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What does it look like when AI learns to land a rocket? PRAXIST coordinates a team of Research Peers, each taking on a different part of the problem. Together, they learn across experiments and turn accumulated evidence into a validated, production-ready solution. See it in action🚀

Sapient Intelligence

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99,588 просмотров • 13 дней назад •via X (Twitter)

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Introducing PRAXIST Beta, your autonomous research team🚀 Define the objective, constraints, and what success looks like. PRAXIST discovers the path. From there, PRAXIST takes on the experimental research loop. Multiple Research Peers explore competing approaches in parallel, share useful findings, and build on accumulated evidence across experiments and generations. In partner-provided environments, PRAXIST reached a 100% safe-landing rate in a rocket simulation and reduced accumulated error in an industrial SLAM system from 9.37 centimeters to 5.01 centimeters. From robotics control to quantitative finance, PRAXIST has delivered measurable advances across fundamentally different problems by combining a shared core research architecture with domain-specific tools, knowledge, constraints, and evaluators. More approaches explored. Faster iteration. Greater R&D capacity without proportionally increasing specialist headcount.

Introducing PRAXIST Beta, your autonomous research team🚀 Define the objective, constraints, and what success looks like. PRAXIST discovers the path. From there, PRAXIST takes on the experimental research loop. Multiple Research Peers explore competing approaches in parallel, share useful findings, and build on accumulated evidence across experiments and generations. In partner-provided environments, PRAXIST reached a 100% safe-landing rate in a rocket simulation and reduced accumulated error in an industrial SLAM system from 9.37 centimeters to 5.01 centimeters. From robotics control to quantitative finance, PRAXIST has delivered measurable advances across fundamentally different problems by combining a shared core research architecture with domain-specific tools, knowledge, constraints, and evaluators. More approaches explored. Faster iteration. Greater R&D capacity without proportionally increasing specialist headcount.

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Welcome to the group chat, but it's all my AI agents. To build a website and plan the social content, I put together a team of AI agents. Each one had a different role. They collaborated, challenged each other's ideas, and reviewed each other's work before anything was finalized. Meet Bloome - a platform where humans and AI agents work together in the same conversation. You can bring different models into one workspace, assign them different jobs, and let them cross-check each other's thinking instead of relying on a single answer. It feels less like chatting with AI and more like managing your own creative team. Try it:

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the one thing i am most impressed about agents is how they can run the full production like if they were a big team they start the script and i see how they are surgically working on each part and validate at each stage... there is a researcher, there is a guy that iterates on the hook until it is perfect, it gives scores for each part, and won't jump into the next part until approved then it gets passed to another agent who takes care of the storyboard... this guy then sends it to another guy who create the visuals, another one who is an expert cinematographer, and so on... all this happening in front of my eyes, a team working together with minimal human input, and the only part i have to take care of, is the part i enjoy the most which is the visuals

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