Alex Bush

Learn what Playwright is, why teams use it, how it compares with other E2E tools, and how to scale tests with parallel execution.

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I think this mixes a real shift with an too early conclusion. Ryan Dahl is right about abstraction winning. It always has.   But “humans writing code is over” collapses several very different things into one soundbite. Syntax has never been the hard part.   Constraints, trade‑offs, failure modes, cost, latency, security, and incentives are. Assembly didn’t disappear because humans stopped understanding machines.   It disappeared because humans understood them well enough to abstract safely. Frameworks didn’t kill engineering.   They moved engineering up the stack and made bad decisions more expensive. LLMs don’t change that. They generate code.   They do not own outcomes.   They do not carry liability.    They do not explain to regulators, auditors, or customers why a system failed (yet) Directing intelligence sounds clean until the system misbehaves in production.   Then someone still has to reason about state, data flow, and edge cases that were never in the prompt. What is ending is not humans writing code.   What is ending is humans writing low‑value, repetitive code by hand. That’s a productivity shift, not the death of engineering. If syntax truly didn’t matter anymore, we wouldn’t see: - Hallucinated APIs   - Broken dependency graphs   - Unsafe defaults shipped at scale   - Teams spending weeks debugging AI‑generated glue Abstraction wins when it is bounded, testable, and reversible.   Unbounded abstraction fails fast and loudly. So yes, humans will direct more.   But someone still has to understand what’s being directed, what it costs, and what breaks when it does. That role doesn’t disappear.   It just gets less forgiving.    If you’re building AI‑heavy systems and feeling the gap between it writes code, and this survives production, happy to help. This transition needs operators, not slogans. Quotes and references: Ryan Dahl: “Most of the code people write today should not exist.”   Marvin Minsky, Society of Mind: “Intelligence emerges from the interaction of many simple processes.”   Fred Brooks, No Silver Bullet: “There is no single development… that by itself promises even one order of magnitude improvement.”   Peter Naur: “Programming is theory building, not typing.” #SoftwareEngineering #AI #LLMs #Abstraction #SystemsThinking #TechLeadership #Programming #RealityCheck

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90% of engineers and engineering managers are embracing AI coding tools, according to the 2025 State of Engineering Management Report from Jellyfish. That’s accompanied by a 25% increase in developer velocity and productivity via AI coding. These findings, derived from interviews of more than 640 CTOs, Engineering Managers, and Engineers, show just how quickly AI is becoming embedded in how technical teams work. If you’re in this field, it’s a must-read. Download the full report here: https://lnkd.in/eC3qQwBE

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Ruby on Rails 8 Beta Has Officially Dropped Rails 8 isn’t just another update. It’s a shift toward: ✅ Simpler deployments ✅ Fewer external dependencies ✅ Leaner architecture ✅ Better SQLite support ✅ Modern asset management Built on Rails 7.2, this release focuses on reducing complexity and improving developer happiness. Over the next few posts, I’ll break down everything new in Rails 8 👇 If you’re building with Rails in 2026 — this matters. #ruby #rails #webdevelopment #backend #fullstack #softwareengineering

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Everyone in the mobile app space should be paying attention. The road to launch on new ventures is getting shorter and shorter…

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I've been developing Ruby on Rails applications for over 20 years, since @dhh first introduced Rails with his famous video. One mistake I've seen engineers commit over and over is failing to trust the framework. I've reviewed many an over-complicated PR that could have been just a few lines of Ruby code. Service objects that could have been concerns, business logic embedded in ERB files, deeply nested dependencies and spaghetti code.... Admittedly, any project can devolve into a mess of spaghetti code. But Rails is such a beautiful framework because if you work with it instead of against it, you can end up with a project that's a joy to maintain. I actually enjoy doing code reviews because it gives me an opportunity to teach about the glories of Rails and demonstrate better patterns to accomplish things that can be simple in Rails, but hard in other frameworks. One of the reasons I love working on my own side projects is because it gives me that unrestricted outlet for my own creativity without having to wade through lines and lines of code committed by engineers who haven't yet learned to trust the framework. @MemberDrive_org is a joy to work on. I have (almost) complete creative freedom, and I get to experiment with the latest Rails features and keep my knives sharp. Now, I just need to get a bit better at marketing...

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Hiring engineers for hard operational problems is different. Most candidates can write clean code. Fewer can sit with a dispatcher at 7am and understand why their shift planning breaks down. Fewer still can go back to the codebase and design something that actually works in production with those constraints. What I look for at Mirage Metrics is not just skill with Python, SQL, or distributed systems. It is a willingness to go into environments that are messy, sometimes chaotic, and keep digging until the real bottleneck shows up. That can mean inventory mismatches hidden in Excel, planning rules written on a whiteboard, or API endpoints that return inconsistent data depending on who queries them. The best engineers I have worked with are the ones who do not flinch at this. They treat debugging a customs declaration pipeline with the same seriousness as debugging a memory leak. They know that solving “boring” problems in data quality or workflow mapping is what unlocks everything else. On the technical side, I test for clarity. Can someone explain the trade-offs between running a model via an API versus hosting it on GPUs without getting lost in jargon. Can they design a schema validator that will still make sense six months later when requirements change. Can they write glue code that is robust, not fragile. On the personal side, I look for stamina. These projects are rarely about building a shiny feature in isolation. They require iteration with operators, late-night debugging of OCR failures, and the patience to integrate into legacy systems that were never designed for AI. If you want to work on AI for logistics, manufacturing, or mining, the question is not only whether you can code. It is whether you can hold your ground in the real world, in front of the people whose work depends on your system. That is the bar.

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