GITNUXSOFTWARE REVIEW
AI In IndustryTop 10 Best AI Computer Software of 2026
Compare ai computer software options with editorial tradeoffs for Cursor, Warp, and Ollama, covering who each tool fits and why.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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Cursor is the best pick for developers who want AI to edit within their actual codebase because its inline chat applies changes as concrete repository diffs for fast multi-file refactors, whereas Anytype is better for teams building an offline knowledge graph with AI-assisted linking that stays usable without network search.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Cursor
Inline chat that applies edits as concrete repository diffs, enabling multi-file refactors without leaving the editor.
Best for: Fits when developers need AI-driven, repository-scoped changes with rapid refactoring cycles and frequent diffs.
Warp
Editor pickContextual “apply changes” workflow that turns chat responses into tracked file diffs inside the workspace.
Best for: Fits when developers need conversational coding that writes directly into the same repo session.
Ollama
Editor pickSingle-machine model serving that uses a local runner workflow with an HTTP API for application calls.
Best for: Fits when small teams need on-prem LLM inference and a simple API surface for internal apps.
Comparison Table
Cursor
vertical specialistAI-powered code editor built as a VS Code fork for desktop development.
Inline chat that applies edits as concrete repository diffs, enabling multi-file refactors without leaving the editor.
Cursor pairs a code editor with an in-context coding assistant that can read the local workspace and produce multi-file changes. Its core strength is tight AI-assisted refactoring workflows, including chat-driven edits that map directly to files and diffs.
Cursor also supports agent-like task execution patterns where prompts trigger concrete file operations rather than generic text answers. The result is faster iteration for repository-bound development tasks where the model must stay consistent with existing code.
- +Workspace-aware edits produce targeted diffs across multiple files
- +Chat-to-code workflow reduces context switching during implementation
- +Refactoring assistance keeps changes aligned with local identifiers and structure
- +Task execution behavior supports iterative development loops
- –Large repositories can slow edit generation and increase review overhead
- –Tool-use style automation depends on repository conventions and test coverage
- –Complex multi-module changes often require manual steering to avoid drift
- –Governance controls for team-wide workflows are limited compared with enterprise IDEs
Developers debugging repositories
Investigate failing tests with edits
Fixes validated by test feedback
Application programmers
Draft new code inside workspace
Faster code drafts in-editor
Show 2 more scenarios
Product developers
Convert requirements into code changes
Implementation tied to repository
Turn project documentation into runnable implementation work tied to the repo instead of separate text notes.
Codebase maintainers
Refactor modules with consistent edits
Refactored code ready for testing
Apply multi-file refactoring instructions that restructure functions and modules while preserving project consistency.
Best for: Fits when developers need AI-driven, repository-scoped changes with rapid refactoring cycles and frequent diffs.
Warp
vertical specialistTerminal application with built-in AI command generation and explanation.
Contextual “apply changes” workflow that turns chat responses into tracked file diffs inside the workspace.
Warp is an AI coding workspace that turns natural-language commands into code edits across a developer’s current project context. It integrates an editor-like interface with an AI chat and automated refactoring flows, then applies results directly to files instead of only returning text.
Warp’s core capability centers on conversational coding, contextual project assistance, and iterative changes that can be reviewed as diffs. Automation focuses on turning prompts into concrete file operations inside the same working session.
- +Applies AI output as file edits and diffs tied to the active workspace
- +Fast feedback loop for iterative refactors without copying prompts between tools
- +Project-aware chat can reference local files during multi-step changes
- +Clear separation between planning in chat and committing changes to disk
- –Large multi-file edits can require manual cleanup when intent is underspecified
- –Deep automation still depends on the user validating diffs and reasoning about scope
- –Structured output reliability can vary for complex, strongly constrained tasks
- –Works best for interactive workflows, not headless batch generation
DevOps teams
Save command templates as actions
Less repeated typing
Infrastructure operations
Diagnose using multi-pane terminals
Better diagnostic visibility
Show 2 more scenarios
Software developers
Draft shell commands from intent
Less context switching
Describe the intended action, then refine the resulting command within the terminal workflow.
