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Prompt: Highly intense scene of the hunter sneaking through the forest and getting ambushed by a large furry creature
— Nicolas Neubert (@iamneubert) 28 mars 2026
Made using Runway's Multi-Shot App pic.twitter.com/tbSLozQ69e
Prompt: Highly intense scene of the hunter sneaking through the forest and getting ambushed by a large furry creature Made using Runway's Multi-Shot App [Translated from EN to English]
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g.co/gemini/share/bedde20a01… [Translated from EN to English]
→ View original post on X — @thegautamkamath, 2026-03-27 23:37 UTC
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I kinda dig "corporate salesman on an openai paycheck". Have read much worse slurs over the last few months. Ignoring that there's now ~7 companies helping out and OAI's sole contribution so far are tokens, but hey.
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"Claudini: Autoresearch Discovers SoTA Adversarial Attack Algorithms for LLMs" This paper shows that an AI coding agent can autonomously invent jailbreak and prompt-injection attacks, and can even beat 30+ human designed methods. So on top of human red-teamers, the new LLM
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"Foveated Diffusion: Efficient Spatially Adaptive Image and Generation" This paper introduces the logic of human vision to diffusion models, where you generate full detail only when the viewer is looking, and becomes low detail in the periphery. With this setup, you can
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Day 83/365 of GPU Programming Looking at DeepSeek's Multi-Head Latent Attention today. The last part of the AMD challenge series is to optimize an MLA decode kernel for MI355X where the absorbed Q and compressed KV cache are given and your task is to do the attention computation. A resource that really helped internalize what MLA does was @rasbt's incredible visual guide to attention variants in LLMs (luckily he posted that last week!), which covers everything from MHA to GQA to MLA to SWA, et cetera. If there's one place to get a visual intuition for recent attention mechanisms, it's this blog post. @jbhuang0604's video on MQA, GQA,MLA and DSA was the best conceptual intro I found on the topic and progressively builds up the ideas from first principles. The Welch Labs analysis of MLA is a great watch as well. Beautiful visualization of the changes DeepSeek made for MLA. Tried out a few kernels once I had a basic understanding of MLA and I think I'm slowly getting more comfortable with at least analyzing kernels. levi (@levidiamode) Day 82/365 of GPU Programming Taking a closer look at Mixture of Experts today, so I can write better MoE kernels. Specifically, to optimize an MXFP4 MoE fused kernel for the GPU Mode challenge. I haven't had much prior exposure to MoEs, so lots of new concepts I learned today. Luckily I found the best intro to MoEs thanks to @MaartenGr visual overview of the topic. I then watched @tatsu_hashimoto's amazing Stanford CS336 lecture on MoEs, which added deeper context around why MoEs are gaining popularity, FLOPs, OLMoE, infra complexity, routing functions (mindblown this works so well…), expert sizes, training objectives, top k routing and DeepSeek variations. Once I had a basic understanding I started playing around with the some AITER kernels but progress there is tbd. Also had a nice chat with @juscallmevyom (who was kind enough to reach out!) about the AMD kernels and the challenge of materialization overhead. — https://nitter.net/levidiamode/status/2037297869518950430#m
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Next steps:
– enable the 50,000 models available in inference providers
– enable the 3,000,000 models available on HF
– local free fast inference with llama.cpp
– train and bring your own model! We don't want a world where you're forced to choose between two or three lookalike
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We have integrated @huggingface as a first-class inference provider in Hermes Agent. When you select Hugging Face in the model picker it now shows 28 curated models organized by use case, with a custom option for the 100+ other models they serve. clem 🤗 (@ClementDelangue) Been really cool to see the traction of @NousResearch Hermes Agent, the open source agent that grows with you! Hermes Agent is open-source and remembers what it learns and gets more capable over time, with a multi-level memory system and persistent dedicated machine access. Starting today, you can use a bunch of @huggingface open-source models thanks to our inference provider partners. Let's go open agents! — https://nitter.net/ClementDelangue/status/2037634211973140898#m
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Gobsmacked by the skepticism in the comments. We’ve suffered 30 years of awful automated menus and slow, unhelpful chat reps… basically since phones existed. Instant, helpful AI voice support will be a massive win-win in many (not all!) cases. Saying 'it’s not ready' ignores
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Google Turbo Quant running Locally in Atomic Chat
— Chubby♨️ (@kimmonismus) 27 mars 2026
MacBook Air M4 16 GB
Model: QWEN3.5-9B
Context window: 100000
Summarising 50000 words in just seconds..
You can do 3x larger context window, processing 3x faster than before!
They are first that have integrated Google… https://t.co/3hrWncqJtg
Google Turbo Quant running Locally in Atomic Chat MacBook Air M4 16 GB
Model: QWEN3.5-9B
Context window: 100000 Summarising 50000 words in just seconds..
You can do 3x larger context window, processing 3x faster than before! They are first that have integrated Google