David Espejo

The Flyte 2.0 SDK is officially here: This release brings some really exciting local development features that you can right out of the box even with connecting to Kubernetes or Docker! The features I've been loving to my local AI workflows: - Terminal User Interface (live workflows and history) - HTML Reports (tracks to workflow version - Caching (skip rerunning a task by reading from cache) - Retries (auto retry a task if it fails - super useful for API calls) - And more All this together gives you: - great lightweight experiment tracking - a lift in reliability - a great interface for debugging large pipelines or AI agent runs I can't wait to show you whats next in both Flyte and @union_ai

It's that time of the year again when I pack my bags and tackle the 21hr journey to meet my awesome colleagues at @union_ai's HQ. As usual, I try to use the time to take on my reading backlog while people around are watching Netflix 😅

@abarbap Que bueno que a alguien mas le guste! Hace poco redescubri la Eroica dirigida por Barenboim. Escucharla con Apple Music Classical en Dolby Atmos, es toda una experiencia.

No joda cipote sinfonia que es la #7 de Beethoven, me gusta el "Allegretto" (Movimiento II). pero el IV es majestuoso. (Opinión de alguien al que le gusta explorar)

@DominikTornow Thank you. I thought in Chapter 2 you'd build an agent from scratch, no frameworks. I've struggled to find a resource like that 😀 Anyways, eagerly awaiting (pun intended) any new chapter.

In Chapter 2 of Systems Engineering for Agentic Applications, we build our own MCP. Not another MCP server but our own protocol We'll discover - What is MCP - What does MCP do - Why would we even need MCP x.com/criccomini/sta…

🚀📕 The GPU + Kubernetes book is finally here. After six months of rabbit holes, I finally understood why this problem was so hard. When I started, I thought GPUs were just fancy parallel processors. Mount the device, set some resource limits, and done. Then I learned that GPUs can't even pause a running kernel. Once computation starts, it runs to completion - no preemption, no time-slicing in the CPU sense, nothing. The hardware was designed this way for maximum throughput, and no amount of software can change it. This fundamental difference breaks every assumption Kubernetes makes about resources. The Linux kernel sees and controls every CPU cycle and memory page. But GPU operations? They happen in a black box managed by the NVIDIA driver. The kernel is completely blind. So I wrote this book. Six chapters that trace the problem from hardware to orchestration: 1️⃣ Why containers work beautifully for CPUs (syscalls, cgroups, namespaces) and why GPUs break every one of these assumptions. You'll understand exactly how device plugins trick Kubernetes into accepting GPUs it can't actually manage. 2️⃣ How traditional Kubernetes isolation completely fails for GPUs. When two pods share a GPU, there's no cgroup enforcement, no memory isolation, nothing. One pod can crash everything. 3️⃣ The truth about "GPU sharing" tools. KAI-Scheduler and NVIDIA's "time-slicing" don't share anything - they just orchestrate turn-taking. Your pods still wait in line for exclusive GPU access. 4️⃣ MIG vs HAMi vs vGPU. When you actually need hardware partitioning (spoiler: probably never), and why seven T4s might serve you better than one H100 with MIG. 5️⃣ Why nvidia-smi lies to you, Kubernetes metrics lie differently, and DCGM reveals that 60-70% of your GPU budget is wasted on idle resources. 6️⃣ How to share GPU clusters across teams without namespace chaos. Virtual clusters give each team its own control plane while efficiently sharing the underlying hardware. Download the free book here: ku.bz/gpu-k8s Huge thanks to Saiyam who co-authored this, bringing real production experience I lacked. To the vCluster team (Rahul Patwardhan!!!) who believed in this project and sponsored my research time. And to Gulcan who edited countless drafts into something readable. 💡 If you want to go deeper, join me for a live discussion this Wednesday, where I will answer your GPU questions and explain how the book came to be ku.bz/g8gXCKW12

Amazon S3 Vectors + Flyte 2.0 = simpler, cheaper AI pipelines Flyte 2.0 users can now use S3 Vectors. That means no separate vector database, cut up to 90% of related costs, fewer moving parts to manage. Learn how it works: union.ai/blog-post/amaz… #S3 #orchestration #agents

