Natively: A Source-Available Interview Copilot That Runs Locally
Natively — Free open-source AI meeting assistant, interview copilot, and note taker. The best alternative to Cluely, Otter, Granola, Final Round AI, Fireflies, and Interview Coder. Real-time transcription, AI meeting notes, lecture recording, local RAG, BYOK, and stealth mode. Runs locally. No subscriptions. No data breaches.
At a glance
- What is it?
- Natively is a free, source-available AI assistant for meetings and interviews, positioned as a Cluely alternative with local processing and BYOK. This review examines its architecture, setup, and the trade-offs of its personal-use license.
- Who is it for?
- Adopt Natively if you are an individual using it for personal, educational, or non-commercial purposes, especially if you want a Cluely-like interface without a subscription and with local data processing. Do not adopt it if you need a permissive open-source license for commercial redistribution, or if you require guaranteed support and formal security audits, which are not documented.
- Can I use it commercially?
- Check first. The repository uses a licence we do not classify automatically, so read its LICENSE file before any commercial use.
- Is it still maintained?
- Yes. The repository received new commits within the last day.
- What is it written in?
- Mainly TypeScript, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What Natively Actually Solves
Natively targets a specific pain: the high cost and privacy risk of AI meeting assistants and interview copilots. The README names Cluely, Otter, Granola, Final Round AI, Fireflies, and Interview Coder as alternatives, and it claims competitors charge $20 to $149 per month and store data on their servers. Natively positions itself as a local, source-available option that costs nothing for personal and non-commercial use. The intended audience is clear: job seekers preparing for technical interviews, students recording lectures, and professionals who want meeting notes without sending audio to a third party. The project leans heavily into the interview-copilot angle, with phrases like 'interview cheating tool' in its metadata, so it is explicitly designed for real-time assistance during live coding or behavioral interviews. That is a controversial use case, and the README embraces it rather than avoiding the topic.
How It Works: Local Capture and BYOK
The core mechanism, as described in the README, is native audio capture on macOS and Windows, with a claimed latency under 500 milliseconds. The project is not a thin API wrapper; the README says it is a 'complete, native intelligence system.' It captures audio and screen content locally, then processes it through an LLM of your choice. The BYOK model means you bring your own API key, so you decide which model provider handles the transcription and note generation. The application runs entirely on your machine, which is why the README can claim 'no data breaches' and 'your keys, your models, your machine.' The interface is a pixel-perfect recreation of Cluely's overlay, so the workflow should feel familiar to anyone who has used that tool. There is no mention of a server-side component, which suggests all processing happens locally, but the actual data flow beyond audio capture is not detailed in the provided material.
Getting It Running: Downloads and Requirements
Natively is distributed as a desktop application, not a command-line tool. The README lists download links for macOS and Windows, with a badge stating 'Platform macOS | Windows.' The system requirements are macOS 12 or later on both Apple Silicon and Intel, or Windows 10 or 11. There is no mention of a Linux build, so Linux users are out of luck. To get started, you download the latest release from the GitHub releases page, install it, and then presumably configure your LLM API key. The README does not include explicit setup commands or configuration keys, so the exact steps for connecting a model provider are not documented in the material I have. That is a gap: for a tool that emphasizes BYOK, the lack of a visible setup guide in the README is a friction point. The release history shows active development, with version 2.8.8 released in August 2026, so the project is being maintained, but the absence of a clear onboarding section is notable.
The License and Its Implications
The GitHub repository lists the license as 'NOASSERTION,' but the README includes a badge that reads 'License: Personal Use Source.' That is not a standard open-source license like MIT or Apache 2.0. The text explicitly says the tool is free for 'personal, educational, research, and non-commercial use.' That means if you are a company using it for internal meetings, or a developer building a commercial product on top of it, you likely need a separate arrangement. The README does not state what that arrangement costs or how to obtain it. This is a critical limitation: it is source-available, not truly open source in the OSI sense. The project's own description calls itself 'free open-source,' but the license badge contradicts that. Anyone evaluating this for a commercial context should read the LICENSE file in the repository before proceeding. The non-standard license is a deliberate choice, but it creates uncertainty about redistribution rights.
