Dimitrios Georgios Akestoridis

Google had nine of my fellow Googlers *arrested* last night. Their "crime"? Peacefully protesting Google's Project Nimbus contract with the Israeli government, and recent revelations that, despite repeated explicit claims by Google to the contrary, this contract includes providing discounted cloud computing and consulting services to the Israeli Defense Force (IDF). You can agree or not with the overall message and goals of #NoTechForApartheid. You can debate whether it's appropriate to call what's happening in #Gaza a #genocide. But you can't deny that this was an act of pure #retaliation against these Googlers. Google claims that the removal of these Googlers was a matter of "workplace safety". Whose safety, exactly, was being compromised by nine peaceful employees inside the workspace of their own employer? You can say they should have used official channels - they did! We have! Hundreds of Googlers have spoken out about Project Nimbus internally through all manner of official channels. We have been consistently ignored, lied to, and given nonsense non-responses. Google culture explicitly encourages escalation - what is the next step when you've been ignored through all official channels but to make yourselves impossible to ignore? We do not consent to the output of our labor being used, directly and indirectly, to fight an unjust war, kill innocent civilians, and commit war crimes. We will not be silenced through illegal retaliation for speaking up about the conditions of our workplace. We will not be used. What can one person do? Use your voice. Speak up. And join with others. In collective action, we have power. Join the Alphabet Workers Union (AWU). Sign the petitions to end Project Nimbus, restore Eddie Hatfield's job, and more (internally or the external petition on notechforapartheid.com ). If you live in the US, contact your representatives in government and tell them that you want to stop funding this unjust war, and that you want clear violations of labor law such as Google's punished, and worker protections enforced and improved. Alone, it can seem like it's impossible. But together, we can make change. #Solidarity #Labor #Union #AWU

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This is the upside of having a monorepo. Successes like this depend on being able to change code at scale safely and with clear understanding of performance impact. The changes themselves are done by a fairly small team, but the systems that enable us to continuously roll changes with critical company wide impact like this are key to make this possible.

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Hey everyone, I am an investor and advisor to Katanemo.... They are reinventing the infrastructure primitives for GenAI and launched Arch - https://lnkd.in/gCrbbkEV - an intelligent prompt gateway. Give the project a look on Github, and star the project if it resonates

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Check out the latest in our "Debuggability" blog series! This blog is authored by Kevin Downey Jason Johl and me and talks about how we built a Debuggability Paved Road at Intuit using Ephemeral Containers and Argo Workflows. We'd love to hear your thoughts on the topic. Also, what tools do you build as-a-platform to help debug service, compute and network issues in Kubernetes at scale? #intuit #kubernetes #debug

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Early this year I started to educate myself in quantum computing. Today I presented my progress on the field at my company eBay. This is a summary of what I have learned so far https://lnkd.in/ekc43Fa4

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For the first time I am happy about certificate which I received today that remind me the new learning, foundation for next year, and the hours I spent over the weekend for the 10 weeks. Little bit about course: NLP with deep learning The best part is the hands on exercises where we learn the foundations of deep learning, neural networks and the NLP. But it will be useful as a starting point and set the direction in that path.

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To measure what I call Total Factor Reliability, we don't just ask: is Temporal robust? We must ask: do our *users* write robust Workflows? These improvements to Signals and Updates will help you ensure the latter. My blog, Robust Message Handlers, explains. We crafted them with love. Hope you enjoy!

