Yes. Number 3 is also deep research inside of ChatGPT or perplexity or you dot com… or many of the synthetic employee startups. (Tricky because you’d think a synthetic SDR would be in category 4 but because they’re non-interactive with the collective, they go in 3).
LLMS
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Why Apple Hasn’t Built Its Own LLM Yet
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Google has Gemini. Meta released Llama. Amazon built Nova. Alibaba open-sourced Qwen. Tencent created Hunyuan.
So the real question is—why hasn’t Apple built its own LLM yet? -
RWKV-7 Goose: State-of-the-art Multilingual Language Model
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RWKV-7 "Goose" with Expressive Dynamic State Evolution RWKV-7 "Goose" is a sequence modeling architecture that achieves state-of-the-art multilingual performance with 3 billion parameters. Despite training on fewer tokens than other top models, it excels in both English and
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Aligning Multimodal LLMs with Human Preferences: Survey
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Aligning Multimodal LLM with Human Preference: A Survey This paper reviews alignment algorithms for Multimodal Large Language Models (MLLMs), which handle tasks involving text, visual, and auditory data, aiming to address issues like truthfulness, safety, and alignment with
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Top 10 Foundation Model Papers: Humanoid Robots and Multimodal LLMs
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This week was huge for foundation models, from generalist humanoid robots to multimodal LLMs that learn from human preferences and negative examples – here are the top 10 papers for the week – DAPO: An Open-Source LLM Reinforcement Learning System at Scale
– GR00T N1: An -
Grok adds DeepSearch and OCR capabilities for JFK archive analysis
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JFK files prompt suggestion is now live on Grok. It triggers DeepSearch across archives dot gov and scans through a bunch of PDF files. A lovely demonstration of the OCR feature
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Robotics for software engineers: AI and machine learning applications
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Robotics for software engineers https://
buff.ly/4JTFJyV #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Qwen AI Model Launch Announcement on Hugging Face
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Time to follow http://
hf.co/qwen to be the first to get it! -
Breadth is free, depth is expensive in neural network compute graphs
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yep exactly, great work spelling it out step by step.
sometimes I talk about it as "breadth is free, depth is expensive" in the imagined full compute graph of the neural net. afaik this was the major insight / inspiration behind the Transformer in the first place. The first time -
Top Resources: Qwen QwQ, Mistral Saba, Hackathons, Batch Processing
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ICYMI, here are some of our top resources from the past few weeks. A Guide to Reasoning with Qwen QwQ 32B
Mistral Saba Added to GroqCloud Model Suite
Guide: How to Win Hackathons with Groq
Build Fast with WLTS
Batch Processing with GroqCloud for AI Inference Workloads
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