As developers, we know the power of LLMs for summarizing documentation, meetings, and research. But there's a glaring issue: feeding our proprietary code discussions and sensitive client meetings into cloud-based APIs is a massive security risk.
I got tired of this trade-off, so I decided to build a solution that relies on local execution by default. I wanted a "second brain" that captures context but runs securely on-device.
Enter Flywall. It’s a desktop application that acts as a local memory for everything you learn.
The Architecture: The core philosophy of Flywall is privacy-first. We don't have user accounts, and we don't use cloud databases for your knowledge. By default, everything from the audio transcription to the vector embeddings and the LLM inference happens on your own hardware (Mac, Windows, or Linux).
Why this matters:
Zero Latency: No API calls mean instant recall.
Absolute Privacy: Your private data never leaves your machine. You don't have to worry about data breaches or model training on your private information.
Offline Mode: It works completely offline on an airplane or in a remote cabin. (Cloud LLMs are supported, but strictly opt-in for heavy lifting).
Building for local-first requires optimizing models to run efficiently on consumer hardware, which is a massive but rewarding challenge. We are currently in closed beta and looking for technical users to push the limits of what our local models can handle.
If you are a developer who wants AI without the privacy compromises, check out the beta here: flywall.com.au
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