Field Theory

Field Theory

Context stacking for voice transcription, screenshots, and portable commands — made for builders and engineers and ready for Cursor, Claude Code, or any input field. The current meta for talking to models is “tell it more.” So we copy and paste a lot. Screenshots, logs, docs, voice transcripts. It’s tedious. Field Theory makes context management fast. Hotkeys for everything. Local transcription. Voice commands. Image and text stacking. And portable commands you can invoke anywhere.

Key Features

• Context Stacking: Sequentially capture screenshots, voice notes, and clipboard text into a single “Block” to paste into AI.
• The Librarian: A searchable history manager for all your captured context stacks.
• Local Transcription: Voice-to-text happens entirely on-device (privacy-first), ideal for sensitive code discussions.
• Portable Commands: Define prompt templates or instructions that can be invoked via hotkey in any text field (IDE, Browser, Terminal).
• Multi-Modal Capture: Natively handles mixing images (OCR) and code snippets without formatting errors.
• Keyboard-First: Designed for speed with global hotkeys for every action, minimizing mouse usage.

How It Works

Users use global hotkeys to capture screen areas, dictate voice notes, or copy logs. Instead of overwriting your clipboard, Field Theory “stacks” these inputs into a unified context buffer. You might screenshot a UI bug, copy the error log, and dictate “Fix this React hydration error.” You then paste this rich, multi-modal context block into any AI interface (like Cursor or a terminal) to provide the model with all necessary background information in a single, structured prompt.

Use Cases

• “Rubber Ducking” to AI: explaining a complex bug aloud while showing the code and error logs simultaneously.
• Bug Reporting: Creating a perfect bug report (Image + Log + Explanation) in seconds to paste into GitHub or Linear.
• IDE Context Injection: Feeding Cursor or Claude Code with external context (e.g., API docs from a browser) without context switching.
• Standardized Prompts: Using “Portable Commands” to insert standard code review instructions into any text box.

Pros & Cons

• Pros: Solves the specific “context dumping” pain point for engineers; Local transcription ensures code privacy; “Stacking” is faster than multiple copy-pastes; “Power user” tool designed for keyboard efficiency.
• Cons: Learning curve for hotkeys/workflow; Overlap with existing screenshot/clipboard tools; Niche audience (builders/engineers); Desktop-only focus.

Pricing

Indie Pricing Model.
* Status: Launched Feb 2026. Likely a low monthly subscription or one-time license for power features, with a free tier for basic local usage. (Specific pricing tiers are currently rolling out).

How Does It Compare?

Field Theory is an “Active Capture” tool, whereas most competitors are “Passive Recorders” or “Simple Buffers.”

• vs. Rewind.ai / Screenpipe:
  • The Difference: Rewind and Screenpipe record everything 24/7 so you can search the past (“What did I do yesterday?”). Field Theory is for intentional capture (“I need these 3 specific things for my next AI prompt”). It doesn’t record your whole day, only what you tell it to stack.
  • Winner for you?: Use Rewind for memory. Use Field Theory for coding and prompting.
• vs. Raycast / Maccy (Clipboard Managers):
  • The Difference: Clipboard managers store a history of single items. You have to paste, switch, paste, switch. Field Theory bundles multiple items (Image + Text + Voice) into one pasteable object.
  • Winner for you?: Use Raycast for launching apps. Use Field Theory for building complex AI contexts.
• vs. Whispr Flow:
  • The Difference: Whispr Flow is excellent for dictation but doesn’t handle screenshots or “stacking” multiple data types. Field Theory treats voice as just one layer of the context sandwich.

Final Thoughts

Field Theory is the tool for the “Copy-Paste Era” of AI engineering. As models get smarter, the bottleneck shifts from “model intelligence” to “context bandwidth”—how fast can I get the relevant info into the model? By treating context as a “stackable” commodity rather than a fleeting clipboard item, Field Theory significantly speeds up the loop between spotting a problem and feeding it to an AI solver. It is a “must-try” for developers living inside Cursor or Claude.