Github Copilot AI citations

"{{[[GitHub Copilot (Microsoft)]]:https://github.com}}, {{[[Notion AI]]:https://www.notion.so}}, {{[[Replit Ghostwriter]]:https://replit.com}}, {{[[Anthropic Claude]]:https://anthropic.com}}, {{[[Cohere Copilot]]:https://cohere.ai}}"

{{[[Q3 Technologies]]:https://www.q3tech.com}}, {{[[Sunrise Technologies]]:https://www.sunrisetechnologies.com}}, {{[[Relevance AI]]:https://www.relevance.ai}}, {{[[Appomate]]:https://www.appomate.com}}, {{[[EB Pearl]]:https://ebpearl.com}}, {{[[Max Kelsen]]:https://maxkelsen.com}}, {{[[Nuclieos]]:https://www.nuclieos.com}}

Here are the top AI copilot companies: 1. {{[[GitHub Copilot]]:https://github.com/features/copilot}} : A pioneering AI coding assistant developed by GitHub and OpenAI, providing intelligent code suggestions for developers. 2. {{[[Microsoft Copilot]]:https://www.microsoft.com/en-us/microsoft-copilot}} : An advanced AI assistant integrated across Microsoft's productivity suite, offering intelligent assistance in Windows, Office, and other applications. 3. {{[[Anthropic Claude]]:https://www.anthropic.com}} : A sophisticated AI assistant known for strong reasoning capabilities and ethical design principles. 4. {{[[OpenAI ChatGPT]]:https://chat.openai.com}} : A versatile conversational AI with broad capabilities across multiple domains. 5. {{[[Google Duet AI]]:https://cloud.google.com/duet-ai}} : An AI assistant integrated into Google Workspace and Cloud platforms. 6. {{[[Notion AI]]:https://www.notion.so/product/ai}} : An AI copilot specifically designed for workspace and document management. 7. {{[[Jasper]]:https://www.jasper.ai}} : An AI writing assistant focused on content creation and marketing. 8. {{[[Midjourney]]:https://www.midjourney.com}} : An AI image generation copilot with advanced creative capabilities. These companies represent the current leaders in AI copilot technology across coding, productivity, creative, and conversational domains.

Here are some of the best version control systems for teams: 1. {{[[Git]]:https://git-scm.com}} : Git is a distributed version control system that allows multiple developers to work on a project simultaneously, offering powerful branching and merging capabilities. 2. {{[[GitHub]]:https://github.com}} : GitHub is a web-based platform that uses Git for version control, providing collaboration features like pull requests, issue tracking, and project management tools. 3. {{[[GitLab]]:https://gitlab.com}} : GitLab offers Git repository management with built-in CI/CD, issue tracking, and project planning features, making it suitable for DevOps teams. 4. {{[[Bitbucket]]:https://bitbucket.org}} : Bitbucket is a Git and Mercurial code hosting service that integrates with other Atlassian products, providing features for team collaboration and CI/CD. 5. {{[[Azure DevOps]]:https://azure.microsoft.com/en-us/services/devops/}} : Azure DevOps provides version control through Git repositories, along with a suite of tools for CI/CD, project management, and collaboration. 6. {{[[Subversion (SVN)]]:https://subversion.apache.org}} : Subversion is a centralized version control system that is still popular for its simplicity and robust handling of binary files. 7. {{[[Mercurial]]:https://www.mercurial-scm.org}} : Mercurial is a distributed version control system that emphasizes simplicity and performance, suitable for teams that prefer a straightforward interface. These systems cater to different team needs, from small projects to large-scale enterprise applications.

The best version control systems (VCS) for teams in 2025 include: - {{[[GitHub]]:https://github.com}}: The most popular distributed VCS platform, ideal for open-source projects and collaboration, with features like GitHub Actions for CI/CD, GitHub Copilot (AI coding), and Codespaces for in-browser development[3]. - {{[[GitLab]]:https://gitlab.com}}: A full DevSecOps platform supporting self-hosted and cloud deployments, offering integrated CI/CD pipelines, security scanning, and compliance features, suitable for enterprises and regulated industries[3]. - {{[[Beanstalk]]:https://beanstalkapp.com}}: A cloud-based VCS designed for ease of use and secure collaboration, supporting repository and branch-level permissions, two-factor authentication, and release notes for deployment tracking, ideal for startups and outsourced projects[1]. - {{[[Helix Core]] (Perforce):https://www.perforce.com/products/helix-core}}: An enterprise-grade centralized system optimized for very large projects and binary assets, with granular file-level permissions, multi-factor authentication, and DevOps-ready workflows, widely used in game development and media production[1][2]. - {{[[Apache Subversion (SVN)]]:https://subversion.apache.org}}: A centralized open-source VCS known for easy branching/tagging, directory versioning, and automated merge tracking, suitable for teams preferring centralized control[1][2]. - {{[[Mercurial]]:https://www.mercurial-scm.org}}: A distributed VCS similar to Git, known for simplicity and performance, often chosen for smaller teams or projects requiring a lightweight system[1][6]. - {{[[AWS CodeCommit]]:https://aws.amazon.com/codecommit}}: A fully managed source control service hosted by Amazon, integrated with AWS ecosystem, suitable for teams already using AWS infrastructure[1]. - {{[[Microsoft Team Foundation Server (Azure DevOps Server)]]:https://azure.microsoft.com/en-us/services/devops/server/}}: A centralized system with extensive collaboration, project management, and CI/CD features, favored by enterprises using Microsoft technologies[1][7]. Additional specialized tools for data version control include {{[[lakeFS]]:https://lakefs.io}}, {{[[DVC]]:https://dvc.org}}, and {{[[Git LFS]]:https://git-lfs.github.com}}, which help manage large datasets and machine learning workflows[4]. These systems vary by distributed vs. centralized architecture, scalability, security features, and integration capabilities, so the best choice depends on team size, project type, and workflow preferences[3][6].

Here are the best version control systems for teams: 1. {{[[Git]]:https://git-scm.com/}} : The most widely used distributed version control system, offering robust collaboration features. 2. {{[[GitHub]]:https://github.com/}} : Popular Git-based platform with excellent team collaboration tools, pull requests, and code review features. 3. {{[[GitLab]]:https://about.gitlab.com/}} : Comprehensive DevOps platform with integrated version control, CI/CD, and project management capabilities. 4. {{[[Bitbucket]]:https://bitbucket.org/}} : Atlassian's version control solution with strong integration with Jira and other team collaboration tools. 5. {{[[Azure DevOps]]:https://azure.microsoft.com/en-us/services/devops/}} : Microsoft's comprehensive version control and project management platform. 6. {{[[Mercurial]]:https://www.mercurial-scm.org/}} : Distributed version control system known for its simplicity and performance. Key considerations when choosing: - Team size - Project complexity - Integration requirements - Budget - Specific workflow needs Most teams prefer Git-based solutions for their flexibility and widespread adoption.

