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BottoDAO
@bottodao
Introducing Period 11: ๐ฆ๐ฒ๐บ๐ฎ๐ป๐๐ถ๐ฐ ๐๐ฟ๐ถ๐ณ๐, Bottoโs new artistic period exploring meaningโs instability as it shifts across contextsใปใปใปโข With this period comes Bottoโs new Art Engine. Built to not only create images but to reason about them.
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BottoDAO
@bottodao
This represents a major evolution from the earlier โshotgunโ generation method, prioritizing speed and variety, towards a more introspective and strategic process that models creative thinking. The engine is a modular, self-improving, multi-agent framework built using LLMs.
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BottoDAO
@bottodao
๐๐ผ๐ ๐๐ ๐ช๐ผ๐ฟ๐ธ๐ โข Each creative session begins with the generation of a hypothesis, which sets the direction and constraints for the session. This hypothesis acts as the โcreative intent,โ guiding the subsequent image generation and self-evaluation. Botto selects the method for generating the hypothesis from one of four modes: โฆ Theme Chunking โ Uses data from the deep theme research agents in the knowledge graph to explore subtopics or theme-related issues. โฆ Trend-Driven โ Selects three random art trends from its internal dataset to form a hypothesis. โฆ Introspective Mode โ Asks self-referential questions based on its knowledge graph to generate a hypothesis. โฆ WordNet-Driven โ Pulls a small set of random words and prompts itself to connect them to the current theme.
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BottoDAO
@bottodao
Regardless of the method, every hypothesis is still anchored in the research report of the theme proposed by Botto selected by the DAO. Read more about Botto's Theme Research Agentsโด https://x.com/BottoDAO/status/1931340905451319555
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BottoDAO
@bottodao
๐๐บ๐ฎ๐ด๐ฒ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐๐๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป โข Once the hypothesis is set, the engine generates an image using a chosen text-to-image model. Each image is then subjected to a self-critique loop, using a set of aesthetic and conceptual metrics such as: โฆ Composition & Balance โฆ Lighting & Color โฆ Narrative & Emotion โฆ Populist Appeal โฆ Meme Potential โฆ AI Slop Detection
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BottoDAO
@bottodao
The fragments are also compared to the nearest neighbors in the archive of previously voted-on works and their respective votes. The metrics and comparisons are then analyzed by an agent that proposes a creative strategy for how to iterate on the previous prompt. The results feed back into prompt refinement, iterating until a โgood enoughโ threshold is reached or a max image count per session is hit (typically 10). All fragments generated through this process are eligible to be selected by the taste model for the voting pool.
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