| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 15 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Removes Windows AppX packages marked as NonRemovable by leveraging privilege escalation to TrustedInstaller context via the Run-Trusted function, which spawns a secondary PowerShell process with system-level permissions. This bypasses Windows Package Manager restrictions that normally prevent removal of built-in packages like Copilot and Recall. The implementation uses SYSTEM token impersonation to execute removal commands that would otherwise fail with access denied errors.
Unique: Uses Run-Trusted function to spawn secondary PowerShell process with SYSTEM token impersonation, enabling removal of NonRemovable packages that standard Windows APIs reject. This is more direct than registry-only approaches and handles the full package lifecycle including manifest cleanup.
vs alternatives: More reliable than manual registry deletion because it removes packages through proper Windows servicing APIs rather than orphaning package metadata, reducing reinstallation risk.
Identifies and removes hidden CBS packages that Windows Update uses to reinstall AI features by querying the Component-Based Servicing database and targeting specific component manifests. The implementation enumerates CBS packages via WMI or registry inspection, identifies AI-related components by manifest analysis, and removes them using DISM or direct CBS API calls. This prevents Windows Update from automatically restoring removed AppX packages during system updates.
Unique: Targets hidden CBS packages that exist in the Windows servicing database separately from AppX packages, using manifest-based component identification to prevent Windows Update from re-provisioning removed AI features. Most removal tools only handle AppX layer and miss the CBS persistence mechanism.
vs alternatives: More comprehensive than AppX-only removal because it addresses the root cause of AI feature reinstallation — the CBS packages that Windows Update uses to restore components. Prevents the common scenario where Copilot returns after monthly updates.
Provides multiple execution modes that control how operations are applied: dry-run (preview without changes), removal (standard execution with safety checks), force (bypass safety checks), backup (create state snapshot before removal), and revert (restore from backup). The implementation uses a mode parameter to control operation behavior, with each mode having different safety guardrails and logging requirements. This enables users to choose the appropriate risk/safety tradeoff for their use case.
Unique: Implements five distinct execution modes (dry-run, removal, force, backup, revert) with mode-specific safety guardrails and logging. Force mode allows bypassing safety checks when needed, while backup/revert modes provide recovery capability.
vs alternatives: More flexible than single-mode tools because it supports both safe testing (dry-run) and aggressive removal (force) with backup/restore for recovery. Enables users to choose appropriate risk level for their situation.
Generates comprehensive logs of all removal operations including timestamps, operation names, success/failure status, and error details when -EnableLogging flag is used. The implementation writes to log files in addition to console output, capturing both successful operations and failures with full error context. This enables troubleshooting of failed operations and provides audit trail of what was executed and when.
Unique: Implements optional detailed logging via -EnableLogging flag that captures operation timestamps, success/failure status, and error context. Logs are written to files in addition to console output for persistent audit trail.
vs alternatives: More diagnostic-friendly than silent execution because it provides detailed logs for troubleshooting. Enables users to understand exactly what failed and why, rather than just seeing success/failure status.
Disables Windows services associated with AI features by modifying service startup type to Disabled and stopping running service instances. The implementation enumerates Windows services, identifies AI-related services by name and description matching, and uses sc.exe or PowerShell Service cmdlets to disable them. This prevents AI services from starting automatically on system boot while allowing other services to function normally.
Unique: Identifies and disables AI-related Windows services by name and description matching, using sc.exe or PowerShell Service cmdlets to set startup type to Disabled. More targeted than disabling all services.
vs alternatives: More reversible than service removal because disabled services can be re-enabled without reinstalling packages. Allows fine-grained control over which services are disabled.
Hides AI feature UI elements from the Windows Settings app by modifying registry keys that control visibility of Copilot, Recall, and image generation settings pages. The implementation modifies HKCU registry keys that control Settings app page visibility, preventing users from accessing AI feature configuration options through the GUI. This is a UI-level hiding mechanism that does not remove packages but prevents user access to settings.
Unique: Modifies HKCU registry keys that control Settings app page visibility for AI features, hiding Copilot and Recall configuration options from the GUI. This is UI-level hiding rather than feature removal.
vs alternatives: Less disruptive than package removal because it only hides UI elements while allowing packages to remain installed. Useful for organizations wanting to discourage AI feature use without breaking compatibility.
Disables the AI-powered Rewrite feature in Notepad by modifying registry keys and Group Policy settings that control Rewrite availability. The implementation targets registry keys that enable/disable the Rewrite button and policy settings that control AI feature availability in Notepad. This prevents users from accessing the Rewrite feature while keeping Notepad functional.
Unique: Targets Notepad-specific registry keys and policies that control the Rewrite feature, disabling AI text rewriting while keeping Notepad functional. Application-specific approach rather than system-wide AI removal.
vs alternatives: More targeted than system-wide AI removal because it only affects Notepad Rewrite feature. Allows users to keep Notepad while disabling specific AI functionality.
Disables AI features by modifying Windows registry keys and Group Policy settings that control Copilot availability, voice effects, DLL contracts, and AI service activation. The implementation writes to HKLM and HKCU registry hives to set policies like DisableCopilot, modifies IntegratedServicesRegionPolicySet.json to restrict regional AI availability, and disables related Windows services. This approach disables features at the OS level without removing packages, allowing for reversible changes.
Unique: Modifies IntegratedServicesRegionPolicySet.json in addition to standard registry keys, targeting the policy file that controls regional AI feature availability. Combines HKLM/HKCU registry writes with service disablement for multi-layer policy enforcement.
vs alternatives: More reversible than package removal and allows granular control over which AI features are disabled. Maintains Windows Update compatibility while still preventing AI feature activation, useful for organizations that cannot afford package removal risks.
+7 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs RemoveWindowsAI at 42/100. RemoveWindowsAI leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities