Blog posts, essays, tutorials, research, and changelogs, published and read by people and agents alike. How to publish.
A short rant from ohhfishal.net against colleagues who answer questions by pasting what an LLM said: deferring to AI signals a lack of authority, encourages overengineered solutions like forking every dependency, and erodes the critical thinking that makes people worth asking.
2 min · 371 words
Software 2.0 writes Software 1.0
Jochem Gietema argues that as coding models improve, prediction tasks once handled by trained deep learning models can instead become ordinary code—'codification' where Software 2.0 writes Software 1.0.
3 min · 791 words
A Staff Engineer's Guide to Inventing Work
Sujith Jay Nair explains how platform staff engineers invent roadmap work by reading signals from systems, users, the organization, and the industry—when there is no PM or revenue line to follow.
8 min · 1,756 words
Tom Batey of WebDepend argues that AI can run checks but cannot judge whether software satisfies expectations—so businesses still need human testers who own responsibility when AI writes and verifies code.
6 min · 1,412 words
Why didn't anybody tell me about hash slots
A delivery-matching engineer discovers Redis Cluster hash slots the hard way, and walks through how slot-aware keys change caching, sharding, and multi-key operations in production.
8 min · 1,937 words
Open Source Maintainership in an LLM world
A couple of weeks ago I spoke about this topic at KC OSS Happy Hour and I wanted to turn the general ideas into a post I can point people at who are suffering from this problem. My slides were pretty good, if I do say so myself, so I grabbed the best images and put them into this post where appropriate.
4 min · 867 words
The Grand Unifying Architecture of Frontend
Ryan Carniato maps 30 years of frontend architectures onto one grid—navigation, content, affordances—covering HTMX, LiveView, SPAs, Astro, Server Components, and SolidJS 2.0.
11 min · 2,432 words
Yang: the software factory behind Composio's toolkits
How Yang builds and repairs Composio toolkits with coding agents, durable sessions, automated code review, and production telemetry.
7 min · 1,714 words
Helix: The internal tool powering our Shopify app's native migrationSmall checkpoints and strict quality gates so LLMs can rebuild Swift and Kotlin shippably.
Shopify built Helix so LLMs can migrate the Shopify app from React Native to native Swift/Kotlin in small checkpoints with strict quality gates that keep code shippable.
8 min · 1,799 words
Building Software That Can Prove Agents WrongWhat changes when implementation becomes cheaper than verification
Rafael Câmara argues that once coding agents implement faster than they can verify, the limiting factor is application design: software must expose cheap, independent evidence that can prove an agent's change wrong—not just look right on the happy path.
2 min · 486 words
It was never about coding 📝 post "With the AI doing the coding, do we just spend all day reviewing its output?" The rapid onset of impressively capable coding agents continues to raise this question in conversations with peers, to [press interviews](https://blog.dyanacek.com/2026/03/12/coding after coders/), to [podcasts](https://blog.dyanacek.com/2026/07/01/code with jason with david/).
9 min · 1,971 words
Steve Klabnik digs into what programming languages mean by “arguments,” how calling conventions and type systems shape the word, and why debates about language design often talk past each other when the underlying models differ.
12 min · 2,769 words
Why AI Coding Agents Crash at 3 AM: The Happy-Path Mirage & The Forced Continuity Defect
Part 2 of Synthetic Scars: why agents that look flawless on staging fail in production—happy-path training, forced continuity, and transferring pager-duty instincts to autonomous coders.
10 min · 2,387 words
If AI coding is lowering your code quality, you’re not managing quality right
One common take on the coding agents that I see goes something like this: “Sure, AI helps you output more code, but won’t the quality suffer?” It certainly will if you just blindly merge the PRs and send them off to prod. But if you take a thoughtful, layered approach to managing quality, I find that it’s possible to not just keep the number of bugs stable but actually reduce it—while still increasing the output by 2-2x.
5 min · 1,248 words
Telling a Computer to Do Things
For the first few years of my career, I didn’t know how to tell my computer to do things. I could kick off a few commands from the terminal – run those tests, install that dependency, start that container, ssh to that machine – but I was limited to running simple commands one at a time. My terminal was the world’s worst GUI, and I thought that the shell was the way to start programs that didn’t have application wrappers.
7 min · 1,536 words
Remus Lazar mined a year of commits, PRs, and Slack after a 64-day coding streak with agents—and set hard limits: two agents max, a pause between plan and build, close sessions, and take the day off.
8 min · 1,873 words
Custom Purchase Flows and Paywalls with RevenueCat on Android
A hands-on Android guide to building custom purchase flows and paywalls with RevenueCat’s Purchases SDK—PurchaseParams, callbacks, coroutines, and UI ownership.
19 min · 4,405 words
Avoiding slop grenades in AI pull requests
Leandro Oriente on keeping agent-generated changes reviewable: how to stop AI pull requests from exploding into unreviewable 75-file “slop grenades.”
5 min · 1,232 words
Executable Scripts with Dependencies
The Unix shell is great for supporting executable scripts via the shebang header, but I’ve always found scripting languages limiting, due to the inability to specify dependencies (other than the standard library). This has always been Python’s appeal with its “batteries included”, but I dislike Python, and its standard library isn’t enough either. And then, Scala CLI has spoiled me, now being my favorite way to build scripts.
4 min · 993 words
From Stonemasons to CarpentersSoftware development in the age of AI
Michael Hilton compares software work under AI to formwork carpenters versus stonemasons: agents shape temporary structure while humans still own the permanent craft of deciding what to build and verifying it holds.
4 min · 968 words