The 2026 PyTorch Docathon will take place May 5 to May 19, a hackathon-style community event focused on improving PyTorch documentation.
Whether you’re new to open source or an experienced contributor, the Docathon offers opportunities to improve tutorials, guides, examples, and website content. Many issues are beginner-friendly, with tasks labeled by skill level and support available through the PyTorch Discord.
Contributors can make an immediate impact while learning more about PyTorch modules, tutorials, and workflows.
Schedule
May 5
Kickoff and Q&A, 10:00 AM PT
May 6 to May 15
Submissions and feedback
May 16 to May 18
Final reviews
May 20
Winner announcements
RSVP to receive updates, participation details, and event instructions.
Three years after launching the Electronic Frontier Foundation’s Tor University campaign, the movement has grown from 13 participating institutions to nearly 50 universities worldwide running Tor relays, including the first university relays in Korea and Taiwan.
In this talk, Roger Dingledine, Tor co-founder and original developer, shares the latest progress of the campaign and explains why universities are vital to strengthening the Tor privacy network. Learn how education, community engagement, and research are helping campuses support online privacy, censorship resistance, and a more secure internet.
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
Andres Rojas (Director of Applied AI Projects at the Vector Institute) explores why the true "moat" in financial services is no longer the AI algorithm itself, but the shared foundations and high-quality data used to train it. He provides a roadmap for staying at the state-of-the-art in a world where AI research is accelerating by 30% year-over-year.
🇬🇧 Join us in London! Catch the latest on Applied AI at OSFF London on June 25, 2026: https://hubs.ly/Q041YV9Z0 (Use Code: 26YTOSFFLN20C)
🕒 Timestamps:
0:00 Setting the Context: Research vs. Application
0:50 The Key Message: Shared Foundations in AI
1:15 Why Algorithms Aren’t IP: The Training Layer
1:45 Transformers and Model Weights
3:05 The Speed of Change: 100,000 Papers a Year
4:45 Proof: Collaboration Results with 200 Companies
5:55 Trust Through Community Validation
6:45 Closing Takeaways: Invest in Your Edge
📊 The Problem: The Myth of the Proprietary Model Many organizations believe their competitive advantage lies in protecting their specific machine learning algorithms as Intellectual Property (IP). However, in modern AI, the logic (like Transformers) is inherently open-source and shared globally; trying to keep these foundations secret only slows down exploration and increases the cost of staying relevant.
🏗️ The Solution: Focusing on the "Differentiating" Layers
Andres Rojas argues that the value shift in AI requires a new strategic focus:
* The Training Moat: IP is created only when open algorithms are trained on your specific, high-quality datasets to create calibrated weights.
* Accelerated Exploration: Collaborating at the foundational layer allows firms to digest the "firehose" of 2,000+ AI papers published monthly and move to production faster.
* Assurance Over Blind Trust: Open foundations allow for community-led validation, building the "Three Lines of Defense" for AI that proprietary "black boxes" cannot provide.
⚙️ Why This Matters for Financial Engineering
* Efficiency Through Sharing: Collective co-development has saved partners over 250,000 hours in development time by mutualizing infrastructure.
* Lead on Openness: Just as AI was launched in Canada, the local financial community can lead by building on verified, open foundations rather than general algorithmic secrets.
The takeaway: Invest your edge in data, domain expertise, and governance—not the algorithm. Andres Rojas proves that the fastest path to trustworthy AI is through shared foundations.
🌐 More about FINOS: https://www.finos.org/
📧 Join our newsletter: https://www.finos.org/sign-up
🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcasts
LinkedIn: https://www.linkedin.com/company/finosfoundation
#FINOS #OSFFToronto #VectorInstitute #AIStrategy #DeepLearning #OpenSource #FinTech #AppliedAI
Security engineer Andy Nguyen’s PS5-Linux project allows original PS5 models on older firmware (3.xx/4.xx) to boot Linux. This transforms your console into a powerful x86-64 machine, capable of running distros like Ubuntu and Arch Linux, and even Steam. A gamer’s dream, if your firmware is outdated! #PS5Linux #GamingPC #TechHacks #LinuxGaming #ConsoleModding
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See what happens when AI plugs into your Meta Quest development workflow ⬆️
This is MCP (Model Context Protocol).
It’s the open standard that connects your favorite AI assistants like Claude and Cursor to device logs, performance traces, documentation, 3D assets, and more. Your AI picks the right tool and pulls the data. 💫
🔗 Tap the link in the comments to get started with MCP
AI has supercharged coding speed, but it has exposed critical weaknesses in the downstream delivery pipeline. The Harness State of DevOps Modernization 2026 report shows that, while AI-assisted developers are shipping code 45% faster, 69% of frequent AI users report more frequent deployment problems, and average incident recovery times have climbed to 7.6 hours. This is the "Velocity Paradox.”
The culprit? A fragmented pipeline where application code moves at machine speed, while database schemas and infrastructure remain trapped in manual, siloed workflows. To survive the AI era, organizations must move toward a unified pipeline that treats the database as a first-class citizen of Continuous Delivery.
In this session, we’ll discuss how to:
– Standardize Across Environments: Deploy application and database changes consistently from dev to staging to production, with the visibility and governance required to scale safely.
– Scale with Agentic Workflows: Move beyond basic scripts. Use AI-driven agents to safely author schema migrations and automate the complex troubleshooting that currently inflates MTTR.
– Implement "Golden Paths" for Data: Enable developer self-service while enforcing rigid security and compliance using OPA (Open Policy Agent) "baked into" the pipeline.
– Bridge the Trust Gap: Learn how to apply "trust but verify" guardrails to AI-generated code and database changes to reduce the 51% increase in code quality issues seen by high-velocity teams.
Just a reminder that the live stream will go unlisted directly after the stream. The edited version will be posted on Saturday night. Live stream replays can be had by joining us on Patreon: https://patreon.com/thelinuxcast
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