LLM Inference Visualizer Made by the LangChain Community Interactive tool visualizing LLM inference through drag-and-drop messages. Uses LangChain's message types to show how context, system prompts, and tool calling workflows shape model outputs in real-time. Watch the
LLMS
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GLM-4.7 ranks 6th on AI Index
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ICYMI: GLM-4.7 lands in 6th place on the Artificial Analysis Intelligence Index, surpassing Kimi K2.
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Personal LLM-as-a-Judge: Continuous Learning System
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The best early application of continuous learning is your personal LLM-as-a-judge. Imagine an LLM that knows exactly how you code, write emails, what you consider to be 'good' legal document or what is a good design – anything that you normally give feedback to LLMs on is
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Proposed Technical Curriculum for AI and Robotics Integration
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Ce que je verrais dans le programme : Ça serait un mélange de DL, du RL, de de modeling 3D, des LLMs , du signal processing, de l’électronique , de l’embarqué, du réseau.
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OpenAI GPT5-Codex-Max Year End Recap Podcast
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Good time to share that we're releasing some great end of year recap pods every day for your holiday listening! just posted: a great end of year convo recapping @OpenAI Codex and GPT5-Codex-Max with @bfioca and @realchillben
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Builders using Anthropic Claude are the future, not emotional support
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Meanwhile all the smartest people I follow are posting about things they are building with Anthropic Claude. The builders are the future. Those who hang onto models for emotional support are not. I'd rather help the builders figure things out. And I know that makes me tone
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Nostalgia for Sonnet 3.5: AI coding progress evolution
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Do you remember how people losing their minds over Sonnet 3.5 for coding? What innocent times
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Recommendation of Claude Opus and Codex models for tasks
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Je comprend , et ca va mieux ? Essaye Claude Opus 4.5 ou Codex 5.2 si tu as le budget.
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Framing consequences for high-stakes LLM responses
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Think about it: When you write an email to a friend → casual
When you write an email that could get you fired → every word matters LLMs learned from both types of text. By framing consequences, you're telling the model: "use the high-stakes mode." -
Why LLMs Respond to Stakes in Text
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You would ask me…Why does this work bro? LLMs are trained on human text and human text is full of stakes. When you add consequences, you're not just giving instructions. You're activating the model's training on how humans think and write when something actually matters.