Benchmarks and news on various repros of TypeSafe's Jev
GitHub - AbdelStark/jev-benchmarks at 0d610cc53e79bcbec691312b0c4adb4a0e371642
Probability-aware evaluation for typed decision models: calibration, selective risk, latency, and reproducible benchmarks. - AbdelStark/jev-benchmarks
MiniMaxAI/OctoCodingBench · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
continuedev/instinct-data · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Jev’s Architecture Unmasked — archerhume
I probed Jev with 10,000 API calls to work out roughly how it’s built, and why most of the grifter takes on X are completely wrong.
Charting and Navigating Hugging Face's Model Atlas
Charting and Navigating Hugging Face's Model Atlas: an interactive visualization and analysis tool for exploring large-scale AI model repositories. The atlas maps model relationships, and helps identify trends and fill in undocumented regions using structural patterns in the data.
Why We Built VIBE Bench: Rethinking Evaluation for Real Workloads
A Blog post by MiniMax on Hugging Face
Train AI models with Unsloth and Hugging Face Jobs for FREE
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
jaredpalmer/kev
tiny Jev-like family of decision models built on top of Qwen3.5 you can train and run on your own
Jev introduces a new shape of LLM—System One, aka Decision Models
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling “System One models” (I’m with Maggie Appleton, I think “decision models” …
Home - Use & Modify
This is a personal selection of beautiful, classy, punk, professional, incomplete, weird typefaces. Open source licenses make them free to use and modify. This selection is the result of deep search and crushes. This selection is yours — Raphaël
Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Generative artificial intelligence (AI) and machine learning (ML) models are being adopted across a variety of domains. As these technologies develop, there is notable diversity in their levels of availability and paths of diffusion. For example, fully closed-source models may be available through chatbots and API calls, but their weights, source code, training data, and other artifacts remain hidden from view. In contrast, open models make some or all of these materials publicly available for developers and downstream users.
Inkling: Our Open-Weights Model
Our first open-weights model: multimodal, Mixture-of-Experts, with controllable reasoning effort. Available to fine-tune on Tinker.
AI Index | Stanford HAI
The mission of the AI Index is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. To achieve this, we track, collate, distill, and visualize dat
