Runs transformer forward+backward training directly on Apple's Neural Engine by reverse-engineering private ANE APIs; includes per-layer ANE kernels, INT8 optimizations and benchmarks. Proof-of-concept only—relies on undocumented APIs, has low utilization and CPU fallbacks.
Provides a rule set and agent skill that lets AI coding assistants review and improve SwiftUI code for modern APIs, performance, accessibility, and maintainability. Packs checks for deprecated APIs, data flow, navigation, and accessibility tailored to mistakes LLMs commonly make.
Generates production-ready App Store and Google Play screenshots from app metadata and style preferences using AI. Scaffolds a Next.js project, composes ad-style slides with localized/RTL support, and exports PNGs at all required Apple and Google resolutions.
Orchestrates parallel CLI-based AI agents in isolated git worktrees so you can run multiple coding agents side-by-side, review AI-generated diffs, and link PRs/CI to each worktree. Desktop client with a mobile companion and BYO model subscriptions.
Delivers an ultra-efficient, edge-friendly multimodal image-and-video-to-text model optimized for on-device deployment. Uses mixed 4x/16x visual token compression, a low-FLOPs visual encoder, and multiple quantized variants for mobile and embedded inference.
Cross-platform native video editor with hardware-accelerated processing and frame-accurate multi-track timeline; core editor is open-source and free while optional Pro AI features (natural-language editing, auto-captions, smart reframing) are paid.
Open egocentric multimodal dataset for embodied AI and robot learning captured on commodity iPhone Pro: ~200 hours and ~10M RGB frames with LiDAR depth, ARKit 6‑DoF poses, IMU, two‑hand MANO mocap, room meshes, and hierarchical action captions.
Runs a full 27B-class language model using end-to-end binary (1.125-bit) weights, cutting FP16 size to ~3.9 GB. Key features: 262k-token context, custom 1-bit kernels for Apple MLX and CUDA, and an optional DSpark drafter for faster decoding. Best when memory footprint matters; trades some FP16 accuracy for on-device feasibility.
Runs a full 27B-class Qwen3.6-derived language model in a ~3.9 GB 1-bit GGUF pack for on-device inference with a 262K-token context; true 1.125 bits/weight binary representation, DSpark speculative drafter, and llama.cpp (CUDA/Metal/CPU) support.
Captures an app's network traffic, extracts its authentication/session bundle, and auto-generates an editable Python client to call the app's API. Supports mitmproxy, cURL and HAR capture backends and provides Session helpers for replay and token refresh.
Lets you write end-to-end tests that mix natural-language goals for an agent with deterministic locators and assertions across web and mobile; agent steps are recorded and replayed to avoid repeated model calls and fit CI workflows.
Lets an AI agent control a real or cloud phone by tapping, typing, scrolling and reading the screen. Works with iPhone via macOS iPhone Mirroring, Android over adb, or rented cloud phones; host-side OCR, accessibility trees and CLI helpers provide verifiable UI actions without jailbreaking. Some manual phone pairing/setup is required.