Hyperlight and Nanvix introduce new model for running services with speed and security

Adds real isolation to containerized code without slowing it down or limiting language choice. Curious what other technologies are striking the right balance between portability, isolation, and speed.

Reasoning engines meet browser automation -> Steel + @nottecore This Cookbook example shows how to connect Notte's reasoning engine to Steel's cloud browser infrastructure via WebSocket. Python implementation ready -> Steel x @nottecore: https://lnkd.in/gkRdrzYb

💡 Making AWS TGW Route Tables Easier to Work With Honestly, AWS should have a simple way to export Transit Gateway route tables — routes, associations, and propagations all in one place. Even though I manage TGW configs through Terraform, it’s not always easy for everyone to read and compare pre- vs post-change outputs. So I built a small Python script (boto3) to export all TGW route tables and save them as files. Now we’ve got clear snapshots for quick diff checks, troubleshooting, or audits — without digging through Terraform plans. Sometimes, small automation like this brings the most clarity. Curious — how are you handling TGW route visibility in your setups? 👇 #AWS #Python #Terraform #Automation #NetworkEngineering #CloudNetworking #TransitGateway

Did you know you could save money just by stopping iteration in Python? Loops look simple, but they’re slow and costly at scale, especially in ML pipelines and cloud workloads. By switching to vectorized operations (like NumPy’s @ operator), you’re tapping into compiled C/Fortran routines under the hood — the same power behind high-performance systems all from the confort of writing Python. 🔗 Deep dive in my latest medium article: “Understanding Vectorization in Python” https://lnkd.in/erxM26Mk

Exciting times for the Python Software Foundation as it modernizes its infrastructure using Arm-based AWS Graviton processors with GitHub's Arm64 runners. This move cuts compute costs by 25%, reduces carbon emissions by 40%, and ensures zero downtime while maintaining reliability for millions. A great example of how open-source can grow responsibly without losing performance. Looking forward to seeing Arm's innovations shaping the future of Python and its community. ⚡️ https://okt.to/o1u3wT

Just wrapped up Module 5 of #mlzoomcamp by DataTalksClub Learned about: 🔹 Deploying ML models as web services 🔹 Serving ML predictions with Flask 🔹 Dependency management with Pipenv 🔹 Dockerizing Python apps 🔹 Deploying ML Model on Amazon Web Services (AWS) Elastic Beanstalk ✨ Key takeaway: ML models are only useful when they’re live & accessible! ➡️ Next up: putting it all into production! Alexey Grigorev #MLDeployment

It's thrilling to witness the Python Software Foundation enhance its infrastructure using Arm-powered AWS Graviton processors and GitHub's native Arm64 runners. This strategic shift enabled the PSF to lower compute costs by 25%, decrease carbon emissions by 40%, and maintain zero downtime, ensuring the reliability that countless developers rely on. This initiative showcases how open-source communities can grow sustainably without sacrificing performance. I'm proud to see Arm's innovation fueling the foundation of Python and the developers who are influencing the future. ⚡️ https://okt.to/4ZnUVo

By migrating its infrastructure to Arm-based AWS Graviton processors and GitHub’s native Arm64 runners, the Python Software Foundation strengthened the ecosystem that millions of developers rely on every day. This move enables the PSF to deliver greater scalability, energy efficiency, and performance across its CI/CD systems and build environments—cutting compute costs by 25%, reducing carbon emissions by 40%, and achieving zero downtime. It ensures the infrastructure behind Python remains as innovative as the community it supports. ⚡️💪 https://okt.to/Eq8sLh

Thanks to our partners Arm, GitHub, and Amazon Web Services (AWS), the PSF migrated #Python’s infrastructure to Arm-based systems; achieving zero downtime, lower costs, and a greener footprint. Results like this show the impact of corporate collaboration with the #Python community 💝 🐍   https://lnkd.in/gA5Q-qhH

It’s exciting to see the Python Software Foundation evolve its infrastructure with Arm-based AWS Graviton processors and GitHub’s native Arm64 runners. By making this move, the PSF cut compute costs by 25%, reduced carbon emissions by 40%, and achieved zero downtime, all while maintaining the reliability millions of developers depend on. This is a great example of how open-source communities can scale responsibly without compromising performance. Proud to see Arm innovation helping power the backbone of Python and the developers who shape what’s next. ⚡️ https://okt.to/ZbTDFN GitHub Amazon Web Services (AWS)