HugiMuni
An open-source AI agent runtime project that provides a sandbox environment, file system access control, a command execution allowlist and cross-session state persistence mechanisms to support continuous operation and tool invocation by AI agents.
Timeline (last 90 days)
HugiMuni is an open-source AI agent runtime project.
Anastasia is an intern at HugiMuni and also a master's student at the Technical University of Munich (TUM).
The survey sample size is about 27-28 people (depending on the question), and it is a non-random sample.
In the multiple-choice question "what should AI do if it thinks the user's request is a bad idea," 74% (20/27) of respondents chose to ask for confirmation first.
In the same question, 33% (9/27) of respondents chose "proceed but flag the risk."
In the same question, 30% (8/27) of respondents chose "object and suggest a better approach."
In the same question, only 7% (2/27) of respondents chose "direct execution".
56% of respondents prioritized judgment (i.e., how AI makes decisions), 44% chose candor, 33% focused on code quality standards, and only 4% chose communication style.
56% of respondents preferred to write instructions themselves rather than choose from preset AI personas.
The HugiMuni team explicitly stated that the survey serves only as an early signal and does not constitute evidence of broader market preferences.
1 more timeline events
All Facts (11)
HugiMuni is an open-source AI agent runtime project.
50%UnverifiedAnastasia is an intern at HugiMuni and also a master's student at the Technical University of Munich (TUM).
50%UnverifiedThe survey sample size is about 27-28 people (depending on the question), and it is a non-random sample.
50%UnverifiedIn the multiple-choice question "what should AI do if it thinks the user's request is a bad idea," 74% (20/27) of respondents chose to ask for confirmation first.
50%UnverifiedIn the same question, 33% (9/27) of respondents chose "proceed but flag the risk."
50%UnverifiedIn the same question, 30% (8/27) of respondents chose "object and suggest a better approach."
50%UnverifiedIn the same question, only 7% (2/27) of respondents chose "direct execution".
50%Unverified56% of respondents prioritized judgment (i.e., how AI makes decisions), 44% chose candor, 33% focused on code quality standards, and only 4% chose communication style.
50%Unverified56% of respondents preferred to write instructions themselves rather than choose from preset AI personas.
50%UnverifiedThe HugiMuni team explicitly stated that the survey serves only as an early signal and does not constitute evidence of broader market preferences.
50%UnverifiedThe HugiMuni team's next research direction is to move from "hypothetical preferences" to "real usage experience," with particular focus on developers who use agents such as Claude Code, Codex, and Cursor in daily work.
50%