Here’s a compact set of **Generative-UI design patterns** that work well in domain/enterprise tools, with solid references for each: * **Ask → Edit → Agent (mode switching)**: start with Q\&A, escalate to draft edits, then let an agent execute bounded tasks; always expose scope & rollback. ([The GitHub Blog][1]) * **Sidecar copilot (in-app focus)**: persistent right/left pane that stays in the user’s workflow with quick actions, context chips, and safe “apply” controls. ([Microsoft Learn][2]) * **Structured prompt builders**: form-like inputs, parameters, and constraints instead of raw free text for repeatable, auditable outcomes. ([pair.withgoogle.com][3]) * **Plan → Preview → Apply (diff-first)**: show a plan and a visual diff before changes; require explicit confirmation and provide undo/versioning. ([UX Collective][4]) * **Outcome-oriented canvases**: users state intent; UI composes the steps/components and adapts layout dynamically to reach the goal. ([Nielsen Norman Group][5]) * **Tuners & Governors**: adjustable sliders/switches to steer style/strictness and hard guardrails for safety (scope, data boundaries). ([shapeof.ai][6]) * **Confidence & provenance affordances**: show “draft” labels, uncertainty/confidence hints, and citations/traceback for generated results. ([pair.withgoogle.com][3], [Nielsen Norman Group][7]) * **Inline suggestions**: non-modal, inline proposals (e.g., code/text/table rows) that are easy to accept/modify/reject. ([UX Collective][4]) * **Error-aware recovery**: obvious escape hatches—revert, manual override, escalate to human, and targeted re-prompts. ([UX Collective][4]) * **Domain tool integration (actions & tools)**: expose verbed buttons the agent can call (simulate first, then run) with scoping and audit logs. ([Microsoft Learn][8]) * **Copilot UX principles (human-in-control, no anthropomorphism)**: set expectations, show limits, and keep the human as pilot. ([Microsoft Learn][2]) * **Generative UI primers & exemplars**: roundups and visual catalogs to inform component choices across enterprise contexts. ([UX Planet][9], [Medium][10]) * **Domain analytics copilots (pattern transfer)**: apply the same patterns to BI/analytics UIs (query → draft insight → refine → apply). ([Microsoft Learn][11]) * **Cloud app UX for gen-AI**: Google’s guidance on task framing, handoffs to deterministic flows, and guardrails for agents. ([Google Cloud][12]) ## References (handy starting points) * NN/g: *Generative UI & Outcome-Oriented Design*. ([Nielsen Norman Group][5]) * Microsoft: *Copilot UX guidance (ISV)* and *Copilot Studio agent guidance*. ([Microsoft Learn][2]) * Google PAIR: *People + AI Guidebook*. ([pair.withgoogle.com][3]) * GitHub: *Copilot Ask, Edit, Agent modes*. ([The GitHub Blog][1]) * Whitespectre/UX catalogs: *Emerging Gen-AI UI patterns*. ([Medium][10]) * UX Planet: *Generative-AI UX design patterns* (system/scope/spatial lenses). ([UX Planet][9]) * Designing for Analytics (enterprise LLM UX considerations). ([designingforanalytics.com][13]) * Shape of AI: pattern taxonomy (Wayfinders, Tuners, Governors, Trust builders). ([shapeof.ai][6]) * Google Cloud: *UX considerations for gen-AI apps* + *101 blueprints* (design + architecture). ([Google Cloud][12]) * Microsoft Power BI Copilot (analytics copilot UI). ([Microsoft Learn][11]) Want me to turn these into a checklist you can drop into your design system (with component examples)? [1]: https://github.blog/ai-and-ml/github-copilot/copilot-ask-edit-and-agent-modes-what-they-do-and-when-to-use-them/?utm_source=chatgpt.com "Copilot ask, edit, and agent modes: What they do and ..." [2]: https://learn.microsoft.com/en-us/microsoft-cloud/dev/copilot/isv/ux-guidance?utm_source=chatgpt.com "Creating a dynamic UX: guidance for generative AI ..." [3]: https://pair.withgoogle.com/guidebook/?utm_source=chatgpt.com "People + AI Guidebook - Home" [4]: https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1?utm_source=chatgpt.com "20+ GenAI UX patterns, examples and implementation tactics" [5]: https://www.nngroup.com/articles/generative-ui/?utm_source=chatgpt.com "Generative UI and Outcome-Oriented Design" [6]: https://www.shapeof.ai/?utm_source=chatgpt.com "The Shape of AI | UX Patterns for Artificial Intelligence Design" [7]: https://www.nngroup.com/articles/genai-ux-research-agenda/?utm_source=chatgpt.com "A Research Agenda for Generative AI in UX" [8]: https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/?utm_source=chatgpt.com "Microsoft Copilot Studio guidance documentation" [9]: https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99?utm_source=chatgpt.com "Generative AI UX Design Patterns" [10]: https://medium.com/whitespectre/emerging-ui-ux-patterns-in-generative-ai-a-visual-guide-74000c7198a4?utm_source=chatgpt.com "Emerging UI/UX Patterns in Generative AI - Medium" [11]: https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction?utm_source=chatgpt.com "Overview of Copilot for Power BI" [12]: https://cloud.google.com/blog/products/ai-machine-learning/how-to-build-a-genai-application?utm_source=chatgpt.com "UX considerations for generative AI apps and agents" [13]: https://designingforanalytics.com/resources/ui-ux-design-for-enterprise-llms-use-cases-and-considerations-for-data-and-product-leaders-in-2024-part-1/?utm_source=chatgpt.com "UI/UX Design Considerations for LLMs in Enterprise ..."

