AI-related job losses (and future non-hires) are the talk of the software town right now, but (at least in the short/near term) a new AI-led tech role has emerged with a massive increase of job postings (800%) over the last 9 months

Changelog NewsDeveloper news worth your attention

Jerod here! šŸ‘‹

So, Spencer Chang made a thing. A thing that made my day.

It’s called the alive internet theory and it makes the case that ā€œthe internet will always be filled with real people: looking for each other, answering calls for help, and sharing laughs even in the midst of arguing.ā€ This is a website that’s better felt than tell’t, so I’ll leave you with this 1k word equivalent šŸ‘‡

A computer desktop from February 15, 2009 littered with various images, movies, and sounds from the internet archive on that date

Ok, let’s get into this week’s news.


šŸŽ§ The world of open source metadata

Andrew Nesbitt builds tools and open datasets to support, sustain, and secure critical digital infrastructure. He’s been exploring the world of open source metadata for over a decade. First with libraries.io and now with ecosyste.ms, which tracks over 12 million packages, 287 million repos, 24.5 billion dependencies, and 1.9 million maintainers. šŸŽ„ VIDEO HERE šŸ‘€

Art for the episode: Smiling faces. Title text. That kind of stuff.

🧨 This new AI role is exploding

AI-related job losses (and future non-hires) are the talk of the software town right now, but (at least in the short/near term) a new AI-led tech role has emerged with a massive increase of job postings (800%) over the last 9 months:

Forerunners in the AI race, such as Anthropic and OpenAI, are actively recruiting software engineering specialists called forward-deployed engineers (FDEs) to help with tailoring AI models to meet customer needs. More than just working with back-office coders, these engineers are embedded within customer and product engineering teams.

Still not sure what an FDE does, exactly?

Unlike traditional software engineers, FDEs go beyond writing code to go out in the field and understand where AI can make the biggest impact. Their mission is to bridge the ā€œlast mileā€ of AI: transforming a general-purpose model into scalable AI solutions that reflect complex client requirements and solve their problems.

If this trend has any staying power, and if you want to be in demand in 2026, now is the time to ensure you can confidently (and truthfully) put FDE on your resumƩ.

šŸ˜“ Younger devs won’t tolerate pain in the AWS

Corey Quinn (who is hilarious, btw) finally realized what I’ve known since the first time I tried shipping a Rails app on EC2: AWS, for the uninitiated, is pure pain:

Recently, I was spinning up yet another terribly coded thing for fun because I believe in making my problems everyone else’s problems, and realized something that had been nagging at me for a while: working with AWS is relatively painful.

Corey lays out what a typical zero-to-one AWS setup often requires, then compares it to the silky smooth experience Vercel provides on top of AWS. His explanation for the discrepancy: it’s generational

This feels generational to me. For folks of a certain age (Gen X and Millenials), AWS and GCP have made their bones. We came of technical age with the platforms and we’re used to their foibles. Azure is of course the Boomer Cloud, but Gen Z is using platforms that aren’t designed as tests of skill to let customers prove how much they want something.

Hat tip to Cory for calling Azure the ā€œBoomer Cloudā€. That’s amazing. However, I don’t think this is a generational thing. There’s an entire group of elder devs, like myself, who have always preferred Heroku-style deployment platforms over AWS.

While his view of the past seems skewed from inside the AWS bubble, he might be right about the future:

AWS spent two decades building the most powerful cloud platform in the world. They may spend the next two watching it become irrelevant to anyone who wasn’t already bought in.

šŸ¤– You should write an agent

Thomas Ptacek makes the case that to truly grok LLM agents (so you can be the best hater (or stan) that you can be) you need to write one.

Agents are the most surprising programming experience I’ve had in my career. Not because I’m awed by the magnitude of their powers — I like them, but I don’t like-like them. It’s because of how easy it was to get one up on its legs, and how much I learned doing that.

I had this experience back in April with Thorsten Ball’s post walked me through it step by step. Thomas isn’t wrong. Building one for yourself brings clarity to what is likely the most important developer-facing technology of the decade.