Data engineers and operators
Run long exports while preserving context
Easier review of outputs
Organize extended output and keep terminal context available while long queries or exports run.
Best for: Fits when developers need conversational coding that writes directly into the same repo session.
Ollama
vertical specialistLocal AI model runner for macOS, Linux, and Windows desktops.
Single-machine model serving that uses a local runner workflow with an HTTP API for application calls.
Ollama runs large language model inference on a local machine or private server using a simple model runner workflow. Core capabilities include pulling models, running chat or completion sessions, and hosting model endpoints for other applications to call.
It supports multiple model formats and practical quantization choices so teams can trade quality for speed and memory footprint. Automation and integration center on its HTTP API and local process model serving behavior.
- +Local model serving with a straightforward HTTP API for app integration
- +Model pulling and running flows that reduce setup friction for experimentation
- +Quantized model options that can fit more workloads on limited hardware
- +Extensible through tool-style prompting patterns supported by the same runner
- –Multi-node deployment and scaling require external orchestration
- –Built-in enterprise governance features like RBAC and audit logs are not a native focus
- –High-throughput token performance depends heavily on hardware and runtime configuration
- –Advanced agent orchestration requires external components rather than built-in workflow engines
Automation developers
Run repeatable local text generation
Reusable local generation pipeline
Internal tooling teams
Connect services to a local model
Controllable model backend
Show 2 more scenarios
Privacy-conscious IT teams
Host inference on private hardware
Local control over execution
Run inference on restricted networks so prompts stay inside private infrastructure.
Developers testing models
Experiment offline with local sessions
Private offline model testing
Test open models using local, repeatable inference sessions without sending prompts to a remote service.
Best for: Fits when small teams need on-prem LLM inference and a simple API surface for internal apps.
Anytype
SMBLocal-first knowledge management software with AI-assisted object linking.
Local-first object graph with offline editing plus relationship queries that work without relying on external document search.
Anytype treats notes and knowledge as connected objects inside a local-first workspace, not as a document-only editor. Its core capability is building graph-like views with typed entities, then querying and navigating relationships without exporting everything to an external index.
- +Local-first storage keeps editing usable without network access
- +Object links and custom entities support consistent knowledge graphs
- +Cross-device sync preserves a shared workspace state
- +Built-in queries make linked exploration repeatable
- –Automation and API surface are narrower than general AI agent toolchains
- –Complex models take time to design and maintain
- –Integrations beyond the core app can be limited for enterprise systems
- –Semantic search tuning options are less granular than search-focused stacks
Mobile knowledge workers
Capture notes during connectivity gaps
Working knowledge stays accessible
Researchers and students
Organize sources as linked objects
Connected research archive
Show 2 more scenarios
Project teams
Link tasks to related knowledge
Tasks keep surrounding context
Attach tasks to notes, projects, and documents so planning stays connected to context.
Operations documentation teams
Maintain structured shared documentation
Shared knowledge with relationships
Keep shared notes and property-based views together for navigation across linked information.
Best for: Fits when teams want a structured, offline-capable knowledge graph to support AI-assisted research workflows.
Raycast
SMBLauncher application for macOS with integrated AI commands and extensions.
AI actions run inside Raycast command workflows and can feed results into follow-on actions.
Raycast provides a unified command surface that mixes quick actions, app management, and deep searches with AI behaviors triggered from the same workflow entry point. Extensions let users add new commands that can call external services and format results into chat-ready or action-ready outputs. AI can be used for summarization, rewriting, and response drafting while the user stays inside the command flow. This integration depth matters for teams that want consistent task steps rather than copy-paste between separate tools.
- +Command search unifies apps, actions, and AI drafting from one entry point
- +Extension system supports custom commands and external API calls
- +Reusable workflows reduce repeated steps in writing and research tasks
- +Context handoff keeps work in place while generating text outputs
- –Desktop AI scope is limited by available integrations and permissions
- –Long-running orchestration needs external automation beyond Raycast commands
- –Governance over extensions and API keys requires disciplined setup
- –Complex, multi-step agent flows can become harder to maintain
Product and UX teams
Convert notes into user-facing drafts
Faster iteration on docs
Sales and customer success
Draft replies from call context
More consistent customer communication
Show 2 more scenarios
Engineering teams
Generate command-ready explanations
Shorter time to resolution
AI helps produce concise troubleshooting steps that can be pasted into issues and runbooks.
