Looks like AWS just published their post-mortem of their big outage this week: you can find it here.
For a lay person, this explanation might suffice.
CNN also has a good synopsis that includes other recent major outages and asks: why does this keep happening?
I think it is safe to say that we will see more of these major outages in this decade. So keep good backups, among other precautions. 🙂
I recently took a vibe coding approach to try and do two things:
Why? For some time I had these two projects in mind:
My main constraint was do it quickly: it was meant to be fun. So don’t spend all weekend getting up to speed on APIs and HTML and CSS: just see what I could do aided by A.I.
First up, to build the web site, I started with Microsoft’s A.I. Since I had some requirements of what I wanted the web log to look like, I told them to Copilot and had it built me the one page blog web site. It helps to be clear on your requirements, but I found that I only needed a few of them to start with. As I went along, new requirements would come to me (e.g. the ability to add photos from the Internet) and I would tell Copilot to now incorporate these new requirements and give me a new web site. My experience vibe coding is that there is a lot of back and forth in order to be effective. As well, there were things that I could just manually do by hand, like add a background tile and change the picture on the header, so I eventually I bailed on using Copilot and finished it by hand. You can see the result here. It’s just what I wanted.
What makes things better was that I asked Copilot to write me a python program which would allow me to easily add to the blog and then push it to AWS. That was a great new addition. Now I can just enter a line on the command line and the blog gets updated.
All in all a very successful project.
As for my Second project with Spotify, I switched from Microsoft to Anthropic. At first Claude produced great code: I asked it to build me a UI that allowed me to type in the name of three songs and then take these songs, use Spotify to build me a playlist built around those songs and lo and behold it did. Things went downhill from there. Much of the code, while long, had numerous errors. I would provide the errors to Claude and it would correct things. The code did get better, but after 30 versions, it was time to call it quits. Instead I took small chunks of the code and using VS Code, manually tried to determine why it was not working. I was able to ultimately nail it down to one Spotify API call. And why wasn’t it working? Because Spotify disabled access to it in 2024. Did Claude know that? I don’t know. It certainly didn’t act like it.
All in all a very unsuccessful project.
What did I learn for this? What would I recommend to you?
Thanks for reading this. I hope it helps.
P.S. When I talk of vibe coding, I am using the definition used by my current employer. The opinions expressed above are mine only and not necessarily those of my employer.
The computer above, and the ad it is, came out in the mid 90s.
It was possible to work from home then, but it was not easy. I used to have a luggable computer that weighed 40 pounds and which I would …lug… home every day one summer to work from home. What I dreamed for, though, was to work from home with a small laptop like Coppola’s. A laptop where I could work from home daily, be it at a desk or in a beautiful kitchen like the one above.
It eventually happened. The laptops got better, the networks got better, and eventually the work cultures got better and I could do this. My kitchen wasn’t as nice, but everything else was nice.
Creative people, keep putting out your visions for a better world. You never know what dreams people will have. It might be as simple as a dream of working on a laptop in a kitchen. A dream that becomes more achievable once people can envision it.
Time for my annual review of what I’ve seen happening in the world of robotics in the last 12 months. Progress in robots, unlike other forms of IT, tends to be slower and incremental. Not surprising: robotics is hard. If you look at the robots I featured last year and compared them to these robots, you won’t see dramatic changes. Still, it is interesting to note the progress made and the limitations still encountered.
Robots in factories are still where the bulk of robots can be found. Despite widespread adoption, they still have issues with basic tasks, as this study of robots in Amazon warehouses shows. Will that mean we are away off from amazon having package delivery humanoid robots? I’d say yes.
Home robots tend to be limited to one floor. But according to this, they are close to navigating stairs. That could be a game changer. Maybe some robots will get around your house like this tiny pogo robot does? Call me skeptical. Still, engineers keep trying to give robots more range, as we see here.
Not all home robots are limited to the floors of your house. This here contratrapion is a brainless soft robot that runs on air, while these 5 robotic lawnmowers hit the great outdoors. Speaking of robots and grass, there may come a day when your caddie is a robot.
It’s not all hard labour for home robots. These cute little fellows are mainly for emotional support for seniors. I question whether this pomodoro desk robot is an actual robot but I do agree it is the “cutest way to boost your productivity “. Speaking of desktop robots, this cute desktop robot gets gloomy when your room becomes unhealthy.
Finally, not all robots are cute: a lifelike robot that mimics human anatomy with complex muscular system is pretttttty creepy (see below).
