Open Superintelligence Stack
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Prime Intellect retweeted
Highest throughput GLM 5.3 🫡
docs.primeintellect.ai/infer…
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Prime Intellect retweeted
We’re hiring a full-time engineer to help build Prime Agent :)
Own features end to end across agent harnesses, cloud agents, and self-improving loops -- integrated with our sandboxes, evals, and hosted training stack.
Full-time, in person in SF: primeintellect.ai/careers/85…
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Prime Intellect retweeted
prime agent v0.9.6 is out:
◆ Support for GPT-6 Sol, Opus 5.5, and Grok 4.7
◆ /mcp plugin catalog with one-click connections to Linear, Notion, Posthog, Stripe, and 60+ more services
◆ Huge perf and reliability pass 🫡
Lots more coming soon :)
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Proud to deliver the most efficient microVM sandboxes on the market at the most competitive prices for our users.
Made by RL teams, for RL teams. Deploy Prime Sandboxes to perform a training run, generate synthetic data, and run an eval or a persistent/remote agent.
Huge push by @a_kirillo and @damian_b 🔥
Introducing Prime Sandboxes:
MicroVM sandboxes purpose-built for RL training.
Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.
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Prime Intellect retweeted
Today, we're releasing Prime VM Sandboxes, co-designed with our research team for large-scale agentic RL training with tens of thousands concurrent sandboxes.
Introducing Prime Sandboxes:
MicroVM sandboxes purpose-built for RL training.
Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.
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Prime Intellect retweeted
Excited to launch Prime Sandboxes: microVMs for large-scale agentic RL training.
We couldn’t find sandboxes that could handle the scale of our RL training, so we built our own.
nitter.net/PrimeIntellect/status/…
Prime Intellect
Introducing Prime Sandboxes:
MicroVM sandboxes purpose-built for RL training.
Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.
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Visit our sandbox page primeintellect.ai/sandboxes
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Read our full blog post here primeintellect.ai/blog/sandb…
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In the near future, we will expand Prime Sandboxes to offer GPU microVMs, state snapshotting, sandbox forking, and shared persistent workspaces. This foundation will enable autonomous research loops that can explore, recover, and compound progress over time.
If you would like to be a part of our roadmap, join our Sandbox Platform team: primeintellect.ai/careers/26…
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Our pricing is built for scale, with no subscriptions or minimum spend.
For our launch through December 22, we’re proud to offer the most competitive pricing on the market for sandboxes, at a third of the cost of other large sandbox providers.
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Here are some tasks you can run with Prime Sandboxes:
1. Perform a training run
2. Generate synthetic data
3. Run an eval
4. Run a persistent, remote agent
All accounts begin with a limit of 1,024 concurrent sandboxes, and teams that need more can contact us directly.
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Prime Sandboxes are available both as standalone infrastructure through our CLI/SDK and as part of our RL suite. Users can enjoy the following features:
1. Full VM fidelity
2. Elastic capacity at scale
3. First-class RL support
4. Bring your own environment
5. Competitive tier-free pricing
With an architecture built for agentic training and pricing designed for tens of thousands of concurrent instances, they are the most cost-effective sandboxes available today.
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Introducing Prime Sandboxes:
MicroVM sandboxes purpose-built for RL training.
Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.
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Read the case study here: primeintellect.ai/case-study…
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The Goodfire team used Prime Intellect to train activation probes to detect reward hacking.
With them, they are able to catch reward hacking in various models.
Their probes are performing similarly or better than frontier LLM-as-judge setups, while being more efficient.
Models know when they’re reward hacking. But they still do it a ton - in 50-96% of rollouts we studied!
We built activation monitors that detect the behavior behind the Hugging Face hack in real time. This can help us stop hacks now - and train future models that don’t cheat. 🧵
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Prime Agent reached 20k stars on GitHub!
Thanks for being such an amazing community 🦋
Seth Karten
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Read our full blog post here: primeintellect.ai/blog/nixl-…
Try prime-rl today: github.com/PrimeIntellect-ai…
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