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Daisy
Record, transcribe, summarise — nothing leaves your Mac
Local-first meeting recorder for Mac — and fully open source (Apache 2.0). Records both sides of a call (mic + system audio) with no bot joining, transcribes on-device with Whisper, and summarises with your AI (Anthropic, OpenAI, Apple Intelligence). Exposes a local MCP server so Claude & Cursor can query your meetings — and doubles as push-to-talk dictation in any app. No cloud, no account, no subscription.
Daisy started as an internal tool. I do a lot of client interviews and discovery calls — design strategy work where the texture of what someone says matters as much as the substance — and for years my options were two:
1. Send the audio to Granola / Otter / Fathom / Shadow. Get fast summaries, lose control of where the transcript lives.
2. Type notes by hand and miss half the conversation.
I tried every cloud meeting tool. They're great products, but they all require a vendor relationship I didn't want for client conversations. The transcript sits on a server I don't own. Every new feature adds a subprocessor to their list. And the moment I asked "where exactly does my Acme client meeting live?", the answer was always "on our infrastructure, encrypted at rest, you can trust us."
I'd rather not have to trust anyone.
So I built Daisy. It records on the Mac — microphone plus the other side of the call captured through ScreenCaptureKit, no bot in the meeting. Whisper Large v3 transcribes on the Neural Engine. For summaries, you bring your own AI provider key — Anthropic, OpenAI, Apple Intelligence on macOS 26, or a local model via Ollama or LM Studio — and the key stays in your Keychain, with requests going straight from your Mac to the provider, never through our servers. The transcript lands in your folder of choice (mine is an Obsidian vault).
The structural thing competitors can't ship: Daisy exposes a local MCP server on 127.0.0.1. Claude Desktop and Cursor can query your transcripts directly — "summarise everything I discussed with Acme this quarter" — and Daisy answers from your own disk. No copy-paste, no upload, no API token sitting in someone else's database.
It's open source on GitHub under Apache 2.0 — OSI-approved, no commercial carve-outs. Built native on macOS, signed and notarised, with Sparkle auto-updates. Free during beta, no account, no subscription gate, no telemetry.
I'd love to hear:
— What's your current meeting-capture stack, and what would have to change for you to try Daisy?
— For developers: what's the ideal local-MCP query you'd want to run?
— For privacy-aware folks: any missing assurance you'd like me to address?
I read every comment. Thanks for taking a look.
— Egor (Addicted Studio)
About Daisy on Product Hunt
“Record, transcribe, summarise — nothing leaves your Mac”
Daisy was submitted on Product Hunt and earned 17 upvotes and 3 comments, placing #16 on the daily leaderboard. Local-first meeting recorder for Mac — and fully open source (Apache 2.0). Records both sides of a call (mic + system audio) with no bot joining, transcribes on-device with Whisper, and summarises with your AI (Anthropic, OpenAI, Apple Intelligence). Exposes a local MCP server so Claude & Cursor can query your meetings — and doubles as push-to-talk dictation in any app. No cloud, no account, no subscription.
On the analytics side, Daisy competes within Mac, Productivity, Developer Tools and GitHub — topics that collectively have 1.3M followers on Product Hunt. The dashboard above tracks how Daisy performed against the three products that launched closest to it on the same day.
Who hunted Daisy?
Daisy was hunted by Egor Sazanov. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Egor Sazanov
Hi everyone,
Daisy started as an internal tool. I do a lot of client interviews and discovery calls — design strategy work where the texture of what someone says matters as much as the substance — and for years my options were two:
1. Send the audio to Granola / Otter / Fathom / Shadow. Get fast summaries, lose control of where the transcript lives.
2. Type notes by hand and miss half the conversation.
I tried every cloud meeting tool. They're great products, but they all require a vendor relationship I didn't want for client conversations. The transcript sits on a server I don't own. Every new feature adds a subprocessor to their list. And the moment I asked "where exactly does my Acme client meeting live?", the answer was always "on our infrastructure, encrypted at rest, you can trust us."
I'd rather not have to trust anyone.
So I built Daisy. It records on the Mac — microphone plus the other side of the call captured through ScreenCaptureKit, no bot in the meeting. Whisper Large v3 transcribes on the Neural Engine. For summaries, you bring your own AI provider key — Anthropic, OpenAI, Apple Intelligence on macOS 26, or a local model via Ollama or LM Studio — and the key stays in your Keychain, with requests going straight from your Mac to the provider, never through our servers. The transcript lands in your folder of choice (mine is an Obsidian vault).
The structural thing competitors can't ship: Daisy exposes a local MCP server on 127.0.0.1. Claude Desktop and Cursor can query your transcripts directly — "summarise everything I discussed with Acme this quarter" — and Daisy answers from your own disk. No copy-paste, no upload, no API token sitting in someone else's database.
It's open source on GitHub under Apache 2.0 — OSI-approved, no commercial carve-outs. Built native on macOS, signed and notarised, with Sparkle auto-updates. Free during beta, no account, no subscription gate, no telemetry.
I'd love to hear:
— What's your current meeting-capture stack, and what would have to change for you to try Daisy?
— For developers: what's the ideal local-MCP query you'd want to run?
— For privacy-aware folks: any missing assurance you'd like me to address?
I read every comment. Thanks for taking a look.
— Egor (Addicted Studio)