Show HN: MiniMax H3 on a 16GB Mac, 5 days after open weights

VPIPE, a new macOS app from developer tgo-app-dev, runs MiniMax H3, a 33B multimodal model generating video and audio, on Apple Silicon Macs with as little as 16 GB RAM, achieving a 5-second 0.5 MP 24p 6-step clip in about 13 minutes on a base-model M5 MacBook Air. The app uses native Metal inference and weight streaming, and benchmarks show it outperforms the h3.c runtime, completing the same 960×544, 124-frame, 6-DiT-step workload in 12 min 15 sec on the M5 MacBook Air versus 16 min 22 sec for h3.c.

Lightweight local multimodal AI pipelines and custom Metal inference for Apple Silicon Macs. Multimodal graph: video, audio, images, text, and tool actions in one pipeline. Native Metal inference: custom kernels and no third-party tensor runtime, for optimal speed. Base-model Mac friendly: weight streaming and 4-bit model preparation for image/video generation on 16 GB machines. Out-of-the-box workflows: image editing, MiniMax H3 video, Qwen chat, realtime VQA, ASR/TTS, and local tool calling. Reproducible Composer: save layouts, prompts, params, and model config with each pipeline spec. Easy to use: macOS app, web UI control, and remote access from a phone browser. Download the macOS app · · Run the first example first-example · Try image editing /tgo-app-dev/vpipe/blob/main/docs/KLEIN-KV.md Try MiniMax H3 video /tgo-app-dev/vpipe/blob/main/docs/MINIMAX-H3.md For the easiest install, choose the largest .dmg in the latest release. Prompt-driven image editing in Composer: live pipeline graph, preview panels, profiler, and reproducible model configuration. - Runs MiniMax H3 FL2VA and REF2VA on an Apple Silicon Mac — a 33B model generating video and its soundtrack together , on as little as 16 GB. Now with Turbo LoRA support See docs/MINIMAX-H3.md /tgo-app-dev/vpipe/blob/main/docs/MINIMAX-H3.md - 5s @ 0.5 MP 24p, 6 steps takes ~13 minutes on a fanless 15-inch base-model M5 MacBook Air, 16 GB 1 user-content-fn-1-ec1d135baf6f7af1e6b5ec6dc1d78d31 - 5s @ 0.5 MP 24p, 6 steps takes - Runs LTX-2.5 on an Apple Silicon Mac — a 22B model generating video and its soundtrack together, again on as little as 16 GB, through the vpipe-ltx-2.5 plugin https://github.com/tgo-app-dev/vpipe-ltx-2.5 . - Image and video generation on base-model Macs with weight streaming — walk through a reference image edit in docs/KLEIN-KV.md /tgo-app-dev/vpipe/blob/main/docs/KLEIN-KV.md - Realtime multimodal pipelines for VQA, ASR, chat, TTS, image editing, video generation, and tool use. Now with Qwen 3.8 27B support - Full-modality runtime under 30 MB , with a mobile-friendly web UI 2 user-content-fn-2-ec1d135baf6f7af1e6b5ec6dc1d78d31 - Extra acceleration from the NAX matmul2d and convolution2d units on M5-generation hardware Matched MiniMax H3 runs using the same 960 × 544, 124-frame, 6-DiT-step workload and settings for both runtimes: | Hardware | VPIPE | h3.c | |---|---|---| | M4 Pro Mac mini, 64 GB RAM | 21 min 50 sec | 27 min 40 sec | | Base M5 MacBook Air 15", 16 GB RAM | 12 min 15 sec | 16 min 22 sec | These numbers are workload-specific measurements, not a universal runtime speed claim. 3 user-content-fn-3-ec1d135baf6f7af1e6b5ec6dc1d78d31 Watch the VPIPE vs h3.c speed & quality comparison on YouTube → https://www.youtube.com/shorts/i3F Fzgt1UM | Workload family | Verified status | |---|---| | Video + audio generation | MiniMax H3 FL2VA/REF2VA, Turbo LoRA, and LTX-2.5 plugin workflows. | | Image generation / editing | FLUX.2-klein-9b-kv: 4-bit reference image editing with 4-step default pipeline and compare-image UI on Apple Silicon. | | Multimodal / LLM / VLM inference | Qwen chat and VQA: local chat with image input, sampler control, stateful turns, and documented per-run token logs. | See docs/MINIMAX-H3.md for the H3 workload, settings, caveats, and Turbo LoRA notes; see and docs/KLEIN-KV.md /tgo-app-dev/vpipe/blob/main/docs/KLEIN-KV.md for image editing and chat workflows. docs/QWEN35-CHAT.md /tgo-app-dev/vpipe/blob/main/docs/QWEN35-CHAT.md | Use case | What VPIPE provides | |---|---| | Local image editing | Prompt tuning, image comparison, mask/reference pipelines, and reproducible Composer layouts. | | Text-to-video and image-to-video | MiniMax H3 pipelines with video and soundtrack generation on Apple Silicon. | | Multimodal chat | Stateful local chat that can read images without re-prefilling the whole thread. | | Realtime VQA and video monitoring | Video