| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Embeds a lightweight HTTP client within VS Code that allows developers to construct, send, and inspect REST API requests without leaving the editor. The implementation integrates with the editor's UI framework to provide request builder panels, response viewers, and header/body management. Requests are executed against specified endpoints with support for custom headers, authentication tokens, and request body formatting, with responses displayed in a dedicated output panel for inspection and debugging.
Unique: Integrates API testing directly into VS Code's editor workflow via Activity Bar and Command Palette, eliminating context switching to external tools like Postman; implementation likely uses Node.js HTTP libraries (http/https modules or axios) wrapped in VS Code's WebView API for UI rendering.
vs alternatives: Faster iteration than Postman for developers already in VS Code because requests and code are in the same window, though lacks Postman's advanced features like request collections, environment management, and automated testing.
Provides direct MongoDB database connectivity and query execution from within VS Code, allowing developers to connect to MongoDB instances using connection strings, browse collections, and execute queries without external database tools. The implementation manages connection pooling, credential handling, and query result formatting, likely using the official MongoDB Node.js driver (mongodb npm package) to establish connections and execute CRUD operations. Query results are displayed in a structured format within the editor's output panel.
Unique: Embeds MongoDB client directly in VS Code using the official Node.js MongoDB driver, eliminating need for MongoDB Compass or command-line tools; connection state is managed within the extension's lifecycle, allowing persistent connections across multiple queries within a session.
vs alternatives: Faster than MongoDB Compass for developers already in VS Code for quick queries, but lacks Compass's visual aggregation pipeline builder and advanced schema analysis tools.
Aggregates nine professional developer utilities (regex builder, JSON formatter, hash generator, and six others not fully documented) into a single, accessible hub within VS Code. The implementation provides a unified UI or menu system for accessing these tools, likely through the Activity Bar or command palette. Tools are integrated into the editor's workflow, allowing developers to perform common development tasks without switching to external applications.
Unique: Consolidates nine developer utilities into a single VS Code extension, providing unified access through Activity Bar and command palette; implementation likely uses VS Code's WebView API to render a dashboard or menu system for tool selection.
vs alternatives: More convenient than managing nine separate browser tabs or applications, but each individual tool likely has less functionality than dedicated alternatives (regex101, JSON.cn, etc.).
Automatically detects and removes console.log statements (and related console methods like console.error, console.warn) from JavaScript/TypeScript code using pattern matching or AST-based analysis. The implementation likely scans the current file or selection for console method calls and provides options to remove them individually or in bulk. This capability integrates with VS Code's command palette and context menu, allowing developers to trigger cleanup on-demand or potentially on file save.
Unique: Integrates console.log removal as a one-click automation within VS Code's editor context, likely using regex or simple pattern matching to identify console statements; implementation may support batch operations across multiple files in a workspace.
vs alternatives: Faster than manually searching and removing console.log statements, but less sophisticated than ESLint rules (eslint-plugin-no-console) which provide linting, auto-fix, and configuration options.
Captures the current code selection or viewport and generates a visually formatted snapshot (likely as an image or styled HTML) suitable for sharing in documentation, chat, or social media. The implementation extracts the selected code, applies syntax highlighting using VS Code's theme, and renders it as a shareable artifact. Snapshots may include metadata like filename, language, and line numbers for context.
Unique: Leverages VS Code's built-in syntax highlighting and theme engine to generate visually consistent code snapshots directly from the editor, eliminating need for external tools like Carbon or Polacode; implementation likely uses VS Code's WebView API to render styled code and canvas/screenshot APIs to export.
vs alternatives: Faster than Carbon or Polacode because it's integrated into the editor and uses existing theme/syntax highlighting, but may lack advanced customization options like custom backgrounds or watermarks.
Provides access to a curated library of 500+ code snippets across 15+ programming languages and frameworks (JavaScript, Python, React, Vue, Node.js, Django, etc.). Snippets are indexed and searchable via VS Code's IntelliSense or command palette, allowing developers to quickly find and insert relevant code templates. The implementation stores snippets as structured data (likely JSON or VS Code's native snippet format) and integrates with VS Code's snippet expansion engine to insert them with proper indentation and placeholder handling.
Unique: Bundles 500+ pre-built snippets across 15+ languages directly in the extension, leveraging VS Code's native snippet expansion engine for seamless insertion with placeholder handling; snippets are likely stored in VS Code's JSON snippet format (.code-snippets) for compatibility with IntelliSense.
vs alternatives: More comprehensive than VS Code's default snippets and faster to access than searching GitHub Gists or Stack Overflow, but less personalized than user-created snippet libraries and lacks AI-powered recommendations like GitHub Copilot.
Allows developers to create, organize, and manage their own code snippets within VS Code, storing them in a personal library accessible across projects. The implementation provides a UI for defining snippet name, description, code content, and placeholder variables, then stores snippets in VS Code's snippet storage format. Custom snippets integrate with IntelliSense and can be shared across the workspace or exported for team use.
Unique: Integrates custom snippet creation directly into VS Code's extension UI, storing snippets in VS Code's native format for seamless IntelliSense integration; implementation likely provides a form-based UI for snippet definition rather than requiring manual JSON editing.
vs alternatives: More integrated than manually managing .code-snippets files, but less feature-rich than dedicated snippet managers like Snippet Manager or Lexi which offer cloud sync, team collaboration, and advanced organization.
Provides an interactive regex builder and tester utility that allows developers to construct regular expressions, test them against sample text, and visualize matches. The implementation likely includes a UI with separate panels for regex input, test text, and match results, with real-time feedback as the regex is modified. May include a library of common regex patterns (email, URL, phone number, etc.) for quick reference.
Unique: Embeds a real-time regex tester within VS Code using JavaScript's native RegExp engine, providing instant visual feedback as patterns are modified; implementation likely uses VS Code's WebView API to render the UI and JavaScript's exec/match methods for pattern testing.
vs alternatives: Faster than regex101.com for quick testing because it's integrated into the editor, but lacks regex101's advanced features like explanation generation, performance analysis, and community pattern sharing.
+3 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 DevSnip Pro at 41/100. DevSnip Pro 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