Ming Liu

After 8 rewarding years, I bid goodbye to Amazon. I strongly believe that successful companies are built with strong culture at their core, and Amazon’s Leadership Principles are some of the most recognizable ones. I'm grateful that Amazon is where I bootstrapped my career. As I reflect on my journey, I also want to share some personal takeaways that shaped my growth. And of course, in the spirit of “if everything is important, then nothing is”, here goes with my top three. 1. 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐰𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 - You should work on: 1) Thing that matters to your team/org/company. If you are not clear, clarify with your leaders and help define one if needed. What matters? If you work backward from the needs of your customers (those who use your product/service), you will get to it. 2) Thing that matters to you. Ask yourself what motivates you and what can help you learn. Find opportunities that push you closer to what you want to achieve in the long run. If you’re lucky, you get to work on 1 & 2 at the same time. Be conscious of what you work on. 2. 𝐋𝐞𝐚𝐫𝐧 𝐛𝐲 𝐨𝐛𝐬𝐞𝐫𝐯𝐢𝐧𝐠 - Over the years, I’ve been fortunate enough to work with a good set of top talents. I learned the most and the fastest by simply observing how they made decisions at high-stakes moments, how they brought order into chaos, how they knew when to move fast and when to slow down, and how they behaved under high pressure, etc. Try to reason about and internalize those behaviors, and adapt to similar situations you face. Like the phrase “Follow the smart money” in the investment world, go identify and learn from the greats. 3. 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐠𝐨𝐞𝐬 𝐚 𝐥𝐨𝐧𝐠 𝐰𝐚𝐲 - Career is made up of ups and downs. Confidence is about not shying away from advocating for your work and celebrating your wins. Confidence is also about resilience - learning from setbacks, carrying the lessons forward, and moving on. Confidence is about showing up with energy, curiosity, and enthusiasm in a way that inspires those around you to produce positive results. Be confident, and just have fun! P.S. None of the above, in my opinion, will be replaced by AI anytime soon. :) #growth #day1

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Coupang is reimagining the shopping experience with the goal of wowing each customer from the instant they open the Coupang app to the moment an order is delivered to their door. The role of machine learning is becoming increasingly important in this innovation. Coupang's machine learning technology optimizes product search, pricing, inventory management, and logistics processing, and improves our customers' lives exponentially better. For more details, visit Coupang's Engineering Blog. ✅ Engineering Blog: https://lnkd.in/eRydERW5 #Coupang #CoupangCareers #CoupangTech #Tech #Engineer #DataEngineering

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If you're a Senior Engineer looking to move up, the next role will likely be a Staff Engineer. And in this guide, Shruti shares tips from her own experience of getting promoted to Staff Engineer at PayPal and Slack. She talks about what Staff Engineers do (and how it's different from Seniors), why you might not be getting promoted, and how to take that next step. https://lnkd.in/g58dnFEG

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David Luan’s Amazon AGI SF Lab marks a significant evolution in Amazon’s AI strategy, distinguishing itself from the company’s traditional AI initiatives. While Amazon has historically focused on scalable infrastructure and consumer-facing products, the AGI SF Lab, under Luan’s leadership, is dedicated to developing autonomous AI agents capable of performing complex tasks across digital and physical environments. The lab’s flagship model, Amazon Nova Act, exemplifies this shift. It outperforms competitors like OpenAI and Anthropic on benchmarks assessing AI agents’ intelligence and aptitude, particularly in tasks involving web interactions . This model integrates reinforcement learning and draws inspiration from robotics to enhance decision-making capabilities, aiming to create AI agents that are dependable and capable of independent actions. Interested to learn more about the AGI SF Lab? Check out the team’s page here: https://lnkd.in/eWHQC7Ps

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📹 ICYMI: Episode 2 of Inside Rakuten AI with Lee Xiong! 👇 "Japanese LLMs are going to be the next wave. This is the beginning of it. The promise of such technology is tremendous." - Lee Xiong, Rakuten Group Vice Director of AI Research Supervisory Department. At Rakuten, we’re pushing the boundaries of AI innovation with groundbreaking work on Japanese Large Language Models (LLMs). Hear from Lee Xiong in Episode 2 of Inside Rakuten AI as he shares his vision for the future of AI and how it’s empowering Japan’s AI development. 🔗 Watch the full episode now: https://lnkd.in/gRTrgvYx 🔗 Read more on Rakuten Today: https://lnkd.in/gReWce6Q #InsideRakutenAI #AIInnovation #JapaneseLLM #Rakuten #AI

