Ryan Newquist, MS

Software agents change coding. AI agents will build the same feature 10 times and select the best version. No limits, no guesswork—just optimized innovation. This is a radical new mindset: iterative, faster than ever, and powered by AI. 🚀 Nikita Shamgunov, CEO of Neon, is focused on the architecture of the future and creating for these next wave of agentic and code-gen applications powered by LLMs. A world where cost, infrastructure, and time barriers are tackled and AI agent development unleashes innovation at scale. The future of software development is parallel, not linear! And we'll need GenAI Friendly serverless database like Neon to make it possible. Watch the full episode to discover: - How serverless Postgres is enabling the next wave of AI-powered apps - Why "breadcrumbs" are crucial for AI agent systems - The architecture behind instant database branching 

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Life of a SWE : Episode 421 #SoftwareEngineering

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Some great hacks coming out from #BeyondTheSpeckleverse

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Longhorn Electric Racing Is Building the Future of Electric Vehicles with Cadence Tools. Longhorn Electric Racing is a student-run racing team at the University of Texas, Austin. Not only are they building the best race cars, but they’re also building the future generation of engineers. Their electric vehicle team is one of their most recent teams, founded in 2016. Some of the challenges they face in designing an electric car are making sure it can go as fast as possible, making sure it has sufficient cooling, and making models effectively. With the help of Cadence CFD and Cadence OnCloud, they’re reaching their goals much more efficiently. https://ow.ly/phqj50RuqK4

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“We need more engineers like Andy!” Sound familiar? As an Engineering Leader, finding candidates that match or exceed the performance of your rockstar engineers can be a challenge. Imagine this: You have your ideal team member—someone who excels at code review, debugging, system design, and more. Now, imagine being able to benchmark every candidate against your top performers, ensuring you’re hiring the best talent every time. Our blog explains it all: How Kodiva.ai Benchmarks Engineers (Without Making Your Brain Hurt) https://lnkd.in/dcYC-2cn Want to level up your technical hiring? Start benchmarking with Kodiva.ai today! 🎯 #TechHiring #EngineerRecruitment #Benchmarking #KodivaAI #EngineeringExcellence #HiringTopTalent

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Leadership in software engineering isn’t just about the code. (It’s about the people behind it.) I used to think being a great engineer was enough to lead a team. Just write clean code, solve problems, and everything else would fall into place, right? Wrong. Here’s what I’ve learned over the years: Being a strong leader in tech isn’t about having all the answers. It’s about helping your team find their own. Here are a few things that have made a real difference for me: 1. Listening more than talking: Your team’s ideas often hold the key to the best solutions. 2. Teaching, not telling: It’s not about being the smartest in the room. It’s about elevating those around you. 3. Showing empathy: Everyone on the team is juggling challenges—technical and personal. A bit of understanding goes a long way. 4. Keeping the vision clear: People want to know where they’re going and why it matters. The most successful teams I’ve worked with weren’t just technically sound—they were connected, empowered, and cared about the mission. What’s one thing you’ve found essential in leading your team? Repost this ♻️ if you’ve had that moment where you realized leadership is a whole different ball game.

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Impressive advancement! Emo's ability to predict expressions will transform human-robot interactions. By anticipating and replicating human expressions, this technology has the potential to greatly enhance communication, build trust, and make interactions with robots feel more natural and intuitive. Excited to see how this will shape the future of robotics and AI-driven systems!

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👨‍💻 Incremental update to my Image-to-3D app - v0.3.1! 🔹 Skew X / Y 🔹 Spherize Be the first to check out these new features: https://lnkd.in/gY-p4J2A #webgl #webgpu #ai

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🚀 𝐄𝐱𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐏𝐚𝐬𝐬𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 🚀 Clean and efficient error handling is crucial for building reliable applications. 💡One approach I've found incredibly useful is the Exception Passthrough Pattern. This pattern focuses on passing exceptions through different layers of your application, allowing the handling to occur at a higher level where the context is clear and a meaningful response can be provided. It simplifies the code at lower layers by keeping exception handling at a minimum, promoting separation of concerns and better maintainability. By adopting this pattern, you: 💡 Keep your codebase DRY (Don’t Repeat Yourself) 💡 Enhance the flexibility of your exception handling strategy 💡 Allow your application to recover from unexpected errors in a more graceful way #ExceptionHandling #CleanArchitecture #SoftwareEngineering #WebDevelopment #TechTips

