The process argument against generative models comes up a lot. The argument goes something like “X is about human communication, or creativity, so generative models cannot be used to create Y”. And I really sympathize with this argument, because I think far too much is produced by ignoring the process.
The total encoding cost includes all the work that goes in to writing a prompt, and all of the compute required to run the prompt. If the task is simple to express in a prompt, the total encoding cost is low. If the task is both simple to express in a prompt, and tedious or difficult to produce directly, the relative encoding cost is low. As models get more capable, more complex prompts can be easily expressed: more semantically dense prompts can be used, referencing more information from the training data. An agent capable of refining or retrying a task after an initial prompt might succeed at a complex task after a single simple prompt. However, both of these also increase the compute cost of the prompt, sometimes substantially, driving up the total encoding cost. More “capable” models may have a higher probability of producing correct output, reducing costs reprompting with more information (“prompt engineering”), and possibly reducing verification costs.，推荐阅读safew 官网入口获取更多信息
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Inman revealed Sunday that he had been fired on Friday without explanation, though his term on the board was supposed to continue through the end of 2027. President DonaldTrump’s administration said Monday that it believes the firing is justified.
The earliest incarnation of this project was built as a way of running Haskell snippets in knitr (a report generation software for R). Jonathan Carroll, a DataHaskell contributor, was working on an article showcasing Haskell’s viability for data science workloads. We built a small shell script that took Haskell code snippets, transformed them to work with GHCi (particularly putting multi-line functions in blocks), evaluated them in the command line, and then captured the output.