NVIDIA and OpenAI’s GPT-5.5-Codex: Engineering at Scale OpenAI’s new GPT-5.5-Codex model (backed by NVIDIA) is touted as a superpower for complex engineering tasks. This development underscores the trend of AI moving from chat to practical code generation and task automation. For open-source enthusiasts, the challenge is to keep pace with proprietary advances while leveraging community-driven … Read more
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.
Video by Open Data Science and AI Conference via YouTube
The year of operationalizing GenAI in healthcare is here
Want to learn more about AI in person? Check out ODSC AI East 2026, coming to Boston this April 28th-30th: https://hubs.li/Q041BP6P0
#DataScience #AI #ArtificialIntelligence #ODSCAI
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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
Microsoft locks devs out of important accounts, the foreign router ban exemptions make even less sense, Backblaze shows that "unlimited" never means that, and attempting to avoid software that’s written with AI.
https://2.5admins.com/2-5-admins-296/
Video by SAP via YouTube
Artificial intelligence is creating new opportunities for social innovation—but many young entrepreneurs are still navigating how to use it effectively.
According to a global study, conducted by The Possibilists – a global alliance for youth innovation and partner of ChangemakerXchange – more than 60% of young impact entrepreneurs believe AI can benefit their work, society, and the broader economy. At the same time, 70% of these innovators say they lack support in navigating AI tools.
Through a long‑standing collaboration, SAP and ChangemakerXchange bring together young social entrepreneurs with SAP volunteers and professionals to exchange knowledge, explore practical use cases, and build confidence in applying AI responsibly.
Matthias Scheffelmeier, Co-Founder of ChangemakerXchange and Alexia von Salomon, Learning Designer at Education Innovation Labs and a changemaker in the European cohort, discuss which skills are essential in an AI-driven future – and how AI can help improve efficiency and scale social impact.
Learn more: https://news.sap.com/?p=241852
00:00 – Future Skills
00:20 – Education Innovation Lab
00:33 – Opportunities of AI in Social Innovation
00:44 – Global AI Report
01:25 – Partnership with SAP
01:29 – changemakerxchange.ai Program
01:59 – Partnerships in the Field of Social Innovation
02:08 – Hopes For AI