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LinkedIn Caption: Tired of struggling to keep your developer documentation up to date? 😵‍💫 Doc-E.ai simplifies the process by turning messy docs into clear, actionable content faster than you can fix a bug! 🛠️ Stop drowning in wires and outdated docs—let us help you maintain accurate, real-time documentation effortlessly. 🚀 Empower your developer team to focus on what they do best while we handle the documentation chaos! 📚💡 #DocEai #DeveloperTools #DeveloperEngagement #DocumentationSimplified #DevRel #TechSolutions

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Style mapping is pretty useful! Here I mapped flight tracks: heading (direction) -> angle grounded (flying/not) -> marker vertical rate (of ascent/descend) -> line_color Check out the docs for more possible mappings! https://lnkd.in/g4BxeHdy

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"AI's moving so fast, we get a new foundational model for every new Kendrick drop. But are we sprinting in the right direction?" In my latest blog post "Socially Awkward Robots", I explore the misalignment between the narrow metrics we use to evaluate AI systems and the grand promises we're sold about the agents' potential as assistants and companions. I argue for a need for diversity in model development and benchmarks that measure the overlooked facets of human cognitive function, such as empathy and judgment. This post is informed by my experiences collaborating with clinicians, neuroscientists, and linguists on dementia research. I'm convinced that we're at a critical juncture for reevaluating our objective function. By drawing on the wealth of knowledge from the humanities and clinical research, we can develop a more comprehensive understanding of what it means to be intelligent. Check out the full post for my take on why we need socially adept AI, not just socially awkward problem solvers: https://lnkd.in/eQxxkUf4 I'd love to hear your thoughts! What skills do you think are essential for AI to master beyond acing tests? How can we foster a more diverse and multifaceted approach to AI development?

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In recent conversations with robotics software engineers, I've noticed a pattern: many, especially those transitioning from the defense sector, are eager to join ethical start-ups where they can tackle real-world problems. FarmWise, with its ground-breaking weeding robots, embodies this ethos, Which is why I am proud to champion them! FarmWise's Vulcan weeder robot leverages AI and machine learning to precisely target and remove weeds, reducing reliance on chemical herbicides and boosting crop yields. According to Sébastien Boyer, co-founder and CEO of FarmWise, “Our robots will increasingly act as specialized doctors for crops, monitoring individual health and adjusting targeted interventions”​ (AgFunder News)​. FarmWise's robots not only enhance productivity but also promote environmental stewardship by eliminating the need for chemical herbicides. If you're a Robotics Software engineer, who is looking to make the transition to agriTech, or you have previous experience in the industry, please get in touch and I would be happy to go through your expectations and to recommend some of the companies I am currently representing in the space. If you are an agriTech company that is in need of recruitment support across, Software, Robotics, Engineering and AI. I would be happy to have a conversation and see if Cubiq could be the right partner for you! #FarmTech #Robotics #AI #SustainableFarming #AgriTech #ROS #RoboticsSoftware #AutomationSoftware

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The latest Full Self-Driving update in my Cybertruck (v12.5.5.2) has me thinking about one of the biggest paradigm shifts in software development. Instead of being powered by hundreds of thousands of lines of explicit C++ code, this update runs on an 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸, trained on millions of video clips. The logic, the "decisions," the behavior—it’s all learned, not hard-coded. Pause on that for a second: A model has replaced over 300,000 lines of code. This is bigger than self-driving cars. It’s a glimpse into the future of software itself: Dynamic Intelligence Over Static Rules Traditional software follows rigidly written instructions: if X happens, do Y. Machine learning breaks this mold. Models learn directly from data, adapting to complexities too nuanced to capture in lines of code. Models as Developers What if future engineers don’t write code—they train models? What if debugging isn’t about fixing syntax but refining datasets? This FSD update is proof of what’s possible when models take center stage in place of traditional programming. From Codebases to Data As the focus shifts, the value of a project won’t be measured in lines of code but in quality data and the strength of the models built on it. Developers will focus more on curation and refinement than on syntax and logic. The implications extend far beyond autonomous driving. Imagine enterprise apps, financial platforms, creative tools—all built by training models, not writing code. Imagine software that evolves organically through feedback and data, not manual updates. Of course, this is just the start. Supervised systems like FSD still require human oversight, and there’s a long road (pun intended) ahead. But the shift is undeniable. For developers, this isn’t a call to abandon coding—it’s an invitation to think bigger. The developer’s role is evolving, as is the foundation of software itself. We’re not just writing code anymore; we’re enabling intelligence. What do you think? Are we ready for a model-first future? It seems we are already there. #AI #FSD #Cybertruck #FutureOfSoftware #NeuralNetworks #MachineLearning

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Edge Vector Search Demo with #Couchbase Mobile 📲

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