Accelerating @Gradient_HQ, The first decentralized AGI platform ex-@Sequoia | @UCBerkeley CS
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Joined August 2013
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From closed models to open intelligence.
The era begins.
🚀 Big shoutout to @Gradient_HQ for making Qwen 235B run on Parallax — the sovereign AI OS we all deserve.
Powerful and built for everyone.
Watch how they did it in minutes 👇
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.
An unverified model leak gets 2.5M likes in hours.
Anthropic's actual IPO filing gets 96K.
We're at the stage where what people imagine about AI generates 25x more engagement than what's actually being built. That gap is going to define the next 2 years of this industry.
Harness engineering + RL will be the most scalable way of creating net new data.
This is the most important "juice" for intelligence generation in the post-training era.
The world is moving from scraping the past to simulating the future.
Great conversation with @supercyclepod.
Building Gradient has been a relentless process, but the real inflection point is ahead. Intelligence belongs to everyone, and we intend to make that a reality.
Watch it in full:
AI should be a public good, not something gatekept by a handful of megacorps
We had Eric Yang, co-founder of Gradient Network, on the pod this week to talk through exactly that.
Gradient's "Open Intelligence Stack" includes:
i) Parallax for distributed model serving
ii) Echo for decentralized reinforcement learning
The whole thesis is that anyone should be able to run large models on consumer hardware (yes, including your Mac Minis + OpenClaws)
Eric breaks down their $10M seed round led by Pantera, Multicoin, and HSG; where he sees the industry heading; and why post-training is going to be the dominant force in enterprise.
Timestamps:
00:00 Intro
01:15 AI market is booming
02:29 Local compute is a hot topic
03:02 Parallax Inference Engine
04:34 Intelligence as a public good
05:46 AI models will become a commodity
07:32 Bottlenecks in AI models accessibility
09:34 Smaller AI models are catching up
11:01 How Gradient's Infrastructure Enables Model Development
12:15 Model post-training
14:24 How does reinforcement learning work?
17:35 AI going rogue
19:20 Gradient's token
23:02 AI entrepreneurs that Eric admires
26:11 Use cases on chain for AI
31:34 The trade-offs of coming to crypto
35:09 How low-spec GPUs will work on Gradient Ecosystem
38:08 Post-training will be the dominating force for enterprise
38:43 Open source models are way cheaper
41:39 Eric's founding story
49:07 Empowering researchers globally
53:37 Why did Multicoin Capital and Pantera Capital invested in Gradient
55:08 One-click deploy agent
58:16 Gradient in 3 years
We kicked off this campaign to demonstrate how you can own your AI. We are constantly shipping new features to the framework.
More exciting stuff coming soon ⚡️
Eric retweeted
The world needs more decentralization.
The compute resources AI is trained with, the infrastructure AI runs on, and the revenue that AI generates has consolidated into a handful of big companies.
The reality is that the only real growth in the American economy is coming from the “Magnificent 7” - who are all capitalizing on AI.
The remaining 493 companies in the S&P 500 are, in aggregate, growing at less than the rate of inflation.
Watch Parallax running LLM across Mac Mini + MacBook + RTX 4070 like a flash ⚡️⚡️⚡️
Try it out @ github.com/GradientHQ/parall…
I’m often struck by how human self-development mirrors Reinforcement Learning.
With strong reward signals from parents, friends, or bosses, we learn fast. With punishment, we avoid mistakes. Sometimes we get stuck in local optima—chasing short-term wins while missing rewards that take years to reveal.
What matters most is:
1) the environment we choose, does it gives good signal to us;
2) can we see through the noise to spot true rewards; and are we willing to explore the uncertain—even when the payoff isn’t clear yet?
We only live once. Make every exploration count.
here we are guys — the swarm is alive. infinite evolution begins.
We’re now hosting the @OpenAI gpt-oss 120B model on the Parallax Playground — powered by a RTX 4090 and an Apple M4 Pro, in different part of US.
In the future, everyone will be able to host locally, or with anyone else around the world.
More exciting stuff coming.