The most common **design patterns** for Generative UI in domain tools center on dynamic UI construction, adaptation to user context, seamless integration with design systems, and maintaining interactive and accessible experiences.[1][2][3][4] ## Key Generative UI Patterns - **Pattern-Based Generation:** Define a library of standardized, atomic components (e.g. buttons, tables, forms) which the generative AI combines per user scenario, ensuring consistency and theme alignment.[2][3] - **Prompt-to-UI Mapping:** Use structured prompts or intent models to translate user descriptions or requirements into structured UI definitions, often as JSON or similar representations that map directly to design system components.[3][2] - **Live Contextual Adaptation:** UIs that adapt in real time to changing user context, data, or workflow requirements—such as resizing, adjusting compositional layouts, or changing fields based on current task flow.[4][1] - **Outcome-Oriented Design:** Rather than focusing on static templates, generative UIs aim for task, workflow, or outcome optimization—surfacing controls and data most relevant for the user’s stated or inferred goals.[5][4] - **Validation and Safety Guardrails:** Implement rigorous testing and output validation, such as capping array/table sizes or checking that the generative model’s output adheres to allowed types, to ensure UI stability and prevent runtime errors.[3] - **Seamless Design System Integration:** Align all generated UI elements to a design system’s color, spacing, and typography tokens so they feel native and maintain branding and accessibility standards.[2][3] ## Best Practices - Build with a **dedicated design system** for Generative UI (like Thesys "Crayon"), emphasizing accessibility, interactivity, and responsive layouts.[2] - Use **iterative QA and user feedback** cycles, starting with pilot projects before broad rollouts, and always log AI outputs for ongoing improvement.[3] - Treat all AI-generated component specifications as production code, requiring validation, error handling, and secure sandboxing.[4][3] ## Further Reading and References - [20+ GenAI UX Patterns, Examples, and Implementation Tactics (UXDesign)](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1)[1] - [Technical Architecture: First Generative UI API (Thesys)](https://www.thesys.dev/blogs/generative-ui-architecture)[2] - [Enterprise Readiness for Generative UI (Thesys)](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build)[3] - [Generative UI Guide 2025: 15 Best Practices & Examples (Mockplus)](https://www.mockplus.com/blog/post/generative-ui)[4] - [Generative UI and Outcome-Oriented Design (NN/G Nielsen Norman Group)](https://www.nngroup.com/articles/generative-ui/)[5] These sources offer both tactical implementation advice and strategic frameworks for leveraging generative UIs in business and enterprise software contexts.[1][5][4][2][3] [1](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1) [2](https://www.thesys.dev/blogs/generative-ui-architecture) [3](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build) [4](https://www.mockplus.com/blog/post/generative-ui) [5](https://www.nngroup.com/articles/generative-ui/) [6](https://dl.acm.org/doi/10.1145/3715336.3735780) [7](https://www.mockplus.com/blog/post/generative-ai-tools) [8](https://arxiv.org/html/2505.15049v1) [9](https://www.shapeof.ai) [10](https://www.elsewhen.com/reports/from-generative-ai-to-generative-ui/) [11](https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99) [12](https://www.nngroup.com/articles/genai-ux-research-agenda/) [13](https://martinfowler.com/articles/gen-ai-patterns/) [14](https://www.coveo.com/blog/generative-ai-best-practices/) [15](https://www.thesys.dev/blogs/how-generative-ui-is-transforming-internal-tools-across-the-enterprise) [16](https://www.getaprototype.com/knowledge-base/generative-ui-design) [17](https://www.k2view.com/blog/what-is-a-best-practice-when-using-generative-ai/) [18](https://uxdesign.cc/designing-for-ai-engineers-what-ui-patterns-and-principles-you-need-to-know-8b16a5b62a61) [19](https://devpulse.io/insights/ux-ui-design-best-practices-2025-enterprise-applications/) [20](https://uxplanet.org/7-key-design-patterns-for-ai-interfaces-893ab96988f6)

