Heather Huynh

Last night, I had the opportunity to return to UCLA to speak in the Philosophy Department’s Being in the World series. It was a full-circle moment. These were the same halls where I first began studying computer science and imagining a career pivot into engineering. We discussed career paths, job markets, AI’s impact on education, and how students can break into tech. But one question stood out: How do you think an undergraduate degree in philosophy has benefitted you both generally and in your field? I’ve thought about that question for years. Generally, philosophy has given me a disciplined way of orienting myself in the world. We are living through profound shifts:  artificial intelligence, ecological destabilization, geopolitical realignment, social fragmentation. Philosophy trains you to examine systems in their totality rather than reducing them to a single domain like economics, politics, or engineering. It provides a scaffolding for moving between disciplines without losing coherence - a space of expansive thought that transcends any particular topic. In my field, philosophy has been equally practical. Software engineering is not fundamentally about writing code. It is about navigating ambiguity, clarifying assumptions, defining terms, and reconciling human intention with technical constraint. At higher levels, engineering becomes the design of logical systems in response to shifting organizational and cultural pressures. The tension between vision and implementation mirrors much older philosophical tensions:  perception and reality, reason and passion, abstraction and instantiation. The questions are ancient. The tooling is new. It has been years since I’ve written on Descartes or Russell, but I remain a philosopher in spirit. Grateful to UCLA Department of Philosophy for the invitation, and especially grateful to the students asking serious questions about their futures.

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The era of the "Vending Machine Developer" is officially over. For the last few years (the ZIRP era), money was cheap, and companies prioritized developer velocity over everything else. It bred a habit of treating development like a vending machine: drop a Jira ticket in, pull a pre-made solution from Stack Overflow or an LLM, and push it to production. But in 2026, the economics have flipped. Companies are optimizing for cloud efficiency, unit economics, and margin. If your debugging strategy is writing a cron job to restart a leaky server at 3 AM, or if your coding style is "vibe coding" with AI until the red squiggly lines disappear—you are becoming a massive liability to your codebase. AI isn't going to replace engineers. It’s going to replace the "ticket movers" who don't understand the abstractions they use every day. I just published a piece on the harsh realities of the 2026 tech landscape and exactly how you can pivot from a "code consumer" to a high-value engineer. Read the full breakdown here: https://lnkd.in/gVwqneYR What do you think is the biggest trap developers are falling into this year? Let me know below. 👇 #SoftwareEngineering #WebDevelopment #TechCareers #AI #ReactJS #NextJS #DeveloperCommunity #FinOps

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Staying disciplined with both studies and coding isn’t always easy, but consistency is something I’ve been consciously working on. Here are a few things that help me stay on track: 🔹 Setting small, achievable daily goals instead of overwhelming myself 🔹 Following a realistic routine that balances college, coding, and rest 🔹 Solving at least 1 problem or writing a little bit of code each day 🔹 Tracking progress so I can see how far I’ve come 🔹 Reminding myself that discipline > motivation — even small steps count It’s not about being perfect every day, it’s about showing up and improving a little at a time. 🚀 #Consistency #LearningJourney #Coding #Discipline #StudentLife #Progress

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Ever wonder why the Bay Area—literal earthquake territory—is the world's tech capital? Maybe it takes a little shaking to build something unshakeable. The rebuilding always makes greater things.🌉 Sam Walton the founder of Walmart, my boss decades ago, he didn’t just lose his first successful store; he lost it because he literally forgot to include a renewal clause in the lease. A total career face-plant. But that “earthquake” forced him to pack up and start over in Bentonville. Without that lease error, there is no Walmart. He’d just be a guy with a nice franchise in Newport. So, if your career feels like it’s hitting a 7.0 magnitude quake right now… relax. It’s not a tragedy. It’s just aggressive renovation. Be ready for something greater and better! #Resilience #GrowthMindset #SamWalton #SiliconValley #CareerAdvice

