Benchmarking OpenAI Models For Automated Error Resolution
- Reilly Oldham tl;dr: AI continues to offer new possibilities in code generation and debugging. Raygun looked into LLMs’ current capabilities and their implications for the future of development practices. Interested? Read the study to see how each of the OpenAI models handled software errors.featured in #540
Scaling ChatGPT: Five Real-World Engineering Challenges
- Gergely Orosz Evan Morikawa tl;dr: An interview with Evan Morikawa, who led the OpenAI Applied Engineering team as ChatGPT launched and scaled. Evan reveals the five engineering challenges along with lessons learned. Challenges are: (1) KV Cache & GPU RAM. (2) Optimizing batch size. (3) Finding the right metrics to measure. (4) Finding GPUs wherever they are. (5) Inability to autoscale.featured in #491
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Using GPT-3 To Explain How Code Works
- Simon Willison tl;dr: "One of my favourite uses for the GPT-3 AI language model is generating explanations of how code works. It’s shockingly effective at this: its training set clearly include a vast amount of source code. Simon shows a few recent examples."featured in #333
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DALL·E: Creating Images from Text
tl;dr: "We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language."featured in #220