Joseph Jacks: …and in order to upgrade the software, you would have to order, and they would have to ship you a new series of floppies, from which you would need to kind of like physically install, and you would own those physical like pieces of technology that would give you an upgrade. And then the pricing model was all built around that, too. It was perpetual licensing, where you would like basically buy that version, and then you would have maintenance that you’d pay for the version, and in order to upgrade a new version of the software you’d have to physically spend a new upfront amount for the new version, and then do that over and over again.

[34:18] Most of the – I would say probably 99% with some interpolation synthesis, mixing and matching of ideas… Most of the categories in software, including SaaS, which have become a little bit more like fractal domain-specific… Cal.com has almost like one feature, and wow, it’s a billion-dollar company. That just happens because of economies of scale, and humans using more and more technology. But most of these ideas were implemented in the seventies, eighties and nineties, even in the mainframe days. Scheduling, booking things… I mean, this is like calendar functionality.

I was recently watching the oral history of Mike Markkula, who was the first CEO of Apple. He was a guy that Sequoia founder, Don Valentine, introduced to Steve Wozniak and Steve Jobs to basically help them with the business plan, and like run the company. And Mike was also a really brilliant programmer, and he wrote Fortran and basic programs that would actually run on the early Apple computers. And the collection of those programs came with a credit, sort of a copyright credit at the bottom, which was Johnny Appleseed. You might remember this from the early Apple computers, this Johnny Appleseed name. So Johnny Appleseed was Mike Markkula. And Mike in the computer history museum describes this – it’s on YouTube, you can watch it. It’s a really great, multi-hour oral history. Mike actually personally wrote the programs for the applications that would ship in the early Apple computers. So including the calculator, calendar app, bookkeeping app… A lot of these basic – a very basic, primitive word processor…

And so I just think back to like the most fundamental, basic things that humans do as part of their work or their lives have been re-implemented and re-implemented so many times in the history of technology, going back to literally the fifties and sixties, that when we have new ways of distributing and innovating in the core technology, what happens is you just reinvent and you re-implement what happened, what came before, with shinier and newer, sexier, more sort of societally-relevant and interesting abstractions. And then those things end up taking off. And oftentimes people are given a choice. There’s sort of like the free version of the thing, maybe it’s open source and it’s like less sophisticated, but you can totally control it… Then there’s the proprietary version of the thing, which is much more polished and sexy and packaged. This is so fascinatingly – for the first time, and sort of skipping to the AI stuff, we’ve seen this incredibly shockingly surprising almost acceleration of equivalency. This actually happened this week - I mean, the latest LLaMA models are state-of-the-art. And by all accounts, the benchmarks are pretty much indistinguishable, and the capital required to produce that is similar. Mark Zuckerberg literally did an interview a couple of days ago saying they’re spending tens of billions of dollars building LLaMA, and they’re giving it away completely. And in fact, they’re making – Yann LeCun was just tweeting about this the other day… They’re making their licensing even more permissive. You can do distillation, quantization… You can use the outputs from LLaMA models to train other models… It’s almost like an Apache or MIT-level permissiveness. The artifact is very different for a neural network that has massive amount of information compression. Not in the Pied Piper sense, by the way, but in the neural network, AI, tokenization of –

Jerod Santo: Here’s a story. In 2015 or so Shea’s employer had layoffs. One of them was the only contributor to part of the codebase that made money for the company. He remembered reading about truck number - which is the same thing; it’s just trucks instead of buses - and thought it’d be fun to write a GitHub enterprise plugin that calculates who you can’t afford to fire. Shea found a research paper called the Truck Factor Research Paper, which actually went through this in a somewhat vigorous way.

“I started writing the plugin and talked five minutes on it at our Thursday afternoon lightning talks.” His co-workers said it would immediately hit Goodhart’s Law - once a metric becomes a measure, it no longer is a good metric - and they say they saw it as a way for management to easily calculate who you can fire. So all good tools can be used for good AND evil.

But long story short, I went out, I used this Truck Factor paper, which actually describes a way you can go about calculating this, at least fuzzily, to find out the key contributors to projects… And ran it against the Linux Founda– no, the Linux Kernel. Don’t run it against the foundation, run it against the Linux kernel. Found a way using the Linguist plugin to filter out documentation and third party libraries just to make sure it’s actually the people who are like working on the Linux Kernel, and found that not only is it small, even on something as large as Linux kernel, it’s going down.

