Tags: story

Being in Croatia last week got me thinking about the country’s history.

I remember the break-up of Yugoslavia, but I was quite out of touch with the news for a while back in 1991. That’s because I was hitch-hiking and busking around Europe with my musical partner Polly from Cornwall. I had my mandolin, she had her fiddle.

We went from Ireland to England to France to Germany to Czechoslovakia (still a single country back then), to Austria to Italy, back to France, and back to England. A loop around Europe.

We set off on August 21st, 1991. The only reason I know the date is because I remember we had been to a gig in Cork the night before.

Sonic Youth were playing in Sir Henry’s (a great venue that no longer exists). The support band was a group from Seattle called Nirvana. I remember that some of my friends decided to skip the support band to stay in the pub next door until Sonic Youth came on because the pints were cheaper there.

By the time Polly and I got back from our travels, Nirvana were the biggest band on the planet. It all happened very quickly.

The same could be said for the situation in Yugoslavia.

I remember when we were stuck for a day at a petrol station in the alps trying to get from Austria to Italy. There was a bureau de change listing currency exchange rates. This was before the euro came in so there were lots of different currencies; pounds, francs, lira, deutsche marks. Then there was the listing for the Yugoslav dinar. It read:

• We buy: 00.00
• We sell: 00.00

That really struck me, seeing the situation summarised so clinically.

But what really got to me was an encounter in Vienna.

Polly and I did well in that city. On our first evening of busking, not only did we make some good money, but we also met a local folk singer. This young man very generously took us in and put us up in his flat.

At some point during our stay, we were on one of the city’s trams. That’s when we met another young couple who were on the road. Somehow there was always a connection between fellow travellers. I can’t remember who spoke to who first, but we bonded straight away.

It soon became clear that our situations were only superfically similar. This was a young couple deeply in love. One of them was Serbian. The other was Croatian. It wasn’t safe for either of them back where they used to call home.

I could return home at any point. I always knew that when I was sleeping rough, or struggling to make enough money to eat.

They couldn’t return. All they wanted was to be together somewhere safe. They started asking us about Ireland and England. “Do you think they’d give us asylum?” they asked with so much hope. It broke my heart to see their desperation, the pleading look in their eyes.

I felt so useless. I wished there was something I could’ve done for them.

I think about them a lot.

Quite a few people have been linking to this list on The Verge of what they consider the greatest tech books of all time.

To be clear, this is a fairly narrow definition of technology. It’s really a list of books about the history of computing. But there’s some great stuff in there.

I’ve been thinking the books about computing and technology that I’ve managed to get around to reading, and which ones made an impact on me. Some of these made it on The Verge’s list too, which is nice to see.

Broad Band by Clare L. Evans

I was blown away by the writing and the stories uncovered in “the untold story of the women who made the internet.” Here’s what I wrote when I read the book:

This book is pretty much the perfect mix. The topic is completely compelling—a history of women in computing. The stories are rivetting—even when I thought I knew the history, this showed me how little I knew. And the voice of the book is pure poetry.

It’s not often that I read a book that I recommend wholeheartedly to everyone. I prefer to tailor my recommendations to individual situations. But in the case of Broad Band, I honesty think that anyone would enjoy it.

Uncanny Valley by Anna Wiener

I read this one in 2020, not too long after it came out. In my end of year round-up, I described it like this:

A terrific memoir. It’s open and honest, and just snarky enough when it needs to be.

Close to the Machine by Ellen Ullman

I read this in 2018, many years after it first came out. Here’s how it came across to me:

Lots of ’90s feels in this memoir. A lot of this still resonates today. It’s kind of fascinating to read it now with the knowledge of how this whole internet thing would end up going.

Abolish Silicon Valley by Wendy Liu

This book is mostly excellent. But as I wrote when I got my hands on an advance copy, the juxtaposition of memoir and manifesto didn’t work for me:

Abolish Silicon Valley is 80% memoir and 20% manifesto. I worry that the marketing isn’t making that clear. It would be a shame if this great book didn’t find its audience.

