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davidbuniat

We let our Software Factory run for 15 hours autonomously. Output was 83 lines of highly optimized C++ code. 714 lines of tests. 8:1 test to code ratio. It fixed the bottleneck in a large codebase. Improved the TPC-H benchmark 2x. Verified memory leak using ASAN. Spent $160 of LLM calls.
Re:Chat with Any GitHub Repo – Demo and GitHub Repo on Wednesday April 26, 2023 @04:19PM (#35719396)
totally, @MuffinFlavored, super-exciting.
nice, pretty impressive to go from 10% to 70%. Do you also do autogpt style looping with reasking and verification until proper DSL is creating?
This is awesome, curious what is your DSL look like and what is the accuracy of LLM translating into your DSL?
my pleasure, let us know if you have any feedback, would love to see you succeed wtih Deep Lake. :)
thank you so much! We've recently announced a broad strategic collaboration with Intel Corporation to advance the field of AI data infrastructure. More specifically, this means initiatives in making sure Deep Lake datasets are trained super-smoothly on 3rd Gen Intel Xeon Scalable processors with built-in AI accelerators, as well as a range of technological improvements to Deep Lake given Intel's know-how in the field. Together, we will hopefully abstract away the need to build complex data infrastructure in-house :)

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