Currently, Shea found a Truck Factor of 12. 12 people on the Linux kernel, down from in the 40s when it was run originally. So from 2015 till now, from the 40s down to 12. And his collaborator, M. Clare, who did some visualizations and stuff, also installed the plugin on her system. She got a factor of 8.

I’m not sure why there’s a discrepancy between the two. This is a developer’s blog post that I’m mining for this information. But yeah, so we have now this tool. I assume you can go take it and run it against other repositories. I would think, depending on the repository, it may or may not be accurate. Because if you have – probably the smaller the project, the harder it is to know that number for sure. But maybe you can know that number just by looking at the list of contributors in that case. But for large projects with thousands of contributors, you can actually understand how many of these are key in order to keep that sucker running. And it turns out, over time, it appears the Linux Kernel in particular has been going down, down, down. What are your thoughts on this, Adam?

Adam Stacoviak: The math being off is just like driving me crazy. I just did it today, I was like, it’s accurate 95% of the time, and then one time it’s like, it does terrible math. I’m like “I could never charge our customer that or our partner that, because that’s not the real number.” And so I’ve got to check it every single time. I’m like, “I just want the word calculator part of it.”

Adam Stacoviak: …is Docker, Ollama, Open Web UI, either a really powerful CPU, or an IGPU. I don’t think Ollama actually recognizes IGPUs, or GPU. And GPUs are scarce, expensive… You can find decent ones on eBay… They’re actually – if you can eBay well, you can find a decent one. Maybe just buy the M4 Mac Pro, and call it done. And maybe you’ll have a limitation, right? That’s essentially an IGPU. It’s an integrated GPU. I think Ollama runs well there, to my knowledge. But if you want to go crazy and do multiple GPUs, you’re going to have to motherboard that thing. That means you’re going to choose between Linux and Windows. And in my case, I was like “Okay, I got better mileage or easier mileage on my shoes when they were stamped with Windows.” And so that’s the route I went. But basically, Docker, Ollama, Open Web UI… Pick your model from there. Capable hardware… And that’s how you can get to local LLM today. You can play with DeepSeek, you can even do a smaller parameter model… LLaMA 3.2 is probably the one that most people can run, because it’s small enough. LLaMA 3.3 is pretty big. It’s 70 billion parameters. It’s sizable. 70 gigs, I believe, which is larger than most people can run on a single machine, unless you’ve got multiple GPUs.

I wanted to get to that point where I could run those things, and what I would love now is once I’ve sort of gotten that mastered and running stable, to begin to – and I don’t even know what the terminology is, Jerod, so forgive me if I’m stupid in this regard, having done what we’ve done for so long; now I’m actually learning it… It’s I want to train one of these models, or teach it a particular trick. The particular trick is like “Okay, here’s all of our agreements, here’s how we price, here’s how we quantify… Make that job easier.” Can it make that job easier? Can it make me faster in those ways, and be just as accurate, and sweat the details just as much as I do, in the real time, doing those exercises myself? That’s what I’m really trying to get to. I would love it if I can get to certain things I do that are deep think things, that are crucial in business to get right… I don’t want to give people the wrong price, give them the wrong contract, or the wrong terms… I want to give this thing certain parameters, certain business parameters to get to certain results, so I can kind of like word-calculator my way to faster, easier, better quotes for people, and stuff like that. I don’t want it to write my novels… I just want it to help me make those kinds of things faster, and be my word calculator, and be very accurate. So that’s my next journey.

Jerod Santo: That’s a funny workaround, because if you’re a calculator for words, you’re not necessarily a calculator for numbers…

And when you can’t do those reliably, then you could just write a program that does it and returns the same thing every time.

Adam Stacoviak: And that’s what I mean about word calculator. You could probably gush about AI for forever. Let’s not do that. Let’s talk about two more things… Especially if you have time. Do you have more time?

Adam Stacoviak: And I was like “Man, I don’t even own this film.” So I bought it on BluRay, because it’s not on 4k… And for whatever reason, whenever I go in to make MKV to rip it, it was naming all the files a dot. And so all the files it was ripping, each one of them was a dot, and it had an increment, but all the files were hidden. And I didn’t notice it until I went back after it was done… And I was like “Where the heck is all the – I see that it successfully ripped, but where’s all the files?” And I’m like “Oh yeah, these are hidden files. Let me go and fix this.” And there’s like 25 of them in this directory. I’m not going to go one by one inside a terminal and rename these things. And I’m also not going to think about a script to automate renaming these things. So what did I do? I went to my good friend, ChatGPT, and I said – I copied the directory and I said “I’ve got, on a Mac, I need to rename all these files so they’re not hidden. Ideally, they’d be in sequential order of sorts.” And it gave me a script. I put that script in, and boom, all my files were there. I mean, that is the beauty of this word calculator, Jerod. Like, I don’t have to think about any of that stuff. Sure, I could have, it would have been a fun exercise, but it would had been a – what do you call it? A squirrel, a yak to shave. I’d have been there for 20 minutes thinking about this, versus one. Not even one. Off to the races, Fight Club is renamed, and boom, it’s on Plex, and I’m watching it, and I’m happy. That’s the way life should be.