One last note: the province of Ontario “began a ten-year pilot program in 2016 to allow the testing of automated vehicles (AVs) on Ontario’s roads under strict conditions, including a requirement to have a driver for safety reasons.” (In my mind, AVs are just another form of robots.) The program is still on going and I expect to see more developments on it. You can read more on that, here.
Thanks for reading this. I’ll be curious to see what happens with robots over the next year. Let’s see!
Over the last year I’ve been working with various Raspberry Pi products, from the Pi Pico to the all-in-1 400 series. Along the way, I’ve found these links / articles to be useful for me. I hope they might help you too.
How To Guides: Here’s a variety of how to articles for various Raspberry Pi products:
LCD/OLED output: If you want to use LCD or oled displays with your Pi, check out how to get Billboard Scrolling with LCD Display & Raspberry Pi Pico. Here’s how to get your raspberry pi working with your oled. More on that, here: using ssd1315 oleds with the raspberry pi pico. Also here .
RPis’ config.txt: If you want to use your Pi with an old composite display, then you need to learn more about the config.txt file. So here’s click on the links to know about the config.txt file: here and here and here and here.
If you are having problems with output display, check those out. Also, how do i shrink the screen with composite out is good. So is this: how to add an rca tv connector to a raspberry pi zero.
Pin settings: Pin settings articles for the Raspberry Pi are here and here and here. Finally, here’s the pin layout for the Digispark Pro. More on the Digispark here.
P.S. Posted on 3/14 for obvious reasons. 🙂
Is Elon Musk incompetent? Is he a genius? Or is he something else?
While some think his recent actions at Twitter and in DOGE indicate he is incompetent, Noah Smith came out and defended Musk in this Substack post: Only fools think Elon is incompetent – by Noah Smith.
Smith starts off by saying that Musk …
is a man of well above average intellect.
Let’s just pass on that, since we don’t know the IQ or any other such measure of the intellect of Musk. Plus, competent people don’t need to have a high IQ.
Indeed Smith gives up on IQ and goes for another measure:
And yet whatever his IQ is, Elon has unquestionably accomplished incredible feats of organization-building in his career. This is from a post I wrote about Musk back in October, in which I described entrepreneurialism as a kind of superpower
So it’s not high IQ that makes Elon Musk more competent than most, it’s his entrepreneurialism. In case you think anyone could have the same ability, Smith goes on the say why Musk is more capable than most of us:
Why would we fail? Even with zero institutional constraints in our way, we would fail to identify the best managers and the best engineers. Even when we did find them, we’d often fail to convince them to come work for us — and even if they did, we might not be able to inspire them to work incredibly hard, week in and week out. We’d also often fail to elevate and promote the best workers and give them more authority and responsibilities, or ruthlessly fire the low performers. We’d fail to raise tens of billions of dollars at favorable rates to fund our companies. We’d fail to negotiate government contracts and create buzz for consumer products. And so on.
Smith then drives home this point by saying:
California is famously one of the hardest states to build in, and yet SpaceX makes most of its rockets — so much better than anything the Chinese can build — in California, almost singlehandedly reviving the Los Angeles region’s aerospace industry. And when Elon wanted to set up a data center for his new AI company xAI — a process that usually takes several years — he reportedly did it in 19 days
And because of all that, Smith concludes:
Elon Musk is, in many important ways, the single most capable man in America, and we deny that fact at our peril.
Reading all that, you might be willing to concede that whatever Musk’s IQ is, not only is he more than competent, but he must be some sort of genius to make his companies do what they do, and that you would be a fool to think otherwise.
But is he some kind of entrepreneurial genius? Let’s turn to Dave Karpf for a different perspective. Karpf, in his Substack post, Elon Musk and the Infinite Rebuy, examines Musk’s approach to being successful by way of example:
There’s a scene in Walter Isaacson’s new biography of Elon Musk that unintentionally captures the essence of the book: [Max] Levchin was at a friend’s bachelor pad hanging out with Musk. Some people were playing a high-stakes game of Texas Hold ‘Em. Although Musk was not a card player, he pulled up to the table. “There were all these nerds and sharpsters who were good at memorizing cards and calculating odds,” Levchin says. “Elon just proceeded to go all in on every hand and lose. Then he would buy more chips and double down. Eventually, after losing many hands, he went all in and won. Then he said “Right, fine, I’m done.” It would be a theme in his life: avoid taking chips off the table; keep risking them. That would turn out to be a good strategy. (page 86) There are a couple ways you can read this scene. One is that Musk is an aggressive risk-taker who defies convention, blazes his own path, and routinely proves his doubters wrong. The other is that Elon Musk sucks at poker. But he has access to so much capital that he can keep rebuying until he scores a win.
