Programming

In its latest labor market report, the New York Federal Reserve found that recent CS grads are dealing with a whopping 6.1 precent unemployment rate. Those who majored in computer engineering — which is similar, if not more specialized — are faring even worse, with 7.5 percent of recent graduates remaining jobless.

.I mean, best in class compensation, strong work life balance and prestige. Hundreds of thousands of students flocked to CS degrees as a no-brainer career. But the music has stopped, and there’s a lot of people without a place to sit. This is definately one of the larger market corrections I’ve seen since 2001. But, yet, I still would not tell people not to pursue engineering. Understanding technology I think is a critical part of the future.

Dohmke [GitHub CEO] described an effective workflow where AI tools generate code and submit pull requests. Developers can make immediate adjustments using their programming skills.

Matches my experience as well. A silly example. I ask AI to add padding to a div and it adds a style inline tag. Not a pt-3 class. AI has been great for getting 80% started. The rest is still up to us.

_{Quote Citation: TECHINASIA, “GitHub CEO: manual coding remains key despite AI boom”, 23 Jun 2025, https://www.techinasia.com/news/github-ceo-manual-coding-remains-key-despite-ai-boom}

Programming, at its essence, is conversation with computers. It’s how we translate human intention into machine action. Throughout computing history, we’ve continuously built better translation layers between human thought and machine execution—from physical wiring to assembly language to high-level languages to the World Wide Web, which embedded calls to backend systems into a frontend made up of human-readable documents. LLMs are simply the next evolution in this conversation, making access to computer power more natural and accessible than ever before.

I was describing GitHub Copilot. Or Claude Codex. Or OpenAI lmnop6.5 ultra watermelon. This isn’t about tools or productivity or acceleration. It’s about the illusion of progress. Because if that programmer-if that thing, that CREATURE-walked into your stand-up in human form, typing half-correct garbage into your codebase while ignoring your architecture and disappearing during cleanup, you’d fire them before they could say ’no blockers'.

A hilarious takedown of the bolder claims of AI coding. The truth is, AI does a great job of orienting and summarizing. But co-pilot.. please. Or as Jj offers ‘Copilot isn’t that. It’s just the ghost of a thousand blog posts and cocky stack-overflow posts’. Real.

Microsoft is not talking about replacing developers. It says GitHub Copilot – and the latest version of the product called “Coding agent” – are tools to partner with developers, akin to a peer programmer.

Seeing as how many of the autonomous bots have failed for me personally, I definately appreciate a pragmatic approach to AI coding detailed here.

_{Quote Citation: GERGELY OROSZ, “Microsoft is dogfooding AI dev tools’ future”, MAY 27, 2025, https://newsletter.pragmaticengineer.com/p/microsoft-ai-dev-tools}

During a fireside chat with Meta CEO Mark Zuckerberg at Meta’s LlamaCon conference on Tuesday, Microsoft CEO Satya Nadella said that 20% to 30% of code inside the company’s repositories was “written by software” — meaning AI.

I wish business had learned their lesson when once upon a time they tried to measure output by lines of code (LOC). Now we have this made up metric of ‘accepted’ suggestions. There’s lots of better ways to measure effectiveness, how fast the printer goes isn’t one of them.

Developer frustrations with AI mandates often surface due to their being handed down by company leaders who don’t have close visibility into engineering workflows. Developers describe executives instituting OKRs and tracking AI usage without any regard for whether it’s actually helping, let alone where it may be making things worse. Code acceptance rate (how often developers accept the code suggestions an AI tool makes) is a popular adoption metric, but some argue it’s a poor measure because it counts people accepting suggestions that may be problematic.

Ginsparg was frustrated because he couldn’t understand why implementing features that used to take him a day now took weeks. I challenged him on this, asking if there was any documentation for developers to onboard the new code base. Ginsparg responded, “I learned Fortran in the 1960s, and real programmers didn’t document,” which nearly sent me, a coder, into cardiac arrest.

Interview with the creator of arXiv which I’ve learned is pronouced ‘archive’. I’ve read so many good papers on this site and none are paywalled. This quote about Ginsparg happily wirting code without documentation especially pleased me. Why not? It works doesnt it?

Software gets more complicated. All of this complexity is there for a reason. But what happened to specializing? When a house is being built, tons of people are involved: architects, civil engineers, plumbers, electricians, bricklayers, interior designers, roofers, surveyors, pavers, you name it. You don’t expect a single person, or even a whole single company, to be able to do all of those.

I mean, this is the business of software engineering. Work gets compressed to whom ever can generate the most revenue per employee.

Every time I’ve built or inherited a team, one trait rises above all others: ownership. Do you ship? Do you own it when it breaks? Do you make the system better for the next person?

Also buried as a footnote ‘business > tech’. Being great at problem solving means being great at whatever tools solve it the most efficiently.

_{Quote Citation: Ben Howdle, “How crawlers impact the operations of the Wikimedia projects”, 02 Apr 2025, https://benhowdle.im/principles-and-implementation}