Andrej Knezevski

Pull Requests shouldn’t feel like this. We’ve all opened a large PR review and thought: “Well... this is going to hurt.” Sure, small PRs are great, but sometimes you need the full picture. That’s where stacked PRs help (been using Graphite for that, a tool for managing PRs and code reviews). Stacked PRs treat your feature development as a chain of commits / PRs layered on top of each other. Think of it like a Jenga tower: each block (PR) builds on top of the previous one. pr1 → main pr2 → pr1 pr3 → pr2 Now it’s getting even better. I just got early access to Graphite Chat 👇 - I can literally ask it “what’s going on in this PR?” and it gives me the full story, codebase history, past PRs, even outside context. - I can just highlight it and ask “explain this”, instant breakdown. - Flags risks before reviewers even show up. - When someone leaves feedback, it helps me make sense of it and suggests fixes. - And the best part? When CI blows up, I can just ask “why did this break?” and actually get an answer. I’ll even be able to pop open the Graphite Editor right inside Chat to view and edit the codebase. Reviews feel lighter, more collaborative. Exactly how they should.

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Want to hire engineers who stick? Drop the unicorn hunt. Focus on real skills, not fantasy lists. The hiring landscape for embedded systems engineers is brutal. You’re tasked with finding talent that blends hardware savvy with software wizardry, thrives under real-time constraints, and can write code that squeezes into the tightest of spaces. It’s a niche, high-stakes game. But here’s the kicker: your biggest obstacle might not be the talent pool. It’s your own HR team. We’ve all seen those job descriptions that read like a wish list for a mythical creature: - "10+ years of C++ on bare-metal systems" - "Expertise in AI/ML AND RTOS AND FPGA" - "Ph.D. in Electrical Engineering REQUIRED" Sound familiar? These fantasy lists aren’t just unhelpful-they’re actively harmful. Here’s what happens when HR gets it wrong: ❌ You end up with a candidate pool of zero. ❌ The hiring process drags on for months. ❌ Your team suffers from missed deadlines and mounting pressure. Let’s be clear: the best embedded engineers aren’t defined by a laundry list of buzzwords. They’re problem-solvers. They’re adaptable. They know how to navigate the quirks and chaos of real-world systems. What we really need is a shift in mindset: → Stop focusing on years of experience; focus on hands-on impact. → Prioritize core competencies over exhaustive checklists. → Collaborate with HR to align on realistic, role-critical needs. The next time you’re handed a unicorn job description, push back. Use data to show what’s realistic. Reframe the role around what actually matters. Educate HR on why embedded is different. It’s not about creating friction-it’s about building a process that works. Pushing back isn’t just about making your life easier. It’s about securing the talent that will stick, perform, and drive your team forward. Want to build a high-performing team without the hiring headaches? Let’s talk. #EmbeddedSystems #EngineeringRecruitment #HiringDoneRight #RunTimeRecruitment

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Good engineering starts with the truth. I once had a boss say, during a sales call: "if anyone asks, we're experts at socket programming in C#". We weren't; not by a longshot. And I wasn't comfortable lying about it, so I didn't. I still won't. The same goes for interviews. If I'm asked about a mistake I made, I won't downplay it; I'll lean in. I'll tell you about a *big* mistake I made - and the even bigger effort I put in to make it right. I don't want to land a job by pretending to be someone I'm not. And I certainly don't want to build systems on that kind of foundation. At this point in my life, success means feeling good when I wake up in the morning.

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We're back with another Audio Programmer tutorial! In this episode, you’ll learn how to remove zipper noise and discontinuities using JUCE's powerful SmoothedValue class. This ensures your plugin parameters update smoothly, without crackles or pops, even when moving sliders quickly. We will: ✅ Explore why zipper noise and discontinuities happen in oscillators ✅ Use LinearSmoothedValue to create smooth parameter changes ✅ Switch from a time-based wrap to a proper phase accumulator for continuous sine waves ✅ Compare before-and-after results so you can hear the difference This episode is perfect if you want your audio products to sound clean, smooth, and professional. 👉 Watch here: https://lnkd.in/e9UeDHQ7

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I’ve been hearing several people in senior technical leadership roles (Head of Engineering at Shopify, CEO at Sourcegraph) talking about how they’re happy to have every engineer in their team spend $1000/month or more on tokens using coding agents if it produces results. What this means at first is that budget that is allocated for product development and used for hiring new engineers is now being used by engineers made of silicon. So there are fewer hires. But at some point you will speed up your delivery and can serve more customers, which means that you can spend more, you’ll hire more developers again to babysit the silicon engineers. The landscape is shifting fast. How are you adapting? #engineering #ai #llm #codingagent

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Agentic DevOps is here 😲 , and it's growing ferociously! Saw an impressive demo from a Vancouver startup, Defang Software Labs at an AI event during Elevate tech week in Toronto 🇨🇦, proving the demand for agent-first workflows on cloud platforms. We're seeing two exciting fronts in this new world: 1️⃣ Innovative Startups: Rapidly building and proving what's possible. 2️⃣ Hyperscaler Platforms: Embedding these agentic capabilities directly into the core developer ecosystem (think GitHub Copilot in your PRs, in your IDE, deploying cloud workloads). The first shows us the future. The second brings that future to the enterprise with the security, scale, and integration they absolutely require. Both are essential for the ecosystem to thrive. It’s a fantastic & exciting time to be in development! 🎉 #AI #agenticDevOps #developerexperience #github

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🚀 Scaling to 20 Billion QPS: How Shopify Reinvents the Global Storefront When you’re the backbone of global commerce, "downtime" isn't just a technical metric, it’s lost revenue for millions of entrepreneurs. Shopify’s footprint is staggering: - Billions of users - Millions of merchants - 175 countries - 6 continents of operation - 24/7/365 availability requirements To handle this demand, Shopify is moving toward a future powered by a distributed, globally sharded database architecture with Yugabyte. The Goal: 20 Billion QPS 📈 Yes, you read that right. Shopify is planning for a massive scale of 20 billion Queries Per Second (QPS). Moving to a cloud-native, distributed SQL database allows Shopify to: 1. Operational simplicity: Having one logical database makes it much easier to manage and do schema changes on. 2. Scale Linearly: Adding capacity becomes a matter of adding nodes, not performing risky migrations. 3. Optimize Latency: By sharding data closer to the user, they ensure a "local" feel for a global audience. So excited to be along for the ride with such a determined team! #SystemDesign #CloudNative #Scalability #Shopify #YugabyteDB #DistributedSQL #TechInnovation #SoftwareEngineering Alan Caldera Brad Dietrich Rohan J. Thomas https://lnkd.in/gspFdZKV

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Hockey. Friggin. Stick. 🏑 😎 Before Julep, everyone was using multi-agent frameworks to build agents. I spent months going to every event in SF, talking to devs. It used to take months to ship AI features that often broke in production, hard to upgrade models, code was a mess of "chains" and "graphs", with prompts hidden away. As a PM, I know most teams care about iterating and shipping fast. They care about owning and writing the business logic, instead of the infra code. So we built a platform - not a framework. Devs could define multi-step workflows in YAML - prompts and code in one file, declarative and readable, and with built-in RAG. We took care of scaling, handling retries, and observability—and made it very simple for AI teams to ship and scale complex multi-step workflows in production.

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You might need a new CTO soon. If your CTO recklessly uses AI everywhere, you'll either (A) burn your customers (B) get into maintenance & bug hell or (C) don't save a single dime but instead put most of your resources on QA. Be smart. Leverage AI, but don't do so recklessly. -- PS: We build software that actually scales and is being used.

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