capture, detection, tracking, audio tagging, overlays, preview, and profiling in one graph. | | Local AI agents | Sandboxed file, shell, Python, and web tools exposed through local MCP-style stages. | | Developer integration | A compact embeddable C++ runtime plus ready-to-run pipeline specs and reference workflows. | What is VPIPE? VPIPE is a local multimodal AI runtime for Apple Silicon Macs. It turns models, media streams, user input, and tool actions into inspectable C++ pipeline graphs. Does VPIPE use MPS, MLX, or Python for inference? No. VPIPE's generative model forward pass runs through Metal kernels directly via its own metal-compute backend. Python support exists only outside that forward path. Can VPIPE run local image and video generation on a 16 GB Mac? Yes, for supported workflows. VPIPE uses weight streaming and 4-bit model preparation to make image editing and video generation practical on base-model Apple Silicon machines. Is VPIPE a ComfyUI alternative for Mac? For some local image and video workflows, yes. VPIPE focuses on ready-to-run, reproducible pipelines with saved UI layouts and a native Apple Silicon compute backend, rather than a large general-purpose node ecosystem. Install install · · quickstart Quickstart · first-example First example · overview Overview Examples For developers: Requirements requirements · · build-from-source Build from source · run Run · tests Tests · structure Structure · acknowledgements Acknowledgements License Download the latest release ▸ Open the .dmg , drag Vpipe Manager to Applications, and launch it. Requires an Apple Silicon Mac running macOS 26 or later . Two builds are published. They are the same app; they differ only in whether FFmpeg travels with it: | Download | Size | Pick this if | |---|---|---| VpipeManager-<version -with-ffmpeg.dmg | ~28 MB | You want it to work immediately. Nothing else to install. | VpipeManager-<version -slim.dmg | ~16 MB | You already have FFmpeg installed — Homebrew's, say — and would rather use it. | The bundled copy is a minimal LGPL build: it covers the common formats and keeps hardware H.264/HEVC/ProRes through VideoToolbox, but it has no libx264/x265. If you take the slim build, point the app at your own FFmpeg — Quickstart quickstart covers where. The app is signed and notarized, so it opens without a Gatekeeper warning, and it updates itself: Vpipe Manager ▸ Check for Updates… What you get.The app is a launcher around the same two binaries the command line uses — it does not reimplement anything. Its real advantage ispermissions: Camera, Microphone and Local Network access are granted to the app under its own name, instead of to whichever terminal you happened to run from. Everything also builds from source. The pipeline core and the non-Apple stages are portable C++20 and build on Linux and Intel macOS; the on-device model stack needs an Apple Silicon Mac. git clone --recursive https://github.com/tgo-app-dev/vpipe.git cd vpipe && cmake -S . -B build && cmake --build build -j That is the whole story when the dependencies are already in place. For prerequisites, build options, the Metal toolchain and the rest, see Requirements and below. Build from source build-from-source Getting from a fresh install to a working browser UI. Everything here is in the app; the command-line equivalents are under Run run . 1. Choose a work directory. Settings ▸ Work Directory ▸ Path ▸ Choose… — it defaults to ~/vpipe . This is where everything lives: models/ for anything downloaded or quantized, sandbox/ for files pipelines are allowed to write, and the LMDB registry and logs. Pick a volume with room. Prepared models run to tens of gigabytes — MiniMax H3 downloads ~115 GB and peaks near 155 GB while it quantizes — and the app shows the free space on the volume you select. Moving it later means moving all of that. 2. Point at FFmpeg — only if you took the slim build. Settings ▸ FFmpeg ▸ Library Path ▸ Choose… Homebrew's is normally /opt/homebrew/lib . The row above the button says whether a usable FFmpeg was found, so you are not guessing. Use Default returns to the bundled copy. Either way the change takes effect the next time you start a pipeline or the server — it is not picked up by something already running. 