Mat Ryer: Yeah, I get it. I get why people – it was like with calculators in my… I remember my cousin wasn’t allowed to use a calculator in one of his exams… But I was younger, and we were allowed to use calculators, and he was outraged. I mean, it was an English exam, so it didn’t help, but still…

[singing] Sometimes your jokes don’t go down so well… Don’t worry. Don’t worry. It’s alright…

Just a little one for me there…

Mat Ryer: Yeah, no, and I think it’s quite convincing. I think you might convince some people… It’s just – we did a hackathon, and I was writing React code, which I’ve never really written… TypeScript, React. I was dealing with that stuff, and probably prototypy quality level, if we’re honest. I would want someone who really knows it to probably rewrite it, frankly, at this moment. But yeah, there’s times where that autocomplete just really does accelerate it.

[01:15:57.05] There are other times where its suggestions lead you down the wrong path. And I have the experience enough maybe to know that, and others might not. And so I do think we have to be cautious with this. But you have to know what to press on a calculator, too.

Johannes Schickling: When you say “How do we build an API?”, I’d love to hear a clarification of that, since one idea around local-first apps is that you actually – you should ask yourself, “Do we still need an API?” It’s all about the client-side experience at the end of the day, or some other things that you need to build. Maybe you want to send an email if a certain thing happens etc. and you might want to do that on some server-side thing. But one of the ideas of local-first is trying to nudge you away from that very API-centric way of thinking of an architecture. If you’re building, let’s say, a calculator app that just happens to synchronize between your phone and your tablet, then you could try to think about that as a “Oh, we’re going to have an API here”, etc. But if you just start out working on that in a single-player experience, you would never think about “Do we need an API for a calculator? No, we just built that thing client-side.” And now, if you have that as a starting point and you want to synchronize data between your different clients, you don’t necessarily need an API for that. You just need a transfer mechanism between the clients, and that’s rather where the local-first idea comes from.

That being said, you can still interact and integrate with APIs, but I think with local-first, you rather think about “How do I synchronize the data?” and less “How do I request-response with an API?”

Russ Cox: Sure, sure. So we’ve been trying to figure out what we should do with AI; what sort of new capabilities the recent AI advances in the particular large language models unlock. And one of the things that I think really helps – so there’s a lot of, I think, unwarranted optimism about exactly these amazing things that LLMs will be able to do. And maybe they will, maybe they won’t. But at the moment, I think that the main strength is - I saw an article, maybe a couple of years ago now, that was sort of framing them as kind of a word calculator. So computers are number calculators, but LLMs are really kind of word calculators.

So in the situations where you have a lot of words and you want to deal with them, having a word calculator sounds like a good thing. And so one of those times is when you’re doing software maintenance and you’re dealing with other people, right? They’re speaking English, and so having some sort of word calculator that can do things with the English text sounds like that would be a win.

And so in particular, we’re looking at what we can do to help open source developers with running their own open source projects, and we’re using Go as a test bed for that. And the goal is really to try to help automate away the stuff that no one really wants to do, like the basic triage of issues, or figuring out where the duplicate issues are.

Right now we have a bot that in the Go repo, when you post a new issue, it looks up using LLMs and vector databases other issues that are very closely related, and it posts a list of at most the top six or seven, maybe ten; I forget exactly what the cutoff is, but there’s a score cutoff of how related they have to be. So sometimes it’ll post nothing. And sometimes it’ll say “Hey, these three issues look very related to this issue.” And originally, we were looking at that for automated duplicate detection and just closing a duplicate, but it’s very hard to tell the difference between “This is a duplicate of the issue” or “Yes, this looks like exactly the same thing, but you thought you fixed it, and now it’s happening again, o maybe it’s different.” You can’t tell the difference between those from the reports.