Performance Claims and Unverified Metrics
The README makes bold performance claims: 'native audio capture (<500ms),' '700+ DAU,' and '0 data breaches.' These are presented as marketing badges, not as results from a reproducible benchmark. As a reviewer, I cannot verify any of these numbers because I have not run the software. The 500ms latency claim is plausible for local audio capture, but the end-to-end response time depends on the LLM provider you connect, which introduces network latency that Natively cannot control. The README quotes a 'Premium User' saying the response time is 'practically non-existent,' but that is anecdotal and self-selected. The '0 data breaches' claim is a historical statement, not a guarantee of future security. For a tool that handles sensitive interview audio, the lack of a published security audit or data-handling policy in the README is a concern. You should treat the performance figures as marketing until you test the tool on your own machine.
Limitations and Wrong Use Cases
The most obvious limitation is the license: it is not free for commercial use. If you are a recruiter or a company wanting to record and summarize internal meetings, Natively is probably the wrong tool unless you obtain a commercial license. The platform support is also narrow: only macOS and Windows, with no Linux or mobile versions. The README does not mention support for cloud-based meetings like Zoom or Teams beyond what is captured on the local screen and microphone, so it may not integrate with meeting platforms the way Otter or Fireflies do. Another failure mode is the interview-copilot use case itself: using AI to cheat in a live interview may violate the terms of the interview process, and if detected, could have professional consequences. The README even lists 'cheating' as a topic. That is a real risk for users, not a technical one. Finally, because it relies on your own LLM API key, you incur token costs for heavy use, and the quality of the output depends entirely on the model you choose.
Alternatives and How They Differ
The most direct alternative is Cluely, which Natively explicitly clones. Cluely is a commercial product with a subscription fee, and it stores data on its servers. Natively's approach is different: it runs locally, uses your own API key, and is free for personal use. That is a fundamental architectural difference. Another alternative is Otter.ai, which is a hosted meeting assistant that transcribes and summarizes meetings in the cloud. Otter does not require you to bring your own model; it provides the transcription and summarization as a service. Natively's BYOK model gives you more control over your data and model choice, but it also puts the burden of API costs and configuration on you. Interview Coder is another named alternative, but the README does not describe its architecture. The key distinction is that Natively is local-first and source-available, whereas the commercial alternatives are hosted and closed. If you prioritize data privacy and cost, Natively's model is appealing; if you want a zero-configuration hosted service, the alternatives are easier.
Maintenance and Upgrade Considerations
The repository shows signs of active maintenance: the last push was September 2026, and there are recent releases including V2.8.8 from August 2026. The project is not archived, and the status badge says 'active.' However, the README does not include a changelog or migration guide, so upgrading between versions may involve manual steps. The download counts and star counts are displayed as badges, but those are vanity metrics and not evidence of stability. The project's homepage is natively.software, but the README does not mention a roadmap or a place to report issues beyond a Telegram chat link. The lack of a formal issue tracker link or contribution guide in the README suggests that the project may be run by a small team or a single maintainer. That means you should be prepared for slower response times to bugs and less predictable release cadence. The sponsor link at the top of the README, for Recall.ai, indicates a commercial partnership, but it does not affect the core tool's functionality.
Editorial conclusion
Adopt Natively if you are an individual using it for personal, educational, or non-commercial purposes, especially if you want a Cluely-like interface without a subscription and with local data processing. Do not adopt it if you need a permissive open-source license for commercial redistribution, or if you require guaranteed support and formal security audits, which are not documented. Before relying on it in a high-stakes interview, verify the actual latency and accuracy on your hardware and with your chosen LLM provider, since the README's claims are unverified and your mileage may vary. The project is active with recent releases, but its license is non-standard, so review the LICENSE file carefully to ensure your intended use is permitted.
Community notes