We all thought we learned how to use AI Turns out we just learned how to code in natural language
LLMS
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Running a One-Person AI Agent Company with OpenClaw and Hermes
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YOU can RUN a one-person AI Agent run company with OpenClaw and Hermes.
— Shubham Saboo (@Saboo_Shubham_) 4 mai 2026
Been doing it for a months now. These AI agent employees run on schedules and works 24/7.
Cofounder 2 just made it available for everyone. https://t.co/VS1aVDv9Fw pic.twitter.com/rFKFB2pXhhYOU can RUN a one-person AI Agent run company with OpenClaw and Hermes. Been doing it for a months now. These AI agent employees run on schedules and works 24/7. Cofounder 2 just made it available for everyone.
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Web2BigTable: Bi-Level Multi-Agent LLM System for Web-Scale Information Extraction
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Web2BigTable A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction paper: https://
huggingface.co/papers/2604.27
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NVIDIA Megatron Core Adds Muon and Advanced Optimizers for LLM Training
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Training Kimi K2 and Qwen3 30B-scale models efficiently requires more than standard data-parallel tricks. NVIDIA Megatron Core now provides end-to-end support for emerging higher-order optimizers like Muon, alongside research optimizers such as MOP and REKLS, to push training
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Speculative Decoding Accelerates RL Post-Training Rollouts
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"Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding" Speculative decoding for RL rollouts! This paper speeds up post-training without changing the target policy’s sampling distribution. So a draft model proposes multiple tokens, and the policy
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Local LLMs Web Stack Setup With SearXNG Firecrawl and Camofox
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PRO TIP Using local LLMs? Give them a web stack My setup: – SearXNG: candidate source discovery – Firecrawl: known-URL scraping and crawling – Camofox: browser fallback when JS/interaction gets annoying Search → Extract → Interact Tell your favorite agent to set this up,
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Tenstorrent Galaxy Blackhole Deploys DeepSeek 671B at 350 t/s/u
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That's a wrap on TT-Deploy! ICYMI we launched Tenstorrent Galaxy™ Blackhole deployed at scale with industry winning benchmarks including: – @deepseek_ai 671B at 350+ t/s/u, with a roadmap to 500 t/s/u at $6 per million tokens, extending the low-cost serving curve where GPU
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AI Hallucinations are Temporary
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Love Germany. I'm half German. When it's nice there it's really nice.
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Nemotron 3 Super Tops Open Source EnterpriseOps-Gym Leaderboard
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Benchmarks should reflect real-world performance. That’s why we’re excited to share that Nemotron 3 Super has topped the open source category on the EnterpriseOps-Gym leaderboard. This agentic gauntlet evaluates performance across 1,150 tasks in fully interactive environments
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MLX vs llama.cpp for Inference on Apple Silicon
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MLX is often better on Apple Silicon from my experience llama.cpp is my fallback method