So Musk wins at poker not by being the most competent poker player: he wins by overwhelming the other players with his boundless resources. And it’s not just poker where he uses this approach to succeed. Karpf adds:
Musk flipped his first company (Zip2) for a profit back in the early internet boom years, when it was easy to flip your company for a profit. He was ousted as CEO of his second company (PayPal). It succeeded in spite of him. He was still the largest shareholder when it was sold to eBay, which netted him $175 million for a company whose key move was removing him from leadership. He invested the PayPal windfall into SpaceX, and burned through all of SpaceX’s capital without successfully launching a single rocket. The first three rockets all blew up, at least partially because Musk-the-manager insisted on cutting the wrong corners. He only had the budget to try three times. In 2008 SpaceX was spiraling toward bankruptcy. The company was rescued by Peter Thiel’s Founders Fund (which was populated by basically the whole rest of the “PayPal mafia”). These were the same people who had firsthand knowledge of Musk-the-impetuous-and-destructive-CEO. There’s a fascinating scene in the book, where Thiel asks Musk if he can speak with the company’s chief rocket engineer. Elon replies “you’re speaking to him right now.” That’s, uh, not reassuring to Thiel and his crew. They had worked with Musk. They know he isn’t an ACTUAL rocket scientist. They also know he’s a control freak with at-times-awful instincts. SpaceX employs plenty of rocket scientists with Ph.D.’s. But Elon is always gonna Elon. The “real world Tony Stark” vibe is an illusion, but one that he desperately seeks to maintain, even when his company is on the line and his audience knows better. Founders Fund invests $20 million anyway, effectively saving the company. The investment wasn’t because they believed human civilization has to become multiplanetary, or even because they were confident the fourth rocket launch would go better than the first three. It was because they felt guilty about firing Elon back in the PayPal days, and they figured there would be a lot of money in it if the longshot bet paid off. They spotted Elon another buy-in. He went all-in again. And this time the rocket launch was a success. If you want to be hailed as a genius innovator, you don’t actually need next-level brilliance. You just need access to enough money to keep rebuying until you succeed.
It seems that the path to success for Musk is not to be good at something, but to be tenacious and throw massive amounts of resources at a problem until you defeat it.
In IT, there is an approach to solving problems like this called the Mongolian horde approach. In the Mongolian horde approach, you solve a problem by throwing all the resources you can at it. It’s not the smartest or most cost effective approach to problem solving, but if a problem is difficult and important, it can be an effective way to deal with it.
It’s interesting that Smith touches on this approach in his post. He brings up Genghis Khan, the leader of the Mongols:
Note the key example of Genghis (Chinggis) Khan. It wasn’t just his decisions that influenced the course of history, of course; lots of other steppe warlords tried to conquer the world and simply failed. Genghis might have benefited from being in just the right place at just the right time, but he probably had organizational and motivational talents that made him uniquely capable of conquering more territory than any other person in history. The comparison, of course, is not lost on Elon himself
It appears that Musk is familiar with the Mongolian Horde approach as well. Indeed, Karpf illustrates the number of times Musk used this approach in order to be successful, whether it’s playing poker or building rockets.
If you can take this approach, with persistence and some luck, you can be successful. Success might come at a great cost, but it likely will come. And in America, if you are successful, people assume you are intelligent and highly competent regardless of your approach. That’s what Smith seems to assume in his post on Musk.
Even with this approach, you do have to have some degree of competency. If you are using this approach to play poker, you have to know enough about the game to win when the opportunity presents itself. But you don’t have to be the world’s best poker player or even a good poker player.
The same holds true for Musk and his other companies. He’s not incompetent, but he’s not necessarily great or even good at what he does. He just hangs in there and keeps applying overwhelming resources until he eventually wins. His access to resources and his tenacity are impressive: his competency, not so much.
P.S. Like many others, I used to think Musk was highly competent. I stopped thinking that when he took over Twitter and turned it into X. This “Batshit Crazy Story Of The Day Elon Musk Decided To Personally Rip Servers Out Of A Sacramento Data Center” in Techdirt convinced me his IT competency is not much better than his poker competency. Indeed, if success was a metric, then he is incompetent at running tech companies, based on this piece in the Verge: Elon Musk email to X staff: ‘we’re barely breaking even’. I won’t count him out until he abandons X, but if the time comes when X is successful, it will be because of him applying massive amount of resources (time, money, etc) to it, not because he is an IT genius.
