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
We get into some homelab updates. Sean has been consolidating hardware, Gary has been implementing high availability with Proxmox, and Shane has been working hard to get Home Assistant working with Kubernetes, as well as downloading YouTube videos.
https://hybridcloudshow.com/hcs56/
Video by SAP via YouTube
H&M and SAP are using Business AI to connect digital, physical, and operational retail experiences in real time.
In this SAP Sapphire Orlando 2026 conversation, Sebastian Steinhaeuser, Chief Operating Officer, Strategy & Operations, and member of the SAP Executive Board, welcomes Ellen Svanström, Chief Digital & Information Officer of H&M, to discuss how H&M is building a digital and AI foundation to strengthen its position as a fashion brand. Ellen explains how SAP supports H&M’s transformation through RISE with SAP, SAP Business Data Cloud, SAP Commerce Cloud, and SAP SuccessFactors.
The discussion highlights how H&M is layering AI on top of that foundation to improve operations, reimagine stores and supply chains, and empower store colleagues with more precise decision-making. One example is the Store Intelligence app, which tracks store performance in real time and uses a Store Intelligence Agent to prepare store readiness and suggest next best actions for store managers.
Ellen also shares how H&M and SAP are exploring a customer-facing Store Concierge Agent that connects online and in-store experiences with personalized recommendations, real-time availability, and contextual advice. Together, these examples show how SAP Business AI can help retailers create more relevant, helpful, and human customer experiences at scale.
Chapters:
00:00 – H&M’s digital and AI journey
00:43 – Building a digital foundation
01:23 – SAP at the core of H&M processes
01:33 – Layering AI into operations
02:15 – H&M and SAP collaboration
02:35 – Store Intelligence app
03:04 – Store Intelligence Agent
03:19 – Agents across the value chain
03:44 – Real-time demand and store recommendations
04:06 – Store Concierge Agent
04:36 – Connecting online and in-store experiences
05:00 – Personalized recommendations and availability
05:31 – AI styling for SAP Sapphire
Watch the full Global Keynote: https://youtu.be/9aa-etRsaLU?si=j0rDQJCtoxUai0fX
Watch all SAP Sapphire replays on demand: https://www.sap.com/events/sapphire.html
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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
#SAP #BusinessAI #SAPSapphire
Video by TWiT Tech Podcast Network via YouTube
SpaceX Starship V3 is stepping onto the world stage with epic upgrades, new engines, and slick design changes. Rod Pyle and Tariq Malik breaks down what’s different and why everyone’s watching on This Week in Space!
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Video by Meta Developers via YouTube
Embedded signup v2/v3 deprecating Oct 15, 2026.
✅ One flow for WhatsApp, Instagram & Messenger APIs
✅ Fewer onboarding screens for your customers
✅ Upgrade takes less than one week
Full migration tutorial → https://bit.ly/4dvLuuM
Developer docs → https://bit.ly/4957smT
#MetaForDevelopers #BusinessMessaging #WhatsAppAPI #Shorts #DeveloperTools
Video by Rasa via YouTube
In this clip from The Dialogue Architects, Eugenia Zeibig, a marketing AI leader in pharma, shares why enterprise AI only becomes useful when it understands the nuance of the business it serves — and why trustworthy outputs matter more than abundant ones.
Watch the full episode: From Reports to Responses: AI in Pharma Marketing
Views shared are Eugenia Zeibig’s own.
#EnterpriseAI #ConversationalAI #PharmaMarketing #AIInnovation #MarketingAI #HealthcareAI #Rasa
The latest wave of open source and AI news signals a clear shift: AI is no longer just a tool for coders—it’s becoming a platform for everyone. From automating entry-level programming jobs to optimizing prompts without hand-tuning, the landscape is evolving fast. For open source enthusiasts, this means both risk and opportunity. The key is … Read more
Video by OpenSource via YouTube
POV: You spent 4 years studying computer science… then AI replaces entry-level jobs.
This graduate’s answer shocked everyone 😳
Do you think AI will make degrees useless or create new opportunities?
#computerscience #ai #programming #softwareengineer #kenya #graduates #artificialintelligence #coding #tech #shorts
Video by OpenAI via YouTube
Codex began as a tool for developers. Today, people are using it for much more: research, planning, file organization, automation, data analysis, presentations, and other everyday knowledge work.
In this OpenAI Forum conversation, Chris Nicholson of OpenAI Global Affairs speaks with Thibault Sottiaux, Head of Codex, about how Codex is evolving beyond software engineering and what that shift means for workers, teams, and organizations.
![[Webinar Recording] Zero Tuning. State-of-the-Art Predictions. How TabH2O Changes Tabular ML](https://i3.ytimg.com/vi/213RMfDLEIE/hqdefault.jpg)
Video by H2O.ai via YouTube
Catch the recording to explore TabH2O, H2O.ai’s foundation model for tabular machine learning.
In this session, you’ll learn how tabular foundation models work and see live how TabH2O delivers predictions in seconds.
Video by MLflow via YouTube
In the eighth installment of Mastering MLflow for GenAI, Jules Damji shows how to go beyond manual prompt iteration (covered in Notebook 1.5 / Episode 5) and use GEPA (Genetic‑Pareto) prompt optimization in MLflow to automatically evolve a baseline prompt into a stronger variant—while keeping everything versioned in the Prompt Registry and measurable with a clear before vs after comparison.
This episode uses a deliberately simple benchmark style inspired by short‑answer QA (similar in spirit to HotpotQA‑style “single token / one‑to‑two word” expectations): the model must stop being verbose and return only the expected short answer so an exact‑match scorer can fire cheaply (pure Python, no LLM calls in the scorer for this demo).
What you’ll learn
🔹 Automated prompt optimization with GEPA using MLflow’s integrated API: mlflow.genai.optimize_prompts
🔹 How to wire the three required pieces: training examples (input + expected output), a predict function (load prompt from registry → fill template → call LLM), and scorers (here: a @scorer exact‑match judge for fast iteration)
🔹 How GEPA’s loop works in practice: Evaluate → Reflect → Improve → Select → Repeat until convergence/budget
🔹 What “budget” means in this context (metric calls / iterations, not “dollars”), plus early stopping when improvements stall ( max_iterations_without_improvement in the walkthrough)
🔹 How optimization produces a new Prompt Registry version (baseline vs optimized), and how to read the run comparison from a weak baseline score to a strong post‑optimization score on the toy setup
Key takeaways
🔹 Scorer design is the product decision: exact match is great for crisp targets; LLM judges are for semantic nuance—but they change cost/latency inside optimization loops.
🔹 Prompt Registry + optimization is the scalable combo: treat optimized prompts as versioned artifacts, not one‑off string edits.
🔹 GEPA is meant to reduce the human “try prompt v17” grind by making improvement systematic—while MLflow keeps the evidence in traces/runs/metrics you can audit.
Resources
🔗 Notebook 1.8: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/08_prompt_optimization.ipynb
🎥 Full series playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR
📚 MLflow prompt optimization docs: https://mlflow.org/docs/latest/genai/prompt-registry/optimize-prompts/