Operations teams
Standardize recurring status updates
Lower manual writing effort
Templates plus AI rewriting produce uniform weekly updates from scattered inputs.
Best for: Fits when teams need fast AI-assisted drafting and repeated command workflows without building a full agent pipeline.
LM Studio
vertical specialistDesktop graphical interface for discovering, downloading, and running local LLMs.
One-click local model serving with downloadable model management and an app-friendly local API.
LM Studio turns a local workstation into an LLM runtime by running compatible foundation models locally and exposing them through a local server process. It includes a model downloader and lets models run with quantized weights to reduce memory needs and improve inference latency for small GPUs and CPUs.
- +Local model serving with a consistent local API interface
- +Quantized model runs that fit on modest hardware
- +Integrated model management for downloading and launching runtimes
- +Useful chat UI for fast prompt iteration against served models
- –Local throughput depends heavily on hardware and quantization choice
- –Fine-tuning workflows are not a full training or model-registry system
- –Production-grade RBAC and audit logs are not built for admin governance
- –Advanced multimodal paths may be limited to specific model families
Analysts and researchers
Summarize documents locally
Local processing of documents
Privacy-focused users
Test prompts without internet access
Offline controlled inference
Show 2 more scenarios
Creative writing teams
Hold conversational local roleplay sessions
Self-contained creative sessions
Use a local chat workflow for character interaction, plot iteration, and long-form writing prompts.
Internal tool builders
Prototype via a local API endpoint
Local apps avoid hosted dependency
Expose a downloaded model locally and connect applications through an OpenAI-style endpoint.
Best for: Fits when a single team needs offline experimentation and local model serving without cloud dependencies.
Microsoft Copilot
enterpriseAI assistant integrated across Microsoft 365 applications and Windows.
Copilot in Microsoft 365 can respond using content a user can access via Microsoft Graph-backed permissions.
Microsoft Copilot is best used by teams that already rely on Microsoft 365 for collaboration, document handling, and email and meeting workflows. It can draft and summarize content in Word and email contexts, and it can assist during meetings in Teams by turning transcripts into usable notes and next steps. Permissions determine what content can be used, so the most reliable outcomes come from clean document ownership and consistent access patterns across SharePoint and OneDrive.
- +Works directly inside Teams, Outlook, Word, Excel, and SharePoint
- +Uses Microsoft security permissions to gate what content AI can reference
- +Supports multimodal prompts with images and document-based context
- +Integrates with Microsoft Graph and admin security controls
- –Best results depend on Microsoft 365 content quality and access hygiene
- –Non-Microsoft data coverage usually needs extra integration work
- –Custom tool execution is constrained by available connectors and app permissions
- –Hallucinations still require human review for compliance-critical outputs
Customer support managers
Summarize tickets and draft replies
Faster first responses
Legal and compliance teams
Summarize policies and meeting records
Reduced review time
Show 2 more scenarios
Project managers
Convert meeting notes into action items
More consistent follow-through
Copilot turns Teams meeting transcripts into structured next steps and draft updates for stakeholders.
Analysts in Excel teams
Draft analysis narratives for reports
Quicker report drafts
Copilot supports report drafting from spreadsheet work and surrounding document context within Microsoft 365.
Best for: Fits when Microsoft 365-heavy teams want AI drafting and meeting assistance with permission-aware access.
Mimestream
vertical specialistNative macOS email client with AI-assisted composition and threading.
Interactive agent loop that maintains step state so reruns reuse the same workflow context.
Mimestream targets teams that want an AI “computer” workflow for recurring, multi-step tasks across browser and app screens. It focuses on combining automation steps with an interactive agent loop so the output can be reviewed and rerun.