3. Decide who can reach the web UI. Settings ▸ Web UI ▸ Bind To This Mac only 127.0.0.1 — nothing else on the network can connect, and no access key is needed. Start here. Automatic this Mac's LAN address , or a specific interface — so a phone or another computer can connect. Anything other than This Mac only makes the web UI reachable from your network, and anyone who reaches it can start and stop pipelines, browse the sandbox and drive models on this Mac. An 8-character access key is the only thing in front of that, so use a LAN binding only when you actually need another device, and not on networks you do not trust. The app warns you in place when you select one. Port defaults to 9876 ; change it if something else is using it. HTTPS Self-Signed is only needed for the low-latency Preview view on another device — browsers restrict WebCodecs to secure contexts — and costs a one-time certificate warning. 4. Start it. Open the Web UI pane and press Start Server , then Open in Browser . If you chose a LAN address in step 3, the pane also shows a QR code : point a phone camera at it and the UI opens already authenticated, with no key to retype. If you kept This Mac only , there is no QR code — a phone could not reach 127.0.0.1 anyway. The access key is still shown, but this Mac connects without it. Open Work Folder and Open Sandbox Folder reveal those directories in Finder. What the QR code contains.Only a URL: http://<this Mac's LAN address :<port /<token . The token is 14 random characters, generated fresh at every start and never written to disk. Nothing about you, your files, your models or your machine is encoded in it — the single identifying detail is the LAN address, and that is a private one like 192.168.x.x , which means nothing outside your own network.Treat it as a password anyway, because the token is not merely information. Scanning it redirects to /?key=<access key and hands the access key over, so anyone who can both see the codeandreach that address gets exactly the control you have: starting and stopping pipelines, browsing the sandbox, driving models on this Mac. A photo, a screenshot or a screen share is enough for someone on the same network. Restarting the server invalidates it. The status row along the bottom shows the machine's thermal state . Sustained image or video generation heat-soaks a Mac, a fanless MacBook Air especially. When it reads Throttling , steps are taking longer because of the hardware, not because something has stalled. Next: the first example, below. The shortest path from a working install to a model answering you: two pipeline files, ~7.7 GB on disk, and no conversion step to sit through — this checkpoint arrives already quantized. Get both pipelines —and prepare-qwen35-9b-optiq-4bit.vpipeline qwen35-9b-chat.vpipeline Raw ▸ Save as , or straight from docs/pipelines/ in a clone . In the web UI from the Quickstart , open the Pipeline Manager, Load the prepare- one and press Start . It downloads the model into your work directory and registers it there. Once, and never again. Load the chat pipeline, Start it, open the User I/O panel — and type at the you prompt. you In one sentence, what is the Pacific Ocean? The Pacific Ocean is the largest and deepest ocean on Earth, covering more than 30% of the planet's surface area and separating the continents of Asia and Australia to the east from North and South America to the west. Five stages: a text input, the chat stage, a sampler stage carrying the values this checkpoint recommends for itself, and a feedback pair that makes it turn-by-turn. You can attach a picture to a turn — this model reads images too. The same pipeline from a phone — scan the QR code the server prints and the browser opens already authenticated. Here a photographed receipt is transcribed, then questioned: the follow-up answers from the same context, and the Mac decodes at ~19.7 tok/s throughout. From a source build the same two files run on the command line, which is where a chat prompt is most at home: cd ~/vpipe your work directory, with both files in it /path/to/vpipe --launch prepare-qwen35-9b-optiq-4bit.vpipeline /path/to/vpipe --launch qwen35-9b-chat.vpipeline /path/to/vpipe is the binary from your build — build/apps/vpipe/vpipe inside the source tree. ▸ docs/QWEN35-CHAT.md — the walkthrough: what each stage is for, why sampling is a stage rather than a config key, and the knobs worth knowing. Then: EXAMPLES.md builds the same chat by hand in the web UI, and adds speech transcription. For image editing from a reference photo, ; for text-to-video docs/KLEIN-KV.md /tgo-app-dev/vpipe/blob/main/docs/KLEIN-KV.md with sound , . Each ships the pipelines it describes. docs/MINIMAX-H3.md /tgo-app-dev/vpipe/blob/main/docs/MINIMAX-H3.md VPIPE has three main surfaces: - Pipeline core — coroutine-based Job stages connected by buffered ports, driven by a runtime that launches and drains them concurrently. Stages are composed into a pipeline from a JSON spec; each stage registers under a type name e.g. rtsp-capture , video-to-rgb , yolo-detection , onvif-discovery , rest-client . This layer is portable C++20. - On-device generative-model stack Apple Silicon — a from-scratch LLM/VLM/ASR/diffusion/video inference stack running on metal-compute , with custom kernels, model loading, quantization support, weight streaming, and resource planning for memory-constrained Macs. It powers stages such as text-chat , visual-qa , realtime-vqa , audio-transcribe , generate-image , diffusion-conditioner , vae-encode , vae-decode , and generate-video . - Web UI and Composer — a self-contained browser UI for launching, inspecting, profiling, and editing pipelines. The Composer can arrange pipeline editors, previews, image comparison views, text I/O, profiler views, files, logs, and stage-provided panels, then save that layout with the pipeline spec so a workflow can be reopened and reproduced. Models are loaded from local directories sharded safetensors / GGUF and are not bundled with the source. Platform note.The pipeline core and the non-Apple stages build on Linux and Intel macOS, but the generative-model stack and the CoreML/Metal stages require anApple Silicon Mac. On arm64 macOS these features are detected and enabled automatically. Everything above describes the app. The rest of this file is the source tree: what it needs to build, how to drive the same session from the command line, from Python or from a test binary, and where things live. - CMake ≥ 3.25 and a C++20 compiler Apple Clang or a recent Clang/GCC . - Git the build pulls a few dependencies as submodules . - FFmpeg development headers — libavformat , libavcodec , libavutil , libswresample . VPIPE compiles against the headers and dlopen s the libraries at runtime, so FFmpeg must also be installed at runtime to decode media.- macOS: brew install ffmpeg - Debian/Ubuntu: apt install libavformat-dev libavcodec-dev libavutil-dev libswresample-dev - macOS: - libcurl — used by the rest-client stage. Provided by the SDK on macOS; on Linux install e.g. apt install libcurl4-openssl-dev . - Python 3 + development headers — only for the optional Python extension built by default . Disable with -DVPIPE BUILD PYTHON=OFF if you don't need it. - Apple Silicon Mac — for the on-device inference stack and CoreML/Metal stages. - Metal shader toolchain Apple Silicon builds only; recommended, not required — by default the build compiles .metal kernel sources into embedded metallibs using xcrun -sdk macosx metal and xcrun -sdk macosx metallib . These compilers are part of Xcode's Metal Toolchain ; the standalone Command Line Tools do not include them, even though the rest of the build never opens Xcode. No toolchain? The build falls back automatically. If metal / metallib aren't found at configure time, the build switches to runtime-compile mode : it embeds the Metal shader source and compiles each kernel on first use via the OS's built-in runtime compiler newLibraryWithSource: , which needs no toolchain on the build or run machine. The Metal Toolchain is therefore optional; the tradeoff is a one-time per-kernel compile on first use instead of at build time. Force either mode with -DVPIPE METAL RUNTIME COMPILE=ON|OFF .To get the faster build-time AOT path, install the toolchain. Two independent things can leave metal / metallib unavailable — both surface the same way, as error: cannot execute tool 'metal' or xcrun: error: unable to find utility "metal" : 1. The Metal Toolchain isn't installed. On Xcode 26 and later macOS 26 the Metal Toolchain is no longer bundled with Xcode by default — it's an optional component you download once. Install it from the command line or via Xcode ▸ Settings ▸ Components ▸ Metal Toolchain ▸ Get : xcodebuild -downloadComponent metalToolchain download + install On air-gapped or CI machines, export once and import where needed: xcodebuild -downloadComponent metalToolchain -exportPath ~/Downloads xcodebuild -importComponent metalToolchain ~/Downloads/metalToolchain.dmg 2. If you installed the CLT and xcrun points at the Command Line Tools, not Xcode. then Xcode, xcrun often still resolves to the standalone CLT, which lacks metal / metallib . Point the toolchain at Xcode: Direct xcrun at the Xcode app run once; needs admin sudo xcode-select --switch /Applications/Xcode.app/Contents/Developer sudo xcodebuild -license accept accept the license if you haven't Verify the active developer dir and that both compilers resolve: php xcode-select -p - /Applications/Xcode.app/Contents/Developer xcrun -sdk macosx -f metal - a path inside Xcode.app not /Library/Developer/CommandLineTools xcrun -sdk macosx -f metallib - likewise xcrun -sdk macosx metal --version To switch back to the Command Line Tools later: sudo xcode-select --switch /Library/Developer/CommandLineTools . 