The Victorian Internet by Tom Standage

Okay, this isn’t technically about computing, it’s about the telegraph. But it’s got the word “internet” in the title, and it’s a terrific read. Here’s what I wrote when I put it in Matt’s book-vending machine:

A book about the history of telegraphy might not sound like the most riveting read, but The Victorian Internet is both fascinating and entertaining. Techno-utopianism, moral panic, entirely new ways of working, and a world that has been utterly transformed: the parallels between the telegraph and the internet are laid bare. In fact, this book made me realise that while the internet has been a great accelerator, the telegraph was one of the few instances where a technology could truly be described as “disruptive.”

When Jason linked to the list of books on The Verge he said:

I’m baffled that Tracy Kidder’s amazing The Soul of a New Machine didn’t make the top 5 or even 10.

I’m more surprised that this book is held in such high esteem. It has not aged well. I read it in 2019 and had this to say:

This is a well-regarded book amongst people whose opinion I value. It’s also a Pulitzer prize winner. Strange, then, that I found it so unengaging. The prose is certainly written with gusto, but it all seems so very superficial to me. No matter how you dress it up, it’s a chronicle of a bunch of guys—and oh, boy, are they guys—making a commercial computer. Testosterone and solder—not my cup of tea.

I woke up today to a very annoying new bug in Firefox. The browser shits the bed in an unpredictable fashion when rounding up single pixel line widths in SVG. That’s quite a problem on The Session where all the sheet music is rendered in SVG. Those thin lines in sheet music are kind of important.

Browser bugs like these are very frustrating. There’s nothing you can do from your side other than filing a bug. The locus of control is very much with the developers of the browser.

Still, the occasional regression in a browser is a price I’m willing to pay for a plurality of rendering engines. Call me old-fashioned but I still value the ecological impact of browser diversity.

That said, I understand the argument for converging on a single rendering engine. I don’t agree with it but I understand it. It’s like this…

Back in the bad old days of the original browser wars, the browser companies just made shit up. That made life a misery for web developers. The Web Standards Project knocked some heads together. Netscape and Microsoft would agree to support standards.

So that’s where the bar was set: browsers agreed to work to the same standards, but competed by having different rendering engines.

There’s an argument to be made for raising that bar: browsers agree to work to the same standards, and have the same shared rendering engine, but compete by innovating in all other areas—the browser chrome, personalisation, privacy, and so on.

Like I said, I understand the argument. I just don’t agree with it.

One reason for zeroing in a single rendering engine is that it’s just too damned hard to create or maintain an entirely different rendering engine now that web standards are incredibly powerful and complex. Only a very large company with very deep pockets can hope to be a rendering engine player. Google. Apple. Heck, even Microsoft threw in the towel and abandoned their rendering engine in favour of Blink and V8.

And yet. Andreas Kling recently wrote about the Ladybird browser. How we’re building a browser when it’s supposed to be impossible:

The ECMAScript, HTML, and CSS specifications today are (for the most part) stellar technical documents whose algorithms can be implemented with considerably less effort and guesswork than in the past.

I’ll be watching that project with interest. Not because I plan to use the brower. I’d just like to see some evidence against the complexity argument.

Meanwhile most other browser projects are building on the raised bar of a shared browser engine. Blisk, Brave, and Arc all use Chromium under the hood.

Arc is the most interesting one. Built by the wonderfully named Browser Company of New York, it’s attempting to inject some fresh thinking into everything outside of the rendering engine.

Experiments like Arc feel like they could have more in common with tools-for-thought software like Obsidian and Roam Research. Those tools build knowledge graphs of connected nodes. A kind of hypertext of ideas. But we’ve already got hypertext tools we use every day: web browsers. It’s just that they don’t do much with the accumulated knowledge of our web browsing. Our browsing history is a boring reverse chronological list instead of a cool-looking knowledge graph or timeline.

For inspiration we can go all the way back to Vannevar Bush’s genuinely seminal 1945 article, As We May Think. Bush imagined device, the Memex, was a direct inspiration on Douglas Engelbart, Ted Nelson, and Tim Berners-Lee.

The article describes a kind of hypertext machine that worked with microfilm. Thanks to Tim Berners-Lee’s World Wide Web, we now have a global digital hypertext system that we access every day through our browsers.

But the article also described the idea of “associative trails”:

Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them, ready to be dropped into the memex and there amplified.

