Ivan Von Turkovich

A developer at a fintech client lost three days to a reconciliation job that was silently dropping records under load. He had shipped eleven features in four months. Real output, not demo output. But this bug lived below the framework, somewhere between the ORM and the connection pool, and he could not see down there. He pasted stack traces into the assistant. The assistant proposed six fixes.…

On June 12, the US government told Anthropic to pull Claude Fable 5. Three days after it launched. The order said no foreign nationals could touch it, including Anthropic’s own foreign employees, and since nobody could verify nationality on every request, the model went dark for everyone. API, Claude.ai, Bedrock, all of it. Gone. On June 30 the controls came off. On July 1 it was back in your…

A developer with 20 years on the job told me something last week. His old burnout used to make sense. Eight hours of code. Brain fried. Go home. Sleep. Repeat. Predictable. His new burnout is harder to describe. He asks AI to do something. He reads what it produced. Something looks off. He asks again. He notices a regression in a file he never opened. He redirects. The AI confidently takes a…

A Quebec translator I read about made roughly six figures in 2023. By late 2025 her work requests had dried up. Total earnings for the year barely cleared €8,000. She is forty-four years old and watching her career end through email, while large language models charge a few cents to do what she charged a few dollars per word to do. Multiply her by a few million. That is the whole argument I…

The Email That Started This Post A friend who is a founder sent me a Telegram message last month. “We’re three engineers. We have an AI feature to ship by Q3. Everyone says Python. Should I just give up on Rails?” He had a working Rails 8 app. Auth done. Billing done. Three years of customer data. Real revenue. And he was about to throw it out because a YouTube influencer told him “AI means…

Someone finally named the thing. There’s a word now for the work I’ve been doing for the last two years without a vocabulary for it. The middle loop. Supervisory engineering. The thing that sits between writing code and shipping it. The thing that didn’t exist before agents started producing code faster than any human could read it. I’ve been in this business for over two decades. Twenty years…

A CTO’s field guide to the 2026 AI job title theater, with a companion reference guide covering all 40 roles. I have been a CTO for most of my adult life. I have hired, fired, onboarded, mentored, and occasionally been forced to explain to finance why a “Senior Applied Generative AI Engineer II” and a “Staff LLM Engineer” should sit in the same comp band. What I can tell you, after twenty years…

The best engineers I know write less code than they did two years ago. They ship more. Everyone wants the clean story. AI replaces developers. AI makes developers 10x. Juniors are cooked. Juniors are saved. Pick a side. The reality is messier. Big Tech new grad hires dropped to 7% of all new hires, down from 25% in 2023. Computer engineering graduates now have the second-highest unemployment…

Two weeks ago, a North Korean state actor compromised the lead maintainer of Axios and published malicious versions to npm. The library has roughly 100 million weekly downloads. The poisoned packages were live for about three hours before anyone noticed. Three hours. That is all it took to potentially compromise tens of thousands of development environments worldwide. The attacker did not find…

There is a growing conviction in the software industry that building software with AI is essentially a solved problem. Or if it is not solved today, it will be within a year. The reasoning goes something like this: a dwindling minority of naysayers at large, slow-moving companies are pretending AI is still bad, while a rapidly growing majority is adopting AI tools daily and actively improving…

When I published The Eternal Promise, tracing sixty years of attempts to eliminate programmers from COBOL through to today’s AI coding tools, the post resonated far beyond what I expected. It reached the front page of Hacker News and generated hundreds of comments from engineers who recognized the pattern in their own careers. But one response in particular stopped me. A commenter articulated…

The most dangerous lie in software engineering is a two-word status update: “It’s done.” In most teams, done means the pull request was merged. The CI pipeline turned green. The ticket moved to the right column on the board. Someone wrote “Done” in Slack, maybe with a checkmark emoji for extra confidence. And then everyone moved on to the next thing. But merging code into a main branch is not…

A week ago my blog post got noticed by developers. I got so many messages and there was a recurring theme. They feel helpless and anxious with the ever growing AI dominance in writing code that might not be perfect but it is almost good enough in many cases. But one question stood out more than others. “What’s the point?” of continuing to practice and learn the skills. As we know most engineers…

There is a question hiding in plain sight behind every celebration of AI-generated code, every prediction that developers are obsolete, every LinkedIn post about building an app 100x faster with a prompt. It is a question that almost nobody in the current hype cycle is asking, and it may be the most important question of all. Where did the AI learn to write that code? The answer is…

Yes, writing code is easier than ever. AI assistants autocomplete your functions. Agents scaffold entire features. You can describe what you want in plain English and watch working code appear in seconds. The barrier to producing code has never been lower. And yet, the day-to-day life of software engineers has gotten more complex, more demanding, and more exhausting than it was two years…

I learned this the hard way while working with Claude Code. AI looks at your existing code and copies the patterns it finds. If you start with clean code, the rest stays clean. If you start messy, the problems pile up faster than any human team could create them. And unlike a junior developer who might occasionally question a strange pattern, AI agents replicate what they see with perfect…

In my previous post on WebMCP, I covered what WebMCP is and why it matters. The response told me something important: developers do not want another think piece about the future of the web. They want to know how to actually build with it. So this is the practical companion. No philosophy. No speculation about whether this will matter. Just code, setup instructions, and patterns you can apply to…