Gradient (@Gradient

Gradient

491 posts

Gradient

@Gradient_HQ

Open infrastructure for open intelligence. Lattica · Parallax · Echo

Joined May 2024

Pinned
Gradient
@Gradient_HQ
Feb 12
They crashed. They fell. They exploded on the pad. Then they got back up. Faster, wiser, stronger. Breakthroughs don't come from one perfect run, they come from the freedom to fail 100 times. Introducing Echo-2, distributed RL that boosts AI research throughput by 10x.
00:00
155K
Gradient reposted
Parallax
@tryParallax
Jun 1
nvidia going all in on local ai. here's our take: it shouldn't depend on which chip you bought. sparks, macs, the 5090 already on your desk, we cluster across all of it and split your favorite model pipeline-parallel so it runs fully private and local.
NVIDIA
@nvidia
Jun 1
NVIDIA RTX Spark: a 1-petaflop superchip, the full CUDA and RTX ecosystem, and Windows-native agents. A new beginning for personal computers.
19K
Gradient
@Gradient_HQ
May 1
CHOI
@arrakis_ai
Apr 30
This GPT Image 2 prompt is going insanely viral right now. “Redraw the attached image in the most clumsy, scribbly, and utterly pathetic way possible. Use a white background, and make it look like it was drawn in MS Paint with a mouse. It should be vaguely similar but also not
Made with AI
61K
Gradient reposted
Eric
@0xEricYang
Apr 29
We're hiring at Gradient. Building open-source environment infrastructure for our distributed RL training stack — reproducible, scalable to thousand-GPU runs Looking for 1–2 RL Environments engineers / tech leads: You've designed verifiers, built sandboxes for agentic RL
53K
Gradient reposted
Yuan ./
@yuangao
Apr 22
Thrilled to see @tryParallax live in production on @Theta_Network. This is exactly why @Gradient_HQ built Parallax: turning the world’s GPU mesh into a sovereign, distributed token factory. Congrats on the milestone! 🫡
Theta Network
@Theta_Network
Apr 20
Replying to @Theta_Network
To make this work, we adapted Parallax, @Gradient_HQ's distributed inference framework, to run across EdgeCloud's global node network. One API endpoint, model split across many machines, no centralized cluster required.
45K
Gradient reposted
Parallax
@tryParallax
Apr 22
glad we could help! with the agentic adoption soaring, privacy and token cost are already the top concerns for both agent and human users. that's what parallax's built for.
Theta Network
@Theta_Network
Apr 20
Replying to @Theta_Network
To make this work, we adapted Parallax, @Gradient_HQ's distributed inference framework, to run across EdgeCloud's global node network. One API endpoint, model split across many machines, no centralized cluster required.
27K
Gradient
@Gradient_HQ
Apr 15
Catch @alex_mirran on DevNTell this Friday. He’ll break down the infrastructure we're building at Gradient and show you exactly how to get started today. RSVP below👇
Developer DAO (🧱, 🚀)
@developer_dao
Apr 13
Ready to learn about the Open Intelligence Stack? 🎙️ This week on DevNTell, we'll be joined by @alex_mirran who is Head of BD at @Gradient_HQ, who'll be giving us an overview of the platform and more! 📅 April 17th 📋 RSVP today luma.com/tdmfpby7
44K
Gradient
@Gradient_HQ
Apr 8
Our cofounder @0xEricYang sat down with @yacinelearning to walk through Echo-2’s distributed RL architecture. Dive in to learn about async RL with distributed infra, and how we are scaling this for businesses to win in the agentic era.
Yacine Mahdid
@yacinelearning
Apr 7
for those interested in distributed reinforcement learning I just finished a ~1h tutorial on the echo2 framework by @Gradient_HQ we check: - how to do async RL - infra split between rollout workers and centralized learner - interview with gradient cofounder eric yang himself!
37K
Gradient
@Gradient_HQ
Apr 8
Full tutorial (~1h) youtu.be/eJL8RoubSKU
25K
Gradient
@Gradient_HQ
Apr 8
When you scale parallel agents, prompt updates degrade fast. The more trajectories you process concurrently, the more generic your learned prompts become. Our researchers worked with @lihanc02 and team on Combee to rethink how aggregation works at scale. Results held up across
Hanchen Li
@lihanc02
Apr 7
Prompt Learning does not scale for parallel agents. More parallel agents 🤖 = worse prompts 😭 Why? Processing too many trajectories concurrently damages the prompt update process 🐝 We fix this with Combee : → preserves high-quality learnt system prompt → scales to more
34K
Gradient reposted
Commonstack
@commonstack_ai
Apr 4
If software no longer needs you to operate it, what does an “application” even mean? That’s what we’re digging into at The Agentic Shift with panels, demos, and speakers from Google, PixVerse, MiniMax + more. SF | Apr 8 Sign up here:
The Agentic Shift · Luma
From luma.com
16K
Gradient
@Gradient_HQ
Mar 31
As fellow training nerds, UniPat AI’s approach caught our eyes. Synthesizing future-event data to fix outcome bias and actually beat human prediction markets is defined something worth checking out!
UniPat AI
@UniPat_AI
Mar 30
Today we’re introducing Echo — our full-stack prediction intelligence system, which turns uncertainty🔮 into profit📈. We Make Prediction General, Evaluable, Trainable and Profitable. 🌐Website: echo.unipat.ai
26K
Gradient
@Gradient_HQ
Mar 27
Everyone's scrambling for more compute. The harder problem is making distributed compute actually work for multi-agent systems. That's what Echo-2 is built for.
The Wall Street Journal
@WSJ
Mar 27
The explosion of agentic AI has kicked off a mad rush for computing power, pushing central processing units back into hot demand on.wsj.com/4uYyMN2
31K
Gradient
@Gradient_HQ
Mar 26
Benchmarks that test what models have memorized are saturating fast. ARC-AGI-3 is asking a harder question: can AI actually learn something new on the fly? One direction we've been exploring: multi-agent orchestration. In our study, coordinating four frontier LLMs across
ARC Prize
@arcprize
Mar 25
Announcing ARC-AGI-3 The only unsaturated agentic intelligence benchmark in the world Humans score 100%, AI <1% This human-AI gap demonstrates we do not yet have AGI Most benchmarks test what models already know, ARC-AGI-3 tests how they learn
GIF
arxiv.org
Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs....
We study multi-turn multi-agent orchestration, where multiple large language model (LLM) agents interact over multiple turns by iteratively proposing answers or casting votes until reaching...
25K