Comparing Major #AI Disciplines by @Python_Dv #ArtificialIntelligence #MachineLearning #ML
AI
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Sesame Releases HER: Personal Voice Agents with Real-Time Mode
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BREAKING 🚨: Sesame just released HER
— 🚨 AI News | TestingCatalog (@testingcatalog) 27 mai 2026
> Sesam iOS app is now available in Preview, offering a collection of 4 personal voice agents.
> Sesame Agents are powered by a SOTA real-time voice mode.
> Agents can search the web, manage reminders, and have memory.
> App rollout is… https://t.co/VeyPHYm3pt pic.twitter.com/cFF2HaHl2VBREAKING : Sesame just released HER > Sesam iOS app is now available in Preview, offering a collection of 4 personal voice agents.
> Sesame Agents are powered by a SOTA real-time voice mode.
> Agents can search the web, manage reminders, and have memory. > App rollout is -
WordPress categories covering AI topics
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Web designers after reading this: https://t.co/yONuEtjT8L pic.twitter.com/p3y16ldruL
— Charly Wargnier (@DataChaz) 27 mai 2026Web designers after reading this:
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Codex for parallel browser-using subagents
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Codex for parallel browser-using subagents: https://t.co/Iqa3RgcBwD
— Greg Brockman (@gdb) 27 mai 2026Codex for parallel browser-using subagents:
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Responses API Now Supported Across SambaCloud, Stack, Managed
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3/ The Responses API is now supported across: SambaCloud SambaStack SambaManaged Starting with:
• gpt-oss-120B
• MiniMax M2.5
• MiniMax M2.7 Built for Codex CLI, Cline, OpenCode, CrewAI, OpenClaw, and custom agent harnesses. -
SambaNova Responses API Accelerates High-Volume Agent Workflows
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2/ Paired with SambaNova’s new Responses API, devs now get the fastest, most reliable foundation for high-volume agent workflows: • Large refactors
• Migrations
• Repo cleanup
• Multi-step coding tasks Because in agentic workflows, speed + cost determine whether an agent is -
MiniMax M2.7 Live: 435 Tokens/Sec for Coding Agents
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1/ @MiniMax_AI M2.7 is live on SambaCloud — running at 435 output tokens/sec, more than 3x the next-fastest provider (per @ArtificialAnlys
). Built for the way coding agents actually work: Reading files Running tests Fixing errors Looping until the job is done -
LangSmith LLM Gateway Prevents AI Agent Overspending
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Your agents can burn through $10k overnight before you notice. LangSmith LLM Gateway stops that. The platform where you already observe, evaluate, and deploy your agents now has a governance layer.
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No filter beats filtered data in large LLM training
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“A Bitter Lesson for Data Filtering” A common consensus is that LLMs need carefully filtered web data, because noisy data can easily hurts in small compute regimes. But this paper shows that with a large enough model size and training, the best filter is no filter. Raw Common
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DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation
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"DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation" End to end backprop stores activations through every layer, which is why deep Transformers is expensive to train. This paper reinterprets residual blocks as diffusion denoising steps, so each