Architecture Review: Waylight for macOS

Waylight for macOS claims to be ChatGPT with context from your tabs, meetings, and docs. Let’s look under the hood.

🛠️ The Tech Stack

Waylight differentiates itself from the sea of “GPT Wrappers” by betting heavily on Edge AI and Local Inference. It is not merely sending your data to OpenAI; it is processing it on your metal.

• Inference Engine: The strict requirement for Apple Silicon (M-Series chips) indicates a reliance on macOS’s CoreML or Apple’s Metal Performance Shaders (MPS) to run quantized Small Language Models (SLMs) locally. This allows for zero-latency responses without API costs.
• Context Pipeline: Unlike web-based wrappers, Waylight utilizes native macOS Accessibility APIs and potentially Screen Recording permissions (OCR) to ingest text from active windows (Zoom, Chrome, Word) into a local vector database.
• Data Layer: A local-first vector store (likely SQLite with vector extensions or a lightweight embedded DB like LanceDB) indexes your “digital footprint” to enable semantic search across temporal data (e.g., “what did I read yesterday?”).
• Privacy Architecture: The “Air-Gapped” design philosophy means no data leaves the device. This is a critical architectural decision that solves the SOC2/GDPR compliance nightmares usually associated with AI context tools.

💰 Pricing Model

Waylight operates on a Freemium model, which is aggressive for a tool that relies on local compute (zero server cost for inference, but high R&D cost).

• Free Tier: Likely includes the core “memory” features and basic chat capabilities, capped by context window or retention history (e.g., “remember the last 7 days”).
• Paid Tier: Unlocks unlimited memory retention, advanced summarization features, and potentially larger/smarter local models for complex reasoning.

⚖️ Architect’s Verdict

Verdict: Deep Tech

Waylight is not a wrapper. It is a piece of Deep Tech engineering that solves the “Context Window Problem” by moving the context window to the OS level.

For Developers: This is a high-utility tool. The ability to ask, “Where is that API documentation snippet I looked at three hours ago?” or “Summarize the architecture decision from the standup meeting” without manually curating context is a massive flow-state preserver.

Pros:

• Zero Latency: Local inference is snappy.
• Privacy: Your proprietary code and docs never hit an external server.
• Integration: Deep hooks into the OS provide context that web apps simply cannot access.

Cons:

• Hardware Tax: Will eat RAM and battery. Running an SLM + Vector DB alongside Xcode/Docker might choke base-model MacBook Airs (8GB RAM).
• Platform Lock: Strictly Apple Silicon for now; Windows support will require a complete re-architecture of the context engine.

Final Call: If you are on an M2/M3 Mac with 16GB+ RAM, this is a production-ready productivity booster. If you are on 8GB RAM, expect swap file thrashing.