Nano Banana 2: Combining Pro capabilities with lightning-fast speed https://
buff.ly/e4FH6vv
#AI #MachineLearning #DeepLearning #LLMs #DataScience
LLMS
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Nano Banana 2: Pro AI capabilities with lightning-fast speed
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Claude Reaches 1 Million Context Tokens in Production
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https://claude.com/blog/1m-context-ga [Translated from EN to English]
→ View original post on X — @alexalbert__, 2026-03-13 18:22 UTC
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Claude Code Opus 4.6 1M Default Model and API Improvements
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Tons of improvements shipped with this one:
– Opus 4.6 1M is now the default Opus model for Claude Code users on Max, Team, and Enterprise plans.
– No more long context price increase in the API.
– No beta header required in the API.
– Include up to 600 images in one request. -

NVIDIA Nemotron 3 Super Now Available in Perplexity Platform
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NVIDIA’s Nemotron 3 Super is now available in Perplexity, Agent API, and Computer.
→ View original post on X — @perplexity_ai, 2026-03-13 18:15 UTC
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Local LLMs 101: Inference, Tokens, and Sequence Explained
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> local llms 101 > running a model = inference (using model weights)
> inference = predicting the next token based on your input plus all tokens generated so far
> together, these make up the "sequence" > tokens ≠ words
> they're the chunks representing the text a model sees
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JTok: Scaling LLMs with Token-Indexed Parameters
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Can LLMs achieve massive capacity gains without massive increases in computational cost? YES, say researchers from Shanghai Jiao Tong University and Xiaohongshu! They introduce JTok, a novel scaling method that uses lightweight "token-indexed parameters" to intelligently
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Evaluation awareness in Opus 4.6: measurement validity concerns
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Eval awareness in Opus 4.6 is a bit alarming TBH. If the model behaves differently when it thinks it's being tested, what are we actually measuring?
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Anthropic releases Opus 4.6 with 1M context window
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Anthropic made Opus 4.6 with a 1M context window, generally available to all Claude Code users.
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Lending stochastic parrots term, refining critique
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Yeah, we’ve got to say it! (Well, after that, I did lend him the term “stochastic parrots,” which he doesn’t use, thanks @Fabien_Mikol
, I need to refine my critique) -

Framework for Understanding Technical Organization Metrics
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A framework for making sense of metrics in technical organizations https://
buff.ly/2KXCf7N
#AI #MachineLearning #DeepLearning #LLMs #DataScience