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An excellent potted history from Cassie on women in computing.

NASA’s “Keypunch girls” would work in cramped rows translating programming instructions onto paper pads, whilst the machine operators would sit in comfort, feeding the code decks through card readers and enjoying the esteem of the end result (I imagine it a bit like Mad Men, but with more sexism and astronauts).

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Generative AI Is Not Going To Build Your Engineering Team For You - Stack Overflow

People act like writing code is the hard part of software. It is not. It never has been, it never will be. Writing code is the easiest part of software engineering, and it’s getting easier by the day. The hard parts are what you do with that code—operating it, understanding it, extending it, and governing it over its entire lifecycle.

The present wave of generative AI tools has done a lot to help us generate lots of code, very fast. The easy parts are becoming even easier, at a truly remarkable pace. But it has not done a thing to aid in the work of managing, understanding, or operating that code. If anything, it has only made the hard jobs harder.

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AI isn’t the app, it’s the UI - Stack Overflow Blog

In some ways, the fervor around AI is reminiscent of blockchain hype, which has steadily cooled since its 2021 peak. In almost all cases, blockchain technology serves no purpose but to make software slower, more difficult to fix, and a bigger target for scammers. AI isn’t nearly as frivolous—it has several novel use cases—but many are rightly wary of the resemblance. And there are concerns to be had; AI bears the deceptive appearance of a free lunch and, predictably, has non-obvious downsides that some founders and VCs will insist on learning the hard way.

This is a good level-headed overview of how generative language model tools work.

If something can be reduced to patterns, however elaborate they may be, AI can probably mimic it. That’s what AI does. That’s the whole story.

There’s very practical advice on deciding where and when these tools make sense:

The sweet spot for AI is a context where its choices are limited, transparent, and safe. We should be giving it an API, not an output box.

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Agile and the Long Crisis of Software

Time and again, organizations have sought to contain software’s most troublesome tendencies—its habit of sprawling beyond timelines and measurable goals—by introducing new management styles. And for a time, it looked as though companies had found in Agile the solution to keeping developers happily on task while also working at a feverish pace. Recently, though, some signs are emerging that Agile’s power may be fading. A new moment of reckoning is in the making, one that may end up knocking Agile off its perch.

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Beginner JavaScript Notes - Wes Bos

A very handy collection of organised notes on all things JavaScript.

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The lines of code that changed everything.

We construct top-10 lists for movies, games, TV—pieces of work that shape our souls. But we don’t sit around compiling lists of the world’s most consequential bits of code, even though they arguably inform the zeitgeist just as much.

This is a fascinating way to look at the history of computing, by focusing in on culturally significant pieces of code. The whole list is excellent, but if I had to pick a favourite …well, see if you can guess what it is.

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