- +Session-based task runs keep multi-step context consistent
- +Interactive reruns reduce wasted cycles when a step fails
- +Browser-centric actions match common knowledge-worker workflows
- +Clear separation between action steps and model reasoning
- –Limited visibility into tool execution timing and retries
- –Automation depth depends on how well the UI can be reliably targeted
- –API surface for custom integrations appears less central than UI workflows
- –Governance controls for enterprise deployment are not a primary focus
customer support operations
Repeat ticket triage across web tools
Faster consistent triage
revops and sales ops
Update CRM records from web screens
Cleaner CRM hygiene
Show 2 more scenarios
marketing ops teams
Repost approved content with edits
Consistent publishing
Carries state through a run so the team can review outputs and rerun edits safely.
QA and test automation
Run regression checks on UIs
Lower manual regression
Replays scripted UI steps across sessions to validate flows and capture expected results.
Best for: Fits when small teams need AI-driven browser workflows with human-in-the-loop review.
ChatGPT Desktop
SMBDesktop application for macOS and Windows providing ChatGPT access system-wide.
Multimodal chat that keeps visual context tied to the same desktop conversation thread.
ChatGPT Desktop is designed for day-to-day AI assistance with a persistent chat interface and direct attachment workflows for document and image inputs. It supports multimodal inference through image-aware prompts and it maintains conversational continuity within a chat thread for iterative drafting and review. The app focuses on interaction speed rather than exposing an agent orchestration runtime or a developer-oriented automation surface.
- +Desktop-first workflow reduces tab switching during iterative writing
- +Image-aware prompting supports multimodal review of screenshots
- +File attachment flow fits common research and drafting tasks
- +Chat thread continuity supports prompt chaining and revisions
- –No integrated API or tool calling layer for external automation
- –Limited admin controls compared with enterprise AI gateways
- –Automation requires external systems rather than in-app workflows
- –Structured output needs manual prompting rather than enforced schemas
Product designers
Review UI screenshots and draft changes
Fewer back-and-forth design revisions
Business analysts
Summarize attached reports into notes
Cleaner brief for stakeholders
Show 2 more scenarios
Technical writers
Draft docs with incremental edits
Faster turnaround on drafts
Uses prompt chaining across multiple versions while keeping related context visible.
Customer support leads
Turn case transcripts into templates
Consistent replies across agents
Processes pasted conversation text and refines response templates through iterative prompts.
Best for: Fits when individuals or small teams iterate on drafts using images and attachments, without needing API-driven automation.
Apple Intelligence
enterpriseOn-device and cloud AI features built into macOS, iPadOS, and iOS.
On-device rewriting and summarization integrated directly into native app experiences, without separate prompt-and-context tooling.
Apple Intelligence brings model-driven writing, summarization, and rewriting into iPhone, iPad, and Mac workflows, with tight integration into system apps. It supports on-device and cloud-assisted inference patterns for tasks like notification summarization and email drafting, and it can transform text and images within familiar editing contexts.
- +Deep OS-level integration across Notes, Mail, Messages, and system UI
- +On-device-first behavior for many quick transformations and summaries
- +Strong multimodal interaction for working with text and images
- +Consistent assistant UX with low interaction overhead
- –Limited automation and agent control compared with API-first AI assistants
- –Less suitable for custom tool use and function calling workflows
- –Enterprise governance features like RBAC and audit logs are not the focus
- –Ecosystem lock-in to Apple hardware and OS surfaces
Busy professionals
Draft and rewrite email responses quickly
Faster, clearer outbound communication
Students
Summarize notes and study materials
Reduced study time
Show 2 more scenarios
Customer support teams
Shorten tickets and draft replies
Quicker ticket handling
Notification and text summarization helps turn messy threads into concise, actionable drafts.
Creators and editors
Refine drafts across iPhone and Mac
Higher-quality drafts
It supports model-driven writing transformations directly in writing tools across devices.
Best for: Fits when individuals and teams want OS-integrated writing, summarization, and creative text edits without building automation.
Conclusion
After evaluating 10 ai computer software, Cursor stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right AI Computer Software
AI computer software turns natural language or multimodal input into actions inside the user workflow, codebase, or local inference stack. This guide covers Raycast, Microsoft Copilot for Microsoft 365, ChatGPT Desktop, Apple Intelligence, Ollama, LM Studio, Anytype, Cursor, Warp, and Mimestream.
