This is a sad day for the AI community… I'm working on a benchmark we will soon release… and these are early results gathering pretty much all models out there, closed and opened. Red = closed source models
y axis = Elo score
x axis = release date
size = task cost … and
LLMS
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Early benchmark results show closed source AI models leading
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Remembering when natural language computer interaction was impossible
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It's hard to remember now, but there was a time when people couldn't talk to their computers in natural language.
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Kog publishes an ultra-fast 2B model on Hugging Face
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Kog has open-sourced on @huggingface the 2B model they used to demonstrate a model running at over 3,000 tokens per second. Very cool work! https://huggingface.co/blog/kogai/kog-laneformer-2b-the-latency-first-model …
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Multimodal AI connects 3D atomistic models with language
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"Atomistic Language Models Understand and Generate Materials" Most materials AI still treats crystals and language separately, either turning atoms into lossy text formats or making LLMs call atomistic tools. This paper makes materials natively multimodal by connecting a 3D
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Brain reads threads and provides a complete deliverable
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Users can mention Brain on any task, doc, or discussion to read the entire thread, extract from connected apps like Google Drive, GitHub, and Slack via MCP, and then provide a complete deliverable. Try it.
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Qwen-AgentWorld: Linguistic World Models for General Agents
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Qwen-AgentWorld Linguistic World Models for General Agents
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Closing the Agent Loop with LangSmith and Context Hub
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"Most agents don't learn, they just leave traces."
— LangChain (@LangChain) 24 juin 2026
In 12 minutes, @jakebroekhuizen breaks down how to actually close the loop.
Surface issues with LangSmith Engine
Write memory updates back to Context Hub
Let the agent actually improve between runs
If you're thinking about how… https://t.co/aIfMwT33hM pic.twitter.com/QcjKez5SQ4Most agents don't learn, they just leave traces. In 12 minutes,
@jakebroekhuizen
explains how to actually close the loop. Surface issues with the LangSmith engine
Write memory updates to the Context Hub
Let the agent -
Give AI Agents Persistent Memory with LangGraph and MongoDB
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3/ Last → users HATE forgetful chatbots. You must give your agents a persistent brain. The 'Memory for AI Apps' module teaches you to use LangGraph and MongoDB to build stateful AI apps with isolated, long-term memory across sessions. Sweet. > https://
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Critique of the Real Intelligence of Claude and ChatGPT
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I have no sophisticated benchmarks other than: I don't know how you can see Claude or ChatGPT conceive in the same way and write in the same way and believe that it is intelligent in any real way.
If there were 'intelligence' in there, this… -
NeMo AutoModel Optimizes MoE Models with Transformers v5
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The rise of MoE models introduced new challenges in training, and @huggingface's Transformers v5 brought first-class support for solving them. Now, NeMo AutoModel builds on top of v5. Part of the NeMo framework for building models at scale, NeMo AutoModel brings optimizations to