I decided to go through all my posts on AI and pull out information that would be useful to anyone wanting to learn more about generative AI (often referred to as gen AI or genAI) and the LLMs they run. If you have used chatGPT, you have used genAI. But there’s much more to the technology than what you find on that site. To see what I mean, click on any of the blue underlined text and you will be taken to a site talking about something to do with gen AI.
Enjoy!
Tutorials/Introductions: for people just getting started with gen AI, I found these links useful: how generative AI works, what is generative AI, how LLMs works, sentence word embeddings which kinda shows how LLM works, best practices for prompt engineering with openai api, a beginners guide to tokens, a chatGPT cheat sheet, demystifying tokens: a beginners guide to understanding AI building blocks, what are tokens and how to count them, how to build an llm rag pipeline with llama 2 pgvector and llamaindex and finally this: azure search openai demo. (Some of these are introductory for technical people – don’t worry if you don’t understand all of them.)
For people who are comfortable with github, this is a really good repo / course on generative AI for beginners. (and check out these other repositories here, too). This here on the importance of responsible AI. and here’s a step by step guide to using generative AI in your business, here.
Prompts and Prompt Engineering: if you want some guidance on how best to write prompts as you work with gen AI, I recommend this, this, this, this, this, this, this, and this.
Finally: Here’s the associated press AI guidelines for journalists. This here’s a piece on how the Globe and Mail is using AI in the newsroom. Here’s a how-to on using AI for photo editing. Also, here’s some advice on writing better ChatGPT prompts. How Kevin Kelly is using AI as an intern, as told to Austin Kleon. A good guide on how to use AI to do practical stuff.
Note: AI (artificial intelligence) is a big field incorporating everything from vision recognition to game playing to machine learning and more. Generative AI is a part of that field. However nowadays when we talk of AI people usually mean gen AI. A few years ago it was machine learning and before that it was expert systems. Just something to keep in mind as you learn more about AI and gen AI in particular.
Imagine you have a database with two tables of information: customer information and account information. The one piece of data that both tables share is account ID. With relational database software, you can use it to tie the two tables together. So if a customer comes to you and asks for their current balance, you can ask them for their account ID and some other personal info. Then you can query that database with the account ID and verify who they are because you can see their customer information and then once you validate them you can also see their account information and you can get the computer to print out their balance. The database, in this case a relational database, relates the two source of information (customer info and account info) and lets you retrieve that information.
Now the nice thing about relational databases is you can further relate that information to other sources of information. If you have a table of products you want to promote to customers depending on their net worth, you can query the database for the accounts that meet the product criteria and then pull up the customer information and mail them the information about their product. You’ve related three different tables of information to do this and pulled it together using a query.
When it comes to generative AI, the prompt you enter is also a query. The gen AI system doesn’t search tables though. Instead it searches a model it has that was build with sources of information it was trained on. If it was trained on Wikipedia, then all those pages of Wikipedia are not unlike tables being queried. The difference is the gen AI system uses its algorithm to determine how all that Wikipedia knowledge relates to your query before it gives you a result. But in many ways you are querying the gen AI systems just like you might query a relational database.
Of course generative AI has much more power than a simple relational database. But in many ways the two things are the same. We need to start looking at it then the same way. we can do many clever things with relational databases but don’t think of them as intelligent. The same should hold for generative AI.
People are worried about data centres.
People are worried about data centres and carbon emissions, especially if they read articles like these: Google’s carbon footprint balloons in its Gemini AI era, or Microsoft’s AI obsession is jeopardizing its climate ambitions or Google’s greenhouse gas emissions are soaring thanks to AI or Exxon Plans to Sell Electricity to Data Centers.
People are also worried about data centres and water usage after they read articles about how much water is used for each ChatGPT query.
And when people read pieces like this, Amid Arizona’s data center boom, many Native Americans live without power, they are no doubt worried about what data centres do to the communities where they reside.
My thoughts on this, as someone who has worked in data centres for 40 years, is that there are valid reasons to be concerned, but there are positive aspects to data centre growth and it’s important to keep those in mind.
Data centres are simply places with a concentration of information technology (IT). One time companies had data centres on a floor of their building, or in special buildings in locations like the one pictured about on 325 Front St in Toronto. These days, many companies are moving from hosting their own technology in their own buildings and moving that tech to cloud computing locations, which are just another form of data centre.
