Meta Harness: Every AI Needs a Harness AI (Claude Code, MIT, Stanford)
Meta Harness: Every AI Needs a Harness AI (Claude Code, MIT, Stanford)
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Gemma-4 26B-A4B + Opencode on M5 MacBook is *actually good*
TL;DR, 32gb M5 MacBook Air can run gemma-4-26B-A4B-it-UD-IQ4_XS at 300t/s PP and 12t/s generation (running in low power mode, uses 8W , making it the first laptop I've used to not get warm and noisy whilst running LLMs). Fast prompt processing + short thinking traces + can actually handle agentic behaviour = Opencode is actually usable from my laptop! -- Previously I've been running LLMs off my M1 Max 64gb. And whilst it's been good enough for tinkering and toy use cases, it's never really been great for running anything that requires longer context... i.e. it could be useful as a simple chatbot but not much else. Making a single Snake game in Python was fine, but anything where I might want to do agentic coding / contribute to a larger codebase has always been a bit janky. And unless I ar
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![[R] VOID: Video Object and Interaction Deletion (physically-consistent video inpainting)](https://preview.redd.it/00ca5c008ysg1.png?width=140&height=78&auto=webp&s=c0e174d6741698b12a9a171d245b4cf7d456846d)
[R] VOID: Video Object and Interaction Deletion (physically-consistent video inpainting)
We present VOID, a model for video object removal that aims to handle *physical interactions*, not just appearance. Most existing video inpainting / object removal methods can fill in pixels behind an object (e.g., removing shadows or reflections), but they often fail when the removed object affects the dynamics of the scene. For example: - A domino chain is falling → removing the middle blocks should stop the chain - Two cars are about to crash → removing one car should prevent the collision Current models typically remove the object but leave its effects unchanged, resulting in physically implausible outputs. VOID addresses this by modeling counterfactual scene evolution: “What would the video look like if the object had never been there?” Key ideas: - Counterfactual training data: paire
Gemma-4 26B-A4B + Opencode on M5 MacBook is *actually good*
TL;DR, 32gb M5 MacBook Air can run gemma-4-26B-A4B-it-UD-IQ4_XS at 300t/s PP and 12t/s generation (running in low power mode, uses 8W , making it the first laptop I've used to not get warm and noisy whilst running LLMs). Fast prompt processing + short thinking traces + can actually handle agentic behaviour = Opencode is actually usable from my laptop! -- Previously I've been running LLMs off my M1 Max 64gb. And whilst it's been good enough for tinkering and toy use cases, it's never really been great for running anything that requires longer context... i.e. it could be useful as a simple chatbot but not much else. Making a single Snake game in Python was fine, but anything where I might want to do agentic coding / contribute to a larger codebase has always been a bit janky. And unless I ar
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