Rudolf Olah

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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Shawn (swyx) Wang wrote last week: “RIP Vibe Coding. [Feb–Oct 2025]” I don’t think it’s dead. I think it’s evolving. > #VibeCoding was great for simple greenfield projects and proof-of-concepts. > #ViableCoding is the next step, making AI useful for real, heavy-duty codebases. In my talk at AI Eng World's Fair 2025, I shared posts from industry leaders exploring this shift: How to move from vibe to viable by • using AI across the entire SDLC (e.g. AI code review, testing, RCA) • tightening integrations across diverse, orthogonal tools (you’ll see more and more of these) • and leveraging a strong context engine to make it all work accurately (critical).

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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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The invisible productivity tax. Every engineering leader I speak to knows it well. A simple UI change should be a quick merge, but instead, it breaks dozens of automated tests. Your build goes red. Deployments stall. Someone has to stop what they're doing and spend the next few hours hunting down selectors. Our data shows teams lose an average of 15 minutes per broken locator. Do the math across dozens of tests and hundreds of UI changes, and you're looking at hundreds of hours spent maintaining tests instead of shipping features. So we at BrowserStack built the Self-Healing Agent. It automatically identifies and fixes broken locators the moment they happen, whether you're using Selenium, Playwright, or Appium across web and mobile apps. It offers both instant remediation and transparent logs, showing exactly how locators were fixed, and even auto-proposes permanent code updates to strengthen your test suites. Teams are seeing up to 40% reduction in automation build failures. Available now on Automate, App Automate and Low Code Automation: https://lnkd.in/dchB595C

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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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If you're looking for a new docs framework for your dev tool, I can't recommend Docusaurus enough. 1. You can change the appearance of any component—no matter how big or small—by ejecting its raw React code into your source tree. I've ejected almost all of its major components and restyled them with Tailwind. 2. It supports content written with MDX, which is like Markdown but better. MDX lets you write JSX tags so you can render React components (like badges and banners) directly inline your content. No more messy templating. 3. It bundles Prism for code snippet highlighting. So you if you want to change how each and every token of your language gets styled—which I do—you can override it with a simple centralized map—which I have. 4. You can swap out its on-site search engine with Algolia, now widely considered the best option for developer sites. It is, in other words, the perfect tool for people like me who are obsessed with every pixel.

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We nearly doubled our team in 2 months while revenue per employee jumped 5x. Here’s how we're achieving efficiency that our competitors can't match: Welcome to Ribbon: Dylan Mendonca, Hayden Caravan, Jerry Liu, and Patrick Foy! Going from 6 to 10 might not sound like much. But when you're processing hundreds of thousands of AI interviews with a tiny team, adding 4 people changes everything. We interviewed over 400 candidates to find these 4. Not because we're picky for the sake of it, but because scaling efficiently requires people who don't just execute, they reimagine how things should work. Each new hire spotted opportunities we hadn't even articulated yet. They came in asking why things worked certain ways, then fixed them without being asked. That's how a 10-person team handles what others need 50 for. We're still small enough that everyone knows everyone. But we're handling enterprise scale with startup agility. The common thread? Every person here has either been ghosted by recruiters or knows someone who has. We're building the solution to our own problem. That's not just team growth. That's building leverage the right kind of talent. The kind that transforms operations, not just maintains them.

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We don't see it often enough, but the start-ups I've worked with who hired *very* senior software developers - often semi-retired folks - part-time got enormous benefits. Most start-ups can't afford to pay for a full-time distinguished engineer, but giving dev teams a chance to be influenced by them even just one or two days a week can really raise the ceiling on what teams realise is possible and where their bar is set.

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