1. Fetch dependencies. LMDB and pugixml are always required; nanobind is needed for the Python bindings, and metal-cpp for the Apple Silicon features: git submodule update --init extern/lmdb extern/pugixml extern/nanobind extern/metal-cpp 2. Configure out-of-source build directory : cmake -S . -B build This defaults to an optimized Release build, so the binaries you get are performant out of the box. Override the build type explicitly if you want a debug build: cmake -S . -B build -DCMAKE BUILD TYPE=Debug 3. Build: cmake --build build -j cmake --build is generator-agnostic; use it rather than calling make directly so the build works regardless of which generator CMake selected. Useful options pass at configure time with -D : | Option | Default | Effect | |---|---|---| VPIPE BUILD PYTHON | ON | Build the vpipe Python extension needs Python dev headers . | VPIPE BUILD APPLE SILICON | auto on for arm64 macOS | Build the CoreML/metal-compute wrappers and the inference stages. | CMAKE BUILD TYPE | Release when unset | Set Debug for an unoptimized debug build. | Install optional : cmake --install build --prefix /path/to/install vpipe-web-ui serves a browser-based Pipeline Manager bound to one VPIPE session. The web assets are embedded in the binary, so no extra files are needed: ./build/apps/web-ui/vpipe-web-ui listens on the LAN address, port 9876 ./build/apps/web-ui/vpipe-web-ui --bind 127.0.0.1 this machine only Then open the printed URL e.g. http://localhost:9876 . By default it binds to the machine's LAN address so other devices can connect; remote connections must supply the 8-character access key printed at startup, while localhost connects without one. Options: --bind ADDR , --port N 0 = any free port , --config CFG inline JSON, a file path, or empty for defaults , --help . The UI has a phone layout, and typing an 8-character key into a phone is exactly the friction that stops anyone from using it. --show-qr prints a QR code to the console next to the usual startup lines: ./build/apps/web-ui/vpipe-web-ui --show-qr Point a phone camera at it and the UI opens already authenticated — no key to read off the screen and retype. The phone layout is selected automatically from the device; ?ui=desktop or the drawer's Desktop layout overrides it, and ?ui=phone is how that layout is developed on a desktop. How it works, and what it costs: Two different secrets. The 8-character access key is short because a human retypes it. The QR link carries its own, longer secret 14 characters of an uppercase alphanumeric alphabet, ~70 bits , because nothing has to read it — it only has to be unguessable. One scan, then the key is gone. GET /<link-secret redirects to /?key=<access key ; the page adopts the key into sessionStorage and immediately strips it from the URL, so it never lands in the address bar, the history, or a bookmark. The key is tab-scoped and disappears when the tab closes. The link is never printed. Only the symbol is rendered — writing the URL beside it would put the secret into the scrollback, a screen share, or a terminal log, which is what the QR code exists to avoid. It is a secret in a URL: treat it as only as private as the console displaying it , and restart the server to invalidate it. Both secrets are per-run , generated at startup, and neither is written to disk. Requests from other computers carry the key as an X-Auth-Key header or a ?key= parameter where the browser cannot set headers, such as a WebSocket handshake or an <img source . Only /api/ is gated, and only for non-loopback peers — static assets stay open so a remote browser can load the page in order to ask for the key in the first place. Note.Add --tls if you want the low-latency Preview view on a phone or any other LAN client: the browser's WebCodecs API is secure-context only, so it needs HTTPS off localhost. The certificate is