CatDoes v3 vs Feynman: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of CatDoes v3 and Feynman — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

CatDoes v3

CatDoes

Freemium

No-code AI mobile app builder that turns plain text descriptions into mobile apps for businesses and personal use.

Natural-Language App Generation: Converts user-written descriptions and requirements into a mobile app structure, letting non-technical users specify features and UI in plain text.
No-Code App Builder: Provides a workflow that removes the need for programming, enabling app creation, iteration, and customization through visual/no-code tools and AI guidance.
Business-Focused Templates: Facilitates rapid creation of apps tailored for business use cases (e.g., service booking, catalogs, customer engagement) to accelerate time-to-market.
Rapid Prototyping: Enables fast generation of working prototypes from ideas so users can validate concepts, gather feedback, and iterate without developer resources.
No-code mobile app creation
Natural-language (words to app) driven workflow
Accessible to non-technical users
Designed for business and personal app development
AI-assisted app generation and scaffolding

Small Business Apps: Quickly build a customer-facing mobile app for bookings, catalogs, or promotions without hiring developers.
Prototype and Validate Ideas: Turn an app concept described in text into a prototype to test product-market fit and collect user feedback.
Solo Entrepreneurs and Creators: Create personal or creator-focused apps to distribute content, manage subscriptions, or engage audiences without technical overhead.
Internal Tools for Teams: Produce internal mobile tools (e.g., simple data collection or workflows) to streamline team operations without custom development.
Educational Projects and Learning: Allow students and non-technical learners to realize app projects and learn product design concepts via no-code creation.
Small businesses building customer-facing mobile apps without hiring developers
Entrepreneurs quickly prototyping and launching MVP mobile apps
Individuals creating personal or portfolio apps without coding
Businesses creating simple internal apps or client-facing tools rapidly

Companion

Free

Open-source AI research agent that reads papers, ranks literature, drafts research and plans experiments from the terminal or a local workbench.

Cited Research Briefs: Asking a research question returns a synthesized brief where each claim is tied to the paper or web source it came from, rather than an unsourced summary.
PaperRank Scoring: Ranks papers on a topic with transparent evidence for citations, methodology, reproducibility and provenance so reading order is a decision you can inspect.
Paper Access Resolver: Resolves a single DOI, arXiv ID, OpenAlex ID, PMID, PMCID or title against OpenAlex, arXiv/alphaXiv, DOI and Europe PMC, with optional full-text fetching.
Local Science Workbench: `feynman serve` opens a standalone app with projects, sessions, chat, notebooks, compute, artifact previews and provenance in one place.
Claim Auditing and Replication: Compares a paper's stated claims against what its code actually does, and generates replication plans with compute targets and gated experiment steps.
Local and Hosted Models: Works with hosted providers via OAuth or API key and with local runtimes including LM Studio, Ollama, vLLM and a LiteLLM proxy.
Skills-Only Install: The research skill library can be installed on its own into Claude, Codex or OpenCode projects without the terminal app or bundled runtime.
Science Artifacts: Reports, data files, spreadsheets, notebooks, LaTeX, chemistry sketches and genomes are browsable together with versions, lineage and execution logs.

Deciding What to Read: Ranking a fresh literature pile on a topic by reproducibility and methodology instead of citation count alone.
Writing a Literature Review: Producing a review that separates where the field agrees from where questions remain open, with citations attached.
Verifying a Paper's Claims: Auditing whether the results a paper reports are supported by the code and data it released.
Planning a Replication: Turning a published finding into a concrete replication plan with a compute target and staged experiment steps.
Running Deep Research Passes: Launching a multi-agent deep dive on a topic that synthesizes findings and verifies them before reporting.
Keeping Research Local: Running the whole pipeline against a local model so unpublished work and private data never leave the machine.
Adding Research Skills to a Coding Agent: Installing the skills bundle into an existing Claude or Codex project to get research workflows without a second app.