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I first joined Amazon in December of 2005. Even though I left for two years, my "Amaversary" still coincides with the end of the year and a natural moment for reflection. As I reached my 17 year mark this December, I also had the privilege of working with Amazon Careers to share some of my journey and thoughts about individual contributor technical leader and people manager career paths. In my ruminating, I put together far more content than could be used for a single article. So, I've decided to post here as a series of Q&As - in case others might find my observations, experiences, and perspectives interesting or useful. Below is the first of 6. 1. 𝘽𝙧𝙞𝙚𝙛𝙡𝙮 𝙨𝙝𝙖𝙧𝙚 𝙮𝙤𝙪𝙧 𝙘𝙖𝙧𝙚𝙚𝙧 𝙟𝙤𝙪𝙧𝙣𝙚𝙮; 𝙬𝙝𝙖𝙩 𝙢𝙤𝙩𝙞𝙫𝙖𝙩𝙚𝙙 𝙮𝙤𝙪 𝙩𝙤 𝙩𝙖𝙠𝙚 𝙤𝙣 𝙙𝙞𝙛𝙛𝙚𝙧𝙞𝙣𝙜 𝙘𝙖𝙧𝙚𝙚𝙧 𝙥𝙖𝙩𝙝𝙨, 𝙄𝘾 𝙫𝙨 𝙢𝙖𝙣𝙖𝙜𝙚𝙧? I started my career as a software engineer 25 years ago. I've worked for startups, enterprise companies, and 7 different organizations over 17 years and two tours at Amazon. I've written firmware for robotic camera mounts; built highly scalable distributed systems; designed restaurant marketing, ordering, logistics, and operations software; created automated test harnesses for mobile games; tested HD DVD encoding; helped launch grocery delivery to customers around the globe - just to name a few of the many cool things I've had the privilege to work on. I've been an entry-level software engineer, a technical lead of teams, a software manager on a couple of occasions, chief software architect reporting to a CTO, and (for the last few years) Amazon Principal Engineer directly supporting a VP with about 200 engineers within my remit. I first became a manager because I mistakenly thought it was the only way to continue growing as a leader, and in many ways I failed. I learned some very humbling lessons about leadership through that experience, which I used to be a better individual contributor technical leader on my path to Senior Engineer. My second time as manager was because my organization had a need that I could fill. I had grown enough as a leader that this time I was much more successful. I said yes to an opportunity that started with managing a small team through delivery of a complex and critical project. Once I grew a high performing team that delivered that initial project, I took on more scope and expanded into multiple teams. After a couple of years of success growing and investing in my teams so that we could deliver greater value to our customers and business, I decided to prioritize being close to my family. For me, at that time, it meant giving up my people management role and returning to the role of an individual contributor technical leader that supports a globally distributed organization. Even though I really enjoyed my time as a people manager, there is no better job for me than that of a Principal Engineer. #amazon #careerdevelopment #technicalleadership #peoplemanagement

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Nvidia GPUs on cloud run

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Xian Ke, this is incredible! The speed at which you and Vishal Punjabi leveraged crewAI, Streamlit, and various LLM models to create Finbot, a powerful company analysis tool for investors, was mind-blowing. ICYMI, Finbot is here: https://lnkd.in/eSQD69qA I couldn't help but chuckle when I saw the Finbot name - it reminded me of the Wall Street Bets crowd. Maybe you can get them to yolo their life savings based on Finbot's analysis? (Kidding, of course. Or am I? 💎🙌) But in all seriousness, your enthusiasm and passion for AI and LLMs is contagious. It's engineers like you who are pushing the boundaries of what's possible with AI and paving the way for a brighter future.🏆 I'd love to extend an invitation to any other software engineers from the bootcamp to join our Devs Do AI Discord server. It's a great place for engineers without an ML / AI background to connect with like-minded devs, share knowledge, and collaborate on LLM projects. DM me for the invite link. 🤝

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Sharing my thoughts on How Waymo (formerly known as the Google Self-Driving Car Project) is Pioneering Autonomous Driving with AI. Waymo has become a trailblazer in self-driving technology, and at the core of it all is AI. Leveraging advanced machine learning and deep neural networks, Waymo’s AI-powered system continuously improves through real-world data, making autonomous vehicles smarter, safer, and more reliable. Perception: AI allows Waymo’s cars to "see" and understand their surroundings by processing data from sensors like cameras, LIDAR, and radar. This helps detect pedestrians, other vehicles, and objects in real time. Prediction: The AI system predicts the behavior of everyone around the vehicle—whether it’s cars changing lanes or pedestrians crossing the street—ensuring smooth and safe driving decisions. Planning & Control: AI makes split-second decisions about accelerating, braking, and steering based on the analysis of complex traffic patterns, enabling Waymo vehicles to safely navigate roads. With millions of autonomous miles driven, Waymo’s AI is constantly learning and improving, helping create the future of safer and smarter transportation. The road ahead is AI-driven! www.rxsmeta.com #AI #AutonomousVehicles #Waymo #SelfDrivingCars #MachineLearning #SmartMobility #TransportationTech #FutureOfTransport #AIinAction #Innovation

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When you need to play with your data in Gen Ai, you need RAG and Chunking is one of the main component in the RAG framework while consuming or processing your data. Excellent post below describing how in different ways chunking in RAG can be implemented..

