AI

Video by H2O.ai via YouTube

Every agent, language model and predictive system is built on the same foundation. Get this part right and the rest of the glossary is easier.

Terms covered:
– Artificial Intelligence: systems that learn, adapt and improve from data instead of following fixed rules written by a programmer
– Machine Learning: the mechanism, where a model learns to tell fraud from non-fraud by seeing thousands of examples
– Features: the variables and attributes that tell a model what to pay attention to
– GPU: the hardware that makes training viable on text, images and video, built for thousands of calculations running at once

AI sets the vision. Machine learning is the mechanism. Features are what the model learns from. GPUs are what make it scalable.

—
AI Fundamentals Glossary | H2O.ai University
Full playlist: https://www.youtube.com/playlist?list=PLAWKlIKS-66E
Free courses and certifications: https://h2o.ai/university

#MachineLearning #AI #GPU #AIGlossary #H2Oai

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Video by Hugging Face via YouTube

Transformers.js v4.3 is out, and the big one is structured output.

With the new @huggingface/transformers-structured-output package you can force a model to follow an exact JSON Schema or regex, directly in the browser. No more parsing markdown fences and hoping the model picked the right property names.

In this video I walk through how constrained decoding works, how the new package hooks into the logits processor in Transformers.js, and the technical story behind it: from a WASM wrapper around llguidance to a pure JavaScript implementation with almost no overhead.

Also in 4.3: a complete overhaul of the documentation pipeline and a bunch of community-contributed fixes and features. Thank you!

Chapters
0:00 Transformers.js 4.3 is out
0:26 New documentation pipeline
1:00 Community contributions
1:19 The problem: unreliable JSON from LLMs
2:18 How constrained decoding works
3:25 Adding the structured output processor
3:37 The new packages folder and plugin approach
6:03 Using regex instead of JSON Schema
6:45 Demo: recipe generator
7:33 Behind the feature: llguidance
9:02 Rewriting it in pure JavaScript
9:22 Performance across Gemma 4, Granite and LFM 2.5
10:26 Try it out

Links
Release notes: https://github.com/huggingface/transformers.js/releases/tag/4.3.0
NPM: https://www.npmjs.com/package/@huggingface/transformers/v/4.3.0
Structured output package: https://www.npmjs.com/package/@huggingface/transformers-structured-output

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Video by PyTorch via YouTube

Sudhan Shushawasa, a graduate student researcher in Electrical and Computer Engineering at UC Davis, discusses how federated learning can enable collaborative medical imaging research without centralizing data, using glaucoma imaging as a case study.

The session covers practical lessons from training across diverse institutional datasets and building privacy-aware AI systems for medical imaging.

Register for PyTorch Conference North America 2026, October 20–21 in San Jose: https://hubs.la/Q04w5M9L0

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Video by CNCF [Cloud Native Computing Foundation] via YouTube

At KubeCon + CloudNativeCon China, Chris Aniszczyk shared how agentic AI is driving massive commit spikes and testing modern system limits.

#CloudNative #Kubernetes #AI #PlatformEngineering #OpenSource #Shorts

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Video by FINOS via YouTube

Stop wasting time rebuilding the same internal AI controls across your engineering teams.

Most organizations are duplicating effort by writing identical policies for AI agents, but these audit mechanisms don’t offer a competitive advantage. We explain why your differentiation should focus on AI agent observability instead of redundant compliance tasks.

Watch to learn where your engineering leadership team should actually invest their resources.

Subscribe for more technical breakdowns on building reliable AI systems.

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Video by Open Data Science and AI Conference via YouTube

What does a practical personal AI workflow look like in 2026?

In this Personal AI Agents Summit session, Brad Groux, Open Source Maintainer for Microsoft Integrations at OpenClaw and Co-Founder & CEO of Digital Meld, shows how developers and teams can move beyond one-off prompting into repeatable, operator-ready AI workflows.

Using OpenClaw Dev Days materials, Brad walks through a hands-on path from messy stakeholder discovery to a scoped product plan, PRD, implementation artifacts, and a working local app. The session explores how OpenClaw supports planning, context management, agentic workflow design, and human-in-the-loop review while keeping safety and judgment at the center.

Watch to learn how personal AI agents can help turn ideas into real applications and join us in New York for the AI for Work Summit: https://summit.ai

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Video by SAP via YouTube

Because AI built externally does not incorporate the full business context. What gets left behind in AI pilots is the full process logic, business rules, security constraints, and relationships that make the data meaningful and allow users to interact with it securely.

SAP’s context-native AI approach means AI has direct access to business rules, relationships, and logic that make business data meaningful. Transforming your entire workflow and business. https://sap.to/6055BGTjpX

#AI #SAP

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Video by TWiT Tech Podcast Network via YouTube

Windows Weekly is about more than Windows. Veteran Microsoft insiders Paul Thurrott and Richard Campbell join Leo for a deep dive into the most valuable company in the world. From consumer to enterprise, AI to Xbox, Windows Weekly is the only Microsoft podcast you’ll ever need.

You can find more about TWiT.tv and subscribe to our full shows at https://podcasts.twit.tv
Join our community at Club TWiT: https://twit.tv/clubtwit

About us:
TWiT.tv is a technology podcasting network located in the San Francisco Bay Area with the #1 ranked technology podcast This Week in Tech hosted by Leo Laporte. Every week we produce over 30 hours of content on a variety of programs including Tech News Weekly, MacBreak Weekly, Windows Weekly, Security Now, Intelligent Machines, and more.

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Video by Meta Developers via YouTube

Should every game add hand tracking?

Not exactly.

Odders Lab’s advice from building ""Chess Club"":

👋 Ask if the game is better with hands
⚖️ Weigh effort vs. impact
♟️ Let real-world actions guide the design
🎯 Avoid extreme edge tracking
✨ Make players feel cool using it

For ""Chess Club"", hands made chess feel more familiar, especially for casual players and VR newcomers.

Full developer story in the comments. 🔗

#VR #MetaQuest #VRDev #GameDev

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Video by The Linux Experiment via YouTube

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Timestamps:
00:00 Intro
00:29 Sponsor: Proton VPN
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#linux #gnome #linuxdesktop

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