Distributed AI Inference at Scale on NVIDIA Dynamo With Gcore and Orange Business https://t.co/eEpHHWYhm6
— NVIDIA AI (@NVIDIAAI) 11 juin 2026
Distributed AI Inference at Scale on NVIDIA Dynamo With Gcore and Orange Business
SYSTEMS
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Distributed AI Inference on NVIDIA Dynamo with Gcore and Orange
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RAG System: The Complete Zero-to-Hero Guide 2026
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RAG System: The Complete Zero-to-Hero Guide – 2026! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding
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First technical blog on building LLM infrastructure
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first in a series of technical blogs of how we build llm infra
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Shoutouts to multi-agent LLM systems, DSPy, and GRASP papers
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shoutouts:
• why multi-agent LLM systems fail? (arXiv:2503.13657) — @mertcemri @melissapan + @istoica05 @matei_zaharia @profjoeyg @adityagp & team • DSPy (arXiv:2310.03714) — @lateinteraction + @hazyresearch lab & co-authors
• GRASP (arXiv:2605.29668) — Jonas Moll, -
Gated approach to agent self-modification via forking and testing
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less novel, but still very interesting impo is the gated approach to self-modification the agent basically forks itself, propose a patch, run through multiple tests (static/sandbox/diff), and something called a binding held out gate before modificaiton lands
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Showcasing controlled self improvement with regime-to-seam approach
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i showcase "controlled" self improvement with a novel regime-to-seam approach where failures are categorized and allowed to fix targeted areas of the agent while interesting, it's more to showcase the type of self-modification that's easy to set up with activegraph
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Refuting claims that satellite latency blocks AI inference or training
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Next thing people are saying you can't do inference or training because satellite to satellite latency is too big Also mostly wrong: https://
x.com/i/grok/share/3
4b7a26aa6954d5ab02d8bbdf63fd2b2
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Cohere Transcribe, open-source ASR model, #1 on Hugging Face benchmark
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Cohere Transcribe, our open-source speech recognition model, is #1 on the new @huggingface Far-Field ASR benchmark.
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LLM Gateway integrates observability and application into a single tool
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Obtaining both observability and application once meant assembling: A separate gateway, A guardrails platform, An observability stack… then correlating signals between the three when a problem occurred. LLM Gateway integrates both into
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Full-text search JSON filtering with custom inverted index for SmithDB
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How do you support full-text search JSON filtering over agent traces that span up to hundreds of MBs, while keeping a median (P50) latency of 400ms? Here’s an inside look at how we built a custom inverted index from scratch for SmithDB.