| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Kaku implements a GPU-accelerated rendering pipeline inherited from WezTerm but optimized for macOS through native CoreText font rendering instead of cross-platform abstractions. The TermWindow core manages a render loop that converts terminal cell state into GPU commands, with platform-specific code paths for macOS CoreText font metrics and glyph rasterization. This approach reduces latency for high-frequency screen updates while maintaining sub-40MB binary size through feature removal and symbol stripping.
Unique: Forks WezTerm's GPU rendering but strips unused features and replaces cross-platform font abstraction with native macOS CoreText, reducing binary from 67MB to 40MB while maintaining frame-rate performance through platform-specific optimizations
vs alternatives: Faster rendering than iTerm2 (GPU-accelerated) and smaller footprint than WezTerm (40MB vs 67MB) while keeping native macOS font rendering that iTerm2 lacks
Kaku ships with sensible defaults (JetBrains Mono font at 13pt, opencode color scheme, optimized for low-DPI displays) embedded in the binary, eliminating the blank-slate problem of WezTerm. Configuration follows a three-tier priority system: CLI arguments override ~/.config/kaku/kaku.lua overrides bundled defaults. The Lua configuration system exposes the full wezterm module API (wezterm.action.SplitHorizontal, wezterm.color.parse, event hooks like gui-startup), allowing power users to customize without losing defaults.
Unique: Implements three-tier configuration priority (CLI > user Lua > bundled defaults) with full WezTerm Lua API compatibility, allowing zero-setup experience while preserving power-user customization without requiring users to redefine all settings
vs alternatives: Faster onboarding than WezTerm (which requires manual config) and more flexible than iTerm2 (which uses plist-based settings with no scripting layer)
Kaku provides clipboard integration that allows terminal applications to read and write the system clipboard via escape sequences (OSC 52 protocol). Toast notifications appear as transient UI elements in the terminal window to provide feedback for actions (e.g., 'Pane split', 'Workspace switched'). The notification system integrates with the rendering pipeline to display toasts without blocking terminal output. Clipboard operations are handled by the platform layer, with macOS-specific code using NSPasteboard for clipboard access.
Unique: Implements OSC 52 clipboard protocol with platform-specific macOS NSPasteboard integration and transient toast notifications that integrate with the rendering pipeline, enabling seamless clipboard operations without external tools
vs alternatives: More integrated than iTerm2's clipboard support (which requires separate configuration) and more reliable than tmux clipboard integration (which requires external tools like pbcopy)
Kaku provides a configuration TUI (Text User Interface) accessible via kaku config that allows users to interactively edit settings without manually editing Lua files. The TUI presents configuration options in a structured format (e.g., font selection, color scheme, keybindings) and validates changes before writing to ~/.config/kaku/kaku.lua. The TUI integrates with the Lua configuration system, allowing users to preview changes and revert if needed. This approach lowers the barrier to configuration for users unfamiliar with Lua.
Unique: Provides a TUI-based configuration editor (kaku config) that allows interactive settings editing without Lua knowledge, with validation and preview capabilities, lowering the barrier to configuration for non-technical users
vs alternatives: More user-friendly than manual Lua editing and more comprehensive than iTerm2's GUI preferences (which don't expose all settings)
Kaku implements workspaces as a grouping mechanism for related windows, tabs, and panes, allowing users to organize work by project or context. Workspaces are named and can be switched via keybindings or command palette. The multiplexer maintains workspace state (open windows, tabs, panes, their layout) during the session. Users can define workspace templates in Lua configuration to automatically create workspaces with specific layouts (e.g., 'frontend' workspace with dev server pane, 'backend' workspace with API server pane).
Unique: Implements workspaces as a first-class organizational unit with Lua-based template support, allowing users to define project-specific layouts and switch between contexts without external tools or multiple terminal windows
vs alternatives: More integrated than tmux sessions (which require separate configuration) and more flexible than iTerm2 profiles (which are limited to window-level organization)
Kaku bundles and auto-installs a curated zsh plugin suite during first-run initialization (kaku init): z for frecency-based directory navigation, zsh-completions for extended shell completion, zsh-syntax-highlighting for real-time command validation, and zsh-autosuggestions for history-based suggestions. Plugins are copied to ~/.config/kaku/zsh/plugins/ and sourced via shell integration scripts that detect shell type and environment. This approach eliminates the need for users to manually discover, install, and configure productivity plugins.
Unique: Bundles and auto-installs a curated zsh plugin suite (z, zsh-completions, zsh-syntax-highlighting, zsh-autosuggestions) during first-run initialization, eliminating manual plugin discovery and configuration while maintaining compatibility with user-installed plugins
vs alternatives: Faster shell setup than Oh My Zsh (which requires manual plugin selection) and more opinionated than bare zsh (which requires users to discover and install plugins individually)
Kaku integrates an AI assistant (kaku ai command) that analyzes failed shell commands and suggests corrections or alternative approaches. The system captures command exit codes, stderr output, and command context, then sends this to configured AI providers (OpenAI, Anthropic, or local models) to generate contextual suggestions. Integration points include shell integration scripts that hook into command execution and a configuration interface (kaku config) for setting AI provider credentials and model preferences. This capability is designed specifically for AI-assisted coding workflows where developers iterate rapidly.
Unique: Implements AI error recovery as a first-class terminal feature with multi-provider support (OpenAI, Anthropic, local models) and shell integration hooks that capture command context (exit code, stderr, working directory) for contextual AI suggestions, rather than treating AI as a separate tool
vs alternatives: More integrated than ChatGPT-in-browser (which requires context-switching) and more flexible than GitHub Copilot CLI (which is GitHub-only and doesn't support local models)
Kaku implements a multiplexer (Mux) architecture inherited from WezTerm that manages multiple windows, tabs, and panes within a single process. The TermWindow core coordinates rendering and input for all panes, with each pane maintaining independent terminal state (scrollback, cursor position, cell grid). Panes can be split horizontally or vertically via wezterm.action.SplitHorizontal/SplitVertical, and workspaces group related windows and tabs. The multiplexer supports both local panes (running shell processes) and remote panes (SSH connections via wezterm-ssh crate), enabling seamless switching between local and remote environments.
Unique: Implements a process-based multiplexer (Mux) that manages windows, tabs, and panes with unified rendering via TermWindow core, supporting both local shell processes and remote SSH connections via wezterm-ssh crate, eliminating the need for external multiplexers like tmux
vs alternatives: More integrated than tmux (no separate process management) and supports SSH domains natively, whereas tmux requires SSH tunneling or separate SSH sessions
+5 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 Kaku at 42/100. Kaku 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