Llama 4 Scout & Maverick are Live! As an official launch partner of @AIatMeta
, the Llama 4 Maverick model in particular is one of the best multimodal vision models to date, running it over 800 tokens/second/user. More info here:
LLMS
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Meta Launches Llama 4 Scout Maverick Multimodal Vision Models
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E5-Mistral-7B-Instruct Fast Embeddings for RAG Solutions
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Fast Embeddings with E5-Mistral-7B-Instruct, delivering cutting-edge English text embeddings for your RAG solutions. Seamlessly integrate this leading open-source embedding model into your workflows using our API. https://
bit.ly/4lMGp4n -
Hassabis: True AGI Requires Generality Across Cognitive Tasks
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Demis Hassabis affirme que la véritable AGI implique une cohérence sur de nombreuses tâches cognitives, et pas seulement d’être meilleure sur quelques-unes.
— VISION IA (@vision_ia) 24 avril 2025
« Ce qui manque, c’est la généralité, pas simplement l’intelligence. »
Autrement dit, tant que les systèmes d’IA… pic.twitter.com/8jWJAXcDi6Demis Hassabis affirme que la véritable AGI implique une cohérence sur de nombreuses tâches cognitives, et pas seulement d’être meilleure sur quelques-unes. « Ce qui manque, c’est la généralité, pas simplement l’intelligence. » Autrement dit, tant que les systèmes d’IA
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LLM-as-Judge Won’t Save Product: Fixing Process Will
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An LLM‑as‑Judge Won't Save The Product—Fixing Your Process Will by @eugeneyan
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Incremental Changes: Balancing Manual Instructions with LLM Assistance
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its hard enough to think of instructions, but yeah incremental changes by me + the llm should be considered
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Claude Plays Pokemon: Interview with Creator David Hershey
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The story behind Claude Plays Pokemon with its creator @DavidSHershey. We chatted about:
— Alex Albert (@alexalbert__) 24 avril 2025
– why pokemon is a great LLM testing ground
– memory harnesses for agents
– funny Claude stories while building this
– tips for making your own agent environments pic.twitter.com/ihlsv0lrXhThe story behind Claude Plays Pokemon with its creator @DavidSHershey
. We chatted about:
– why pokemon is a great LLM testing ground
– memory harnesses for agents
– funny Claude stories while building this
– tips for making your own agent environments -
Developers share AI model preferences for faster inference engine
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Calling all developers! Which models are you most excited to use next?
Are you building with tool calling, multimodal capabilities, or long context windows? We’re building the fastest inference engine for real-time AI, and we want your input to help shape what comes next. -
Llama 4 Performance Benchmarks on SambaNova Cloud
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What can you expect out of #Llama4 on SambaNova Cloud? Scout at 697 t/s Maverick up to 800+ t/s Tested & verified by @ArtificialAnlys Safe to say we're on the list of best providers — llama-nate it.
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Creating RAG Agents with Reflection for Improved Performance
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How to Create a RAG Agent with Reflection RAG is a powerful technique that supplies your agent with external information, and can improve agent performance. However, relying on RAG alone, agents can often pull in irrelevant documents to the user question. What if you
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Using o3 model on ChatGPT and enjoying the experience
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Yes generally enjoying it. I only use o3 on ChatGPT