Seems like you are leaking an ElevenLabs API key in your web demo. The OpenAI completions endpoint also has the API key in the request header but that seems to already be revoked and is returning a 401.
Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
91–100 of 167 posts
Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#92Earlier quoted context omitted.
This post is about the launch of a YC company and its product. It's reasonable that when a company launches a product, the discussion is focused on the product that it is launching. We moderate that way whether it's a YC company's Launch HN or anyone else posting a Show HN. Keeping discussion on-topic is one of the most important things we moderators do, and is the main reason HN is a place where people like to parti…
The idea of giving spammers a second chance seems truly bizarre to me. Have you ever un-blocked an email address that you previously blacklisted for spamming you? Do you think recipients of spam from this company want to give them a second chance? I'm not necessarily saying the people behind this should be completely blacklisted from the entire industry, but when a company earns a place on my block list for behavior…
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Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#93> Apple M3 or later required. MetalRT uses Metal 3.1 GPU features available on M3, M3 Pro, M3 Max, M4, and later chips. M1/M2 support is coming soon. On M1/M2, RCLI automatically falls back to the open-source llama.cpp engine.
So, no support for M5 Neural Accelerators, eh? (Requires Metal 4) ¯\_(ツ)_/¯
Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#94Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#95this is the company that cold emailed many people via email on GitHub.
Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#96Seems like you are leaking an ElevenLabs API key in your web demo. The OpenAI completions endpoint also has the API key in the request header but that seems to already be revoked and is returning a 401.
I am pretty sure we don't have balance. It's a bait :)
Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#97Earlier quoted context omitted.
They said it didn't work installed from homebrew, so I assume they went back and did the curl | bash install option
This option didn't work either. I tried it. Also, the install script… installs Brew. So at the end, it's the same?
Oh dear.
if ! command -v brew &>/dev/null; then
info "Installing Homebrew..."
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
eval "$(/opt/homebrew/bin/brew shellenv)"
fiRe: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#98I’m a bit confused by what you’re offering. Is it a voice assistant / AI as described on your GitHub? Or is it more general purpose / LLM ? How does the RAG fit in, a voice-to-RAG seems a bit random as a feature? I don’t mean to come across as dismissive, I’m genuinely confused as to what you’re offering.
Fair question, let me clarify.
RunAnywhere is an inference company. We build the runtime layer for on-device AI.
There are two pieces:
MetalRT, a proprietary GPU inference engine for Apple Silicon. It runs LLMs, speech-to-text, and text-to-speech faster than anything else available (benchmarks: https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-e...). This is our core product.
RCLI, an open-source CLI (MIT) that demonstrates what MetalRT enables. It wires STT + LLM + TTS into a real voice pipeline with 43 macOS actions, local RAG, and a TUI. Think of it as the reference application built on top of the engine.
On RAG specifically: voice + document Q&A is a natural pairing for on-device use cases. You have sensitive documents you don't want to upload to the cloud, you ingest them locally, and then ask questions by voice. The retrieval runs at ~4ms over 5K+ chunks, so it feels instant in the voice pipeline. Its not random, it's one of the strongest privacy arguments for running everything locally.
The longer-term vision is bringing MetalRT to more chips and platforms, so any developer can get cloud-competitive inference on-device with minimal integration effort.
Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#99Very cool, congrats! I'm curious how you were able to achieve this given Apple's many undocumented APIs. Does it use private Neural Engine APIs or fully public Metal APIs? Either way, this is a tremendous achievement and it's extremely relevant in the OpenClaw world where I might not want to have sensitive information leave my computer.
Fully public Metal APIs, no private frameworks, no Neural Engine, no undocumented entitlements.
MetalRT is built on the public Metal API. The performance comes from how we use the GPU, not from accessing anything Apple doesn't document.
We specifically chose to stay on public APIs so that MetalRT works on any Apple Silicon Mac without special entitlements or SIP workarounds. This also means its App Store compatible for future macOS/iOS distribution.
The results speak for themselves: 1.1-1.19x faster than Apple's own MLX on identical model files, 4.6x faster on STT, 2.8x faster on TTS. Full methodology published here: https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-e...
Appreciate the kind words, the "OpenClaw world" framing is exactly why we built this.
Re: Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon
#100The fact that Apple didn't ship this in years after Siri acquisition is an indictment of its Product leadership
This is not different from mlx-lm other than it uses a closed-source inference engine.
Respectfully, the benchmarks show it is different.
MetalRT and mlx-lm use the exact same model files, identical 4-bit MLX weights. That makes it a pure engine-to-engine comparison:
LLM decode: MetalRT is 1.10-1.19x faster across all models tested
STT: 70s audio in 101ms vs 463ms (4.6x faster)
TTS: 178ms vs 493ms (2.8x faster)
mlx-lm is a general-purpose array computation framework that also supports inference. MetalRT is purpose-built for inference only. That focus is where the performance gap comes from.
You can reproduce these numbers yourself: rcli bench runs the same benchmarks we published. Full methodology: https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-e...
Yes, MetalRT is closed-source. We're transparent about that. The performance difference is the reason it exists.