I believe that’s this migration to the cloud is a good thing. As this states:
Research published in 2020 found that the computing output of data centers increased 550% between 2010 and 2018. However, energy consumption from those data centers grew just 6%. As of 2018, data centers consumed about 1% of the world’s electricity output.
Moving workloads from on premise infrastructure to cloud infrastructure hosted in big cloud data centres saves on energy consumption. One of the reason for the savings is this:
…normally IT infrastructure is used on average at 40%. When we move to cloud providers, the rate of efficiency using servers is 85%. So with the same energy, we are managing double or more than double the workloads.
You might think all this data centre growth is being driven by things like AI and crypto, but according to this IEA report:
Demand for digital services is growing rapidly. Since 2010, the number of internet users worldwide has more than doubled, while global internet traffic has expanded 25-fold. Rapid improvements in energy efficiency have, however, helped moderate growth in energy demand from data centres and data transmission networks, which each account for 1-1.5% of global electricity use.
That’s the key point: the demand for digital services is driving the growth of data centres. Every time you watch a video on your phone or pay your bills on your computer, you are using a data centre. Even things like the smart meter on your house or the computer in your car or the digital signs you see interact with a data centre. You use data centres pretty much all day, sometimes without knowing it.
The good news is that there are innovations to make them greener are happening with them, like this new method for liquid-cooling data centres that could make the waste heat useful. And it’s good that IT professionals are moving towards green cloud computing. But it’s not good that with the rise of technologies like generative AI, IT companies are having a difficult time keeping up with the demand and sticking to their green targets.
Speaking of gen AI, I think energy costs associated with AI will peak and come down from the initial estimates. Indeed, when I read this article Data centres & networks – IEA, and in particular this…
Early studies focused on the energy and carbon emissions associated with training large ML models, but recent data from Meta and Google indicate that the training phase only accounts for around 20–40% of overall ML-related energy use, with 60–70% for inference (the application or use of AI models) and up to 10% for model development (experimentation). Google estimates that ML accounted for 10-15% of its total energy use in 2019-2021, growing at a rate comparable with overall energy growth (+20-25% per year over the same period).
… then I am optimistic that energy costs will not be as bad as initially estimated when it comes to AI. I am not optimistic that we will not decrease our demand for digital services any time soon. Because of that demand, we need not just more data centres, but better ones. It’s up to companies to build them, and it’s up to citizens to keep the companies accountable. The best way to keep them accountable is to better understand how data centres work. I hope this post went some small way to doing that.
P.S. All the opinions expressed here are my own and do not represent the views of my employer.
This week I successfully set up five Raspberry Pi devices at home: 3 Pi Zeros, 1 Pi 400, and 1 Pi original. Plus I have two old C.H.I.P. computers that work. I had struggled with using them in the past, but this time it was a breeze due to the lessons I’ve learned. Here’s some of these lessons:
Get wireless ones: I originally had Pi Zeros and Picos without wireless capability. And that can be fine if you know you don’t need it. But it is helpful to be able to have them communicate wirelessly and it gives you more flexibility, even if it costs a few more bucks.
Get headers: again, I had some Pi Zeros and Picos without headers. Unless you are good with soldering, get the ones with headers. It just makes it easier physically connect them to other technology. The Pi Zero above has no headers, the one below does.
Keep track of all the connectors you need and kept them handy: With the Pi Zeroes, I have a set of adapters that allow me to connect it to power, USB and HDMI. Once I have it set up, I just need a cable to provide power and I run it in headless mode (which I can do because of wireless). I have a special box for all that stuff so I can easily find it.
Give your Pis unique hostnames: if you are going to be connecting to them via ssh or scp, then give them a unique host name. You can do this when you set them up. What’s nice about that is once they connect to the wireless network, I can easily identify them. For example, I can ping pizero1 or I can ssh myuserid@pizero2 versus trying to find out their IP address of 192.168.0.??
Designate a machine for setting up the Pis: for me, I have a Pi 400 that I use to program the Picos. And I have a Ubuntu machine to format the SD cards. But you do what works best for you.Having a consistent environment means when you run into problems, the problem is likely not with your environment but with the SD card or the Pico.
Avoid obsolete or tricky technology: in the past I got discouraged by trying to get old or tricky technology to work. I had old dongles that gave me errors when trying to build the SD cards properly; I had old unsupported Digispark devices that would not work at all; and I had some Adafruit devices that were cool but the path to success with them was challenging. In the future, I am sticking with tried and true technology from Arduino and Pi. Don’t make working with such devices any harder than it has to be.