self-signed and cached under ~/.vpipe/webui-tls , so expect a one-time browser warning — and a QR scan lands on that warning rather than the UI until it is accepted. vpipe launches pipelines straight from the terminal — a thin command-line front end over the same session and stages the web UI drives. It dynamically links libvpipe . A full pipeline from a saved spec file, or from inline JSON: ./build/apps/vpipe/vpipe --launch my-pipeline.vpipeline ./build/apps/vpipe/vpipe --launch '{"id":"tick","stages": {"id":"c","type":"chrono","config":{"count":5}} }' A single stage wrapped in a one-shot pipeline handy for utility stages : ./build/apps/vpipe/vpipe --launch-stage onvif-discovery ./build/apps/vpipe/vpipe --launch-stage model-fetch \ --stage-cfg model path=mlx-community/Qwen3.5-4B-MLX-4bit --stage-cfg overrides stage config: key=value after --launch-stage , or stage-id::key=value to target a stage inside a --launch spec. Repeat --launch / --launch-stage to run several pipelines concurrently ; Ctrl-C stops them cleanly. vpipe --help lists every option, and EXAMPLES.md shows fetching a model from the terminal. The extension lands in build/python/ . Importing the package creates a default session: python PYTHONPATH=build/python python3 -c "import vpipe; print vpipe.vpipe version " Startup configuration is resolved from VPIPE CONFIG / VPIPE CONFIG FILE , an ./init.vpipe file, or built-in defaults. Call vpipe.create session config=... to make your own session. The build produces a unit-test executable: ./build/vpipe test run everything ./build/vpipe test --list tests ./build/vpipe test --filter '<pattern ' supports and ? wildcards ./build/vpipe test --color off for captured/non-interactive output Some tests exercise real models and are gated on environment variables that point at local model directories; when a variable is unset, the corresponding test skips. | Path | Contents | |---|---| pipeline/ , common/ , interfaces/ , include/ | Pipeline core: jobs, ports, runtime, session, shared services. | stages/ | Pipeline stages capture, decode, detection, REST, the LLM/VLM stages, … . | generative-models/ | On-device LLM/VLM/ASR stack model families, tokenizers, encoders . | apple-silicon/ | metal-compute backend and CoreML C++ wrappers. | gpu-kernels/metal/ | Metal compute kernels attention, GEMM, quant, … . | apps/ | Executables: vpipe CLI , web-ui , db-log-reader . | python/ | Python bindings nanobind . | tests/ | Unit tests. | extern/ , 3rd-party/ | Vendored dependencies. | VPIPE builds on these projects: — the multimedia framework vpipe uses to decode and encode audio and video; compiled against its headers and FFmpeg https://ffmpeg.org dlopen ed at runtime. LGPL-2.1-or-later some optional components are GPL . — the memory-mapped key-value store behind vpipe's databases: logs, the model registry, camera records. LMDB https://github.com/LMDB/lmdb OpenLDAP Public License 2.8. — a light XML parser, used for the SOAP and WS-Discovery exchanges that find ONVIF cameras. pugixml https://github.com/zeux/pugixml MIT. — the C++/Python binding layer the nanobind https://github.com/wjakob/nanobind vpipe Python extension is built with. BSD 3-Clause. — header-only C++ bindings for Apple's Objective-C runtime; vpipe uses its metal-cpp https://github.com/bkaradzic/metal-cpp Foundation headers under the CoreML and metal-compute wrappers. Apache-2.0. — a header-only FFT, used by the audio feature extractors to build mel spectrograms. pocketfft https://gitlab.mpcdf.mpg.de/mtr/pocketfft BSD 3-Clause. MLX https://github.com/ml-explore/mlx MIT is Apple's array framework for machine learning on Apple Silicon. VPIPE does not link MLX and does not use it in the forward pass, but it does vendor a small set of MLX's Metal kernel headers — the "steel" GEMM and attention templates and their supporting helpers — under gpu-kernels/metal/vendored/mlx/backend/metal/kernels/steel . Those headers are include d by vpipe's own .metal sources and compiled into the embedded metallibs. Full copyright and license texts for everything bundled or vendored are in THIRD PARTY LICENSES.md /tgo-app-dev/vpipe/blob/main/THIRD PARTY LICENSES.md . VPIPE is licensed under the Apache License, Version 2.0 — see LICENSE /tgo-app-dev/vpipe/blob/main/LICENSE . Bundled and vendored third-party components and their licenses are documented in . /tgo-app-dev/vpipe/blob/main/THIRD PARTY LICENSES.md THIRD PARTY LICENSES.md Brought to you by T-Go LLC, registered in California.