Flyte 2 isn't just an AI orchestrator. It's a durable, dynamic agent runtime. union.ai/flyte/2-0-anno…

✈️ Introducing Flyte 2.0 ✈️ Dynamic, crash-proof, resource-aware AI orchestration. Plus, a fully functional agent runtime. This is big. Learn more and access the beta: union.ai/blog-post/intr…

Pandera passed 100 million downloads this weekend! (not the sandwich shop, the open source data validation project) Huge thanks to our incredible OSS community at Union.ai. Here’s to 100 million more! union.ai/pandera

A free 698-page PDF ebook with everything you need to know about math:

@damianplayer n8n

I compiled 200+ n8n automation templates you can copy & paste into your business or sell to other companies. Just straight plug-and-play systems for: – Lead gen -Content creation – Email outreach – CRM updates – AI workflows – Slack/Discord bots …and more. Follow + Reply “n8n” and I’ll send it over. This is completely FREE. Don't even want your email.

I am at GTC and found like 10 Companies built on top of Flyte - crazy way to spread fast.

@ketanumare Agree :) The fact that objects bring immutability and simplicity is maybe what contributed to S3's unparalleled success. Design decisions that remain right 19 years after its inception are a good substrate to build on!

Please comment if you agree that Object stores are the future of most storage besides a simple relational database.

Can folks believe that S3 initially was designed to handle 2B objects? 1/ List and Range were the biggest problems 2/ Hot shards and replication strategy would slow down writes And we @union_ai have taken a bet on Object Store as the only data storage api needed

🚨We’re at GTC (booth 2022) with a huge announcement—Union now serves AI models and apps! Learn common AI serving mistakes in our whitepaper: hubs.la/Q03ckdcp0 Union serves 2x faster than SageMaker, in any cloud. #GTC2025 #MLOps #Serving #Inference #CompoundAI

@HowDevelop @nebiusaistudio @AllThingsOpen @couchbase @union_ai This is great, thank you Shivay!

Fully packed room here at my presentation on how to fine tune LLMs with Flyte (programmatically) and @nebiusaistudio at @AllThingsOpen AI conference. Extremely relevant conversations, and 1000+ attendees. Despite being 1st edition you know it's going to be great since it's ATO

🚀 Exciting news! Union is heading to #gtc2025 next week! 🎉 Join us at Booth 2022 to see how we’re revolutionizing AI workflows and model serving. Want to see it in action? Want to learn how Union can Unify your AI Development? 🔗 Book a chat: cal.com/team/unionsolu…

How is AI orchestration powering the future of #bioinformatics? Union’s platform empowers researchers to tackle computationally intensive problems like protein folding with ease, scalability, and precision. Learn more in our latest blog. #ML hubs.la/Q031WPpR0

@akshaykagrawal @themylesfiles Very interesting, thanks for sharing! Also, check out how we're also trying to add reproducibility, versioning, and container-based execution environments to Notebooks @mecoli1219/develop-run-flyte-workflows-with-jupyter-notebook-163eaac2d363" target="_blank" rel="nofollow noopener">medium.com/@mecoli1219/de…

Jupyter notebooks suffer from a reproducibility crisis. A 2019 paper found just 24% of 800k Jupyter notebooks on GitHub could be rerun, and only 4% reproduced the same results! @themylesfiles and I are fixing notebooks because this is untenable for science/data engineering 🧵

Just tried @genmoai's Mochi model for video generation on @union_ai serverless with an A100 GPU 🎥✨ The video quality is impressive! Check it out: dub.sh/jhR7T6D

@johncodes Wow congrats John! I'm sure your work will benefit the entire OSS ecosystem, and projects, including @flyteorg.

What an amazing journey! 🍕 After nearly 2 years of building big AI platforms and RAG data pipelines, I'm excited to also be joining the Linux Foundation as their VP of Infrastructure and AI Engineering! Excited to help pave the future of infra, data, and AI for the LFX!