High workload, running 120b models locally.
MACHINE LEARNING
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Databricks Grounded Reasoning Cup sponsors: Anthropic, OpenAI, Google DeepMind
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Introducing the lab sponsors for the Databricks Grounded Reasoning Cup at #DataAISummit 2026: @AnthropicAI
, @OpenAI
, and @GoogleDeepMind
. Each lab is partnering with leading academic teams to build agents that tackle grounded reasoning over complex government data using the -
Gemma 4 12B: encoder-free open model with agentic reasoning, vision, audio
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Gemma 4 12B shipped today under the label "encoder-free." A local 12b model that shows really good results. I'm a big fan of Gemma Gemma 4 12B is out: a dense, fully open model (Apache 2.0) that runs on a 16GB laptop and does agentic reasoning, vision and audio at a quality
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Scaling PEFT: Towards Million Personal Models of Trillion Parameters
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"On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters" Right now LLM personalization mostly means prompts, memory, or retrieval on top of one shared assistant. This paper instead keeps one trillion-parameter base model shared, and give each user a tiny
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Benchtalks #2 discusses ProgramBench where frontier models scored 0%
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Benchtalks #2 is up with @vincentsunnchen. @jyangballin of @stanfordnlp, creator of @SWEbench, on ProgramBench, the benchmark every frontier model scored 0% on at launch.
— Snorkel AI (@SnorkelAI) 3 juin 2026
They dive into end-to-end code generation, why models reward-hack once they get internet access, and the… https://t.co/WZmhUqa8yaBenchtalks #2 is up with @vincentsunnchen
. @jyangballin of @stanfordnlp
, creator of @SWEbench
, on ProgramBench, the benchmark every frontier model scored 0% on at launch. They dive into end-to-end code generation, why models reward-hack once they get internet access, and the -
Open-source models: Faster, cheaper, more control, and privacy
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Routing and post-training open-source models won't only give you more accurate systems but also meaningfully faster and cheaper systems as most companies are currently learning (in addition to giving you more control and privacy). The idea that a "frontier" model (by frontier we
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Positive endorsement of ’50 ML Projects to Understand LLMs’ book
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Here is my editorial endorsement: This book perfectly reflects its title "50 ML Projects to Understand LLMs". The entire book consists of exactly that: 50 projects, with tasks and subtasks pleasantly outlined, explained, and presented in a beautifully instructive and consistent
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Simpler approach in Unified Embedding Decoder Architecture category
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Conceptually, it fits nicely into the Unified Embedding Decoder Architecture category that I wrote about a while back: https://
magazine.sebastianraschka.com/p/understandin
g-multimodal-llms
…
I think it's the refreshingly simple(r) approach of the two. -
AI Programming with Python: From Zero to Hero covering ML and DL
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AI Programming with Python — From Zero to Hero: https://
amzn.to/43TkNea Thorough introductions to #AI, #MachineLearning, and #DeepLearning Hands-on introductions to #Python Discussions of supervised and unsupervised learning Explorations of classification and -
Data Without Labels: Unsupervised Machine Learning Fundamentals and Data Cleaning
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Data Without Labels — Models and Algorithms for Practical Unsupervised #MachineLearning: https://
amzn.to/4q5bbYz 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷: Fundamental building blocks and concepts of machine learning and unsupervised learning
Data cleaning for structured and