Decode vs Worktrunk: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Decode and Worktrunk — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Decode

Entropik Technologies

Freemium

A human insights platform that uses emotion AI, webcam eye tracking, and predictive models to test creative, products, and experiences before launch.

Emotion AI Measurement: Face emotion, voice emotion, and text sentiment analysis reveal how respondents actually feel during a study rather than only what they report in an answer.
Webcam Eye Gaze Tracking: Real eye tracking runs through a participant's own webcam with zero hardware, producing attention heatmaps that show where people look first and what they miss.
AI Creative Insights: Neuro AI predicts attention, emotional resonance, brand recall, and conversion impact for ad creative, packaging, OOH, and web layouts before media spend is committed.
Synthetic Audience: Build reusable synthetic personas and compare how each creative performs persona by persona ahead of fielding a study with real respondents.
AI Moderator: Runs moderated and unmoderated interviews at scale, then extracts themes, emotions, and supporting evidence from raw interview and video feedback automatically.
Shopper and Shelf Simulation: Simulates real-world shelf and pack testing with attention heatmaps, shelf visibility analysis, planogram optimization, and purchase-intent prediction.
UX Research Suite: Prototype testing, unmoderated task studies, usability and wireframe testing, card and tree sorting, and live website and app testing, each enriched with gaze and emotion data.
Global Respondent Panel: Access to more than 103 million respondents worldwide, or bring your own panel free of charge on any plan.

Pre-Flight Ad Testing: Comparing creative variations and messaging options to predict which version earns attention and recall before buying media.
Packaging and Shelf Decisions: Testing pack designs and planograms in a simulated retail environment to forecast visibility and purchase intent.
Product Concept Validation: Screening product concepts, storyboards, and innovation ideas for early-stage market fit before committing development resources.
UX Friction Discovery: Running prototype and usability studies where webcam eye tracking and emotion signals expose confusion users cannot articulate.
Qualitative Research at Scale: Using the AI Moderator to conduct and synthesize many interviews into structured themes instead of manual transcript coding.
Brand Tracking and Price Testing: Running recurring consumer studies on brand perception, pricing, and the customer journey across multiple markets.

max-sixty

Free

A Rust CLI that makes git worktrees as easy as branches, built for running several AI coding agents in parallel without collisions.

Branch-Addressed Worktrees: wt switch, wt remove, and wt list refer to worktrees by branch name with paths computed from a configurable template, replacing multi-step git worktree incantations.
Agent Launch in One Command: wt switch -c -x claude <branch> creates the worktree, enters it, and starts the agent in a single invocation.
Lifecycle Hooks: Run commands automatically on create, pre-merge, and post-merge to automate setup and teardown for every new worktree.
LLM Commit Messages: Generates commit messages from the diff so parallel agent branches stay legible without hand-writing every message.
One-Command Merge Workflow: Squash, rebase, merge, and clean up the worktree and branch in a single step rather than a sequence of git commands.
Interactive Picker: Browse worktrees with streaming CI status alongside diff, log, PR, and comment previews before switching.
Shared Build Caches: wt step copy-ignored gives ten worktrees their own target/ and node_modules/ without rebuilding or copying, using reflinks on APFS, btrfs, and XFS.
Per-Worktree Dev Servers: A hash-port template filter assigns each worktree a unique port so parallel dev servers do not conflict.

Parallel Agent Runs: Give each of five to ten concurrently running AI coding agents its own worktree so their edits never collide.
Fast Branch Context Switching: Jump between in-flight changes by branch name instead of navigating sibling directories by path.
Pull Request Review: wt switch pr:123 checks out a pull request's branch directly for local inspection or testing.
Monorepo Iteration: Share heavy build artifacts across many worktrees so each new branch is usable immediately instead of after a full rebuild.
Automated Branch Setup: Use create hooks to install dependencies, copy env files, or start services whenever a worktree is made.
Multi-Branch Status Review: wt list --full shows CI status and AI-generated summaries for every active branch in one view.