HM Godoy 
Introduction: The Open Source AI Revolution Open source AI is no longer a niche experiment—it’s a full-blown revolution reshaping the economics, ethics, and global balance of power in artificial intelligence. From drug discovery to banking, open models are driving costs down and transparency up, while raising thorny questions about safety and national competitiveness. In this … Read more
Why Open Source Is at an Inflection Point The latest videos from OpenWorld.news reveal a dynamic open source ecosystem facing both opportunities and challenges. From the rapid evolution of Linux to the rise of open source AI models, community-driven projects are reshaping technology. Yet, as AI bug-finding efforts barely scratch the surface of software maintenance, … Read more
Video by OpenProject | Open Source Project Management via YouTube
What often stands out to new joiners at OpenProject?
Our People & Organization Manager, Dorothee Steiner, shares what new colleagues often notice when they first join the team.
Curious about our culture or wondering if OpenProject could be a good fit for you? Have a look at this video. 👀
Explore career opportunities at OpenProject:
👉 https://www.openproject.org/careers/
And feel free to reach out.
Video by H2O.ai via YouTube
For most of AI’s history the focus was analyzing and predicting. Generative AI creates. Here is what changed, and how to choose between proprietary and open source models.
Terms covered:
– Generative AI: AI that produces text, code, images, summaries and answers on demand
– GPT (Generative Pre-trained Transformer): the architecture behind most large language models
– Proprietary LLMs: powerful, easy to start with, priced per token, and your data goes to a third-party server
– Open Source LLMs: weights you can download, deploy on your own infrastructure and fine-tune, with no per-token cost and no data leaving your environment
Most serious deployments need both. Proprietary for speed and access, open source for control, privacy and cost at production scale.
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AI Fundamentals Glossary | H2O.ai University
Full playlist: https://www.youtube.com/playlist?list=PLAWKlIKS-66E
Free courses and certifications: https://h2o.ai/university
#GenerativeAI #LLM #OpenSourceAI #AIGlossary #H2Oai
Video by PyTorch via YouTube
Following a great community gathering at Graphcore’s London office, momentum is building for PyTorch Conference North America 2026.
Join PyTorch Foundation board member Sylvain Viguier and fellow open source developers, researchers, and technical leaders in San Jose, California on October 20 and 21. We look forward to hearing about your latest projects, sharing technical insights, and connecting in person.
Register now to save your spot: https://hubs.la/Q04v4SL60
Video by CNCF [Cloud Native Computing Foundation] via YouTube
Join Akihiro Suda at KubeCon + CloudNativeCon North America, November 9-12 in Salt Lake City, Utah.
In “Lima as the Universal AI Sandbox,” Akihiro will explore how Lima can isolate AI agent workloads across host and guest operating systems, helping limit the potential damage caused by malicious instructions.
The session will also cover GPU acceleration, plugin infrastructure, MCP server support, expanded operating system compatibility, and new tools for running AI agents.
Explore the session:
https://bit.ly/4y7tJuH
Register:
https://bit.ly/3BDI5XL
Video by FINOS via YouTube
Linux kernel development moves at a truly staggering pace.
We examine the raw metrics behind the kernel’s growth, including changes per hour and weekly CVE counts, to understand the reality of open source maintenance. This perspective breaks down why even massive AI bug-finding efforts barely scratch the surface of software engineering at this scale.
Subscribe for more technical breakdowns on Linux kernel internals.
Video by Open Data Science and AI Conference via YouTube
Real-time analytics systems need to be fast but speed alone is not enough. They also need to be correct, consistent, and reliable under real production workloads.
In this ODSC AI East 2026 session, Deep Patel, Senior Data Engineer at Robinhood, explores how to build event-time-consistent analytics pipelines using Apache Kafka, Apache Flink, and Apache Pinot.
The session covers the challenges that often cause metric drift in streaming and OLAP systems, including late-arriving events, replay, backpressure, ingestion delays, and multi-tenant workloads. Deep introduces architectural patterns for exactly-once analytics, event-time watermarks, deterministic segment assignment, idempotent upserts, and Flink checkpoint alignment.
Watch to learn practical strategies for building low-latency analytics pipelines that deliver fresh, trustworthy insights at scale.
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