The smartest AI used to live in giant data centers. That’s changing fast. For the same electricity, AI on your own computer is 5x smarter than two years ago. Enough that a Mac now goes toe to toe with the best paid AI. Here’s why.

Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence. > 6 model instances, across text, audio, vision, and omni modalities, generative and decision models. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding > 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages. > 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language. > 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input. > fine-tune on 1 GPU. > a new tri-hybrid architecture > architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒 Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device. 1/ 🧵

We need an "Antifragile Internet" built for the agentic era. The hacks over the past few days (PHP, Railway, eMudhra, LiteLLM etc) show how fragile the existing Internet has become. AI agents are challenging the Internet's structure in 3 distinct ways: 1/ On the offense side, agents are a weapon - AI finds and exploits bugs on a scale not seen before, and the cost of attack asymptotes to 0. Bugs that used to take human experts weeks and months are easily found, adapted, and exploited by AI agents. Decades of legacy code that ran on "security by obscurity" no longer holds against agents. 2/ On the defense side, agents are a target - AI generated code is often bug-prone. AI agents themselves, as machines of unconstrained output are liable to prompt injections and other types of attacks that can leak keys, PII, and other pieces of information. 3/ On the identity side, agents are dominating Internet activity - this year, machine traffic has already passed human traffic, and may continue to grow 1000x over the next few years. Content and webpages used to human requests and behavior are breaking with agents, and it is increasingly difficult to tell human content from AI content. All this means we will need to see a ground-up redesigning of the Internet, one with more cryptographic verifiability, identity protocols, rule-enforcement, and other security primitives that eliminate bug categories rather than simply patch them - an "Antifragile Internet" that gets stronger and more secure as more agents participate in it. This is an emerging thesis area for us at @usv, if you're building in the space please reach out! More thoughts soon.

🚀Excited to announce Nanbeige4.3-33B! To push the performance ceiling of small agents, our strategy in Nanbeige4.3 starts with building a powerful teacher first. Nanbeige4.3-33B delivers SOTA agentic performance among open models at ~30B parameters: * Strong results on agentic coding and cowork benchmarks. * When integrated into mainstream agent harnesses, it tackles real-world tasks with cost-efficient inference. * It will serve as a teacher for upcoming smaller Nanbeige models, starting with Nanbeige4.3-3B. huggingface.co/Nanbeige/Nanb… huggingface.co/Nanbeige/Nanb… #LLM #Agent

Here's a snippet of the @OsaurusAI iOS app that I'm currently working on. It's being built entirely in UIKit with no traces of SwiftUI for maximum performance and backward compatibility. Excited to bring the Mac app to mobile with Day 0 support for the new iPhone Duo!

Thanks for calling local models the frontier. Intelligence per watt is improving fast on its own curve. Soon local models will match what feels like frontier today. When that happens, there’s an enormous opportunity: tools, infrastructure, and support for people building businesses on the electrons they already paid for. Labs like @ornith_ , @Alibaba_Qwen , @liquidai , and @nanbeige are already pushing this forward with almost no revenue model. We’re building the infrastructure for it. Reach out if you want to learn more.

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

Your AI agent isn't yours if it needs someone else's server. In 2019, I read an essay about local-first software that I never got over. Seven years later, I built Osaurus around it. Here's why the thing that knows how you think should belong to you.