AI

Video by OpenAI via YouTube

For many families living with a rare disease, the hardest part is not having a name for what is happening. Even with modern genetic testing, about half of people with rare diseases remain undiagnosed.

In this OpenAI Forum conversation, researchers from Boston Children’s Hospital’s Manton Center for Orphan Disease Research discuss how they used OpenAI o3 Deep Research to reanalyze difficult pediatric cases and surface new leads for expert review. The conversation explores how AI can help scientists move faster, give families a better chance at answers, and support advanced medical research, while keeping geneticists and clinicians at the center of diagnosis and care.

Join the OpenAI Forum to explore more conversations with experts working at the forefront of AI, science, and society: https://forum.openai.com/

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Video by NetApp Instaclustr via YouTube

Cassandra is now focusing on streamlining operations and scalability, but what are the exact CEPs underway?

Developer Advocate Mariah McLaughlin walks through the five accepted CEPs focused on improving operations.

Learn more by reading the blog breakdown: https://www.instaclustr.com/blog/whats-coming-in-cassandra-key-apache-cassandra-ceps-to-watch/

Time stamps:
00:00 – 00:21: Intro
00:22 – 01:28: CEP 38 – CQL Management API
01:29 – 02:32: CEP 45 – Mutation tracking
02:33 – 03:31: CEP 49 – Hardware accelerated compression
03:32 – 04:30: CEP 60 Flexible placements
04:31 – 05:29: CEP 62 Cassandra configuration via Sidecar
05:30 – 6:08: Conclusion

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Video by FINOS via YouTube

In this clip from OSFF London 2026, Dov Katz (Morgan Stanley) reframes how engineering leaders should evaluate developer productivity in the AI era. Rather than measuring raw output through merged pull requests, Dov demonstrates how enterprise security productivity is quantified by burning down CVEs across thousands of transitive software dependencies.

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LinkedIn: https://www.linkedin.com/company/finosfoundation

#Fintech #OpenSource #FINOS #DevSecOps #Cybersecurity

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Video by Open Data Science and AI Conference via YouTube

What does it mean to be a data scientist in the age of AI agents?

In this episode of the ODSC Ai X Podcast, Sheamus McGovern sits down with Eric Ma, Data Science Leader at Moderna, to explore how AI coding agents are changing the way data scientists work—and why human expertise matters more than ever.

Eric shares practical insights on agentic data science, coding agents, loop engineering, evaluation frameworks, fine-tuning, AI engineering, and why data scientists should focus on the science of measurement rather than trying to become full-stack software engineers.

00:00 Why Data Scientists Should Focus on Measurement
00:56 Introduction and ODSC AI West
02:10 Meet Eric Ma: From Bioengineering to Data Science
03:44 What Agentic Data Science Means in 2026
05:07 Delegating Analysis to Coding Agents
06:13 Why Data Scientists Still Need to Work by Hand
08:13 What Should Data Scientists Still Do Themselves?
10:14 Slowing Down and Verifying AI-Generated Analysis
12:51 How AI Coding Tools Free Up Cognitive Capacity
13:46 Using AI to Learn New Technical Domains
15:54 Does AI Widen the Gap Between Experts and Beginners?
18:10 The Most Important AI Skills for Data Scientists
20:12 Loop Engineering and Precise Agent Instructions
22:12 Thinking in Systems and Designing Feedback Loops
24:35 What Is an Agentic Harness?
25:01 Coding Agents, Marimo Notebooks, and Pair Programming
26:29 Building a Personal Knowledge Base with AI
29:31 When Should Teams Fine-Tune an LLM?
30:31 Cost, Privacy, and Security Considerations
32:19 Common Failure Modes in Agentic Data Science
34:03 Should Data Scientists Become Full-Stack Engineers?
35:17 Why Measurement Is the Core Data Science Skill
36:52 Data Scientists vs. AI Engineers
38:23 Essential Practices for Working with AI Agents
40:25 Will the Data Scientist Role Still Exist in 10 Years?
41:34 How Important Are Engineering Skills for Data Scientists?
42:09 Final Takeaways and Closing

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Video by SAP via YouTube

Discover how AAF moved from a two-system landscape to a three-system landscape with SAP Cloud ERP to support ERP harmonization, governance, and future growth.

