Compute by Hyperspace (https://compute.hyper.space) is a new platform for deep, agent-driven AI research and task execution. It generates multi-step AI agents to tackle complex tasks through planning, research, and optimization.
The platform runs on Groq hardware by default, using the speedy Mixtral 8x 7 B model. But it’s flexible – you can use other models like Llama 3.1 70B or even run it on your laptop or the Hyperspace peer-to-peer AI network.
What sets Compute apart is its focus on iterative improvement. The agents don’t just execute tasks – they test, fix errors, and optimize until they produce high-quality results. This makes it ideal for thorny research problems that need multiple rounds of refinement.
The use of open source models from Meta AI and Mistral AI is another key strength. It allows for community involvement and transparency in the underlying AI. The peer-to-peer network option is intriguing too, potentially offering a more decentralized and secure environment for AI tasks.
If you’re looking to push the boundaries of agent-based AI research, Compute by Hyperspace is worth checking out. But test it thoroughly on your specific use cases.
For more on evaluating AI models and platforms, check out my post on choosing between Claude and GPT. The principles there apply to assessing newer offerings like this as well.
