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4,134 ideas

RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon

Hi HN, we're Sanchit and Shubham (YC W26). We built a fast inference engine for Apple Silicon. LLMs, speech-to-text, text-to-speech – MetalRT beats llama.cpp, Apple's MLX, Ollama, and sherpa-onnx on every modality we tested. Custom Metal shaders, no framework overhead.Also, we've open-sourced RCLI, the fastest end-to-end voice AI pipeline on Apple Silicon. Mic to spoken response, entirely on-device. No cloud, no API keys.To get started: brew tap RunanywhereAI/rcli https://github.com/RunanywhereAI/RCLI.git brew install rcli rcli setup # downloads ~1 GB of models rcli # interactive mode with push-to-talk Or: curl -fsSL https://raw.githubusercontent.com/RunanywhereAI/RCLI/main/install.sh | bash The numbers (M4 Max, 64 GB, reproducible via `rcli bench`):LLM decode – 1.67x faster than llama.cpp, 1.19x faster than Apple MLX (same model files): - Qwen3-0.6B: 658 tok/s (vs mlx-lm 552, llama.cpp 295) - Qwen3-4B: 186 tok/s (vs mlx-lm 170, llama.cpp 87) - LFM2.5-1.2B: 570 tok/s (vs mlx-lm 509, llama.cpp 372) - Time-to-first-token: 6.6 msSTT – 70 seconds of audio transcribed in *101 ms*. That's 714x real-time. 4.6x faster than mlx-whisper.TTS – 178 ms synthesis. 2.8x faster than mlx-audio and sherpa-onnx.We built this because demoing on-device AI is easy but shipping it is brutal. Voice is the hardest test: you're chaining STT, LLM, and TTS sequentially, and if any stage is slow, the user feels it. Most teams fall back to cloud APIs not because local models are bad, but because local inference infrastructure is.The thing that's hard to solve is latency compounding. In a voice pipeline, you're stacking three models in sequence. If each adds 200ms, you're at 600ms before the user hears a word, and that feels broken. You can't optimize one stage and call it done. Every stage needs to be fast, on one device, with no network round-trip to hide behind

Hacker News6mo agoToolAI

82FL score
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Klaus – OpenClaw on a VM, batteries included

We are Bailey and Robbie and we are working on Klaus (https://klausai.com/): hosted OpenClaw that is secure and powerful out of the box.Running OpenClaw requires setting up a cloud VM or local container (a pain) or giving OpenClaw root access to your machine (insecure). Many basic integrations (eg Slack, Google Workspace) require you to create your own OAuth app.We make running OpenClaw simple by giving each user their own EC2 instance, preconfigured with keys for OpenRouter, AgentMail, and Orthogonal. And we have OAuth apps to make it easy to integrate with Slack and Google Workspace.We are both HN readers (Bailey has been on here for ~10 years) and we know OpenClaw has serious security concerns. We do a lot to make our users’ instances more secure: we run on a private subnet, automatically update the OpenClaw version our users run, and because you’re on our VM by default the only keys you leak if you get hacked belong to us. Connecting your email is still a risk. The best defense I know of is Opus 4.6 for resilience to prompt injection. If you have a better solution, we’d love to hear it!We learned a lot about infrastructure management in the past month. Kimi K2.5 and Mimimax M2.5 are extremely good at hallucinating new ways to break openclaw.json and otherwise wreaking havoc on an EC2 instance. The week after our launch we spent 20+ hours fixing broken machines by hand.We wrote a ton of best practices on using OpenClaw on AWS Linux into our users’ AGENTS.md, got really good at un-bricking EC2 machines over SSM, added a command-and-control server to every instance to facilitate hotfixes and migrations, and set up a Klaus instance to answer FAQs on discord.In addition to all of this, we built ClawBert, our AI SRE for hotfixing OpenClaw instances automatically: https://www.youtube.com/watch?v=v65F6VBXqKY. Clawbert is a Claude Code instance that runs whenever a health check fails or the user triggers it in the UI. It can read that user’s

Hacker News6mo agoToolAI

78FL score
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Open-source browser for AI agents

Hi HN, I forked chromium and built agent-browser-protocol (ABP) after noticing that most browser-agent failures aren’t really about the model misunderstanding the page. Instead, the problem is that the model is reasoning from a stale state.ABP is designed to keep the acting agent synchronized with the browser at every step. After each action (click, type, etc), it freezes JavaScript execution and rendering, then captures the resulting state. It also compiles the notable events that occurred during that action loop, such as navigation, file pickers, permission prompts, alerts, and downloads, and sends that along with a screenshot of the frozen page state back to the agent.The result is that browser interaction starts to feel more like a multimodal chat loop. The agent takes an action, gets back a fresh visual state and a structured summary of what happened, then decides what to do next from there. That fits much better with how LLMs already work.A few common browser-use failures ABP helps eliminate: * A modal appears after the last Playwright screenshot and blocks the input the agent was about to use * Dynamic filters cause the page to reflow between steps * An autocomplete dropdown opens and covers the element the agent intended to click * alert() / confirm() interrupts the flow * Downloads are triggered, but the agent has no reliable way to know when they’ve completedAs proof, ABP with opus 4.6 as the driver scores 90.5% on the Online Mind2Web benchmark. I think modern LLMs already understand websites, they just need a better tool to interact with them. Happy to answer questions about the architecture, forking chrome or anything else in the comments below.Try it out: `claude mcp add browser -- npx -y agent-browser-protocol --mcp` (Codex/OpenCode instructions in the docs)Demo video: https://www.loom.com/share/387f6349196f417d8b4b16a5452c3369

Hacker News6mo agoToolAI

76FL score
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Prism (YC X25) – Workspace and API to generate and edit videos

Hey HN — we’re Rajit, Land, and Alex. We’re building Prism (https://www.prismvideos.com), an AI video creation platform and API.Here’s a quick demo of how you can remix any video with Prism: https://youtu.be/0eez_2DnayIHere’s a quick demo of how you can automate UGC-style ads with Openclaw + Prism: https://www.youtube.com/watch?v=5dWaD23qnroAccompanying skill.md file: https://docs.google.com/document/d/1lIskVljW1OqbkXFyXeLHRsfM...Making an AI video today usually means stitching together a dozen tools (image generation, image-to-video, upscalers, lip-sync, voiceover, and an editor). Every step turns into export/import and file juggling, so assets end up scattered across tabs and local storage, and iterating on a multi-scene video is slow.Prism keeps the workflow in one place: you generate assets (images/video clips) and assemble them directly in a timeline editor without downloading files between tools. Practically, that means you can try different models (Kling, Veo, Sora, Hailuo, etc) and settings for a single clip, swap it on the timeline, and keep iterating without re-exporting and rebuilding the edit elsewhere.We also support templates and one-click asset recreation, so you can reuse workflows from us or the community instead of rebuilding each asset from scratch. Those templates are exposed through our API, letting your AI agents discover templates in our catalog, supply the required inputs, and generate videos in a repeatable way without manually stitching the workflow together.We built Prism because we were making AI videos ourselves and were unsatisfied with the available tools. We kept losing time to repetitive “glue work” such as constantly downloading files, keeping track of prompts/versions, and stitching clips in a separate video editing software. We’re trying to make the boring parts of multi-step AI video creation less manual so users can generate → review → edit → assembl

Hacker News6mo agoToolAI

60FL score
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