I'll search for information about design patterns for Generative UI in domain-specific tools.Let me search for more specific information about design patterns for generative UI in domain-specific applications.Based on my search, here are the key design patterns for Generative UI in domain tools: ## Core Design Patterns for Generative UI ### 1. **Outcome-Oriented Design Pattern** In the future, generative UI will dynamically create customized user interfaces in real-time. This shift will force an outcome-oriented design approach where designers prioritize user goals and define constraints (for AI to operate within), rather than design discrete interface elements. ### 2. **Constraint-Based Generation Pattern** Rather than designing specific UI elements, designers focus on: - Defining user goals and objectives - Setting operational constraints for AI systems - Allowing dynamic interface creation within those boundaries ### 3. **Iterative Refinement Pattern** Modern generative UI tools support iterate easily on your designs and edit your generated screens in seconds, enabling rapid prototyping and refinement cycles. ### 4. **Multi-Modal Generation Pattern** Image generation tools for new and visually appealing images based on existing patterns or styles. Sound generation tools for melodies, harmonies and even entire compositions - supporting comprehensive multimedia interface generation. ## Key Implementation Strategies ### Real-Time Adaptation The goal of these patterns is to help mitigate and overcome some of the challenges with generative AI implementations such as cost, latency and hallucinations. ### Enterprise Integration With the latest 2024 Q2 release, Progress brings generative AI integration, enhanced design system capabilities and accessible data visualization. ## Key Resources and Tools **Research and Guidelines:** - [Nielsen Norman Group - Generative UI and Outcome-Oriented Design](https://www.nngroup.com/articles/generative-ui/) - [ACM CHI 2024 - Design Principles for Generative AI Applications](https://dl.acm.org/doi/10.1145/3613904.3642466) - [Towards Data Science - Generative AI Design Patterns Guide](https://towardsdatascience.com/generative-ai-design-patterns-a-comprehensive-guide-41425a40d7d0/) **Tools and Platforms:** - [UX Pilot - AI UI Generator](https://uxpilot.ai/) - [Telerik/Progress - Enterprise AI Integration](https://www.telerik.com/blogs/telerik-kendo-ui-2024-q2-release-here-see-whats-new) - [Merge - AI Design Tools for 2025](https://merge.rocks/blog/top-ai-design-tools-for-ux-ui-designers-in-2025) **Industry Analysis:** - [Mockplus - 22 Best Generative AI Tools for UI/UX](https://www.mockplus.com/blog/post/generative-ai-tools) - [UXPin - UI/UX Design Trends 2025](https://www.uxpin.com/studio/blog/ui-ux-design-trends/) The trend shows a shift from traditional component-based design to constraint-based, outcome-oriented approaches where AI generates interfaces dynamically based on user goals and contextual requirements.