But just even pointing out “Hey, look, these other issues might be related” has been incredibly helpful, because you get an issue that comes in and you’re looking at it and you didn’t even know about this other issue that someone else took care of. And you say “Oh wait, that does look like exactly the same thing.” In fact, the bug fix for that one has a subtle mistake in it, and that would cause this new one. Seeing those connections is really, really helpful. And in particular, having this kind of database of the context for the project, and having the computers take care of saying “Hey, this looks like related context that you should know about”, that turns out to be incredibly helpful. Because once the project is bigger that you can’t keep it in your head - none of us can keep Go in our heads anymore, all the stuff in the Go project. Having that kind of automated retrieval is actually just incredibly helpful.

So we’re looking for how can we use LLMs and sort of recent advances in AI to help with that kind of stuff, the stuff that people aren’t good at, and that honestly, isn’t that much fun. Groveling through all the issues to try to find the related issues is not something I want to spend my time doing. Whereas, you know, we’re not that interested in having the AIs write all the code, because that’s the fun part, right? Like, why would we take away the fun part? Let’s take away the not fun part.

So that’s the basic idea of the project… It’s just “Let’s figure out how we can point AI at the stuff we don’t want to do”, and also, just learn a bit about what AI can do. To me, it’s sort of still a bit of an experiment to just see what are LLMs actually good for, because none of us really know.

Ryan Worl: Yeah, at WarpStream we know that that’s like us. A very important part of what we do. But it’s always easier to walk into a sales conversation with the hard facts numbers, and not the – a lot of vendors use those exact attributes to describe, to attribute a lot of savings to their product… Which is probably true. But they feel a little bit more wishy-washy compared to the hard facts numbers. So that’s why we lead with those in our pricing calculator.

And obviously, those are still things that we highlight when we’re talking to potential customers, to help them understand the value of the product. But we like to think of that as more like the icing on the cake stuff, and the cost savings is what we’re promising them, basically. Everything else is just icing on the cake.

Tejas Kumar: Yeah, that’s a good question. I just follow a number of experts’ definitions of this thing. I tend not to try and coin terms myself, mainly because I’m just not very credentialed, if we’re being honest… So how do I see agents? I summarize it, I summate it - I’m trying to find the right word - I deduce it from definitions from industry experts who have done it before me. So people like Andrew Ng, the founder of Coursera, and now the founder of DeepLearning.ai - I think he’s got some great content about this, where he defines agentic workflows as workflows that have LLMs perform three tasks, either all three or a subset of them. And those are reflection, meaning generate some output and reflect on it, “Is it good, is it not?”, and then iteratively work on it until it cannot be improved further. So there’s reflection. There’s tool calling, as I mentioned, with RAG, where the large language model will, sort of like a human being, recognize – for example, if you ask me to do a complex calculation, like 324 divided by 9 times 7, I’ll just be like “It’s time to get a calculator.” I’ll recognize that this is the sort of boundary of my capabilities, and go use a tool. So number two is tool calling. And number three - I think it was agent collaboration, where you have – yeah, it’s LLM as judge. It’s this model where a capable model (pun intended) coordinates lesser capable models towards an outcome you want. So it’s like GPT 4.0 being the most capable of Open AI’s models would orchestrate like three or four different 3.5 turbo models that are doing various tasks or generations. And so those three, either one of them or all of them, make up according to Andrew Ng an gent workflow.

[05:47] According to David Khourshid, AI agents are an implementation of the actor model, which is just a programming model where you have an entity called an actor, that sort of acts in response to observing its environment. So the classic implementation of the actor model is PacMan. Actually, a great example of AI, but rule-based AI, where the rules are known ahead of time, is PacMan, where you have PacMan, the little yellow pizza thing, and it’s observing the environment: where are the ghosts, where are the cherries, where are the dots… And you as the player take on the role of the actor. But there’s also demo mode, where the actor model is in play. And according to David Khourshid, this implements agentic workflows. However, it’s rule-based, it’s not generative, but it’s still an agentic workflow, where PacMan is an agent.

So I just take a mishmash of those two - these are the preeminent leaders in the space in my mind - and marry them, and that’s the working definition that I have for an agent. So it’s not a sentence, it’s not a nutshell, but I’m trying to give you more sort of a broad framework of how I see agent workflows. I have seen this term abused, where people will build – maybe abused is too strong… But people will build a custom GPT; this is a feature you can use from Open AI’s GPT 4. They’ll just build a custom GPT, add a system prompt, add some knowledge that GPT 4 can do RAG on, and call this an agent. I disagree; I don’t think that’s an agent, that’s just a RAG application. It doesn’t really do any of the things like we talked about: reflection, tool calling, collaboration, or observing an environment and responding accordingly. So I’d say those four tenets make an agent an agent.

