Introducing Kernels on the Hugging Face Hub ✨ What if shipping a GPU kernel was as easy as pushing a model? - Pre-compiled for your exact GPU, PyTorch & OS - Multiple kernel versions coexist in one process - torch.compile compatible - 1.7x–2.5x speedups over PyTorch baselines https://t.co/U0qDdxCWkd

0.3B params. Small enough that your coding agent can OCR a whole dataset, and the bill barely moves. Just added Falcon-OCR to uv-scripts/ocr. `hf jobs uv run` command, bucket or dataset output, AGENTS.md in the repo so your agent figures out the rest. https://t.co/VdY5FrNrCk

We recently worked with @databricks to make the best @huggingface × @ApacheSpark integration, and went a step further Introducing support for HF Storage Buckets in Spark ! Enabling fully optimized access to both AI datasets and buckets on HF on Spark https://t.co/pgvWj7XCot

Getting started takes about 60 seconds. >Install the CLI with npm, run the installer, and launch Claude Code like normal. Every turn gets logged from that point forward. Sessions, tool calls, subagents, token usage, all in a structured trace you can inspect in Weave. If you're iterating on prompts, context, or skills, this is how you figure out what's working and what's not. Repo 👇 https://t.co/Fw8Pfds7Tu

Now the masked_token_weighted is learning. We ablated the inpainting task, swapped MSE for SmoothL1Loss (more robust to outliers), and per-dim normalized the reconstruction targets, significantly reducing curvature-dim dominance. ref: https://t.co/FL5X61xpbQ https://t.co/0j03IXFXR2

Wandb is one of my most used tools these days as an AI/ML engineer, and it's pretty cool. Training a model using CNN architecture. Understanding how AI works under the hood will be a great leverage going forward. AI in healthcare 😍 https://t.co/bqzcFfO6d4

@bollineni1234 @mriduljoshi_ https://t.co/YkNsJq7RuX

Committing to tracking all my experimentation via wandb and making it public. https://t.co/BPKKTLT1Zu

Committing to tracking all my experimentation via wandb and making it public. https://t.co/BPKKTLT1Zu

@luaroncrew Hey! So, what we can do is give you inference credits to use for our W&B Inference service if you like? Send us an email with your org id or email you use for your account and we'll hook it up. https://t.co/2QXC2j0c0a

You start a training run. You leave for a jog. Your phone buzzes. "Loss diverged." Sprint home. Or... "Loss below threshold." Keep jogging. W&B Automations are now LIVE for everyone! https://t.co/kmDNkMsfVZ

Our Long-Term Benefit Trust has appointed Vas Narasimhan to Anthropic's Board of Directors. Vas brings more than two decades of experience in medicine and global health, including as CEO of Novartis. Read more: https://t.co/DSvtZUTone

Researchers just proved that every single elementary function, sin, exp, log, sqrt, comes from one single binary operator. It is like finding the “God Particle" for calculus. In computer science, every complex program breaks down to a single logical operator: the NAND gate. It is the fundamental building block of all digital reality. But for continuous math, physics, engineering, machine learning, we thought we needed a massive toolbox. Addition. Subtraction. Trigonometry. Logarithms. Every scientific calculator and neural network has to juggle all of them. Until today. But this paper proved that every single mathematical function can be generated by a single, bizarre binary operator. eml(x,y) = exp(x) - ln(y). Combine that with the number 1, and you can build everything. Pi. The square root. Sine and Cosine. Arithmetic. It is all just the exact same operator, repeating over and over again in a binary tree. Nobody anticipated this existed. It was found by systematic exhaustive search. But the implications for AI are massive. Instead of an AI struggling to combine different mathematical rules to discover a new scientific law, it can just use a single, uniform architecture. One trainable circuit. One repeatable node. We thought the language of the universe was complex. It turns out, it's just one equation repeating in the dark.

Circle Nanopayments lets agents send as little as $0.000001 USDC. Completely gas-free. So we’re turning this into a real build challenge at the Agentic Economy on Arc Hackathon. Powered by @BuildOnCircle @arc @GoogleDeepMind @MindsDB → https://t.co/u5i3ErvAsU. https://t.co/ordyKntmRs

7 years ago https://t.co/uhoUx2groa

7 years ago https://t.co/uhoUx2groa

PI HARD starring Neil deGrasse Tyson and Elon Musk https://t.co/JdwD0bHbjU

@standrew0x https://t.co/lUXBhbgHb6

https://t.co/tmyxJuaFlT

https://t.co/tmyxJuaFlT

South African politicians openly chant for the death of Whites. https://t.co/1HlTnjh82T

The latest AI Index shows just how fast the technology is moving. But it also highlights a growing gap between rapid progress and the systems meant to manage its impact, from policy to infrastructure to society. The takeaway is clear. AI’s trajectory is accelerating, but whether its benefits are evenly distributed will depend on how thoughtfully it is guided. https://t.co/dE1lXYOCn8 @StanfordAILab @StanfordHAI

Today I’m incredibly excited to announce Wendy. Wendy is an operating system and developer platform for Physical AI — built to make it dramatically easier to build and deploy on NVIDIA Jetson, Raspberry Pi, and other edge devices. We think robotics, edge AI, industrial systems, autonomous machines, and smart cameras should be far simpler to create. Less setup. Less infrastructure pain. Faster time to first demo. This is the start of something big. Get started at https://t.co/yGruInmaZ6

Le AI index report de Stanford (@erikbryn et al.) est sorti aujourd'hui et le premier graphique est assez douloureux https://t.co/JWqWJTROUw https://t.co/NhpK23vrUX

They're catching on https://t.co/g4NxnHpQp8

[HF ML Club India] The IRL event "Gemma x Hugging Face" is on the 18th of April (Saturday) in Kolkata. We have floated a form which needs to be filled by tomorrow (14th April). Help spread the news! https://t.co/DkRA5dt8b7

Introducing DDTree: accelerates speculative decoding by drafting a tree with one block diffusion pass, then verifying multiple likely continuations together. Paper: https://t.co/cgYBw70O5i Project page: https://t.co/ygFukxrZLB Code: https://t.co/2z7U00NsuH https://t.co/XJh6YXNgrf

Together AI presents Introspective Diffusion LM - The first DLM to match the quality of AR while outperforming DLMs in both model quality and serving efficiency - Delivering about 3× higher throughput than prior SotA DLMs https://t.co/Ji6Ys7DvnS

abs: https://t.co/6dSiAlmzhw repo: https://t.co/GQk9x6CyfW model: https://t.co/V5oR5ajhIP

Interesting: "Currently, 38% of Americans live within 5 miles of at least one operational data center... Living near a data center doesn’t have much of an effect on public opinion about the facilities." From now on, it looks like most DCs will be rural. https://t.co/URcr0s4L40

We have a lot of things in common. https://t.co/fgAWGr5H3M