minware

People talk about replacing engineers with AI — what about engineering managers? Managers identify anti-patterns (e.g., not reaching out for help soon enough), provide coaching, and track progress. This seems easier to do with AI than building software itself. Of course, AI can’t deal with politics or human problems like lack of motivation. But, it could do a lot of the job, especially for junior-to-mid-level engineers who face similar issues.

Tell ChatGPT to write a performance review based on what you know about your own work this year. If it’s better than your actual performance review from your manager, then it’s time to look for a new job.

The difference between a good and a great engineer is completing the same project with half the features and 90% of the value. Boards and CEOs who drive their teams to increase velocity miss this point: what you do is more important than how quickly you do it. (And if you don’t give your engineers a say in what you do, then you are beyond help…)

If you're worried about people "cheating" with LLMs during your interview process (beyond the initial screening), then your process is horribly broken to begin with.

Running a startup requires juggling an inhuman number of responsibilities. The best productivity hack I’ve found is *extreme single-tasking*. Scheduling focus time and going on do not disturb is great, but it’s not enough to avoid the eventual creep of interruptions and delays as an organization grows. Extreme single-tasking takes things to another level, structuring the entire organization so that everyone can complete their work from start to finish in one sitting. Achieving this level of productivity requires a deep commitment to single-tasking. This article talks about how to build an extreme single-tasking culture and offers advice about how to eliminate the most common causes of context switching. If you ever wanted to know how it’s possible for a small team to get more done than competitors several times their size, this is the secret.

The #1 benefit of AI copilots is that they force you to write better code (comments, variable names, etc.), otherwise they won’t work. Rewarding developers for writing down how their code should function by letting them type less is how we should be thinking about AI’s productivity impact. In a way, making the least experienced developers (and students) use it more may help them get better faster than directly writing code.

Researchers discover 9.5% of developers have no commits. Customers who’ve used a particular competitor tell us commits disappear due to squashes, rebases, and force pushes. Coincidence? 🤔 #dataquality

I hate having to talk to salespeople or submit my email so they’ll spam me. It’s like being forced into a car dealership before Tesla. Soon we will look back on SaaS companies who gate stuff behind email forms or don’t have full self-service product demos like e-commerce sites that used to make you create an account before checking out. Remember that?

VCs are pouring billions into AI writing code, while engineers spend hours filling out Jira tickets, tracking time, and writing status reports. Instead of replacing engineers, maybe first we should automate tedious non-engineering work that wastes their time.

So much of this AI hype is people who have never experienced automation getting access to the equivalent of a very junior engineer for the first time. Saying it will replace developers is like people who take one JavaScript class thinking they can build Facebook. For senior engineers, AI is like a squire — it may clean mud off your boots (and be good value at $20/month), but it isn’t going to fight your battles.