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tales.fromprod.com | ||
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simonwillison.net
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| | | | | Anthropic have added one of the most important missing features to Claude Artifacts: apps built as artifacts now have the ability to run their own prompts against Claude via a ... | |
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til.simonwillison.net
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| | | | | My LLM tool has a feature where you can set a LLM_OPENAI_SHOW_RESPONSES environment variable to see full debug level details of any HTTP requests it makes to the OpenAI APIs. | |
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mydeveloperplanet.com
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| | | | | This blog explores using WireMock for mocking external system behavior in unit integration tests for Spring Boot applications. It emphasizes its utility when no container image is available, providing step-by-step guidelines for setup, stubbing requests and responses, and testing both non-streaming and streaming APIs effectively. | |
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jalammar.github.io
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| | | Discussions: Hacker News (397 points, 97 comments), Reddit r/MachineLearning (247 points, 27 comments) Translations: German, Korean, Chinese (Simplified), Russian, Turkish The tech world is abuzz with GPT3 hype. Massive language models (like GPT3) are starting to surprise us with their abilities. While not yet completely reliable for most businesses to put in front of their customers, these models are showing sparks of cleverness that are sure to accelerate the march of automation and the possibilities of intelligent computer systems. Let's remove the aura of mystery around GPT3 and learn how it's trained and how it works. A trained language model generates text. We can optionally pass it some text as input, which influences its output. The output is generat... | ||
