Ying Liu

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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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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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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While there are certainly individual variations in skill and experience when discussing modern software development the story is much more nuanced than pure individual effort.  * software development is a team sport -- individual performance is heavily impacted by the system in which they are asked to operate in.  * we pair skill and experience to this model of understanding performance by expecting people to be able to influence and adapt in direct proportion to their seniority, aka the more senior you are the more responsible you are for the outcomes of your work, the less senior you are the more your manager and management chain are responsible.  * humans want to succeed, we're social animals who derive purpose from demonstrating skill, and working collectively. All of which is to say there is no way you have over 1000 people underperforming at your company without senior leadership deeply implicated in.  If nearly 10% of your company is failing, versus just having the market move against you, you need to fire the CEO.

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A data-driven approach to driving wound healing outcomes Marcella Gomez, University of California, Santa Cruz https://lnkd.in/gAkWZYJT Meet: https://lnkd.in/g4AAWkcK YouTube Stream: https://lnkd.in/gwtqaTmf Talk details: https://lnkd.in/gWn3B3cU Subscribe for talk announcements: https://lnkd.in/g5ciuNuX Abstract: Precision medicine requires an ability to predict the response of an individual to a prescribed treatment regimen a priori. Thus, advancement in the field is challenged by a lack of predictive models and, arguably, a lack of time-series information for a highly dynamic system. Here, we discuss work in wound healing, for accelerating wound closure. We argue that timing of treatments is as critical to consider as the choice of drug or therapy. Due to system size and complexity, data-driven methods need to be explored to develop multi-dimensional quantifiable indicators tracking systemic changes. In this work I discuss how bioelectronic devices enhanced with deep learning can help facilitate real-time sensing and actuation for automated decisions in treatment for wound healing and preliminary work in transcriptomic based classification of wound states. Bio: Marcella M. Gomez is an associate professor at UC Santa Cruz in the department of Applied Mathematics and Associate Dean for Diversity, Equity, and Inclusion for Baskin Engineering. She received her PhD from Caltech in 2015 and a B.S. from UC Berkeley in 2009; both degrees in Mechanical Engineering. Her research interests are in the broad field of bio-control leveraging methods in machine learning and control theory. Applications range from controlling single-cell response to driving complex systems such as wound healing. #modeling #simulation #medicine #healing #trauma #predictivemodeling #datascience #data #woundhealing #biology #bioengineering #systemanalysis #sensing #omics

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🎉 Big congratulations to our incredible team (Haoyu Chen, Nick Greenquist,Yuxing Wang, Sridevi Maharaj, Ruiyang Wu and many more) for delivering several quarters-long impactful features on the Play Store! 🚀 We’re thrilled to have launched key updates like Search Home, Sports Moment, and WearOS recommendations, all designed to enhance personalization and help billions of users worldwide search and discover content they love. 🌍✨ Check out the Play Store Q3 updates: https://lnkd.in/dn5wx8Mi #GooglePlay #Personalization #Recommendation

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Project update! My application tracking extension is now doing fuzzier matching and injecting new information into LinkedIn (and other sites) to keep me from doubling up on applications. Next up: local storage and bulk import/export operations...

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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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What does it feel like to be an engineering manager at Amazon: Since the RTO announcement it became popular to write shit about Amazon, but it's still one of the best companies around the world. A few weeks ago, Gilad Naor shared with the 'Leading Developers' readers 3 lessons from his time as an engineering manager at Amazon. Here is the first one, read the full article here: https://lnkd.in/dHBxv2Ha 𝟭. 𝗦𝗼𝗹𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 My first meeting at Amazon was baffling. I entered a room with about a dozen Software Development Managers (SDMs) and Product Managers (PMs). Everyone sat in silence and looked at their laptops or the printed papers they had before them. It was my second day at Amazon, and I picked up one of the extra sheets and started reading. After those first ten awkward minutes, everyone finished reading and the meeting began. Why do Amazon meetings always start this way? This is a specific example of how Amazon approaches problem-solving. It is the "Good Intentions Don't Matter" approach. In a famous internal video, Jeff Bezos talks about why good intentions are irrelevant. When things don't work, it does not matter what people wanted. All that matters is fixing the problems with mechanisms. For example, many busy managers come to meetings without first reading the prep material. They all intended to read the 6-Pager before the meeting. They just got sidetracked by the latest emergency. Scolding the managers won't solve the problem. A mechanism will. This is why all Amazon meetings start with a study period. The remaining meeting time is then spent on high-quality decision-making. Meta solves the same problem by creating cultural norms. I can't count the number of times that I heard a senior leader cut off the presenter: "Stop, assume that everyone here already read the slides. Let's get to the point." This is a culture-first approach. I have seen this work well in some teams, and not work at all in other teams. At Amazon, the solution is part of the company's "operating system". ------------------------- Anton here - by now everyone is familiar with the 6-pager concept, but that "operating system" thinking can be used almost anywhere. Think about something annoying that your developers do, and how can you solve it. For example, I remember being annoyed that people don’t update the Jira tickets. Finally, after requesting multiple times, now we just take 20 seconds from the standup to update them.

