Engineers can learn how open hardware, AI, and collaborative projects like OCP are crucial for achieving environmental sustainability goals in tech. It highlights practical applications of AI in reducing carbon footprints for IT infrastructure and data centers.
Most people have heard of open-source software. But have you heard about open hardware? And did you know open source can have a positive impact on the environment?
On this episode of the Meta Tech Podcast, Pascal Hartig sits down with Dharmesh and Lisa to talk about all things open hardware, and Meta’s biggest announcements from the 2025 Open Compute Project (OCP) Summit – including a new open methodology for leveraging AI to understand Scope 3 emissions.
Learn about the history of OCP and its growth into an organization with more than 400 companies contributing to it. You’ll also hear how AI and open hardware are helping Meta push to achieve net zero emissions in 2030, including how AI is being used to develop new concrete mixes for data center construction.
Download or listen to the episode below:
You can also find the episode wherever you get your podcasts, including:
The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
Send us feedback on Instagram, Threads, or X.
And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.
The post Open Source Is Good for the Environment appeared first on Engineering at Meta.
Continue reading on the original blog to support the author
Read full articleThis pattern solves 'context interference' where LLMs degrade as they gain more knowledge. By separating orchestration from domain reasoning, engineers can build more reliable AI agents that handle complex, multi-stage workflows without constant manual prompt tuning.
Engineers can leverage Ax, an open-source ML-driven platform, to efficiently optimize complex systems like AI models and infrastructure. It streamlines experimentation, reduces resource costs, and provides deep insights into system behavior, accelerating development and deployment.
Meta is shifting AI infrastructure from proprietary silos to open, Ethernet-based standards. These innovations in liquid cooling, petabit-scale fabrics, and standardized switch layers enable the massive scale required for next-gen AI models while fostering an open hardware ecosystem.
This shift from ID-based to semantic tokenization allows recommendation systems to leverage LLM world knowledge. It solves the cold-start problem for new items and enables better scaling, leading to measurable improvements in conversion accuracy and cross-campaign learning.