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Alibaba Group’s #Qwen team just dropped FP8 builds of Qwen3-Next-80B-A3B in both Instruct and Thinking flavors. Key details: ⚡ FP8 (block-128) → cuts memory + bandwidth without giving up scale 🧩 Hybrid-MoE → 80B total, ~3B active (512 experts: 10 routed + 1 shared) 📏 Context → 262K native, ~1M validated via YaRN 🧠 Thinking build → defaults to <think> traces, pairs with a reasoning parser 🔮 Features → multi-token prediction + serving commands for latest sglang/vLLM nightlies 📊 Benchmarks shown are from BF16 versions, so FP8 performance needs re-validation. ✅ Licensed under Apache-2.0 → open for commercial + research use. Another step in pushing massive reasoning-capable MoEs onto commodity GPUs. Link : https://lnkd.in/gTtJw9Mh

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How 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 is 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 #𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁? #𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗔𝗜 𝗮𝗻𝗱 #𝗔𝗴𝗲𝗻𝘁𝗶𝗰𝗔𝗜 𝗮𝗿𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗵𝗼𝘄 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁, 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗱, 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗲𝗱 𝘄𝗶𝘁𝗵. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗮𝗻𝗱 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗲𝘆 𝗯𝗿𝗶𝗻𝗴: 🔄 Shift from Manual Coding to AI-Assisted Development: Traditional development: Requires human programmers to write, debug, and test code manually. Gen AI challenge: Tools like GitHub Copilot, ChatGPT, and Code Whisperer can now, Autocomplete code intelligently; Generate boilerplate or even complex logic; Translate between languages; Suggest fixes or optimizations; 📌 Impact: Reduces the need for repetitive coding; Lowers the barrier to entry for non-developers; Raises expectations for faster development cycles; 🤖 Autonomous Software Agents: Traditional development: Applications run predefined logic and require user commands or inputs. Agentic AI challenge: AI agents can, Plan, reason, and make decisions independently; Interact with APIs, databases, and other software without being explicitly programmed for each task; Adapt and learn from the environment; 📌 Impact: Moves from static systems to dynamic, adaptive ones; Introduces a new development paradigm: "specify the goal, not the steps"; Challenges existing architectures which are rigid and procedural; 🛠️ New Development Paradigms: Traditional software: Built using structured design, with heavy focus on deterministic logic and clear data flow. AI Agents approach: More probabilistic and data-driven; Can evolve through fine-tuning and prompt engineering; Uses "natural language" as a development interface (e.g., prompt-based programming); 📌 Impact: Engineers must now also be prompt designers or data curators; Testing and debugging becomes less about syntax errors and more about behavior alignment; Version control of prompts, training data, and models becomes crucial; 📦 Challenges to DevOps & Maintenance: Traditional model: CI/CD pipelines manage code changes, testing, and deployments. Agentic AI complications: Code is partly generated dynamically — versioning is less straightforward; Models might change behavior after updates or retraining; Monitoring and auditing AI behavior requires new tools (e.g., for explainability, fairness, or hallucination detection); 📌 Impact: Observability and debugging tools need to evolve; Governance, compliance, and reproducibility become more complex;

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11 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗽𝗮𝗽𝗲𝗿𝘀 I wish I had read as a junior engineer (these helped me learn so much on my journey in the last 6 years from junior to senior SWE) 1. LinkedIn Kafka (PubSub) [https://lnkd.in/gJrDkNPv] 2. Designing Container-Based Systems [https://lnkd.in/gxJzAKi7] 3. Amazon Dynamo (SOSP 2007) [https://lnkd.in/gY-UJ5JP] 4. Bitcoin: A Peer-to-Peer Electronic Cash System [https://lnkd.in/gcqquJyQ] 5. Real-time Data Infrastructure at Uber [https://lnkd.in/g3WvfcMx] 6. Hierarchical Text-Conditional Image Generation with CLIP Latents [https://lnkd.in/gH4j_PQG] 7. Google File System (GFS) [https://lnkd.in/gTJQtVHw] 8. Google Chubby Locking Service [https://lnkd.in/gHAT7cBR] 9. Meta XFaaS: Hyperscale and Low-cost Serverless Functions [https://lnkd.in/eHqbPXpH] 10. Facebook Cassandra (Distributed NoSQL DB) [https://lnkd.in/eD9erCNu] 11. Facebook Memcache (KV Store) [https://lnkd.in/eYZM5SPb]

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