Meta's 'Avocado' AI Model Delayed as Internal Tensions Rise - The Tech Buzz
Meta's 'Avocado' AI Model Delayed as Internal Tensions Rise The Tech Buzz
Could not retrieve the full article text.
Read on GNews AI Llama →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
model
Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes
arXiv:2603.06928v2 Announce Type: replace Abstract: Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media. Using a hexapedal robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced per

Why APEX Matters for MoE Coding Models and why it's NOT the same as K quants
I posted about my APEX quantization of QWEN Coder 80B Next yesterday and got a ton of great questions. Some people loved it, some people were skeptical, and one person asked "what exactly is the point of this when K quants already do mixed precision?" It's a great question. I've been deep in this for the last few days running APEX on my own hardware and I want to break down what I've learned because I think most people are missing the bigger picture here. So yes K quants like Q4_K_M already apply different precision to different layers. Attention gets higher precision, feed-forward gets lower. That's been in llama.cpp for a while and it works. But here's the thing nobody is talking about. MoE models have a coherence problem. I was reading this article last night and it clicked for me. When

qwen3.5 vs gemma4 vs cloud llms in python turtle
I have found python turtle to be a pretty good test for a model. All of these models have received the same prompt: "write a python turtle program that draws a cat" you can actually see similarity in gemma's and gemini pro's outputs, they share the color pallete and minimalist approach in terms of details. I have a 16 gb vram gpu so couldn't test bigger versions of qwen and gemma without quantisation. gemma_4_31B_it_UD_IQ3_XXS.gguf Qwen3_5_9B_Q8_0.gguf Qwen_3_5_27B_Opus_Distilled_Q4_K_S.gguf deepseek from web browser with reasoning claude sonnet 4.6 extended gemini pro from web browser with thinking submitted by /u/SirKvil [link] [comments]
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models

Failure Mechanisms and Risk Estimation for Legged Robot Locomotion on Granular Slopes
arXiv:2603.06928v2 Announce Type: replace Abstract: Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media. Using a hexapedal robot on a tiltable granular bed, we systematically measure locomotion speed together with slope-dependent normal and shear granular resistive forces. While normal penetration resistance remains nearly unchanged with inclination, shear resistance decreases substantially as slope angle increases. Guided by these measurements, we develop a simple robot-terrain interaction model that predicts anchoring timing, step length, and resulting robot speed, as functions of terrain strength and slope angle. The model reveals that slope-induced per

qwen3.5 vs gemma4 vs cloud llms in python turtle
I have found python turtle to be a pretty good test for a model. All of these models have received the same prompt: "write a python turtle program that draws a cat" you can actually see similarity in gemma's and gemini pro's outputs, they share the color pallete and minimalist approach in terms of details. I have a 16 gb vram gpu so couldn't test bigger versions of qwen and gemma without quantisation. gemma_4_31B_it_UD_IQ3_XXS.gguf Qwen3_5_9B_Q8_0.gguf Qwen_3_5_27B_Opus_Distilled_Q4_K_S.gguf deepseek from web browser with reasoning claude sonnet 4.6 extended gemini pro from web browser with thinking submitted by /u/SirKvil [link] [comments]

Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!