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𝗔𝗿𝗲 𝘆𝗼𝘂 𝗹𝗼𝘀𝗶𝗻𝗴 𝘀𝗹𝗲𝗲𝗽 💤 ? because you can't pinpoint your CI bottlenecks or spot problematic trends? At Pinterest, we faced the same challenges—but we’ve found a solution. 🌟 In our latest Pinterest Engineering blog post 🔗 https://lnkd.in/eJJ_GpGW We share how we’re leveraging honeycomb.io to identify CI root causes through cross-data correlations. Discover our innovative approach for categorizing CI failures in real-time using job log analysis. Learn how to segment CI jobs by agent wait time, script execution, specific Bazel targets, or any other level of detail tailored to your needs. Our enhanced CI observability metrics have let us improve CI stability and boost developer productivity—and they could do the same for you! Check out the blog for the full breakdown and see what’s possible when data-driven decisions guide your CI workflows. 🚀 Let’s make CI faster and more reliable together! #DevOps #DevX #CICD #honeycomb #observability #engineering #softwareengineer

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I reached 17 years at Google two weeks ago. Google has changed quite a bit over the years, though I won't get into that now. Sometimes, though, it's not the big things you've landed, but the smaller things that unexpectedly turned into bigger things. I frequently reflect on: - Android lint testing for Google apps - Fake "Save" button in Google Docs - The algorithm for Google Calendar's event layout - A couple of random test frameworks I created along the way, e.g. one that repeatedly prevented new crashes in Android OS itself - Some novel algorithms for detecting production issues in Android - Helping land XSS-free autoescaping for one of our most common HTML templating languages Many of these were things I started on a whim because I thought they might be valuable. Some of them I landed myself, but some were just started and took on lives of their own. Of course, I tried a lot of other things that didn't work, but it's important to try and experiment even when not everything works. What small things bring _you_ disproportionate pride?

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Come join us tomorrow in SF to learn more about the latest updates to run AI workloads on Google Cloud!

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LLMs and LangChain: The Dynamic Duo 🦸‍♀️🤖 Just finished a fun project where we built a Q&A bot using these two powerhouses. It's like having a personal AI assistant that's always ready to answer your burning questions! 🔥 Here's what I learned: LLMs are wizards: They can conjure up answers from thin air (or, uh, the internet). 🧙‍♂️ LangChain is the glue: It holds everything together and makes it easy to use. 🔗 Endless possibilities: This tech can be used for anything from customer support to writing catchy social media posts. 📝 Want to see it live (obvio a test server link 😛 ): https://lnkd.in/dZNAVCSV Note: it's a render link, they will take down the instance because of no use, you can hit the link and wait for two minutes, they will spin up the instance and you can try it out. Want to see the code? Check it out here: https://lnkd.in/dzjh4r47 #AI #MachineLearning #NLP #LangChain #LLM #Python #Developer #FunProject"

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ModernBERT is an open-source embedding model which represents a significant leap in encoder-only transformer architecture merging: > Long context (8k tokens) > Local-global attention for efficiency > Up-to-date code and text training data (2 trillion tokens) > FlashAttention & Unpadding to push performance on consumer and server GPUs > High retrieval and classification accuracy that sets new benchmarks across GLUE, BEIR, code tasks, and more. Learn more about this exciting new embedding model here: https://lnkd.in/en5ud9VE

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Very excited that we shared our blockchain solution, LETUS, at SIGMOD-2024. LETUS introduce a re-designed architecture with delta-encoding and version-based indexing schema, also with block tiering and state pruning proposed. LETUS is also universal for different blockchains, adaptable for Ethereum, BNB Smart Chain, etc. Evaluations shows that, LETUS improves the throughput by up to 15.8 times for the ERC-20 smart contract transfer workload. And reduces the space usage by up to 80.3%, reduces disk bandwidth usage by up to 95%. During the 2023 Asian Games in Hangzhou, AntChain (augmented by LETUS) successfully supported the digital flame ignition event, where over 100 million torchbearers from more than 100 countries lit the digital torches in real-time online. Final version paper link: https://lnkd.in/g5h736Zb PDF link: https://lnkd.in/gyNqy4r4

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My Journey to Securing 7 Offers for Management and IC Roles Across FAANG and Startups I’ve been considering opportunities beyond Amazon since 2020, but I was fortunate to be offered enriching experiences year after year, which kept me from making the leap. By early 2024, after laying the foundations for AWS App Studio, I decided to explore other roles within #Amazon. However, I realized that Amazon’s presence in the Bay Area was shrinking, and with its #RTO policies, the management opportunities in #bayarea were limited to just a few roles, none of which aligned with my interests and goals. A combination of limited internal opportunities, and changes in Amazon culture and policies, motivated me to explore external options. Initially, I had two interviews with a FAANG company and a startup, but I failed them miserably. Securing new interviews proved difficult due to factors like scarce roles, high competition, and increasing expectations in both technical and people-management skills. I quickly realized I needed to make every interview count by preparing more methodically and strategically. In a future post, I’ll dive into how I: a) Adjusted my job search to land new interviews consistently b) Staggered my interview timeline to maximize preparedness and increase the likelihood of receiving multiple offers c) Prepped for technical skills, as many companies now expect managers to be as technically competent as senior engineers I also decided to interview for individual contributor (IC) roles for several reasons: a) I’m still hands-on with my side projects b) Companies are reducing management layers c) I enjoy both IC and management roles d) Compensation for ICs at some companies can be higher than for managers at others e) Since I was preparing for technical interviews anyway, I figured I might as well explore IC opportunities too I segmented my job search into four categories: Startup/FAANG and IC/Management, staggering my applications to focus on startups first and then #FAANG. This approach created an avalanche effect: I started receiving IC and management offers from startups, followed by multiple FAANG offers across both IC and management roles. Here are the benefits of this approach: a) I was able to fairly evaluate startup opportunities without bias toward cash components. One startup came close to winning me over, but by that point, I had FAANG offers and ultimately decided to pursue those. b) I gave myself a chance to objectively reconsider an IC role, as I’m still mid-career c) I ensured that for any managerial role I considered, the company culture and its policies aligned well with my values. d) This approach helped me negotiate effectively and secure a top-of-market compensation package. e) I was at my best in the final interviews (both FAANG and startups) after having multiple real-world practice sessions with other companies.

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