crjohns648

Even before AI, I've worked with people who would produce a huge wall of code and ask for review, and sometimes that code was completely off base or needed a significant rework.

I would always feel bad in those cases, because it's clear they spent a lot of time, and I'm going to have to say "no" and they will feel like they wasted a ton of effort.

The thought process around this has started shifting for me in the last few weeks. I'm a lot more comfortable saying "no" with a list of concerns when I suspect the code is AI-generated, and I see others doing the same. CLs that would be sitting around for days because no one wants to be the first to say, "this is bad, don't do this" now get quicker feedback.

The good thing is this feedback doesn't feel like as big a deal as it used to because people are less personally attached to code they generated in 30 minutes vs. code they hand crafted over a week. I had at least 2 LLM-generated PRs that were complete, correct, tested, and pre-reviewed by me, but I got feedback that they were going in the wrong direction. This would have been 8 hours of wasted effort a year ago, but now it's just an extra 30 minutes to rework the direction with LLM assistance.

I don't think a threatening name would be unheard of in a hijacking scenario. Someone calls saying they have a bomb on board, and as evidence there is a Bluetooth device called "bomb," showing they have an accomplice on board. They then make demands. This scenario doesn't seem unreasonable in light of pre-9/11 hijacking attempts.

Yes, this was a huge reaction to something that was almost certainly benign, but "almost certainly" isn't an acceptable risk for 100s of people in an explosive flying cylinder. It truly sucks, there maybe can be better procedures, but "100s of people majorly inconvenienced" is better than "100s of people dead in fireball."

I have also seen the learning acceleration, there's a significantly increased set of techniques and technologies I have learned how to apply.

From a person perspective though, I'm apprehensive about the effect AI will have on the human "very well read intern." People who know a lot very deeply about specific areas are fascinating to talk to, but now almost everyone is able to at least emulate deep knowledge about an area through the use of AI. The productivity is there, but the human connection is missing.

I think this article does a great job at conveying the new skills you need from a work-completing perspective as a manager, but there's another aspect that is much more subtle and long-term: being a guide for other people's careers.

I've seen dozens of people start managing, stop managing, change roles (including myself), etc, and there are two extremes that stand out:

1. Management out of necessity. They became a manager because they wanted to solve a problem that is too big for them to solve alone, and no one else was willing to fund it. So they got headcount, hired a team, and set them to work on solving the hard problem. But the problem they're solving is the only focus. This manager tends to have an elite team of low-maintenance engineers who just get things done. They are very effective, but eventually when those reports start asking questions like, "how do I get promoted? What's the next step in my career?" their manager has to suddenly learn this new set of skills or risk losing their highest performers.

2. Management to be a mentor. They became a manager to help other people grow. Sure they are solving problems with the team, but this manager spends the time to help higher-maintenance engineers grow their own skills. This is time-consuming, this can be frustrating, progress is going to be slower, but eventually you can reach very high throughput, and also feel very accomplished knowing you helped someone else reach their potential. This, however, has to be balanced with not moving so slowly that you frustrate your top performers.

There's nothing wrong with either of these extremes so long as everyone in the manager-report relationship knows what to expect, and many managers will be between these two extremes.

The main tl;dr takeaway is: as a manager, you are not just responsible for people's tasks, you are responsible for their career. Managers need to take this seriously and address it head-on to build those skills before the first time a report asks, "so how do I get promoted?"

I stopped using Digg a long long time ago. It just felt too slow to get the news I care about.

I was an avid Slashdot user way back in the day, but the site was basically the same throughout the day, and I wanted faster updates. Digg did this perfectly for a time, but eventually I migrated entirely to Reddit (even before whatever that drama was that caused a big exodus from Digg).

I think Reddit right now is the sweet spot: up to date information, longer-term articles to read, and easy to catch up on things I missed. I was recently pressured to sign up for X (or Twitter or whatever), and I had to turn off all of the notifications since I was constantly spammed with "BREAKING: X RESPONDS TO Y ABOUT Z!!!!"

Right now having Reddit for scrolling and Hackernews for articles+discussion feels like it works for me.

A good manager is more like a transparent umbrella. They protect the team from unnecessary stress and pressure, but don’t hide reality from them.

I'm absolutely going to steal this metaphor going forward.

Being a "transparent umbrella" does require knowing the personalities of your reports, some people do get distracted when they think higher-up decisions or unhappiness are going to affect their team. Most people, however, really appreciate the transparency. It helps them feel more in control when they know what is happening around them, and when things do change they can tie it back to something that was said previously.

Very cool. I think everyone should try making all of these foundational technologies themselves: A programming language, an OS, a bootloader, etc.

It's one thing to read about them in books, but you learn so much nuance by actually stepping through the inherent problems and difficulties in actually making them work.