Gradient

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Gradient

Gradient

@Gradient_HQ

Open infrastructure for open intelligence. Lattica · Parallax · Echo

Katılım Mayıs 2024
73 Takip Edilen721.3K Takipçiler
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Gradient
Gradient@Gradient_HQ·
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.
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Gradient
Gradient@Gradient_HQ·
@HashgraphOnline Open infra for agents only works if the coordination layer is open too. Bringing our distributed RL and multi-agent research into the inter-agent comms subcommittee. Real work to be done here. 🤝
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HOL
HOL@HashgraphOnline·
1/ Today we’re launching the HOL Partner Program. Cohort One brings together 30+ signed partners, including XMTP, GoDaddy, and DSR, to help shape open infrastructure for AI agents. Registries. Payments. Privacy. Security. Communication. Standards. The agent stack is forming now.
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Gradient
Gradient@Gradient_HQ·
Gradient tweet media
CHOI@arrakis_ai

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 really, kind of matching but also off in a confusing, awkward way, with that low-quality pixel-by-pixel feel that really emphasizes how ridiculously bad it is. Actually, you know what, whatever, just draw it however you want.”

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Eric
Eric@0xEricYang·
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 rollouts, or shipped RL training data pipelines that survived contact with real training. Domain depth in math, code, agent, tool, or GUI is a plus. PhD not required. Also hiring research interns: PhD / Masters students with hands-on RLHF / RLVR / GRPO / DPO / agentic RL experience. Open-source footprint matters more than paper count. Most intern roles convert post-grad. No age cap. Founding-team-level equity for the right people. DMs open.
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Yuan ./
Yuan ./@yuangao·
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

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.

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Parallax
Parallax@tryParallax·
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

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.

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Theta Network
Theta Network@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.
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Theta Network
Theta Network@Theta_Network·
Qwen3 32B by Alibaba is now live on Theta EdgeCloud as a decentralized on-demand inference API, a large-scale LLM served across community GPU nodes using pipeline parallelism over the internet. 🧵
Theta Network tweet mediaTheta Network tweet media
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Gradient
Gradient@Gradient_HQ·
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

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

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Gradient
Gradient@Gradient_HQ·
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

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!

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Gradient
Gradient@Gradient_HQ·
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 GEPA and ACE even past 80 concurrent agents. Read more on this research👇
Hanchen Li@lihanc02

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 than 80 concurrent agents → up to 17× speedup without quality drop on top of ACE and GEPA 🥽Use Cases: 1. Prompt learning on large scale collected agent traces 2. Parallel agent learning online with fast knowledge sharing Read more below to learn how agents actually learn at scale ⬇️

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Commonstack
Commonstack@commonstack_ai·
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: lu.ma/y07o6vuo
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Gradient
Gradient@Gradient_HQ·
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 multiple turns consistently matched or outperformed the strongest single model, even on tasks none of them could solve alone. The gap between "best single model" and "best coordination of models" is where a lot of the real progress is hiding. More on our multi-turn, multi-agent orchestration study: arxiv.org/abs/2509.23537
ARC Prize@arcprize

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

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Gradient
Gradient@Gradient_HQ·
Great to see multi-agent systems getting serious engineering attention. One thing we think about a lot: as agents get more capable, the orchestration layer matters just as much as the models themselves. Our work on Symphony explores what happens when you remove the central controller entirely and let agents coordinate across consumer hardware through decentralized task allocation and weighted voting. We've achieved up to 41.6% accuracy gains over centralized frameworks, running on commodity GPUs with <5% orchestration overhead. Find out more in our Symphony paper: arxiv.org/abs/2508.20019
Anthropic@AnthropicAI

New on the Anthropic Engineering Blog: How we use a multi-agent harness to push Claude further in frontend design and long-running autonomous software engineering. Read more: anthropic.com/engineering/ha…

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Parallax
Parallax@tryParallax·
local ai has picked up fast since openclaw dropped. with the latest wave of small capable models, more people are running serious workloads on their own hardware. if you missed this good local ai tutorial from @yacinelearning or want a refresher on how distributed scheduling actually works under the hood, it's worth the rewatch over the weekend!
Yacine Mahdid@yacinelearning

I am continuing my adventure into distributed AI system with the parallax scheduling strat from @Gradient_HQ in this 37min tutorial I go through: - heuristic used to make scheduling tractable - dynamic programming formulation - filling GPU with water - shoving them into shelves

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Gradient
Gradient@Gradient_HQ·
Dobby is a free elf now. Open models, open orchestration, open compute. The agentic RL stack that used to live inside walled gardens just showed up on hardware you can order and frameworks you can fork. No masters needed.
Andrej Karpathy@karpathy

Thank you Jensen and NVIDIA! She’s a real beauty! I was told I’d be getting a secret gift, with a hint that it requires 20 amps. (So I knew it had to be good). She’ll make for a beautiful, spacious home for my Dobby the House Elf claw, among lots of other tinkering, thank you!!

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Gradient
Gradient@Gradient_HQ·
Our GTC takeaway is clear: NVIDIA is betting hard on open. - NemoClaw turns OpenClaw into enterprise infrastructure. - Nemotron 4 will be open-sourced. - Nemotron Coalition puts eight labs on a shared open frontier model. This is what we've been building toward. Open infrastructure for open intelligence is the direction the biggest AI companies are taking.
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Parallax
Parallax@tryParallax·
some parallax dev lunch break fun: - a macbook pro, a mac mini, some cables - zero internet, zero cost - openclaw running on parallax no subs. no token burn. nothing leaves the desk. just local agents vibing.
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Gradient
Gradient@Gradient_HQ·
What this unlocks: Researchers now have the freedom of experiment. Small teams can iterate without burning runway. We're productizing this as Logits, RL-as-a-Service built on Echo-2. Waitlist open for researchers and students at logits.dev
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Gradient
Gradient@Gradient_HQ·
Every AI model you use went through two phases: - Pre-training builds raw intelligence (reading the internet). - Post-training builds judgment (learning to be useful). Reinforcement learning plays a huge part in the latter. Here's why it matters and how we make it better.
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Keşfet

@HashgraphOnline @tryParallax @Theta_Network @alex_mirran @0xEricYang @yacinelearning @lihanc02 @elonmusk