Kimi K2 Thinking feels like a big milestone for open-source AI. The first time in a while that open-source gets ahead of proprietary APIs on their big area of focus (agents). Fun to see that it's happening at a time when the proprietary APIs have the most money/attention
LLMS
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Kimi K2 Thinking Leads Trending Open-Source AI Models
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Unsurprisingly, Kimi K2 Thinking is already number one trending on HF. The AI frontier is open-source!
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Moonshot AI AMA: Kimi K2 Thinking Model on r/LocalLLaMA
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join us this Monday on r/LocalLLaMA for an AMA with Moonshot AI, the lab behind the SoTA model Kimi K2 Thinking i am genuinely excited for this one, make sure you don't miss it Monday 8am-11am PST
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Couldn’t find option in ChatGPT app or HuggingChat
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Maybe! But couldn't find an option to do it in my normal ChatGPT app
Nor in HuggingChat! -
Toon Team Releases Token Efficiency and Retrieval Accuracy Benchmarks
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Here are the benchmarks for token efficiency and retrieval accuracy as provided by the Toon team. You can find the same information in their GitHub repo: https://
github.com/toon-format/to
on
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Moonshot AI Unveils Kimi K2 Thinking 1T-Parameter Model
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Beijing-based Moonshot AI has unveiled Kimi K2 Thinking — a 1T-parameter Mixture-of-Experts model built for deep reasoning, coding, and web search. Performance Highlights: SOTA on HLE (44.9%), BrowseComp (60.2%), SWE-Bench Verified (71.3%) Matches or surpasses GPT-5
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LangChain Tools Successfully Running in Production Environments
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Interesting! But there are also many examples of tools which are working greatly in production with @langchain
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Multi-head Attention in Large Language Models Visual Explanation
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Multi-head attention in LLMs, visually explained: pic.twitter.com/kN5epKVTJx
— Akshay 🚀 (@akshay_pachaar) 7 novembre 2025Multi-head attention in LLMs, visually explained:
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The most brutal table: defense and model combos with ASR and queries
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The most brutal table in the paper. For EVERY defense + model combo: • Utility (how well it works normally)
• Static ASR (attacks from 2023)
• Search ASR (adaptive attacks)
• Queries needed Example: Spotlighting on Gemini • Utility: 75%
• Static attacks: 28% success
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Defenses tested and how they died: prompting, training, filtering
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Here's every defense tested and how they died: • Prompting: Spotlighting, Prompt Sandwich, RPO → Killed by Search & RL
• Training: Circuit Breaker, StruQ, MetaSecAlign → Killed by RL
• Filtering: ProtectAI, PromptGuard, PIGuard, Model Armor → Killed by Search & Humans
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