Join host Yannick from SAP Product Success with Uwe Detroy from AAF, Hans Tscherwitschke from implementation partner XEPTUM, and Calin Cernea from SAP Product Management as they explore AAF’s real-world transformation journey. AAF, also known as American Air Filter and part of Daikin Group, needed to consolidate multiple standalone ERP systems, standardize processes across Europe, and create a scalable foundation for continued rollout and innovation.

In this Expert Talk, you’ll:
✔️ Learn why AAF chose SAP Cloud ERP to harmonize ERP processes across European entities
✔️ Understand the business challenges behind fragmented systems, manual alignment, and master data complexity
✔️ Hear why the two-system landscape became difficult for transport management, governance, and rollout planning
✔️ See how AAF, XEPTUM, and SAP approached the move to a three-system landscape
✔️ Explore how test data conversion helped support continuity in the new test environment
✔️ Learn how SAP Cloud ALM, transport management, and localization capabilities can support scalability
✔️ Hear how the new landscape helps AAF prepare for faster innovation, future rollouts, and AI capabilities with Joule

Whether you’re running SAP Cloud ERP, evaluating a 2SL to 3SL migration, or supporting a multi-entity ERP rollout, this Expert Talk shows how a modern system landscape can help organizations move from technical complexity to a more flexible, governed, and innovation-ready ERP foundation.

Explore SAP Cloud ERP:
https://www.sap.com/products/erp/s4hana.html

Chapters:
00:13 – Welcome and AAF transformation story
01:29 – Why AAF chose SAP Cloud ERP
03:23 – Standardizing processes across Europe
07:16 – Why the 2SL setup became challenging
10:52 – Moving from 2SL to 3SL
13:17 – The 3SL conversion journey with XEPTUM
20:19 – New capabilities, scalability, and AI readiness
25:28 – Key takeaways and closing

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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://www.sap.com/index.html

#SAPCloudERP #SAPS4HANA #ERPTransformation

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Video by TWiT Tech Podcast Network via YouTube

On MacBreak Weekly, Mikah Sargent, Andy Ihnatko, and Jason Snell talk about Apple’s Q3 2026 earnings, why Apple’s stock took a slide following their earnings report, and why investors are worried about supply issues with Apple’s products in the future.

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Video by OpenAI via YouTube

Your new birdwatching buddy is here.
Make your own: https://github.com/openai/birdingpal

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Video by MLflow via YouTube

In the tenth and final tutorial of the Mastering MLflow for GenAI series, Jules Damji (Databricks) builds a multi-agent supervisor pattern with LangGraph and MLflow, routing queries to specialized agents, synthesizing results, and evaluating the full workflow with granular traces and scorers.

This session simulates a Databricks Agent framework–style supervisor using LangGraph’s StateGraph for acyclic, state-machine-like execution. The use case is a FEMA disaster-response assistant over roughly simulated records.

What You’ll Learn:
🔹 Multi-agent supervisor pattern: a supervisor fans out queries to specialized agents, then returns one cohesive answer
🔹 Supervisor router (Node 1): classify natural-language queries and route to a Genie-like agent, a knowledge assistant, or both
🔹 Genie-like agent (structured data): Natural Language → SQL with an LLM chain, executed against FEMA data via Python Pandas + SQLite
🔹 Knowledge-based assistant (unstructured data): document retrievers, OpenAI embeddings, and cosine similarity over in-memory files like PDFs
🔹 Synthesizer (Node 3): combine one or both agent outputs into a single response
🔹 Granular MLflow tracing: define agent tools: query, tool, embedding, and retriever functions with MLflow span markers
🔹 Built-in scorers: use MLflow’s 60+ scorers for answer relevance, safety, and guideline adherence on an evaluation dataset
🔹 Custom routing accuracy: implement a customer MLflow @scorer metric that compares supervisor fan-out against ground truth
🔹 Extra practice: supplementary notebooks for Deep Agents and CrewAI multi-agent frameworks

Resources:
🔗 Notebook 1.10: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/10_multi_agent_supervisor.ipynb
🎥 Full Series Playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR

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