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The session unfortunately wasn't recorded, but here are the slides from my talk at LFMS on quantifying the ROI of contributing to OSS https://lnkd.in/gwdQa3UZ

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Want a preview of what I'm going to be talking about in my keynote for RubyConf? You can catch a glimpse of it here!

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I am excited to share the latest blog, co-authored by myself, Junhwi Kim and Vyas Sekar, detailing how we managed to successfully localized and improved LLM by combining Fine-Tuning and RAG. https://lnkd.in/gKDkgGPq

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Recently, there has been much discussion about Nvidia's CEO's approach of not having 1:1 meetings while leading 60 direct reports, emphasizing the benefits of a flat organizational structure and portraying 1:1s as unnecessary. Like with many trends, the focus has been primarily on these two aspects. Leaders have attempted to replicate this model by increasing the number of direct reports per manager or even eliminating 1:1s altogether. It's refreshing to learn about another side of the story that many are trying to emulate, one where valuing employees and placing them at the center of the organization is crucial for the company's success.

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Did you hear? Anthropic's upgraded Claude 3.5 Sonnet model is now on Vertex AI! When building with the upgraded Claude 3.5 Sonnet on Vertex AI, you get a platform to take your workloads from concept to production with fully managed infrastructure, robust security and privacy, and advanced developer tools. Get all the details, here →

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8 years ago, I started at Google, drawn to the job and mission of supporting customers, building cloud architecture, and creating tools. Back then, our team of 35 focused on "Cloud Platform products like App Engine and Compute Engine". Funny enough, there was no mention of Kubernetes or GKE, which is now a cornerstone of cloud architecture and my specialization! My Googleversary always brings reflection, especially after the intensity of the last few years. Each year feels like a checkpoint; an opportunity to analyze what went well so far and how to build on it. I thought it would be really cool to have someone summarize for me the last 8 years working for Google, my team, and shed some light on what does a Solutions Engineer exactly do? I turned to Google's new notebookLM for this. notebookLM is a web app similar to Google Docs, but it ingests documents, sources, and videos, allowing you to ask questions in natural language. I fed notebookLM, my resume, recognitions, awards,  assessments (including growth feedback), conference notes, presentations, product requirement docs, trip reports, ~8 years worth of performance evaluation and a few rants ;-). notebookLM has a podcast feature that creates conversations from your uploaded documents. You can give it guidelines, but you can't heavily influence the output. I asked it to stop using internal code names, customers, partners, and other names. I also had to cut sections where it tried too hard, "the biggest company in X sector in Y region", that's just too revealing. The summaries were surprisingly insightful. Sometimes, it mentioned things I'd forgotten, which I then fact-checked. notebookLM responds with the exact section in the document where it found something. I appreciated this perspective on my contributions, especially the balance it showed between my technical skills and leadership, training and developing my team. This gave me valuable insights into my own strengths and how I contribute to my team. I must also say a lot of these achievements can't happen without the fantastic teammates, partner teams, customers etc that help us build the cloud community. Incredibly blessed to have spent the last 8 years doing this, and very thankful for all of it. In the end notebookLM gave me a beautiful product, but it's also not lost on me the times i've stumbled, wasn't too sure if I was focusing on the things that would ultimately make my team successful. There was a broadcast/podcast that was talking about a sport team and saying "having pressure is a privilege"...  and in the moment, I myself may not fully believe that, but it's a sentiment I'd love to embody someday! Very thankful for the 8 years of growth. *If you're an AI practitioner, and you have an idea on how to take this audio and generate something cool with it! shoot me ideas! Here's the podcast summary it generated! https://lnkd.in/gfH4zwQQ

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You don’t normally see LLM researchers and engineers talking about their day-to-day work. Big shoutout to this YouTube video from the Anthropic LLM Interpretability team, the team behind the Golden Gate Claude. It’s amazing to see that most of the team members have worked for the team for only one year, and yet they have made such a big breakthrough in interpretability research. Another thing I want to highlight is the unique challenges facing the team and how they approach these challenges, that is, through fast trial-and-error iterations and constant evaluation of what may work and what to prioritize next. #artificialintelligence #machinelearning #deeplearning https://lnkd.in/eUaPAYch

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