I'm a hands on engineering leader for a team of about 20 engineers and I've been spending the past 6 months trying to get my team to understand just this.

On Friday we had a coffee hour to share how we've been working recently and they all seemed perplexed at the workflows I've been adopting. It seems natural to me as someone who's been a manager for some time now, but very alien to all those who've never gone down that path.

That's not to say my workflows are superior, but they're extremely different to some of my teams now. In reality it's just leaning heavily on things like prds, limiting communication between agents, etc.

> You can make lots of money by making something appear novel and undiscovered, and consequently make yourself sound smart and cutting edge.

This is pretty much the crux of it. It's very similar to the strategy of undercutting a market with VC subsidies until it dies and can be replaced.

>You can make lots of money by making something appear novel and undiscovered, and consequently make yourself sound smart and cutting edge. Nobody raises a round to apply a well understood discipline correctly.

I'm curious about this. I thought investors preferred safe bets?

On the other hand, I know that if you're too early, it can be impossible to make a business work (or even to pitch the idea in the first place).

Related: You can't tell people anything (2004)

https://web.archive.org/web/20091025030730/https://habitatch...

Relatedly, I fully expect the software business to fail to learn from (nearly) literally every other industry how to operate when your marginal costs are no longer zero. It drives me nuts the number of people in software who think that people in other industries are slow because they’re just not as smart as us, rather than because when it takes months to get a cast part made, it better be right the first time.

Seems like this could be categorized as yet another reason why software developers are not engineers.

That’s because most Software Engineers aren’t Engineers, they’re computer science majors. It’s a completely different discipline.

This is mostly wrong as far as motivation is concerned. Some people just by default figure stuff out with what they know. Others accumulate knowledge. If you go too far in either direction you are probably going to struggle, but it's just different people having different nature.

He cites Brooks as proving that communication friction is quadratic in headcount. The exponent does not need to be exactly, or even nearly, 2; 1 + epsilon is already fatal. This is very closely related to Coase's ceiling.

A comments section about an HN comment about a post. We can recurse even deeper.

Solo programming is like managing a team of developers that can only communicate and contribute in one temporal direction.

Sometimes two or more developers may appear to coexist, but it's actually a simulated concurrency achieved by rapid context switching

Every time you close a file to open another, you are setting up a different context, therefore you are different agents.

Man I love and agree with nearly everything about this post except that I wouldn't take the advice to follow waterfall or PMBOK literally.

The post laments that it's hard to find Dr. Royce's original waterfall paper. That's probably true, I have my copy from a compilation book “Ideas that Created the Future: Classic Papers of Computer Science” edited by Lewis [1].

I do agree that it's important to do things like scope out your demands of your AI agent, check-in on progress, give as clear a requirement and test cases as you can. But you've always been able to do that with agile methods, and LLMs are fast enough that you don't need to go full waterfall (and if anything it would be counter productive).

Dr. Royce's paper talks about literally thousands of pages of documentation being needed for any reasonably useful system. Good luck fitting that into even a 1M context window :P.

But the main point to thesis, that you can't just let your coders loose to do whatever and expect the right results even pre-dates Fred Brooks. I'd argue it goes all the way back to the beginning, to the comments about Baggage's computing machine where British politicians asked if it would generate the correct answers even with incorrect inputs.

The answer then is the same answer today: of course not, and expecting anything different is foolishness.

[1] https://www.amazon.com/dp/0262045303

He says "engineers" but he means code monkeys. Actual engineering is all about learning from past failures

Take my money. This is better than "The Pragmatic Engineer".

As someone that has studied engineering, designing a solution is very different than managing the resources around the design and implementation of that solution. Books like The Mythical Man Month and Making Things Happen deals with the management side, (from a team level) and The Pragmatic Programmer is from a personal level.

But TFA is ignoring the actual engineering side where you have to grapple with some primitives and assemble them in a way that do something valuable. And so in a way that is cost effective. That part is always answered by hand waves.

Nobody gets rewarded for rejecting the newfangled thing. Even when the newfangled thing should be rejected. Same as scientific papers are very biased against reporting nulls.

There’s a good list of reasons why we do this, but it leaves out the biggest one, at least for me: building stuff is fun. Reinventing stuff is fun. It’s the most natural hammer to reach for whenever I encounter a nail. Not necessarily the best, but such is life.

I don’t quite understand the management analogy. Agents aren’t people, they’re code generation machines. Part of managing is delegating and trusting the output of your employees without having to verify everything yourself. But if you’re “managing” an agent, how does that work? You can’t exactly hold an LLM responsible if it fucks something up.

Or are people really just yoloing and not even verifying that the code generation is correct? I know it’s a bit of a meme, but are people actually doing the meme in irl where there are actual consequences??

Another obnoxious behaviour I’ve observed lately, is trying to attach whatever pre-existing pet methodologies one had to the AI-hype bandwagon like some sort of personal vindication orgy.

See also: people.

You know the old saying: "Those who can't do, teach. Those who can't teach, go into software engineering."

I think it needs to be updated to just say "Those who can't do, go into software engineering". Teachers don't deserve that crap.