Why would in-context learning be that important in AI? (i'm not talking about just "storing new info", which could be achieved for LLMs by enriching prompt, but about "updating reasoning processes") Like, for a human, I get it, we are limited by both lifespan and memory size
LLMS
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Claude Code coming to mobile app, hidden from public, almost ready
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Vibe coders will soon be able to use Claude Code on the go, directly in the Claude mobile app!
— 🚨 AI News | TestingCatalog (@testingcatalog) 18 octobre 2025
It is almost ready for release and already working, but hidden from the public. pic.twitter.com/0DyHlVJXNFVibe coders will soon be able to use Claude Code on the go, directly in the Claude mobile app! It is almost ready for release and already working, but hidden from the public.
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Share Insights on LLMs AI Agents and Machine Learning
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If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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GPT-6 will not arrive before the end of the year
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No, GPT-6 is not coming before the end of the year. It is over, @patience_cave won
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PaDT: MLLMs Generate Visual Detection Outputs Directly
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Ever wonder if an AI could do more than just describe an image and actually show you where things are? PaDT (Patch-as-Decodable Token) is a unified paradigm enabling Multimodal Large Language Models (MLLMs) to directly generate visual outputs like detection boxes and
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Router-R1: Reinforcement Learning Framework Coordinates Multiple LLMs
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Sometimes, a single LLM isn’t enough, and we could coordinate multiple models to solve complex tasks together. Router-R1 is a reinforcement learning–based framework that routes and aggregates multiple LLMs like an intelligent conductor. Key ideas: – Formulates multi-LLM
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Natural Language APIs with Reasoning: Infrastructure and Margins
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Oh this is cool. Might be harder to do if you don’t own inference cuz you’re paying margins on it. Seems like a natural language APIs endpoint with reasoning baked in on the API side. This makes sense to me since X knows X best, so it’s great to offload the reasoning load of how
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Open Source LLM Inference: Current State of Chaos
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this is what opensource LLM inference
looks like in my head > pure chaos
> lousy & misplaced integrations > everything half-broken, somehow still runs we're so early, and there's a lot of work to do -
OpenAI Agents Are Fine-Tunes: Operator and Deep Research
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All OpenAI agents are fine tunes: Operator, Agent Mode, Deep Research (same goes for Google), Codex now too. I think even Canvas was too at some point.
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Does Grok learn from 100M+ daily posts?
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100M+ posts a day is a huge data. Does Grok learn something new from it as well?