Jerod Santo: [00:15:37.08] For sure. For sure, because it demos so well. And it just – it’s just so impressive. I mean, it’s the stuff out of the future, right? I mean, you basically take your story problem, that your teacher gives you, and you just write it down with your pencil, and draw some little diagrams and lay it out, and then boom goes the dynamite. But again, I don’t think – I mean, I don’t even have an iPad, but I think some of that is in… Math notes have come to also to the Notes app, and I’m not sure if that’s in there, or that’s just the iPad Calculator… But regardless, I will probably never use this feature myself.

Jessica Sachs: You mean like flights and stuff, and like calculator, all those things?

Jared Zoneraich: Yeah, 100%. And just to be on the cutting edge, and know that nobody really knows what they’re doing here. Some people do, but there’s very few of them. So regarding the APIs, ChatGPT, the way I think about it, and the way I usually explain it is it’s basically a very thin wrapper on top of the API. Every time you talk to ChatGPT, behind the scenes it’s using this LLM technology, it’s sending your message, and a little bit of a preamble before your message. And the preamble is basically - we can call it the prompt, and it’s basically saying “Hey, you’re an AI assistant. Make sure to be helpful to the user. Maybe don’t be controversial. You can use a calculator if you need”, stuff like that, and then giving the user messages. And that’s all it is. And the process of prompt engineering is “How do you tweak that preamble? How do you get it to respond in the way you want to respond by telling it what to do?”

When I talk about the API, I’m talking about the things Open AI has exposed to let you build your own track up and let your build your own products on top of it. And I just think Open API has done – if you want to get started and you haven’t touched these APIs today, the best, best thing to do is just go to Open AI’s docs and just read the Getting Started tutorial. It’s really well done, and I think - as someone who’s running a developer tools company, I’ll tell you how hard it is to write good docs. I think our docs – from my perspective, I think they should be much better. Other people say they’re great, but… Well, there’s always room for improvement, and it’s just a very hard thing to do. So that’s where you should go.

Adam Stacoviak: Yeah, for sure. Can we talk about some subtle human leveling up that’s practical for ChatGPT? I mean, I know it’s not Cody. Do you mind riffing a little bit? So last night my wife and I, we were hanging up pictures of our beautiful children. We took pictures of them when they were less than one week old, and then we have pictures of them in the same kind of frame at like their current ages, and one’s seven and one’s three. So it doesn’t really matter about the age. They’re just not one week old anymore. So you have this sort of brand new version of them, and then like current version, to some degree. And it’s four pictures, because we have two sons, and we want to hang them on the wall. And my wife was trying to do the math - and we can obviously do math; it’s not much. It’s like an eight foot wide wall; we want to put them in a [unintelligible 00:15:29.05] with even spacing, all that good stuff. I’m like “Babe, we should ask–” And like, I’m more versed in this than she is; not so much that she doesn’t use it often, she just doesn’t think to. And I think that might be the case of why it’s not being more widely used, is they don’t think to use this. And I’m like “I don’t want to use it. It’s a word calculator.” Or “I want to use it, it’s a word calculator.” I don’t want to think about this problem myself. I don’t want to do the math. I could just tell it the problem; my space, my requirements, and it will just – it will tell me probably too much, but it will give me a pretty accurate answer. I’m like “Let’s just try it”, and she’s like “Okay.” And so I tell it “Hey, ChatGPT, I have this eight foot, five inch wall in width, and I want to have these pictures laid out in a grid. They’re 26 inches squared, 26 wide, 26 tall, and I want to have them evenly distributed on this wall in a four grid.”

It gave me the exact answer, told me exactly where to put them at. We did it in five minutes, rather than like doing the math, and making a template, and writing all these things on the wall. It was so easy, because it gave us the exact right answer. That’s cool.

Justin Garrison: Yeah. Maybe Excel is too difficult. If I go look at Excel and I’m like “Actually, there’s too many functions here. I don’t need all of them”, I can hide most of them. But at some point, I just need like maybe a calculator. Because actually, what I needed – I didn’t need Excel to do that thing.

Jerod Santo: The way Simon Willison described it really resonated with me. He called it a calculator for words. And so it’s going to be very good at taking words - you can put a lot of words into it and have it summarize those down, compile them down into less words, and like Adam just said with the smart diagnostics, “Tell me what this means” or “Highlight the difficult parts”, or whatever. It can also take a small amount of words and expand them into much more words. And those two use cases - and there’s many permutations of those - are hugely valuable, beyond just Q&A.

