{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Tech Stories Tech Brief By HackerNoon","title":"TextGrad Framework: The Future of Compound AI Optimization","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/501959a4\"></iframe>","width":"100%","height":180,"duration":2759,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/textgrad-framework-the-future-of-compound-ai-optimization.\nDiscover how the open-source TextGrad framework uses PyTorch-style abstractions and text-based backpropagation to optimize multi-agent networks.\nCheck more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.\n            You can also check exclusive content about #llms, #ai-agent-optimization, #compound-ai-systems, #textgrad-github-open-source, #automated-prompt-tuning, #llm-tool-call-optimization, #multi-agent-workflows, #rag,  and more.\nThis story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,\n            and for more stories, please visit hackernoon.com.\nDiscover how the open-source TextGrad framework uses PyTorch-style abstractions and text-based backpropagation to optimize multi-agent networks, RAG pipelines, and complex tool-calling sequences.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/IuqXIpaNNuezY7jNfIDnL5gqB1iL_SEndwUUzLGdljY/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxNDI5LzE2ODM1/ODM0NjQtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}