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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
Chris ended up with a managed M4 Macbook Air at work with no sudo or root. So how does a Linux user get on with his first ever Mac? Turns out pretty well, thanks to lots of open source software and a terminal.
linuxafterdark.net/linux-after-dark-episode-120/
Video by SAP via YouTube
When your business outgrows its processes, the instinct is to patch the gaps. But temporary fixes become business as usual – and suddenly you’re reacting to growth instead of ready for it.
SAP GROW is a complete AI-embedded cloud ERP that connects finance, operations, supply chain, and HR in one place. Industry-specific processes built in from day one. Go live in weeks, not years. Start with what matters, expand when you’re ready. No disruption, no starting over.
00:00 The challenge of scaling
00:34 What is SAP GROW?
00:50 AI built in from day one
01:04 Faster decisions, smarter teams
01:14 Go live in weeks, not years
Bring it with SAP GROW. https://sap.to/6052BB10NG
Follow us on social:
LinkedIn: https://sap.to/6053BB10NH
Instagram: https://sap.to/6054BB10Ny
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Threads: https://sap.to/6056BB10NK
About SAP:
As a global leader in enterprise applications and business AI, SAP stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit: https://sap.to/6057BB10Nz
#SAPGROW #CloudERP #AI
Video by TWiT Tech Podcast Network via YouTube
NASA’s budget slash could mean pulling the plug on over $13 billion in active missions, including Chandra, New Horizons, and Venus spacecraft. Discover in This Week in Space with hosts Rod Pyle, Tariq Malik, and guest Jennifer Vaughn, CEO of The Planetary Society, on what’s on the chopping block possibly for NASA if the proposed budget for the organization goes through for next year.
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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.
Video by Rasa via YouTube
We set up an agent harness — Claude Code, a Rasa MCP server, and some markdown files — and use it to build a flight upgrade assistant from scratch, live on camera.
The airline bot on WhatsApp is actually pretty slick. You’re already logged in, your bookings are there. But the conversation feels like filling in a form. Say anything off-script and it’s lost.
So we build something better: an upgrade agent that can check availability, show seats, quote prices, and handle hesitation naturally. "That’s a lot" doesn’t break it — it re-asks.
The whole thing is built in one sitting using Claude Code with Rasa’s MCP tools. No slides, no prep.
*Try it yourself:* https://gist.github.com/amn41/537a607ccbc3ca557e7f23a97233f0be
*What’s in the harness:*
– Claude Code (the base)
– Rasa MCP tools (train, test, inspect — without leaving the editor)
– CLAUDE.md (workflow instructions that make Claude Code self-validating)
*Links:*
– *Rasa Docs:* https://rasa.com/docs
– *Code from this video:* https://gist.github.com/amn41/537a607ccbc3ca557e7f23a97233f0be
Video by OpenAI via YouTube
"GPT-5.5’s superpower is that it can just get things done.”
It’s so cool to see how masters of the AI universe like NVIDIA’s Dennis Hannusch are leveraging GPT-5.5-Codex for complex engineering tasks.
Be like Dennis.
Build with GPT-5.5: https://openai.com/index/introducing-gpt-5-5/
Video by H2O.ai via YouTube
How to fine-tune domain-specific LLMs for tasks like text-to-SQL and multimodal QA using H2O Enterprise LLM Studio.
When prompt engineering alone is insufficient, fine-tuning a domain-specific model can reduce costs while improving accuracy. H2O Enterprise LLM Studio walks through the full instruction tuning process—leveraging LoRA adapters, built-in AutoML for hyperparameter optimization, and real-time training metrics like loss curves and validation perplexity. Models are evaluated for safety and quality, then exported directly to Hugging Face for distribution across the organization.
Technical Capabilities & Resources
➤ Multimodal Generative AI Tuning: Train models for domain-specific tasks including multi-modal causal language modeling and image/text classification.
🔗 https://docs.h2o.ai/h2o-enterprise-llm-studio/get-started/what-is-h2o-enterprise-llm-studio#use-cases
➤ Instruction Tuning & DPO Alignment: Fine-tune base models using labeled data, automated hyperparameter search, and preference optimization.
🔗 https://docs.h2o.ai/h2o-llmstudio/guide/experiments/supported-problem-types#dpo-modeling
➤ Augmentation for Fine-Tuning Datasets: Use LLM DataStudio to augment and prepare training data for downstream instruction tuning.
🔗 https://docs.h2o.ai/h2o-llm-data-studio/guide/augment/augmentation-datasets
Video by MLflow via YouTube
In this webinar, MLflow Ambassador and Machine Learning Engineer Thor Steen Larsen from DSB demonstrates how to move beyond messy local files to a structured, enterprise-ready machine learning environment using MLflow. Thor explains how MLflow provided essential tools for experiment tracking, model evaluation, and various deployment strategies suitable for both Data Scientists and ML Engineers.
Topics Covered:
🔹 Getting Started with MLflow
🔹 MLflow for AI & GenAI
🔹 Deployment Strategies
🔹 MLOps Environments
🔹 Algorithmic Prompt Optimization
🔹 Deploying to production with MLflow and DABs
🔹 New & Upcoming Features
🔹 Tracking Image Datasets with MLflow
🗣️ Hosted by Pål de Vibe, Databricks MVP
💻 Monthly Euro Databricks Community Slack Hangout
Video by Hugging Face via YouTube
Reinforcement learning is becoming central to agentic systems, but moving from RL for LLMs to RL for agents introduces a new set of challenges: environments, rollouts, tool use, inference bottlenecks, reward design, and evaluating multi-step behavior in the real world.
In this live Hugging Face workshop, we bring together researchers and builders working on the frontier of RL for agents. The session will feature short talks followed by a discussion on what is working today, where open methods still fall short, and what comes next.
Speakers include:
– Lewis Tunstall, Hugging Face
– Will Brown, Prime Intellect
– Ofir Press, Princeton University
– Alex Zhang, MIT CSAIL
additional guests TBA
Topics include:
– training agents with open source tools
– scaling RL for language agents
– multi-step verification and reward design
– benchmarking agent capability beyond static tasks
– recursive reasoning and new agent architectures
Video by NetApp Instaclustr via YouTube
Provisioning a production-ready Kafka cluster using Terraform onto Instaclustr – all without using the Instaclustr console. Your infrastructure can be version-controlled, repeatable, and ready to scale from day one.
Video by CNCF [Cloud Native Computing Foundation] via YouTube
As enterprises transition from AI experimentation to real-world production, Private AI is quickly becoming a strategic imperative. In this CNCF Fireside Chat, Chris Wolf, Global Head of AI and Advanced Services at VMware by Broadcom, and Pankaj Gupta, Senior Director of Modern Application Solutions at VMware by Broadcom, share firsthand perspectives from customers who are successfully building and scaling AI in secure, well-governed, and cost-efficient environments.
From real-world use cases like contact center modernization and computer vision at the edge, to the critical role of cloud-native technologies such as Kubernetes and key CNCF projects, this conversation explores what it באמת takes to operationalize AI at scale.
We’ll also dive into the biggest challenges organizations face -from Day 2 operations and data sovereignty to rising infrastructure and energy costs – and how a cloud native approach is helping solve them.
