yep it's a unified system between http://
claude.ai and claude code https://
support.anthropic.com/en/articles/11
014257-about-claude-s-max-plan-usage
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LLMS
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Unified System Between Claude AI and Claude Code
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Google AI Studio Adds Code Assist
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ICYMI: Google AI Studio now has code assist in the Apps section. Now you can do vibe coding straight inside AI Studio from anywhere with diffs and all that stuff. pic.twitter.com/1ekucQyvp2
— 🚨 AI News | TestingCatalog (@testingcatalog) 25 mai 2025ICYMI: Google AI Studio now has code assist in the Apps section. Now you can do vibe coding straight inside AI Studio from anywhere with diffs and all that stuff.
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Next Word Prediction and Loss Functions for Model Understanding
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This is a brilliant explanation of why next word prediction is a good loss function to achieve understanding. In addition to predicting the next bit, one can also use masking losses and diffusion to achieve understanding. The general intuition is to minimise the difference… https://t.co/tUDUQWP4NJ
— Nando de Freitas (@NandoDF) 25 mai 2025This is a brilliant explanation of why next word prediction is a good loss function to achieve understanding. In addition to predicting the next bit, one can also use masking losses and diffusion to achieve understanding. The general intuition is to minimise the difference
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Build Philosophical AI Agents with LangGraph and FastAPI
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PhiloAgents Build AI agents that impersonate philosophers with LangGraph in this OSS repo covering RAG implementation, real-time conversations, and system architecture with FastAPI & MongoDB integration. Start building philosophical agents! https://
github.com/neural-maze/ph
iloagents-course
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AI Agents Now Code Alongside Developers in Big Tech
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Big in big tech: AI agents now code alongside developers https://
search.app/TThW9 #bigtech #RAG #AgenticAI #AIagentInnovation #AIAgents #LLMs #LLM #GenerativeAI #GenAI #technology #TechRevolution #tech #Engineering #ArtificialIntelligence #AI @lexfridman @KirkDBorne -
GRIT: Teaching MLLMs Grounded Visual Reasoning with Images
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10. Teaching MLLMs to Think with Images GRIT is a new method that enables MLLMs to perform grounded visual reasoning by interleaving natural language with bounding box references.
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AdaptThink: RL Framework for Dynamic Reasoning Strategy Selection
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7. AdaptThink This paper introduces AdaptThink, an RL framework designed to help reasoning models decide when to use detailed chain-of-thought reasoning (“Thinking”) versus directly producing an answer (“NoThinking”), based on task difficulty.
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MedBrowseComp: LLM Agents Medical Fact-Finding Benchmark
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8. MedBrowseComp MedBrowseComp is a new benchmark designed to evaluate LLM agents’ ability to perform complex, multi-hop medical fact-finding by browsing real-world, domain-specific web resources.
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LLM Reasoning in Dynamic Environments Beyond Static Benchmarks
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6. Towards a Deeper Understanding of Reasoning in LLMs This paper investigates whether LLMs can adapt and reason in dynamic environments, moving beyond static benchmarks.
