open source

Video by The Linux Experiment via YouTube

Try out Proton Mail, the secure email that protects your privacy: https://proton.me/mail/TheLinuxEXP

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Timestamps:
00:00 Intro
01:04 Sponsor: Proton Mail
02:16 SystemD
07:04 Wayland
12:27 Ubuntu
15:38 Snaps
20:24 Flatpak
24:11 Gnome
30:34 Conclusion
31:11 Sponsor: Tuxedo Computers

#linuxdesktop #linuxnews #linuxdistro

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Video by OpenSourceLowTech via YouTube

The whole story so far; Catalunya, Berlin, Scotland, Nepal.
This open source turbine can be built for about half the cost of a 120 watt solar panel, and will produce about ten times the power per day. It can be plugged into any appropriate waterway without the need for earthworks, and uses only basic off the shelf and recycled materials, and basic hand tools.
The turbine is now working and producing power.

Build tutorial: https://www.youtube.com/watch?v=PmuW1i20K3c

This technology was produced in partnership with COACT, an open source green tech incubator in Barcelona.

And thanks to the good people at Ca La Fou (https://calafou.org/) for the rest.

My name is Daniel Connell. I prototype and develop basic technologies which anyone can make using recycled materials and simple tools.
The aim is for everyone everywhere to be able to build and maintain their own infrastructure; producing their own energy, food, clean water, communications, and anything else they need.
All designs are open source and license free for any purpose, and full construction tutorials and how-tos are available on the opensourcelowtech.org website.

00:00 Catalunya
06:50 Berlin
23:12 Scotland
34:56 Nepal
50:25 Results and Data

#water #turbine #DIY #hydro #renewable #energy

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Video by OpenAI via YouTube

See how Box, Ramp, Figma, Cognition, Balsyny Asset Management, and CXO.dev are already using GPT-6 Astra in core business and coding workflows.

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Video by H2O.ai via YouTube

Knowing what an agent can do is one thing. Knowing how it works inside is another, and it is more structured than most people expect.

Give a general-purpose agent a goal and it plans the steps, picks its tools, executes, checks its own results, self-corrects when something looks wrong, and delivers a summary or output files. Plan, act, check, self-correct, deliver. That loop is what separates an agent from a chatbot.

Terms covered:
– Agent Tools: the specific actions an agent can take, from web search to running code to calling APIs
– Tool Builder: creating custom tools so an agent can reach internal databases, custom APIs and legacy systems
– Agent Builder: the environment where you set the agent’s goal, tools, instructions and constraints, without deep engineering

The internal loop gives it intelligence. The tools define its reach. The builder is how you put it together.

—
AI Fundamentals Glossary | H2O.ai University
Full playlist: https://www.youtube.com/playlist?list=PLAWKlIKS-66E
Free courses and certifications: https://h2o.ai/university

#AIAgents #AgenticAI #AI #AIGlossary #H2Oai

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Video by CNCF [Cloud Native Computing Foundation] via YouTube

In this episode of ChatLoopBackOff, CNCF Ambassador Henrik Rexed explores OpenEverest for the first time, with CNCF Maintainer Sergey Pronin live on stream to navigate obstacles and elaborate on features as they are encountered.

OpenEverest is a CNCF Sandbox project that provides an open-source database-as-a-service (DBaaS) platform for Kubernetes environments.

Join us on September 24 for this practical, live dive into OpenEverest!

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Video by FINOS via YouTube

Learn how location information is becoming a fundamental part of modern business operations rather than just a simple add-on.

This discussion focuses on the shift toward integrating data about the physical Earth into our daily workflows. We examine why this shift is happening and how organizations are moving beyond basic mapping to treat spatial intelligence as a core asset. For professionals and decision-makers, understanding this evolution is critical for staying ahead in a data-driven environment.

As we integrate more geospatial data into our systems, the way we handle complex projects changes significantly. We look at the practical reality of these data operations, where spatial context is no longer optional but essential for success. Recognizing this pattern helps teams prioritize the right technical infrastructure for long-term growth.

Subscribe for weekly insights on industry trends and leave a comment sharing how you currently use location data in your own work.

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Video by Open Data Science and AI Conference via YouTube

This event is part of the ODSC AI Skills Accelerator, bringing together AI and data professionals for practical, expert-led sessions on building real-world AI systems.

Just like we don’t put crude oil in a car—we refine it into gasoline—AI can’t run on raw data. It needs continuous, refined context. Serving fresh context is the missing architectural layer for reliable AI.

In this session, learn how IBM, Confluent Intelligence, and Google Cloud turn operational data into fresh context for AI agents to take the right action at the right time. See how Confluent’s data streaming platform turns operational data into automated decisions, and how Google Cloud adds a layer of real-time investigation and explanation on top—all grounded in context that reflects the current state of the business.

We’ll demo a real-time architecture for anomaly detection and investigation—streaming racing telemetry through Confluent’s Real-Time Context Engine, where a Confluent Streaming Agent detects anomalies and recommends pit timing in real time. You’ll then see a Gemini agent in Google Antigravity, connected via MCP, investigate those decisions and explain the reasoning behind them, with governed data synced to BigQuery for historical record and analysis.

You’ll leave with practical, streaming-native patterns to move from brittle pipelines and demos to production-grade AI on Google Cloud while reducing TCO.

Speakers:

– Karan Sachdeva, IBM
– Sean Falconer, Confluent
– Sohrab Rahimi, Google Cloud

Join us on September 9 for a new ODSC AI Skills Accelerator session, bringing together experts from IBM, Confluent, and Google Cloud to explore how real-time data and fresh context can power more reliable AI agents.

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