Introducing Almond

Most people have accepted a strange tradeoff in modern writing: the better your ideas get, the slower it feels to capture them.

You pause to type. You edit while still thinking. You lose the exact phrasing because your hands cannot keep up with your head.

Almond started with a simple product question: what if writing could begin at speaking speed, without sending your words to a cloud pipeline?

What we built, in plain terms

Almond is local dictation for macOS that focuses on end-to-end writing velocity, not just raw transcription accuracy. You trigger it, talk naturally, and get usable text where your cursor already is.

That means no mode switching to a separate editor, no copy and paste loop, and no cloud wait after you stop speaking.

Our product thesis

Fast dictation is not one number. It is a chain:

• How quickly you can start speaking.
• How long finalization takes after speech ends.
• How much cleanup is needed before the text is usable.
• How quickly the text lands in the app you are already using.

Almond is designed around this full chain. If one step is slow, the workflow feels slow.

Why deterministic on-device processing matters

When dictation depends on cloud inference, your finalization time depends on network conditions and service variability. Sometimes it is fast. Sometimes it is not. That unpredictability breaks writing flow.

Almond keeps transcription local. By removing cloud round trips from the critical path, timing becomes far more stable. Stable timing builds trust, and trust is what turns dictation from a novelty into a daily tool.

What this unlocks in real work

The biggest gains show up in high-context writing tasks:

• Prompting ChatGPT, Claude, or Gemini with long constraints and examples.
• Drafting product docs and strategy notes in Notion or Docs.
• Writing status updates and incident summaries in Slack.
• Explaining bugs and architectural intent in coding tools like Cursor and VS Code.

These are not one-line snippets. They are multi-step ideas, and speaking them is usually faster than typing them.

How we validate performance claims

We publish a specific benchmark boundary: time from end of dictation to visible final text. This is the delay users actually feel after they finish talking.

If you want details, start with our benchmark page and then the methodology breakdown.

What comes next

Our roadmap stays focused: faster completion, better cleanup quality, and stronger workflows for AI prompting and coding.

Almond is not trying to be everything. It is trying to be the fastest way to turn thought into text on Mac.