AI’s Open Source Crossroads This week’s digest highlights a pivotal tension: AI is becoming both more accessible and more risky. From multi-agent orchestration to hidden censorship biases, the open source community must navigate powerful capabilities alongside new vulnerabilities. The stories below show how developers and enterprises are grappling with these trade-offs. Multi-Agent AI Gets a … Read more
See how ChatGPT for Excel can help finance teams review a CFO performance workbook before it goes out.
In this demo, ChatGPT checks tie-outs across model tabs, flags issues like stale source data and revenue mismatches, creates a QA issue log, drafts owner questions, and gives a clear readiness verdict.
How h2oGPTe enables no-code Super Agent customization and auto-generated multi-agent Python code using CrewAI and LangGraph.
Enterprises often need agents tailored to specific workflows beyond built-in defaults. h2oGPTe supports two approaches: the Super Agent can be rapidly customized using system prompts, collections, and tool configurations without any code. For advanced requirements, the Agent Builder generates fully executable Python source code by accepting a natural language workflow description, selecting the right framework (CrewAI or LangGraph), and running an internal build-test-refine loop. Agents also generate standardized A2A protocol files for cross-framework interoperability.
Technical Capabilities & Resources
➤ Super Agent Customization: Configure task-specific agents using custom system prompts, collections, and tool orchestration—no code required.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/agents#how-it-works
➤ Agent Builder & Code Generation: Generate production-ready Python code for custom agents using CrewAI, LangGraph, or the OpenAI SDK.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/agents
➤ Multi-Agent Ecosystems (A2A Protocol): Automatically generate A2A communication files for agent interoperability across different frameworks.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/agents/agent-builder/a2a-protocol
April saw lots of updates with Kafka, ClickHouse, and more:
Powering analytics with ClickHouse and FSx.
https://www.instaclustr.com/blog/powering-low-latency-analytics-with-clickhouse-and-amazon-fsx/
Kafka Client Telemetry
https://www.instaclustr.com/blog/introducing-kafka-client-telemetry-centralized-client-metrics-for-instaclustr-managed-apache-kafka/
Version bumps for Kafka:
Released Kafka 3.9.2 and Kafka Connect 3.9.2 to General Availability
Support Karapace Schema Registry 5.2.0 and Karapace Rest Proxy 5.2.0
Released PostgreSQL 18.3, 17.9 and 16.13
More product updates in April so subscribe for more video content like this about Kafka, ClickHouse, Cassandra, and more.
Large Language Models (LLMs) can inherit hidden biases from their training data—including censorship bias. This talk explores how training on censored internet content can shape AI outputs, especially in Simplified Chinese datasets affected by state censorship.
Learn about a new research method comparing responses in Simplified Chinese and Traditional Chinese to detect censorship bias in popular AI models from Google, Meta, OpenAI, and Anthropic. The findings reveal evidence of censorship bias across multiple leading LLMs.
We’ll also discuss the wider implications for AI privacy, security, and trust, including risks of generative AI in the software supply chain and how open source communities can respond.
FOSSASIA Summit 2026 held in Bangkok, is Asia’s leading Open Source tech conference featuring sessions on #AI, #Cloud, #DevOps, #Open Hardware, #Security, #Web #Mobile Technologies, #Web3, and #Databases. Learn more: http://summit.fossasia.org
Video by CNCF [Cloud Native Computing Foundation] via YouTube
Collaborating on GitHub is great, but solving a production bottleneck over coffee? That’s where the real magic happens. ☕️
The "hallway track" is where the best cloud native insights are born—from whiteboarding new architectures to hearing raw migration stories you won’t find in a PR.
Join the community this summer to build the future of cloud native, face-to-face:
🇮🇳 Mumbai, India | June 18-19
🇯🇵 Yokohama, Japan | July 29-30
Register here: https://www.cncf.io/kubecon-cloudnativecon-events/
I just want to give you some explanation. I’m sure most of you barelly notice I haven’t posted in a while. It’s been a month or so, and I know it, believe me. Sometimes life happens. I have a job that is incredibly demanding lately, and I’ve been having to play catch up quite a bit. I’m not usually the person catching up. I’m usually the one on the cutting edge of anything in the tech world, but with AI being a massive focus at my company, I’ve been left behind a bit.
Additionally, I’ve been working, with my wife, on a huge project to landscape our yard. It’s a large-ish yard (garden for you Brits) at a bit over a half acre (2026 sq meters). This takes an immense amount of watering, and living in a desert region…that’s super expensive. So we are Xeriscaping a large portion of the front yard. This eats up all of my weekends lately as well. I do have new videos in the works, so please stick with me, and new content is coming.
=== Links ===
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=== Get My Novel ===
Entanglement – by Brian McGonagill
Dave is just a normal guy with a normal job that he doesn’t like that much. That is, until he meets Leo, and gets the job he never imagined. Action, adventure, betrayal, love, loss, and some serious weirdness happen throughout this science fiction thrill-ride.
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=== Special Thanks ===
Gabriela Lopez – Video Editor
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Video by Open Data Science and AI Conference via YouTube
Clustering is one of the most widely used and most frequently misapplied techniques in machine learning. While it promises to uncover hidden structure in data, practitioners often encounter unstable results, arbitrary groupings, and clusters that are difficult to interpret or act on. These challenges are especially pronounced with high-dimensional data such as text embeddings and behavioral signals.
This talk presents a practical, end-to-end approach to clustering using modern, open-source tools built for real-world data. We begin by examining why common approaches like k-means often fall short when assumptions about cluster shape, density, and separability do not hold. We then introduce a more robust workflow: using UMAP to reveal structure in high-dimensional data, applying HDBSCAN to identify clusters without forcing arbitrary decisions, and leveraging datamapplot to make results interpretable and actionable.
Through examples, including real reviews and other data types, we show how clustering can surface meaningful themes and patterns that drive product and business decisions. We also highlight common pitfalls, such as over-interpreting clusters, misconfiguring parameters, and ignoring noise, along with practical guidance on how to avoid them.
Attendees will leave with a clear mental model for when clustering works, when it does not, and how to apply a modern workflow that turns messy, high-dimensional data into useful and explainable insights.
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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.
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
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