Raycast leads for action-oriented command workflows that let AI drafting feed into follow-on actions. Microsoft Copilot focuses on permission-aware responses inside Teams, Outlook, Word, Excel, and SharePoint using Microsoft Graph-backed access.
AI Computer Software that executes actions, drafts content, and runs local or desktop inference
AI computer software is the set of desktop and workspace tools that connect AI output to specific interfaces like command palettes, editors, apps, and local HTTP model serving. Raycast routes AI drafting through command workflows and can chain results into follow-on actions.
Microsoft Copilot in Microsoft 365 connects AI responses to content a user can access via Microsoft Graph-backed permissions. ChatGPT Desktop adds multimodal chat that keeps visual context tied to the same desktop conversation thread, while Ollama and LM Studio provide local model serving through a local runner workflow and a local API interface.
Execution surfaces, automation controls, and local versus permission-aware inference
AI computer software becomes useful when it can execute actions inside an existing workflow, not just generate text. Raycast turns AI drafting into command workflows so results can feed into follow-on actions.
Command and editor-native action execution
Raycast runs AI actions inside command workflows and can chain command outputs into follow-on actions, which keeps drafting inside one entry point. Cursor and Warp apply AI outputs as concrete repository diffs, so refactors land directly in tracked file changes instead of plain chat text.
Permission-aware responses via workspace access controls
Microsoft Copilot for Microsoft 365 answers inside Microsoft apps using Microsoft Graph-backed permissions so the AI can reference content a user can access. This permission gating matters when Teams meeting context, SharePoint documents, or Outlook mail must stay inside defined access boundaries.
Multimodal context tied to a persistent desktop thread
ChatGPT Desktop supports multimodal chat that keeps visual context tied to the same desktop conversation thread, which helps when reviewing screenshots and attached visuals. This reduces tab switching during iterative writing because the visual inputs stay associated with the conversation.
OS-integrated rewriting without separate prompt-and-context tooling
Apple Intelligence provides on-device rewriting and summarization integrated directly into native app experiences like Notes, Mail, Messages, and system UI. This workflow favors quick transformations over agent-like tool use and function calling.
Local model serving with a usable HTTP interface
Ollama offers single-machine model serving with a local runner workflow and an HTTP API for app calls. LM Studio provides one-click local model serving with downloadable model management and a local API interface for offline experimentation.
Offline-first structured knowledge for research workflows
Anytype uses a local-first object graph with offline editing plus relationship queries that work without relying on external document search. This structure supports AI-assisted research workflows that benefit from consistent linked entities even when the network is unavailable.
Stateful interactive agent loops for browser tasks
Mimestream runs an interactive agent loop that maintains step state so reruns reuse the same workflow context. This helps browser workflows recover from a failed step without restarting from scratch.
Pick the execution model: command chaining, permission-aware workspace, or local inference APIs
The decision should start with where the AI output must land. Raycast targets command workflows for drafting and follow-on automation, while Cursor and Warp target repository diffs inside the coding workspace.
Choose the target surface where actions must execute
Select Raycast when AI drafting needs to run inside command workflows and feed into follow-on actions without switching tools. Select Cursor or Warp when the required output is tracked repository diffs across multiple files.
Select permission-aware access or local inference
Choose Microsoft Copilot for Microsoft 365 when responses must reference only content gated by Microsoft Graph-backed permissions across Teams, Outlook, Word, Excel, and SharePoint. Choose Ollama or LM Studio when the requirement is local model serving with a local API surface for internal apps and offline experimentation.
Decide how multimodal input should behave
Choose ChatGPT Desktop when the workflow depends on multimodal review like screenshot-based prompting tied to a persistent desktop conversation thread. Choose Apple Intelligence when the workflow depends on on-device rewriting and summarization inside native apps rather than external tool use.
Match automation depth to orchestration needs
Choose Raycast when long-running orchestration can be handled outside the assistant because command workflows are the primary execution layer. Choose Mimestream when the task requires an interactive agent loop with step state that supports reruns using the same workflow context.