Our browsing histories are a kind of associative trail. They’re as unique as fingerprints. Even if everyone in the world started on the same URL, our browsing histories would quickly diverge.

Bush imagined that these associative trails could be shared:

The lawyer has at his touch the associated opinions and decisions of his whole experience, and of the experience of friends and authorities.

Heck, making a useful browsing history could be a real skill:

There is a new profession of trail blazers, those who find delight in the task of establishing useful trails through the enormous mass of the common record.

Taking something personal and making it public isn’t a new idea. It was what drove the wave of web 2.0 startups. Before Flickr, your photos were private. Before Delicous, your bookmarks were private. Before Last.fm, what music you were listening to was private.

I’m not saying that we should all make our browsing histories public. That would be a security nightmare. But I am saying there’s a lot of untapped potential in our browsing histories.

Let’s say we keep our browsing histories private, but make better use of them.

From what I’ve seen of large language model tools, the people getting most use of out of them are training them on a specific corpus. Like, “take this book and then answer my questions about the characters and plot” or “take this codebase and then answer my questions about the code.” If you treat these chatbots as calculators for words they can be useful for some tasks.

Large language model tools are getting smaller and more portable. It’s not hard to imagine one getting bundled into a web browser. It feeds on your browsing history. The bigger your browsing history, the more useful it can be.

Except, y’know, for the times when it just make shit up.

Vannevar Bush didn’t predict a Memex that would hallucinate bits of microfilm that didn’t exist.

Picture someone tediously going through a spreadsheet that someone else has filled in by hand and finding yet another error.

“I wish to God these calculations had been executed by steam!” they cry.

The year was 1821 and technically the spreadsheet was a book of logarithmic tables. The frustrated cry came from Charles Babbage, who channeled his frustration into a scheme to create the world’s first computer.

His difference engine didn’t work out. Neither did his analytical engine. He’d spend his later years taking his frustrations out on street musicians, which—as a former busker myself—earns him a hairy eyeball from me.

But we’ve all been there, right? Some tedious task that feels soul-destroying in its monotony. Surely this is exactly what machines should be doing?

I have a hunch that this is where machine learning and large language models might turn out to be most useful. Not in creating breathtaking works of creativity, but in menial tasks that nobody enjoys.

Someone was telling me earlier today about how they took a bunch of haphazard notes in a client meeting. When the meeting was done, they needed to organise those notes into a coherent summary. Boring! But ChatGPT handled it just fine.

I don’t think that use-case is going to appear on the cover of Wired magazine anytime soon but it might be a truer glimpse of the future than any of the breathless claims being eagerly bandied about in Silicon Valley.

You know the way we no longer remember phone numbers, because, well, why would we now that we have machines to remember them for us? I’d be quite happy if machines did that for the annoying little repetitive tasks that nobody enjoys.

I’ll give you an example based on my own experience.

Regular expressions are my kryptonite. I’m rubbish at them. Any time I have to figure one out, the knowledge seeps out of my brain before long. I think that’s because I kind of resent having to internalise that knowledge. It doesn’t feel like something a human should have to know. “I wish to God these regular expressions had been calculated by steam!”

Now I can get a chatbot with a large language model to write the regular expression for me. I still need to describe what I want, so I need to write the instructions clearly. But all the gobbledygook that I’m writing for a machine now gets written by a machine. That seems fair.

Mind you, I wouldn’t blindly trust the output. I’d take that regular expression and run it through a chatbot, maybe a different chatbot running on a different large language model. “Explain what this regular expression does,” would be my prompt. If my input into the first chatbot matches the output of the second, I’d have some confidence in using the regular expression.

A friend of mine told me about using a large language model to help write SQL statements. He described his database structure to the chatbot, and then described what he wanted to select.

Again, I wouldn’t use that output without checking it first. But again, I might use another chatbot to do that checking. “Explain what this SQL statement does.”

Playing chatbots off against each other like this is kinda how machine learning works under the hood: generative adverserial networks.

Of course, the task of having to validate the output of a chatbot by checking it with another chatbot could get quite tedious. “I wish to God these large language model outputs had been validated by steam!”

Sounds like a job for machines.