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🧠 AI Rights Historical Wrongs Researchers at Stanford and Princeton have fine-tuned a large language model to uncover racially discriminatory housing clauses buried in millions of historical property deeds — accelerating a process that could have taken a decade by hand. 📍 The problem: Many property deeds in Northern California — especially in Santa Clara County — still contain illegal racial covenants from as far back as the 1850s, banning people of color from owning or living in certain homes. 🔎 The solution: The team used OCR to digitize 5.2 million deed pages, then fine-tuned Mistral-7B via LoRA on a hand-labeled dataset of discriminatory phrases. The model learned to identify harmful language in context, avoiding false positives like the surnames "Black" or "White." 📊 Results: Detected 24,500 lots with racial clauses (~25% of homes in 1950) Found that 10 developers were responsible for 1/3 of all discriminatory clauses Model reviewed all pages in 6 days at a cost of $258 Equivalent manual effort: 10 years, $1.4M 💥 This work doesn't just use LLMs to build the future — it uses them to understand and repair the past. The open-sourced model now enables other U.S. counties to follow suit. #AI #MachineLearning #LLM #ResponsibleAI #Stanford #Princeton #HistoryMeetsAI #TechForGood #EthicalAI #SocialImpact #PropertyTech https://lnkd.in/dtec-zbx

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I recently started a new role as a Software Engineer at Meta, and like many engineers joining a new team, the first challenge is ramping up quickly in a large and complex codebase. One tool that has been incredibly helpful during this process is AI. I’ve been using it as a learning accelerator in several ways: • Understanding code deeply – When navigating unfamiliar parts of the codebase, AI helps me reason through patterns, break down complex logic, and explore how different pieces fit together. • Finding context faster – In large systems, the hardest part is often discovering the right context. AI helps surface connections between components that would otherwise take much longer to uncover. • Synthesizing information – Between documentation, internal resources, and public engineering blog posts, there’s a lot to absorb. AI helps me distill large amounts of information into clearer mental models. • Accelerating learning loops – Instead of being blocked for long periods, I can quickly test ideas, ask questions, and refine my understanding before validating with teammates. Of course, AI doesn’t replace the importance of great mentors, thoughtful code reviews, and discussions with teammates. But it has been a powerful complement that helps shorten the feedback loop when learning something new. I’m excited about how tools like this can help engineers ramp up faster, learn deeper, and spend more time focusing on solving meaningful problems. Curious how others are using AI during onboarding or when joining a new team. #AI #SoftwareEngineering #Meta #Learning #CareerGrowth

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$100k can be low income. Even for a single-person household. Of course this isn’t referring to a small town in the countryside. According to the California Department of Housing and Community Development’s 2025 income levels (linked below): In Marin, San Francisco, and San Mateo counties <$109700 is low income for single-person households. In Santa Cruz county <$111100. And in Santa Clara county <$111700. It’s pretty well known that Silicon Valley and surrounding areas are fairly expensive. And for many people the salaries in tech make up for and reflect that. Still, when the median income in Santa Cruz county (e.g. $92950 for a single-person household) is lower than the low income threshold… It’s pretty astonishing to think about. Accounting for income, is the SF bay area the most costly city in the world for the average resident?

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Had an incredible time at the SF10X Hackathon this weekend! 🚀 Our team built Neighborhood Net, a platform to help San Francisco’s unhoused community access real-time food, shelter, and hygiene resources. It also gives shelters and food banks a simple way to update availability—ensuring those in need always have the most accurate info. - Mapbox integration allows users to easily view nearby resources on an interactive map. - Gemini chatbot helps users find the closest and most relevant resources with natural language queries like “Where can I sleep tonight?” Grateful to work on something both technically exciting and socially impactful. Huge thanks to the organizers and everyone building for good —and major shoutout to my amazing teammates Ellen Damaso Soniya Phaltane Here is the link we deployed it on: https://lnkd.in/gju8Epsu #Hackathon #TechForGood #SocialImpact #SanFranciscoTech #FullStackDevelopment #Mapbox #GeminiAI #Chatbots #ReactJS #TypeScript #OpenData #AIforGood #SoftwareEngineering

