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Crispin Almodovar's picture
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Crispin Almodovar

calmodovar
bcnailab's profile picture
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AI & ML interests

NLP, log anomaly detection, cyber intelligence

Recent Activity

reacted to abidlabs's post with 🔥 about 14 hours ago
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 fdaudens's post with 🚀 about 14 hours ago
See that purple banner on the Llama 4 models? It's Xet storage, and this is actually huge for anyone building with AI models. Let's geek out a little bit 🤓 Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better. Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration. Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes. Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this. This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! 🧨 Check out the models collection: https://huggingface.co/collections/meta-llama/llama-4-67f0c30d9fe03840bc9d0164
reacted to daavoo's post with 🔥 about 14 hours ago
After working on agent evaluation🔍🤖 the last weeks, we started to accumulate code to make trying different agent frameworks easier. From that code, we have built and just released a small library called `any-agent`. Give it a try and a ⭐: https://github.com/mozilla-ai/any-agent ```py from any_agent import AgentConfig, AgentFramework, AnyAgent agent = AnyAgent.create( framework=AgentFramework("smolagents"), # or openai, langchain, llama_index main_agent=AgentConfig( model_id="gpt-4o-mini" ) ) agent.run("Which Agent Framework is the best??") ```
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logfit-project's profile picture test012345's profile picture

calmodovar's activity

liked a model 19 days ago
liked 5 models 11 months ago
liked a dataset about 1 year ago