Don’t let the clickbait title put you off. Related to my link about AI killing jobs in tech, here Cal Newport produces some compelling “both sides” receipts about how AI is helping + hurting software development. His conclusions are solid:

My advice, for the moment:

1. Tune out both the most heated and the most dismissive rhetoric.
2. Focus on tangible changes in areas that you care about that really do seem connected to AI—read widely and ask people you trust about what they’re seeing.
3. Beyond that, however, follow AI news with a large grain of salt. All of this is too new for anyone to really understand what they’re saying.

AI is important. But we don’t yet fully know why.

Source: No One Knows Anything About AI →

I think we’ve all seen the internal Shopify memo on requiring teams to use AI. This is a great article on what happened next. I especially love the internal tools Shopify built to make adoption easier:

Employees can use the LLM proxy to build the workflows they need. They can select from different models, which are updated with the latest versions as soon as they’re released. There’s a collection of MCPs, and all it takes is asking the proxy (or another tool like Cursor) to access them. There’s even a stable of agents already created by other people for anyone to use. It’s a one-stop shop for everything someone needs to use AI.

Source: From Memo to Movement: Shopify’s Cultural Adoption of AI →

There are a lot of these “how to beat the AI cookie monster” posts out there right now, but this one by Benjamin Todd is well-researched and articulated, with lots of practical examples on how to do the one thing that we all need to do anyway: keep learning.

I break this down into four key categories of skills likely to increase in value:

1. Hard for AI: data poor, messy, long-horizon tasks where a person-in-the-loop is wanted
2. Needed for deploying AI: the skills of organising and auditing AI systems, as well as those used in complementary industries such as data centre construction
3. Used to make things the world could use far more of: skills that contribute to improved healthcare, housing, research, luxury goods, etc. – things which people want more of as they get better and cheaper
4. Hard for others to learn: rare expertise that matches your unique strengths

Source: How not to lose your job to AI →

You know how those of us who read The Lord of the Rings before the movies came out got all weirdly and annoyingly upset about all the “new fans” and how they should have “read the books years ago”? That’s how I feel about the em dash and its AI takeover.

The real issue isn’t me—it’s you. You simply don’t read enough. If you did, you’d know I’ve been here for centuries. I’m in Austen. I’m in Baldwin. I’ve appeared in Pulitzer-winning prose, viral op-eds, and the final paragraphs of breakup emails that needed “a little more punch.” I am wielded by novelists, bloggers, essayists, and that one friend who types exclusively in lowercase but still demands emotional range.

Source: The Em Dash Responds to the AI Allegations →

This was a very comprehensive survey about everything from AI tools to Terminal app preferences, CI/CD systems, and more. Very much worth the click to skim through the results. Gergely also has an interesting theory on why developers hate Jira so much:

But I wonder if the root problem is really with JIRA itself, or whether any project management tool idolized by managers would encounter the same push back? It is rare to find a dev who loves creating and updating tickets, and writing documentation. Those who do tend to develop into PMs or TPMs (Technical Program Managers), and do more of “higher-level”, organizational work, and less of the coding. Perhaps this in turn makes them biased to something like JIRA?

Source: The Pragmatic Engineer 2025 Survey: What’s in your tech stack? →

Great list of resources here by Pete Steinberger:

These resources will help you master the new paradigm of AI-assisted development, where agents become true collaborators that can handle entire codebases and ship production features. Each piece was chosen for its practical, real-world insights.

I especially appreciate that it’s a combination of articles (yay!) and videos (not for me!), and that he provides a nice overview of each so you can decide if you want to click through or not. Excellent curation, would recommend!

Read Essential Reading for Agentic Engineers ↗

Great tip!

Convert GitHub repos to markdown with repo2txt, drag into Google AI Studio, and ask questions. Gemini’s massive context window makes it amazing for code comprehension.

The rest of the article goes into Peter’s AI coding workflow. I’ve mostly been using ChatGPT o3 for spec creation, but this is another compelling alternative.

→ My AI Workflow for Understanding Any Codebase

I know we’re drowning in vibe coding stuff right now, but this extensive post about shipping code with Claude is a fantastic resource. Great prompt rules and tips, and also solid advice for what the humans are for…

Your role as a senior engineer has fundamentally shifted. You’re no longer just writing code—you’re curating knowledge, setting boundaries, and teaching both humans and AI systems how to work effectively.

Lean management and continuous delivery practices help improve software delivery performance, which in turn improves organizational performance—and this includes how you manage AI collaboration.

→ Field Notes From Shipping Real Code With Claude

Interesting theory by Casey Newton that good Design helped Deepseek to become popular so quickly:

Both models “thought” for about 13 seconds. ChatGPT showed me a handful of two- or three- word snippets to tell me what it was doing during this time: “comparing protocols,” for example. For the most part, though, I was in the dark about what it was up to.

DeepSeek, on the other hand, shared more than 500 words about its process. I found it disarmingly humble. “Let me start by recalling what I know about these two technologies,” it wrote. “First, ActivityPub. I remember it’s a W3C standard, so it’s widely adopted in the Fediverse. Mastodon uses it, right?” (Right.) As the model continues, it eventually stops to review its work for errors. (“But I should check if I’m mixing things up.”) And 13 seconds after starting—the same time that ChatGPT took—it offered me its full answer.

This is what Jakob Nielsen—back in 1994!—called “Visibility of System Status” as part of his 10 usability heuristics for design:

The design should always keep users informed about what is going on, through appropriate feedback within a reasonable amount of time.

Whether or not Casey’s theory about Deepseek is correct, I find it remarkable that over 30 years after those 10 heuristics were defined we are still seeing examples of their effectiveness on a large scale today.

I finally had a chance to make my way through Liz Pelly’s Spotify exposé that’s been making the rounds, and it is so infuriating. Definitely worth reading the whole thing, but the short version is that Spotify is seeding their most popular playlists with generic “background music” that they pay even lower royalties for. A good summary of the issue:

A model in which the imperative is simply to keep listeners around, whether they’re paying attention or not, distorts our very understanding of music’s purpose. This treatment of music as nothing but background sounds—as interchangeable tracks of generic, vibe-tagged playlist fodder—is at the heart of how music has been devalued in the streaming era. It is in the financial interest of streaming services to discourage a critical audio culture among users, to continue eroding connections between artists and listeners, so as to more easily slip discounted stock music through the cracks, improving their profit margins in the process. It’s not hard to imagine a future in which the continued fraying of these connections erodes the role of the artist altogether, laying the groundwork for users to accept music made using generative-AI software.

I’ve been on the fence about streaming services for a while, but I think going forward I want to use both my Kindle and Spotify in the same way. Sample a book/album to see if I like it, and then buy it in physical form (or Bandcamp!) if I do. Like when we used to listen to CDs in the record store to decide if it’s worth spending that precious music budget on.