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Day 61 of posting until I get hired. For Day 61, I spent most of the day on campus at the University of Washington, where I met with one of my teammates to discuss an important topic for our project The Marketplace application. As you probably know by now, our project is a marketplace app, and one of the most critical components of any marketplace platform is the listing system paired with a smart, efficient search engine. The search feature needs to be capable of handling unconventional or imperfect user queries while still understanding the context and intent behind them. Our goal is to ensure that even if a user’s search isn’t an exact match, the system can still return relevant results that satisfy what the user is looking for. Before our meeting, we both did some research on how search engines operate, and during our discussion, we went over our findings in detail. We talked through the full process how a user search request would move through an API, reach the database, and return results. We also explored how traditional web crawlers work with priority queues and heaps, and then adapted those concepts for our use case since we’re building an app-based search system rather than a web one. Instead of using a crawler, we plan to develop our own internal system that serves the same purpose but is optimized for our application’s data. Our initial step will be to build a basic CRUD API as the foundation of the search engine. From there, we plan to integrate our own AI-powered model that utilizes Natural Language Processing (NLP) and Natural Language Understanding (NLU) to interpret user intent. This AI component will help us analyze queries, apply filters, and return the most relevant results even suggesting similar items when an exact match isn’t available. The goal is to create an intuitive, intelligent search experience that feels seamless to the user. After our meeting, I spent some time doing code reviews for the same project and applied for a few more jobs, as always. I also contacted a few offices to handle some paperwork and legal documentation mostly administrative tasks that will be useful for future work and app development efforts. Overall, it was a productive day. We made great progress on planning the search engine, and we also started discussing how we want to design the messaging system for the marketplace, which we’ll explore further next week.

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Had some awesome conversations about compliance and AI the University of Southern California Startup Career Fair today! Lots of great companies made it out and getting a lot of work done! Key takeaway is that there are a ton of applications out there looking to solve data entry and compliance but it isn't a one size fits all!

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I recently caught up with Adrian Callies, an APU Alum and former Intuit engineer, to talk about the gap between a CS degree and the actual job hunt. He shared some advice that completely shifted my perspective. 1. Consistency over intensity: - Treat LeetCode like drinking water. It should be a daily habit rather than a two-week panic. Adrian introduced me to Exponent Interview Prep to help master the core patterns instead of just memorizing solutions. So far, it has been a fantastic resource! 2. The "Ceiling" Myth: - Coming from a smaller program like APU, it is easy to worry about the curriculum gap. But that does not define my ceiling. The gap is real, but it is closeable through self-study and targeting roles where I will actually be coding. 3. Play the game: - There is a disconnect between interview questions and day-to-day engineering. It can be frustrating, but we have to respect the process to get to the work we love. Huge thanks to Adrian for the mentorship and the resources. If you are an APU alum or working as a Software Engineer, I’d love to connect! #SoftwareEngineering #Exponent #BackendEngineering #Mentorship #APU

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Recently, while working with a capstone project team of UCI students, I was reminded of an important point about industrial AI. Industrial AI today is often discussed through the lens of large language models such as ChatGPT, Gemini, Claude, or LLaMA. These technologies are impressive and have transformed how we interact with information. But in many industrial environments, real progress in AI depends on something different: 𝐝𝐨𝐦𝐚𝐢𝐧-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. Industrial systems are fundamentally different from the text and images that general-purpose models are trained on. Engineering data appears in forms such as CAD geometry, sensor signals, time-series data, manufacturing processes, and physical simulations. These systems are governed by physics, constraints, and complex interactions between components. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬 𝐦𝐨𝐝𝐞𝐥𝐬 𝐝𝐞𝐬𝐢𝐠𝐧𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐨𝐟 𝐭𝐡𝐚𝐭 𝐝𝐚𝐭𝐚. For example, learning from CAD models requires geometric and topological reasoning. Predicting equipment behavior often relies on time-series analysis of sensor data. Manufacturing optimization may require graph-based representations of processes and assemblies. In these contexts, domain knowledge is not optional, it is essential. What we are beginning to see is a new layer of AI emerging in industry: models trained specifically on engineering and operational data. These models combine machine learning with physics, system constraints, and domain expertise. Rather than replacing engineering tools, they augment them and helping engineers analyze complex systems, detect patterns across large datasets, and explore design spaces more effectively. Large language models will certainly play a role, particularly as interfaces that help orchestrate workflows or make complex tools easier to access. But the real breakthroughs in industrial AI will likely come from deep integration between machine learning and domain-specific knowledge. In other words, the future of industrial AI will not be built by generic models alone. It will be built by 𝐜𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐝𝐞𝐞𝐩 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐨𝐟 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡𝐢𝐧 𝐞𝐚𝐜𝐡 𝐝𝐨𝐦𝐚𝐢𝐧. #ArtificialIntelligence #IndustrialAI #MachineLearning #DomainSpecificAI #EngineeringAI #DigitalTwin #Manufacturing #CAD #Industry40 #Engineering

