Videos

Video by Rasa via YouTube

🤖 Everyone asks whether AI will replace researchers.

Aleks Bass sees a different future.

Instead of spending hours manually coding every interview and comment, AI can handle the repetitive work—giving researchers more time to validate insights, spot patterns, and ask better questions.

✨ More context.
📊 Better analysis.
🧠 Smarter decisions.

The future of research isn’t AI *instead* of humans.

It’s AI helping humans understand people better.

🎧 Watch the full conversation with Aleks Bass, CPTO of Typeform, on *The Dialogue Architects*.

#AI #CustomerResearch #QualitativeResearch #ProductManagement #Typeform

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Video by The Linux Cast via YouTube

The LUG returns. If you want to join in – https://meet.thelinuxcast.org/lug

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Video by OpenAI via YouTube

More about our next generation voice model here: https://openai.com/index/introducing-gpt-live/

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Video by PyTorch via YouTube

PyTorch 2.13 introduces updates across attention, compilation, distributed training, memory efficiency, Python support, and accelerator platforms. Highlights include FlexAttention support on Apple Silicon with up to approximately 12x speedup over SDPA on sparse patterns, the CuTeDSL "Native DSL" backend for key GPU operations, and nn.LinearCrossEntropyLoss to reduce peak GPU memory by up to 4x during large-vocabulary language model training.

On Wednesday, July 22, 2026, at 11 a.m. PT, PyTorch maintainers and contributors will provide a brief overview of the PyTorch 2.13 release and answer questions from the community live.

Topics will include:

– FlexAttention support on Apple Silicon and deterministic backward computation on CUDA
– The CuTeDSL "Native DSL" backend for Inductor
– nn.LinearCrossEntropyLoss for reducing peak GPU memory
– torchcomms for large-cluster training
– FSDP2 communication overlap improvements
– Torch wheel support for Python 3.15 on Linux, including free-threaded 3.15t builds
– Expanded ROCm, Arm, and Intel XPU platform support

The live Q&A will feature expert panelists Alban Desmaison, Andrey Talman, and Piotr Bialecki, with Chris Gottbrath moderating.

PyTorch 2.13 includes 3,328 commits from 526 contributors since PyTorch 2.12.

Register for the live Q&A today!

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Video by NetApp Instaclustr via YouTube

Learn how OpenSearch’s knn_vector field type powers semantic search — and see it running live on an OpenSearch cluster, all in under 6 minutes.

In this video, Ramya breaks down what knn_vector is, the key parameters you need to know when configuring it, and walks through a complete working example on OpenSearch Dashboards. By the end, you’ll know how to create a vector index, index a document with an embedding, and run your first semantic search query.

▶️ Full blog article (deep dive): https://www.instaclustr.com/blog/understanding-opensearch-vector-field-types-part-1-knn-vector/

▶️ Try NetApp Instaclustr free trial: https://console2.instaclustr.com/signup

▶️ How to create an OpenSearch cluster on Instaclustr: https://www.youtube.com/watch?v=2RU-YqW5zsk

NEW TO OPENSEARCH?

If you’re new to OpenSearch itself, check out the other videos on this channel for the basics — they’ll give you a helpful foundation for what’s covered in this video.

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Video by FOSSASIA via YouTube

Run Large Language Models (LLMs) directly in the browser with WebLLM and WebGPU—no cloud APIs required. In this FOSSASIA Summit 2026 session, Warat Wongmaneekit explores how to build fast, private, and cost-effective on-device AI chatbots using open-source models and modern web technologies.

Learn how browser-based AI works, how WebGPU accelerates LLM inference, and the architecture behind deploying high-performance AI entirely on the client side. Perfect for web developers, AI engineers, and anyone interested in privacy-first, edge AI applications.

FOSSASIA Summit 2026 held in Bangkok, is Asia’s leading Open Source tech conference featuring sessions on #AI, #Cloud, #DevOps, #Open Hardware, #Security, #Web #Mobile Technologies, #Web3, and #Databases. Learn more: http://summit.fossasia.org

Session slides: https://eventyay.com/ev/88882f3e/talk/BB8XBKPW3B5W/

#FOSSASIA #FOSSASIASummit #opensource #FOSS

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Video by CNCF [Cloud Native Computing Foundation] via YouTube

In this episode, CNCF Ambassador Thomas Schuetz explores Capsule for the first time, with CNCF Maintainer Oliver Baehler live on stream to navigate obstacles and elaborate on its design. Capsule is a sandbox project providing multi-tenancy and policy governance for Kubernetes.

For its core capabilities, Capsule features tenant isolation by grouping namespaces into logical tenant environments within a single shared cluster. Its self-service delegation empowers tenant owners to provision resources safely within boundaries without cluster-admin access. For policy governance, it automatically applies resource quotas, network isolation, and RBAC rules across specific tenants. Finally, it supports a native ecosystem, leveraging native Kubernetes custom resources and admission webhooks without adding complex layers.

Join us for this practical, live dive into Capsule!

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Video by Meta Open Source via YouTube

Jakob Engel, Director of Research at Meta Reality Labs Research presents “How Humans Move and Interact with the World at CVPR 2026 in Denver, Colorado. The talk covers the addition to the Nymeria dataset – NymeriaPlus.

🔗 Links & Resources:

Read the full paper: https://arxiv.org/abs/2603.18496
Download the dataset: https://github.com/facebookresearch/nymeria_dataset
Learn more about Nymeria: https://www.projectaria.com/datasets/nymeria/

#CVPR #CVPR2026 #ComputerVision #AI #Surreal #TechTalk #MachineLearning

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Video by Open Data Science and AI Conference via YouTube

LLMs can solve Olympiad-level math, complex coding problems, and expert benchmarks—but still fail in surprisingly simple ways.

In this ODSC AI East 2026 keynote, Nouha Dziri, PhD, Senior Research Scientist at Cohere Labs, explores what it takes to build more trustworthy large language models. Drawing on her research in reasoning, post-training, and AI safety, Nouha examines the limits of current LLMs, why “jagged intelligence” creates real-world reliability challenges, and how better training and evaluation can improve model behavior.

The session covers key challenges in LLM deployment, from reasoning failures and out-of-distribution generalization to reinforcement learning approaches that strengthen capabilities and scalable safety systems like automated adversarial testing and moderation.

Watch to learn how researchers are working to advance LLM capabilities while making them safer, more robust, and more reliable in practice.

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Video by The Late Night Linux Family via YouTube

Support us on Patreon and get an ad-free RSS feed with some early episodes. https://www.patreon.com/LateNightLinux

The condiment we’d pick if we could only have one. With Allan from 2.5 Admins, May from Linux After Dark, and Shane from Hybrid Cloud Show. This is a short episode because Joe is having a summer break.

Patrons got this this in their feed two weeks ago.

https://askthehosts.com/ath38/

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