Stars and mentions
Last 30 days, Oct 5 – Nov 3
Package downloads
Cumulative recorded downloads · since 2026-04-08
Across 3 known packages in repositories MatrAIx-ai owns. Cumulative totals include recorded downloads before the selected range. Missing observations leave gaps; totals may be incomplete. PyPI excludes known mirrors.
Top repositories
MatrAIx-ai’s repositories ranked by new stars, as on trending.
Contributes to
Repositories outside MatrAIx-ai’s account where MatrAIx-ai opened or reviewed pull requests in the past 365 days.
Who noticed MatrAIx-ai
Everyone who starred or forked MatrAIx-ai’s repositories since the archive began in 2011.
Notable accounts
Ordered by the stars their own repositories have received.
Biggest fans
Accounts that starred the most of these repositories.
Breakout weeks
Each repository’s best week of new stars, with the release published around it.
- Gold
- 01st place finishes
- Silver
- 02nd place finishes
- Bronze
- 03rd place finishes
- Awarded repositories
- 0Owned by MatrAIx-ai now
No trending awards yet
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A repository earns an award for finishing first, second or third in a completed trending day or week, ranked by new stars, new forks or pull request authors. Awards follow a repository through renames and transfers and count toward its current owner. Weeks run Monday to Sunday, UTC.
Mentions
PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas in four groups, rewrites each instruction in that user's own words and answers the CUA's questions, while the CUA sees only the dialogue, never the profile or the original instruction. Across nine backbones (3,330 runs), mean task completion falls by 36% relative to the original requests. The losses have two sources: in 59.9% of cells that lose points, the agent engages with what the user asked for and still gets it wrong, and 11.2% are stops the user requested that the checker scores as failures. Even when a persona request is fully completed, the user gives the top satisfaction rating in only 62.7% of cells, against 84.1% for the original requests. CUAs should therefore be evaluated not only on task completion but also on whether they meet the requirements and constraints users express. Code: MatrAIx-ai/PersonaCUABench. Project page: matraix.ai Comments: 31 pages, 13 figures, 24 tables