Kris Brandow: Yeah. I’ve ran a few little tiny plugins to do things like converting between binary and decimal, or hexadecimal and decimal, stuff like that, which is just like – I hate googling for that, because there’s not a Google calculator that just does it.

Kate Bradley Chernis: Yeah. I mean, I think my first advice would be just calm down, everybody. Hold on. So think about humans - we’re the only mammals that when we come out of the womb, we’re completely helpless. We can’t feed ourselves, can’t defend ourselves, can’t stand up; can’t even hold our heads up. Nothing. If AI was a human, it’d be about three months old. So I think that’s just one important thing to remember, is that human guidance is required.

Certainly, as we talked about at the top, the prompting expertise is going to be something that people are looking for as well, because in order to get the robot to do what you want, it has to be asked just the exact right questions; what are those questions?

With technology, as you guys already know - I mean, technology happens; it’ll continue to happen, and it’ll continue to improve lives and replace jobs… And jobs will evolve. And we’re in the same boat here. This strategy piece is where humans are still really absolutely necessary. AI is not sentient. Unfortunately, Hollywood really has given us a grave misunderstanding of what the definition means. I think it maybe it was Paul Roetzer from Marketing AI Institute, but they said “AI still lacks emotional intelligence.” Which is also true, you know.

[38:22] And then another quote, and I don’t know who said this, but it’s “If you’re not using AI, you’re going to be falling behind.” But the people who are using AI in tandem with the work they’re doing - just like that calculator we talked about before - they are the ones that are going to be getting ahead of the game. So you have to figure out a way to obviously embrace it. And I think it’s so funny that companies are abandoning generative AI, because remember when everybody banned Facebook? I think there’s still government entities that can’t use Google Docs. And you’re like “Are you kidding me? Get with the program.”

Simon Willison: I think that’s the thing I was talking about that does 15 trillion operations a second. It sounds like that’s that neural processor chip… Which - yeah, so Apple don’t let you access it directly. People have hacked it. George Hotz has a GitHub repository where he… He did a livestream last week where he apparently managed to get his own code to run on it by jailbreaking the iPhone, or maybe it was on a laptop… So yeah, it sat right there…

And yeah, I mean - all of these language models, they all boil down to matrix multiplication, right? You’re just doing vast numbers of matrix calculations. My understanding at the moment is for every token that it produces, it has to run a calculator here, and there’s all 175 billion parameters. But again, 15 trillion - that’s going to do you a lot of those token estimations in a second.

Break: The top story from Sunday’s edition of Changelog Weekly was Matt Healy announcing, “Your next smart home device is a $30 used Kindle”. He says:

“… a lot of tools that used to be separate have now converged into one: the phone.

You might not buy another camera again, and you probably won’t buy a calculator. For the most part, it’s a good trade-off: what we lose in the tactile pleasures of a good single-purpose device, we gain back tenfold in having all those devices in our pocket all the time.

That’s all fine for calculators, cameras, music players, and the like — things that are there when we need to use them, and can forget about when we don’t. But our phones are also the nexus of all kinds of data we want pushed to us — news, messages, notifications, alerts, status updates. Things we want to know about without going looking for them.

A phone isn’t actually great for this kind of ambient awareness. It’s usually in a pocket or purse, so the ways it can get our attention are limited: a buzz, a notification on the lock screen, a red dot on an app icon. I can choose to let myself be interrupted, or I can compulsively check in to see what the latest alerts might be.

I’ve found myself wanting a less intrusive channel for ambient data. Something I can glance at in passing, without giving my full attention or opening myself up to whatever other interruptions my phone might have in store. It should be easy to check, but just as easy to ignore.”

The device he landed on is, you guessed it, a wifi-connected Kindle 4 mounted on the wall. In the post he walks you through why this is the perfect setup: why e-ink, why a kindle, why a screenshot of a web page… he also provides instructions so you can set up a $30 Kindle to do the same.

Audun Fauchald Strand: But we actually – it was a lot of reuse. We had some strange components; we had the calculator that people used to figure out what they could get in a benefit if they needed to. But that calculator kind of had the functionality we needed to get the data, the data to calculate what people should get in these new benefits.

So basically, you used the calculator as an API. So we kind of grabbed things from everywhere, and the payment system had this old file-based interface that we used… So some of the integrations was like totally modern, with Kafka and asynchronous, and another one was writing files to disk, and a Bash script moving that file to another desk…