Back to News
research

Talat’s AI meeting notes stay on your machine, not in the cloud

Sarah Perez
Loading...
6 min read
0 likes
⚡ Quantum Brief
A Mac developer launched Talat, a privacy-focused AI notetaker that processes audio and transcripts locally—never uploading data to cloud servers—unlike competitors like Granola, which rely on hosted models. The app leverages Apple’s Neural Engine via FluidAudio, a Swift framework enabling low-latency, on-device transcription, and defaults to Qwen3-4B-4bit for summaries, with options to switch to other local or cloud LLMs. Talat offers a one-time $49 purchase (rising to $99 at 1.0 release), no subscriptions, and no account requirements, targeting users seeking control over data storage and export options like Obsidian or webhooks. Features include real-time speaker identification, editable transcripts, and post-meeting AI summaries, with future integrations planned for Google Calendar and Notion. Built by Nick Payne and Mike Franklin, the 20 MB app supports M-series Macs, offering 10 free hours for testing, emphasizing configurability and open-source tools like AudioTee.
AI Audio Summary
0:00 / 0:00
Click to play
Untitled design (36).png
Quantum News · Media Library

The AI-powered notetaking app Granola, valued at $250 million, has become a popular tool among tech industry founders and VCs. But one developer believes there’s demand for a more private, local-only alternative that’s available for a one-time fee and without a subscription. That’s led to the creation of a new Mac app called Talat. Yorkshire, England-based developer Nick Payne, a self-described computer nerd, says the idea to build a local AI notetaker came about mostly because of a series of happy accidents. “I think Granola is awesome; it’s a shining example of what you can do with an Electron app [a framework for building desktop applications] given enough love and care,” he told TechCrunch. “When I first tried it, I was fascinated that it managed to record system audio on my Mac without recording video, which was the standard workaround at the time. That led to a ton of research, discovering a relatively new and poorly-documented Apple API.” To make it easier to work with that API (Core Audio Taps, which lets developers tap into a Mac’s audio streams), Payne decided to create an open source audio library, AudioTee. “During that time, I was slowly piecing together a toolkit, but I never found anything that felt like it could stand on its own as a product rather than just a cool tech demo,” Payne said. “The state-of-the-art hosted transcription models — the same providers folks like Granola use — are incredible, and it’s viscerally cool to see your speech unfurled onscreen in near real-time. But it always nagged me that the tradeoff required providing not just my data, but my audio data; my actual voice,” he added. He then stumbled upon a software toolkit called FluidAudio, a Swift framework that enables fully local, low-latency audio AI on Apple devices. It lets you run small, fast transcription models directly on the Mac’s Neural Engine — Apple’s dedicated hardware for AI processing. That was the piece that made Payne realize he could turn his research into an actual product — one where your audio never leaves your Mac, and your transcripts aren’t stored on another company’s servers. Talat, which was built alongside Payne’s longtime friend and former colleague, Mike Franklin, is the result of Payne’s interest in the audio space. The result is a 20 MB, one-time purchase that doesn’t require you to create an account or even share analytics data back with the developers. There are no ongoing fees, either. While some AI notetakers may have more bells and whistles, Talat offers a streamlined set of features. It captures audio from your computer’s microphone when you’re in meeting apps like Zoom, Teams, Meet, and others, and transcribes it in real time. The app tries to assign speakers in real-time, but you can reassign them as needed. You can also take notes, plus edit, delete, or split transcript segments. When the meeting finishes, a local LLM generates a summary with key points, decisions, and action items. The notes, transcripts, and summaries are all searchable in Talat, too. In addition to the privacy angle, Payne said the goal is to give users more options. “We’re leaning into configurability and letting users control where their data goes: pick your own LLM, auto-export to [note-taking app] Obsidian, webhooks that push data out when a meeting finishes, an MCP server,” which is a standardized way for AI tools to connect to outside data sources, “to pull it on demand,” he explained. Under the hood, the AI is a mixture — “mostly stitched together and abstracted behind FluidAudio,” Payne noted, which he credits with doing a lot of the heavy lifting. For the summarization piece, the app defaults to an Al model called Qwen3-4B-4bit, which can run on even fairly modest hardware. However, users can opt to switch that out to any cloud LLM provider of their choice, or they can choose between two Parakeet variants — speech recognition models developed by Nvidia — or point it at Ollama (a tool for running AI models locally), giving them more control over the experience. In time, Talat will add support for more built-in choices, as well, as well as integrations for other apps, like Google Calendar and Notion. At launch, users with M-series Mac computers (those running Apple’s own processors, starting with the M1) can download the app and try it out for free with 10 hours of recordings before deciding to purchase. Talat is available for $49 while in this pre-release version, which is still under active development. When the app hits a 1.0 release, the price will increase to $99. Payne and Franklin are bootstrapping Talat and plan to keep the core product a one-time purchase going forward. Topics AI, ai apps, Apps, Apps, granola, meeting notes, talat, TC Sarah Perez Consumer News Editor Sarah has worked as a reporter for TechCrunch since August 2011. She joined the company after having previously spent over three years at ReadWriteWeb. Prior to her work as a reporter, Sarah worked in I.T. across a number of industries, including banking, retail and software. You can contact or verify outreach from Sarah by emailing sarahp@techcrunch.com or via encrypted message at sarahperez.01 on Signal.

View Bio June 9 Boston, MA Actively scaling? Fundraising? Planning your next launch?TechCrunch Founder Summit 2026 delivers tactical playbooks and direct access to 1,000+ founders and investors who are building, backing, and closing. REGISTER NOW Most Popular Someone has publicly leaked an exploit kit that can hack millions of iPhones Lorenzo Franceschi-Bicchierai Zack Whittaker Delve accused of misleading customers with ‘fake compliance’ Anthony Ha Cyberattack on vehicle breathalyzer company leaves drivers stranded across the US Zack Whittaker Jeff Bezos reportedly wants $100 billion to buy and transform old manufacturing firms with AI Lucas Ropek Employees had to restrain a dancing humanoid robot after it went wild at a California restaurant Amanda Silberling Nothing CEO Carl Pei says smartphone apps will disappear as AI agents take their place Sarah Perez Nvidia is quietly building a multibillion-dollar behemoth to rival its chips business Rebecca Szkutak

Read Original

Source Information

Source: TechCrunch

Discussion

0 professional contributions

Sign in to join this professional discussion.

Be the first to add a constructive contribution.