Artifacts 23: Laguna S2.1, Inkling, & Kimi K3 show the utility of open models on the Pareto frontier. In this issue, a total of 24 models from june/july you should be aware of, from: Thinking Machines @thinkymachines (2) Tencent @TencentGlobal (2) Poolside @poolsideai (2) DeepSeek @deepseek_ai Moonshot AI @Kimi_Moonshot Meituan LongCat @Meituan_LongCat Motif Technologies Swiss AI Initiative @apertusllm AMD @AMD Upstage @upstageai InclusionAI @TheInclusionAI Moondream @moondreamai Baseten @baseten IBM @IBM Mistral AI @mistralai Google @GoogleAI Nanbeige @nanbeige Kwaipilot @KwaiAICoder InternLM @intern_lm Microsoft @Microsoft fal @fal Read the issue below.

Artifacts 22: Zyphra, Cohere, Poolside, and others are expanding the breadth and diversity of the ecosystem. In this issue, a total of 30 models from may/june you should be aware of, from: NVIDIA @NVIDIAAI (3) Cohere @Cohere_Labs (2) Zhipu @Zai_org Zyphra @ZyphraAI (3) Poolside @poolsideai Moonshot AI @Kimi_Moonshot StepFun @StepFun_ai Dolphin @dphnAI Google @GoogleAI (3) Nex AGI @NexEcosystem Liquid AI @liquidai MiniMax @MiniMax_AI Swiss AI Initiative @apertusllm JetBrains @jetbrains Microsoft @Microsoft H Company @hcompany_ai Datalab @datalabto (2) Baidu @Baidu_Inc PaddlePaddle @PaddlePaddle Ideogram @ideogram_ai KREA @krea_ai Photoroom @photoroom_ML Read the issue below.

Latest open artifacts (#21#): Open model bonanza! Gemma 4, DeepSeek V4, Kimi K2.6, MiMo 2.5, GLM-5.1 & others. On CAISI's V4 assessment. An eventful month with one flagship release after another

One of my key strategies with Interconnects is to develop the practice of making my work obviously compelling to a wider audience, keeping them hooked over time and wondering what I'm up to, etc. It pays off so much on the downstream influence of your work to have direct audiences for it. These types of articles are a pure grind, but the directly convert hard earned reputation into success for projects. Many people, especially academics, too far too many projects and invest too little in each. The amount of work just on distributing each project is fairly high. If you don't work on distributing it, don't expect to get new readers. It'll seem like a lot of your audience already knows everything if you've mentioned it once, but repetition is key. People are busy, algorithmic feeds are fickle, and people don't remember points/brands until they've seen it many times. Do the work (and read my post contextualizing why all my recent efforts are important)!

Excited to launch the accompanying free RLHF Course for my book. To kick it off, I've released: - Welcome video - Lecture 1: Overview of RLHF & Post-training - Lecture 2: IFT, Reward Models, Rejection Sampling - Lecture 3: RL Math - Lecture 4: RL Implementation I'm going to add question & answer videos throughout the lecture to go deeper on topics that need it, and potentially cover some topics that are too recent and in flux to go in print. I expect 10-15 videos in total over the next few months. At the same time, development around the code for the book is picking up. It's a great time to build the foundation for post-training methods. YT playlist and course landing page below.

My book, Reinforcement Learning from Human Feedback, is wrapping up and going into final production (copyediting, making pretty, formatting, etc.). Shipping to you in 1-2 months! It's a wonderful project to create a foundation of knowledge for the research communities that I love and operate in. It’s the book I wish I had when starting on my LLM journey about 3 years ago. The book’s deepest cut is on core reinforcement learning methods, intuitons, and implementations for LLMs. These don’t live in isolation, and it’s presented in the broader context of post-training methods and unsolved problems in RLHF. A nice balance of depth and breadth. I’m always asked about the title, and I am staying firm that this is THE book documenting the organization of the field of RLHF. Any other topic is too dynamic, where writing a book today would be immediately outdated. RLHF is largely being overshadowed by lots of other developments in AI, but will always be around and at the forefront of human-AI interactions. The topic deserves coverage in depth and this platform. Thank you for all your support. More projects related to the book being announced soon 🎥 I'm excited to reconnect with the community through in-person book events this summer and fall.

Gemma 4 looks great on paper, just what many people need, but mostly I'm focused on the questions around what makes an open model succeed in the long term. We desperately need to build a field of research on "what makes a finetunable model" if we want the open model economy to succeed. Too much of that research happens behind closed doors of companies. Plus, 240 days after The ATOM Project was released, I share reflections on the recovery of American models, and what that means for the next phase.