Chi-Ling Hsiao

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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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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After 19 years at Google, I experienced a layoff that initially felt shocking but has since brought a sense of relief upon reflection. I had reached a point of complacency, feeling neither energized nor hungry for new challenges. Over the years, I've witnessed a significant shift at Google, particularly in the wake of the pandemic. What was once a vibrant hub of innovation driven by engineers and visionaries has increasingly become focused on business decisions and safe bets. In my view, Susan Wojcicki represented the last empathetic stand against corporate greed and maintained integrity in leadership. Unfortunately, large corporations often become myopic, prioritizing numbers over their impact on users. Google's intense focus on AI could potentially undermine its ads business in search, jeopardizing its credibility and income stream. Meanwhile, YouTube appears uncertain about the role of AI on its platform, with executives seemingly unconcerned about the long-term implications. Ultimately, the relentless pursuit of quarterly profit margins is detrimental to our country, its citizens, and the competition posed by smaller companies. So far, no executive I've spoken with has articulated a financial plan addressing AI, unemployment, and its effects on our consumer-driven economy. The bubble is real.

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Do we get some a free DGX Spark depending on the number of companies we have worked in our career that showed up today during Jensen Huang's NVIDIA GTC keynote? 🤣

Want to truly understand how AI agents work? Build them yourself. I found a repository that teaches you exactly that—without LangChain or any abstractions. It focuses on raw code and real understanding. The course consists of 9 chapters that build on each other: - Loading local LLMs - Prompt engineering - Function calling (tools) - Persistent memory - ReAct reasoning loops Here's why this matters: Frameworks are beneficial until something breaks. When that happens, you're left debugging code you never fully understood. This repository addresses that issue. Start by building everything from scratch. Then, when you later use LangChain or CrewAI, you'll have a clear understanding of what's happening underneath. Check it out here: https://lnkd.in/gy_hVF6r

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What I learned about hidden complexity as a Software Engineer at Amazon Engineers, including me, love to build. We get excited about features we could add, new stacks we could try, and all the customers we will serve. That creative energy is a big part of why we love what we do. But here’s the trap: every new creation comes with a hidden price tag. - That extra service we spun up? It needs monitoring, on-call rotations, and upgrades. - That shiny library we added? It’ll break with the next version and someone has to fix it. - That feature you shipped quickly? It now has to be supported, documented, and debugged long after launch. At Amazon, we still support the first-generation Kindle from 2007. Let that sink in! An almost 20-year-old device still has systems and people behind it to keep things running. For each new product or service we build, we have to (or aim to) live with for the long term. In the moment, complexity feels like progress, but in the long run, it shows up as maintenance debt: More work for future teams, slower velocity, and higher risk. Good design isn’t about how much you can add. It’s about how much you can simplify while still meeting the constraints, and how prepared you are to live with it years down the road. 👉 What’s one “hidden complexity” you’ve seen come back to bite a project? --- Hello, I’m Charles, and I am committed to share my two cents on career-related topics for 100 consecutive days. In these uncertain times, I hope to support those facing layoffs or career challenges. Follow me, and let's navigate this together! (52/100) #SoftwareEngineering #SystemDesign #Leadership #CareerGrowth

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The U.S. immigration system runs on a hidden digital backbone. It's built by the same tech giants whose products you use every day. A recent analysis shows federal immigration agencies have spent hundreds of millions on technology from Palantir, Microsoft, Amazon, and Google since 2023. This isn't just about cloud storage. It's about powering the entire enforcement machine. Here’s a snapshot of the scale: • Palantir: ~$122M for AI-driven deportation dossiers and case management systems. • Microsoft: ~$175M for Azure cloud and automation tools for legal and investigative units. • Amazon: ~$209M for hosting critical data warehouses and surveillance infrastructure. • Google: Over $8M for cloud services and generative AI to summarize documents. These contracts are often routed through intermediaries, which can obscure the final use. The technology enables a 'collect-it-all' approach, repurposing data in ways that weren't originally intended. This creates a fundamental tension. We rely on these companies for innovation and convenience. Yet their tools are also being used in systems that profoundly impact human lives. Employee petitions at some of these firms show the internal ethical struggle is real. It’s a reminder that technology is never neutral. Its application defines its impact. When the same platform that organizes your work email also helps automate a deportation process, where do we draw the line for corporate responsibility? #TechEthics #ResponsibleAI #DigitalTransformation 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gRV3JEc7 Abdul Fahumitha Sivasai Meesala

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🦺 🛟 Agent Safety > Model Safety LLMs didn't break your app. Agents can. As we wire LLMs to tools, browsers, payments & code the risk shifts from bad text to bad actions. Top failure modes (seen in the wild & in papers): 🚩 Prompt/Content Injection -> Tool Misuse. Malicious web pages or data instruct agents to steal secrets or make unintended API calls. This is now a primary risk for browsing and MCP - style tool agents. 🚩 Over permissioned tools & long-lived tokens. Agents get "god mode" scopes. A single injection becomes account takeover or irreversible ops. Security notes for MCP emphasise least privilege and short-lived auth. 🚩 Unsafe web autonomy. Benchmarks show web agents will attempt harmful tasks unless constrained (posting misinformation, illicit sales, etc.). You must measure misuse not just assume guardrails. 🚩 Supply-chain & retrieval poisoning. Agents trust plugins, third-party tools & indexed data that an attacker can taint. OWASP’s newest GenAI Top 10 calls this out explicitly. 🚩 Process safety gaps. NIST's GenAI Profile (AI 600-1) warns that organizations ship agents without role clarity, human-in-the-loop or incident playbooks. ✅ Model safety reduces bad text. Agent safety prevents bad state. Treat the agent like a junior SRE - with narrow permissions, audited actions and clear escalation paths. If you're building agents, what's the one control you won't ship without? Also drop a comment if you'd like me to share a follow-up post on how to tackle each of these risks. #AgenticAI #AISafety #AIAgents #GenAI #CyberSecurity #MCP #OWASP #NIST #AITrust #ResponsibleAI #AIForBusiness

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Farewell, Amazon! After nearly 5 years, hundreds of projects shipped, dozens of tech design documents written, thousands of operational issues resolved, hundreds of meaningful connections (many of which turned into lifelong friendships), countless interviews conducted, an infinite number of Sev3s, and unforgettable Sev2s - it’s time to close this chapter. I’m feeling that familiar mix of excitement and nostalgia that comes with any meaningful goodbye. Reflecting on my journey, I’d like to share a few takeaways that made the biggest impact on me. In the spirit of “if everything is important, then nothing is” – here are my top 3: 1. Obsess Over Your Customers. Build what customers need – not what you want. Listen, observe, add metrics that guide data-driven decisions, and never rely on assumptions. Always start from a real customer need and work backwards. 2. Own Everything - your projects, your career, your growth, your failures. Ownership is one of the strongest forces that can drive you forward. It’s truly transformational. 3. Prioritize Relentlessly. Your time is your most valuable asset - and it’s limited. How you spend it shapes your future. Use it wisely. Full of gratitude, full of learnings, full of memories. Amazon - thank you for shaping me more than I could have imagined. Amazonians - thank you for being the heart of this experience. The journey continues – onward and upward

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