šŸ’° Why GH actions/checkout is slow for 98.5% of orgsThanks to Depot for sponsoring Changelog News

Depot just dropped another deep-dive, and this one hits home for anyone using GitHub Actions. They analyzed thousands of workflows and found that 98.5% of organizations are running actions/checkout slower than they need to.

Turns out, the default settings most teams use are…not great. Cold clones, missing shallow fetches, and bloated histories waste precious CI minutes. And this is BEFORE your build even starts. Depot’s post breaks down why this happens, how much time it’s costing you, and what you can do to fix it.

The takeaway? CI performance isn’t just about bigger runners. It’s about smarter ones. Depot’s obsessed with shaving seconds off every step, and this new data proves there’s a ton of low-hanging fruit hiding in your pipelines.

🪦 Dead framework theory

Paul Kinlan says he was wrong last October when he predicted that LLMs would abstract away framework choice. Well, maybe not wrong. But wrong about the timeline.

The reality is more interesting and more permanent: React isn’t competing with other frameworks anymore. React has become the platform. And if you’re building a new framework, library or browser feature today, you need to understand that you’re not just competing with React—you’re competing against a self-reinforcing feedback loop between LLM training data, system prompts, and developer output that makes displacing React functionally impossible.

When he says ā€œself-reinforcing feedback loopā€, he’s not exaggerating. TIL Replit, Bolt, and tools like them are literally hardcoding React into their system prompts.

They have to. If you’re building a tool today to attract developers, you need to give them code they can maintain. And ā€œcode developers can maintainā€ now means ā€œReactā€ for the vast majority of web developers.

I remember back in 2022 when Josh Collinsworth declared, ā€œReact isn’t great at anything except being popular.ā€ (He even debated this with us on a pod)

Turns out that might be all it needed…

šŸ™ƒ Stop vibe coding your unit tests

We’re still trying to figure out this agentic coding thing.

Should we make the agent write the tests and write the implementation ourselves?
Should we write the tests and make the agent write the implementation?
Should we just sit back and say, ā€œagent, take the wheelā€?

Andrew Gallagher has thoughts:

There is a growing sentiment that LLMs are good for CRUD, boilerplate, and tests. While I am not so sure about how good AI is at making CRUD or thumping out boilerplate, a year of working as an SWE in the modern LLM-powered AI codescape has proven to me that LLMs write unconstructive, noisy, brittle, and downright-bad unit tests. Please do not vibe code your unit tests.

Andrew does say there’s a way to get good tests from LLMs, but right now it requires you to make them write tests one at a time. Ain’t nobody got time for that!


šŸŽ™ļø #define: sheer resistance

On this seventh iteration of our award-worthy game show filled with obscure jargon, fake definitions, and expert tomfoolery: past winners battle to determine the champion of champions. (Also, Adam.) šŸŽ„ VIDEO HERE šŸ‘€

Art for the episode: Smiling faces. Title text. That kind of stuff.

šŸ‘¾ Reviving classic Unix games

Juan M. MƩndez Rey shares his 20-year journey though software archaeology:

How I spent two decades tracking down the creators of a 1987 USENET game and learned modern packaging tools in the process.

šŸ“” Off-grid, long range, decentralized mesh networks

MeshtasticĀ® is a project that enables you to use inexpensive LoRa radios as a long range off-grid communication platform in areas without existing or reliable communications infrastructure. This project is 100% community driven and open source!

When they say long range they mean loooOOOooong range (331km record)

šŸ§žā€ā™‚ļø What is special about MCP?

Jessica Kerr on ā€œthree things MCP can do, and an infinite number of things it can’t do (all of which make it great).ā€


šŸ“ Don’t forget your (un)ordered list

That’s the news for now, but stay tuned for Wednesday when Hacker News’ favorite blogger, Sean Goedecke, joins the show!

Have yourself a great week,
the hand of the diligent makes rich,
and I’ll talk to you again real soon. šŸ’š

–Jerod