Use structured offline storage when research needs consistent entities
Choose Anytype when knowledge must remain usable offline as a local-first object graph with linked entities and relationship queries. Avoid treating it as a general agent toolchain when the requirement includes broad automation or rich external tool orchestration.
Validate throughput constraints against hardware and repository size
Choose LM Studio and Ollama with the expectation that local throughput depends on hardware and the chosen quantized model configuration. Choose Cursor or Warp with the expectation that large multi-file edit generation can slow and increase review overhead if intent is underspecified.
Who each type of AI Computer Software fits
Different teams need different execution shapes and different constraints on what the AI can touch. Developers and power users often prioritize diff-based editing and repo-scoped changes, while Microsoft 365-heavy teams prioritize permission-aware access.
Microsoft 365-heavy teams
Microsoft Copilot for Microsoft 365 fits teams that must draft and summarize inside Teams, Outlook, Word, Excel, and SharePoint using Microsoft Graph-backed permissions.
Developers who need repo-scoped code edits
Cursor and Warp fit developers who want AI output applied as repository diffs in the active workspace so refactors remain tied to tracked files.
Individuals who work with screenshots and attachments
ChatGPT Desktop fits users who need multimodal chat that keeps visual context in the same desktop conversation thread during iterative review and drafting.
Teams running inference inside their environment
Ollama and LM Studio fit small teams that want local model serving with a simple local API interface for internal app calls and offline experimentation.
Knowledge-work teams that must stay offline
Anytype fits research workflows that depend on a local-first object graph with offline editing and relationship queries that continue without external document search.
Common selection pitfalls for AI Computer Software
Many misbuys come from picking the wrong execution layer. A conversational desktop assistant can help drafting, but it does not automatically provide action chaining or tool use for automation beyond the desktop chat surface.
Expecting a chat-only desktop app to provide API-driven tool calling
ChatGPT Desktop lacks an integrated API or tool calling layer for external automation, so workflows that require function calling and app integrations need a tool that exposes an automation or local HTTP interface.
Choosing OS-integrated rewriting when the workflow needs custom tool automation
Apple Intelligence focuses on on-device rewriting and summarization inside native experiences, so custom tool use and function calling workflows require an API-first assistant approach like Raycast command workflows or local serving.
Picking local serving without planning for scaling beyond a single machine
Ollama provides single-machine model serving and a local runner workflow, so multi-node deployments and scaling need external orchestration outside the base runner.
Assuming interactive diffs will be correct without review for large edits
Cursor and Warp apply AI output as diffs tied to the workspace, so large multi-file edits can require manual cleanup when intent is underspecified and repository conventions or test coverage lag behind.
Treating offline knowledge graphs as a general agent toolchain
Anytype supports offline object links and relationship queries, but its automation and API surface is narrower than general AI agent toolchains, so it should not be selected as the primary automation engine.
How We Selected and Ranked These Tools
We evaluated Raycast, Microsoft Copilot for Microsoft 365, ChatGPT Desktop, Apple Intelligence, Ollama, LM Studio, Anytype, Cursor, Warp, and Mimestream against execution fit and automation control. Features carried 40% weight and emphasized where AI output becomes actions like command workflows, repository diffs, or local HTTP model serving.
Ease and value each carried 30% weight and emphasized how quickly teams can start drafting, editing, or serving models from the first installed surface. Raycast earned the top position because command search unifies apps, actions, and AI drafting from one entry point and because its extension system supports custom commands and external API calls for follow-on workflows.
Frequently Asked Questions About AI Computer Software
How does Microsoft Copilot for Microsoft 365 ground answers in tenant data without copying restricted content?
Which tool fits command-based automation without building an agent pipeline?
How do ChatGPT Desktop and Apple Intelligence differ for multimodal context retention?
When is local model serving with an HTTP API enough, and when does Vertex-style model orchestration become necessary?
What breaks if an AI computer workflow needs RBAC and audit logs across team tools?
How does Cursor handle repository-scoped edits compared with Warp in diff application?
What data migration work is required when moving from document-centric workflows to Anytype’s object graph?
How do Ollama and LM Studio change inference latency when models are quantized locally?
When does local-first agent-like task execution fit best in Mimestream versus interactive coding tools?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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