aurora-m/redteam
Viewer • Updated • 6.07k • 34 • 8
Aurora-M
community
AI & ML interests
Omni Lingual Models
Recent Activity
View all activity
huu-ontocord
updated a
dataset 3 days ago
huu-ontocord
updated 2
models 6 days ago
huu-ontocord
updated a
Space 6 days ago
marzena
authored 5
papers 29 days ago
AI translation of literary texts is "fine", but readers still prefer human translations
Paper • 2606.26040 • Published • 6
Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation
Paper • 2406.11580 • Published
AI use in American newspapers is widespread, uneven, and rarely disclosed
Paper • 2510.18774 • Published • 1
CaLMQA: Exploring culturally specific long-form question answering across 23 languages
Paper • 2406.17761 • Published
OverThink: Slowdown Attacks on Reasoning LLMs
Paper • 2502.02542 • Published
Muennighoff
submitted 2
papers to Daily Papers about 1 month ago
huu-ontocord
published a
dataset about 2 months ago
Ziyang
authored a
paper 2 months ago
Ziyang
submitted a
paper to Daily Papers 2 months ago
ajibawa-2023
posted an update 2 months ago
Post
3688
Technical-Architectures-Large
Dataset: ajibawa-2023/Technical-Architectures-Large
This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.
These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
Dataset: ajibawa-2023/Technical-Architectures-Large
This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.
These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
Chaning License to ODC-By
1
#2 opened 3 months ago
by 
huu-ontocord
Chaning License to ODC-By
1
#2 opened 3 months ago
by huu-ontocord
felfri
authored a
paper 3 months ago
PSaiml
authored a
paper 3 months ago
ajibawa-2023
posted an update 3 months ago
Post
7085
Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices