials and distribute trained Apache models, without direct redistribution of the original files? Thanks!

CC (Expresso paper authors whose handles I could find on HF) @wnhsu @adavirro @bowenshi @itaigat @TalRemez @JadeCopet @hassid @felixkreuk @adiyoss @edupoux
reacted to clem's post with 🔥 3 days ago
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3814
Before 2020, most of the AI field was open and collaborative. For me, that was the key factor that accelerated scientific progress and made the impossible possible—just look at the “T” in ChatGPT, which comes from the Transformer architecture openly shared by Google.

Then came the myth that AI was too dangerous to share, and companies started optimizing for short-term revenue. That led many major AI labs and researchers to stop sharing and collaborating.

With OAI and sama now saying they're willing to share open weights again, we have a real chance to return to a golden age of AI progress and democratization—powered by openness and collaboration, in the US and around the world.

This is incredibly exciting. Let’s go, open science and open-source AI!
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