Wednesday, 1st April 2026
I want to argue that AI models will write good code because of economic incentives. Good code is cheaper to generate and maintain. Competition is high between the AI models right now, and the ones that win will help developers ship reliable features fastest, which requires simple, maintainable code. Good code will prevail, not only because we want it to (though we do!), but because economic forces demand it. Markets will not reward slop in coding, in the long-term.
— Soohoon Choi, Slop Is Not Necessarily The Future
- The
llm_prompt_context()plugin hook wrapper mechanism now tracks prompts executed within a chain as well as one-off prompts, which means it can be used to track tool call loops. #5
- Removed features relating to allowances and estimated pricing. These are now the domain of datasette-llm-accountant.
- Now depends on datasette-llm for model configuration. #3
- Full prompts and responses and tool calls can now be logged to the
llm_usage_prompt_logtable in the internal database if you set the newdatasette-llm-usage.log_promptsplugin configuration setting.- Redesigned the
/-/llm-usage-simple-promptpage, which now requires thellm-usage-simple-promptpermission.
- This plugin now uses datasette-llm to configure and manage models. This means it's possible to specify which models should be made available for enrichments, using the new
enrichmentspurpose.
- Now uses datasette-llm to manage model configuration, which means you can control which models are available for extraction tasks using the
extractpurpose and LLM model configuration. #38
- The
actorwho triggers an enrichment is now passed to thellm.mode(... actor=actor)method. #3
- The same model ID no longer needs to be repeated in both the default model and allowed models lists - setting it as a default model automatically adds it to the allowed models list. #6
- Improved documentation for Python API usage.