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Limitations

Documenting what Whittl does NOT do is as important as documenting what it does. If you know where the edges are, you can trust the middle more.

Platforms

  • macOS is not supported. No build, no in-development macOS target. Whittl may work on Linux-in-a-VM on a Mac, but I can't help troubleshoot it.
  • Windows 10 is the minimum. Windows 7 / 8 / 8.1 will not install.
  • Linux — tested on Ubuntu 22.04+, Fedora 38+, Arch, Debian 12+. Older distributions may work but aren't tested.
  • 32-bit systems are not supported. Both Whittl and its bundled Python runtime are 64-bit only.

Languages

Whittl generates Python. That's the whole ecosystem. It does not generate:

  • JavaScript, TypeScript, Rust, Go, C, C++, Java, etc.
  • HTML / CSS (unless your Python app outputs HTML, like a Flask template)
  • SQL (beyond what a Python app needs to execute — no standalone schema designer)

If you need a non-Python target, Whittl isn't your tool.

GUI frameworks

Whittl's four targets cover most desktop and CLI use cases but not all:

  • Kivy is not supported.
  • wxPython is not supported.
  • Web frontend (React, Vue, Svelte) is out of scope — Whittl ships local apps, not web apps.
  • Game engines (Godot, Unity, Unreal) are not supported. Pygame works in the General Python target but there's no dedicated pipeline.
  • iOS apps are not supported (Flet targets Android only in Whittl).

See Choosing a Target for what IS covered.

AI backends

  • Only the five supported backends. Custom endpoints are limited to OpenAI-compatible via a configured base URL — no fully custom protocol support.
  • No batched generation. One generation per turn. Want 10 variations of the same prompt? Run it 10 times.
  • No fine-tuning pipeline. Whittl uses base models from each backend. You can't upload a custom-fine-tuned model directly (you can route one via OpenRouter if you've hosted it there).

Mobile

  • iOS builds are not supported (Flet target is Android-only in Whittl).
  • APK builds are experimental. They work; they may need troubleshooting on specific devices or OEM skins.
  • No Play Store publishing pipeline. Whittl produces signed APKs; you handle Play Console uploads manually.
  • No automatic cross-platform mobile testing. Install to your own device and test there.

Code generation

  • The AI makes mistakes. Autofix rules catch many, but not all. Some generations crash; some produce code that runs but doesn't do what you asked. Iterate.
  • Large single-file projects degrade quality. Past ~1,500 lines in one file, the AI starts making more mistakes. Split into modules.
  • Very domain-specific tasks (compiler writing, advanced numerical methods, hardware drivers) are usually outside the sweet spot of the base models. Whittl is best at application code.
  • Real-time / low-latency requirements (audio processing under 20ms, real-time physics) are hard — Python isn't fast enough for some of these, and the AI may not pick up on your latency budget even if it understood the requirement.

Build output

  • No code signing on Whittl's own binaries (v2.4). Windows SmartScreen warns on first install of Whittl itself; users click through "More info" → "Run anyway." Azure Trusted Signing deferred until revenue supports it. This is separate from apps YOU build with Whittl — those are also unsigned by default, and you can sign them yourself if you've got a cert.
  • No automatic distribution channels. Whittl produces builds; it doesn't upload them to itch.io, Steam, the Play Store, etc. You handle distribution.
  • No incremental installers. Each release is a full installer. In-app updates (v2.4+) download the full installer, not a delta.
  • No macOS .app / .dmg. Covered above.

Privacy & security edges

  • Whittl is not encrypted at rest beyond API keys. Your code and project files are plain .py files. Full-disk encryption is the OS's job (BitLocker, LUKS).
  • No multi-user mode. Whittl runs per-user. On a shared machine, every user has their own ~/.whittl/ and projects don't cross over.
  • No cloud sync built-in. Whittl is local-only. You can layer Dropbox / rclone / git on ~/.whittl/projects/ to sync across machines, but nothing is built in.

Skills & Commons

  • Skills library is text-only (markdown). No binary skill data (embeddings, vectors, code corpora).
  • Whittl Commons isn't shipped yet (planned for v2.5). The "download community rules weekly" flow doesn't exist in v2.4.

What's NOT a limitation

To cut some common misconceptions:

  • You do NOT need an internet connection if using Ollama
  • You do NOT need Python installed separately
  • You do NOT have to pay for the AI — Gemini and OpenRouter have free tiers usable indefinitely
  • You do NOT need an Anthropic / Google / etc. account for Whittl itself — only for the backend you choose
  • You do NOT need to create a Whittl account. There is no account system. The app is local.

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