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🚀 🚀 How I helped build a rocket as a software engineer 🚀 🚀 Full Article: https://lnkd.in/gFavf3eY Last year, Shawn Wei Chew and I had the amazing opportunity to serve as software leads for our school’s rocket team, Rocket Project at UCLA. We wrote code for a hybrid propellant rocket that reached an apogee of 19,200 ft, making it the most successful flight in our team’s history. Coming from a traditional SWE background, we didn’t know what to expect, and from our first outing messing with microcontrollers, multi-threading & communication protocols - we’ve learnt tons along the way So we wrote this article hoping that it would be helpful for prospective club applicants, future batches, or at the very least, interesting to the average reader. Enjoy! Acknowledgements - this article (and rocket) would not have been possible without: • The larger Rocket Project organization, who helped build every single other part of the rocket from scratch that was not code — to me, software is tremendously easier than engineering so I don’t know how they do it LOL • Shawn, my fellow co-software lead and one of my best friends, who helped review and proofread this article. • Angela & Yifan, our amazing electronic leads who helped read this article and put out countless of fires when Shawn and I couldn’t • Our two other software leads who helped build this software system with Shawn and I • My wonderful friends who I roped in to build the web app with us • The electronics team, who were all extremely fun to be around and helped us figure out the hardware side of things so I could just write code

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Choosing a master’s in the US can feel overwhelming 😅 so many factors to consider: courses, location, career opportunities… it’s a lot For me, what made University of Southern California stand out wasn’t just the classes ✨ it was the network and opportunities. Alumni genuinely reach out, share advice, and guide you through the job market 🤝 The cohort is smart and pushes you to grow 📈 Courses are challenging but that’s exactly what prepares you for real-world problems and interviews 💻 And being in California 🌴 the access to tech companies, startups, and networking events is next-level — it’s not just a city, it’s a playground for opportunities 🚀 Of course no place is perfect ⚖️ large batch sizes, etc. exist but for me the growth, exposure, and connections completely outweighed the challenges If you’re considering a master’s 🎓 focus on where you’ll grow, the people you’ll meet, and the opportunities you can access, not just the brand name I’d love to hear from anyone else deciding on their master’s ✨ what factors matter most to you? #usc #uscalumni #mscs #msinus

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A few years ago, I had the chance to teach a pixel art class at a high school through Mission Bit. I walked into the classroom thinking I was there to share my love for art 𝒃𝒖𝒕 𝑰 𝒍𝒆𝒇𝒕 𝒕𝒉𝒊𝒏𝒌𝒊𝒏𝒈 𝒂𝒃𝒐𝒖𝒕 𝒄𝒐𝒏𝒇𝒊𝒅𝒆𝒏𝒄𝒆 𝒂𝒏𝒅 𝒄𝒓𝒆𝒂𝒕𝒊𝒗𝒊𝒕𝒚. While talking with the students, I started noticing something. Some jumped right in and filled the page. Others hesitated, erased, and stopped. For many of them, a blank page felt intimidating, one student even told me they felt a single wrong mark could ruin everything. Some chose not to begin at all. That moment stayed with me. 𝐈 𝐤𝐞𝐩𝐭 𝐰𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐰𝐡𝐚𝐭 𝐰𝐨𝐮𝐥𝐝 𝐡𝐚𝐩𝐩𝐞𝐧 𝐢𝐟 𝐭𝐡𝐞 𝐜𝐫𝐞𝐚𝐭𝐢𝐯𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐤𝐢𝐝𝐬 𝐜𝐨𝐮𝐥𝐝 𝐟𝐞𝐞𝐥 𝐦𝐨𝐫𝐞 𝐚𝐜𝐭𝐢𝐯𝐞, 𝐩𝐥𝐚𝐲𝐟𝐮𝐥, 𝐚𝐧𝐝 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐯𝐞, especially if physical drawing could respond back, almost like a game. What if visual feedback could encourage children to keep going? Those questions led to 𝐃𝐫𝐚𝐰𝐢𝐧𝐠 𝐁𝐮𝐝𝐝𝐲. I wasn’t alone in thinking about this. Together with Yoojin Leem and Ryugwang(Josh) Jang, we kept exploring and developing a creative system that’s intuitive, responsive, and supportive for both children and caregivers — not to replace imagination, but to help children see their ideas unfold while preserving their sense of ownership. Working on this project keeps reminding me how often confidence is the real barrier to creativity, and how thoughtful design can make that first mark feel a little less scary. Leave us some thoughts, I’d love to hear what helped you take that first creative step!🎨🖼️

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