weakening

s its verbose239regression outputs contain large execution traces with sparse hypothesis-relevant content.240MetaScience resists condensation entirely, its compact, already-aggregated outputs leave241little noise to remove.

Performance varies substantially: crisis communication and264expense anomaly score highest (85, 82

10,000 unique person mentions

Stage 3: Interesting findings extraction. To build the culture layer, a separate week-147batched pass uses elevated temperature to identify 3–10 qualitatively interesting moments148per week—humor, prescience, fun facts, cultural artifacts—each with a catchy title, de-149scription, category, excitement rating (1–10), and source email references. Findings below150excitement level 6 are discarded, yielding 382 curated findings across 119 weeks.1514

emoving emails381with bodies <50 characters

3)Temporal density—email provides daily or even hourly granularity over121months or years;

2)Comprehensiveness—email captures strategic119discussions, operational coordination, social exchanges, and administrative processes within120a single medium;

five-layer model

simplicity, mobil-ity, and immutability

odels to obsolesce

esource e"ciency.

the ability to directlymanipulate model weights allows users to bypass safety training entirely, modifying models so that they no longerrefuse any request

Some participants argued that moredata is necessary to capture the full range of cultural expressions, while others contended thatthe focus should be on developing thicker development pipelines that incorporate expertiseand context. They discussed the limitations of current models, which often operate on crudemetrics and may not adequately represent the richness of cultural data.

They highlighted the importance of interdisciplinaryteaming at specific points in the development pipeline, imagining pairs of experts fromtechnical and qualitative fields working together step-by-step to negotiate approaches thatmeet shared goals. This collaborative approach would ensure that both qualitative insightsand quantitative rigor are incorporated into AI development.

common basic language for evaluating iterations of AI that do not assumethere is a linear or universal path of improving AI for all users, regardless of context.This shared language would help clarify where disciplinary specificity is needed and whereinterdisciplinary collaboration can be most effective.

how small language models can contribute to responsible innovation, and howto design decentralized infrastructure architectures that enable users to choose how theyshare and distribute their data and models

the need for alternativeapproaches to AI development that prioritize sustainability, justice, and inclusivity

The role of metadata is complex

ocused on the gaps between community knowledge and com-putational knowledge

Institutional aspects and interdisciplinarity play a significant role in the cultures of AIproduction. There is a need for alternative imaginaries of technology that go beyond thecorporate inclusion of data.

first exercise of the day was a series of “speed dating” rounds.