Get cases for your Pis: if you are going to use them on the regular, get a case. Even a cheap case make it look like a finished and working device and not some hack. Not only does it look better, but it will likely work better (i.e. the cables will not move around and lose a connection). And make sure the case you get is made for your device so it will fit properly.
Document as you go: keep some log of what worked and what didn’t. Take photos of successful set ups. Save all the good web sites that helped out. Better still, blog about it. (If you search this blog for “raspberrypi” you will find the things I have found and written about.)
Good luck with your projects. May they go smoothly.
If you’re like me and you’re doing work with an Arduino or a Raspberry Pi, you are going to want to hook it up to something. Now the something might just be a simple button or an LED, or it might be more sophisticated like an infrared receiver or a heat sensor. If that’s you, then you want to consider getting this: KEYESTUDIO 37 in 1 Sensors Starter Kit for Arduino Mega R3 Nano Raspberry Pi Projects (on amazon.ca).
It says Sensors starter kit, but it has a nice collection of LEDs and buttons, too. Each of the 37 items are easy to plug into a breadboard or you can connect them to your Pi or Arduino with wires.
Other great things:
I’ve purchased sensors in the past, and the problems I’ve had with them are gone due to this kit. I’m glad I bought it.
Here’s my latest post on AI with 60+ worthwhile links found since my last post on it in June. It comes in three sections: the first section is about AI technology, the middle section is on AI in the news and the oped pages, and at the end there’s a good but odd bunch worth a look. Enjoy!
Technology – LLMs:
Technology – Ollama / Flowise:
Technology – RAG:
Technology – watsonx.ai:
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AI in the news / oped sections:
Last but not least:
How long have you had your router in your house? Is it relatively new? If so, that’s good. However you might be like me and have a router that’s 3 or 4 years old. If that’s the case, it’s time to replace your router. Contact your internet provider and ask them if they can refresh your router with a newer one. (And if they can’t or won’t, consider switching internet providers.)
You might think: I don’t want to go through the hassle of that. That’s what I thought too. It turns out it was a very easy thing to do in my case. I suspect that will hold true for you.
Hassle aside, what I also noticed is that I started getting much better upload and download speeds with the newer router without having to upgrade my plan. You might find the same thing, and that’s a good thing indeed.
So if you haven’t refreshed your router in a number of years, consider getting a newer one.
PS it doesn’t have to be a new device. In fact, the upgrade cost might be free if it’s slightly older than new but more recent than your current router.
Yep, it’s true. If you have some technical skill, you can download this repo from github: rasbt/LLMs-from-scratch: Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step and build your own LLM.
What I like about this is that it demystifies LLMs. LLMs aren’t magic, they aren’t Skynet and they’re not some sentient being. They’re software. That’s all.
So ignore all the hype and handwaving about LLMs and go make your own.
Prefer to read it in dead tree form? You can get the book here.
I haven’t written about robots in awhile. That’s not for a lack of news stories about robots. We are finding them popping up all over the place.
Robots have always been used in manufacturing. Now they are moving into other businesses. Here’s a story of how robots are moving into restaurants. I’ve already seen one of these…it was less than impressive.
Drones are a form of robot. Here is a story on how IKEA is using drones for inventory management.
Wildlife has to be managed, too. This robot dressed up like a predator makes flights safer at airports by keeping wildlife away from runways.
The military is known for using robots. Here’s a piece on gun carrying robot dogs in the Chinese army. Not to be outdone, the Canadian armed forces are ramping up with the use of drones.
Back at home, this unit from Samsung can vacuum and steam clean your floors. And this home robot comes with an arm. (No word on if it can unload the dishwasher.)
Speaking of vacuuming robots, this quadruped robot can pick up cigarette butts on beaches. That’s a good use of robots.
Back at work, these BMW robots being tested in their factories in South Carolina give off Terminator vibes. (see below.) More on them, here.
Boston Dynamics has it’s own Terminator like robots too. This dog like robot gives off Boston Dynamics vibes, but isn’t from B.D.
Here’s a robot that can become your child’s protector, teacher, and even playmate. Meanwhile, this one hangs out in your kitchen and acts as an air purifier.
Here’s is a robot that is part of a work of art.
Finally, is the robot backlash starting? It already has in San Francisco, where a crowd vandalized a Waymo driverless taxi.
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How great is Teenage Engineering? Let me count the ways. Or devices. In this case, three very special devices designed by them.
Device #1: the play.date
First up is the Playdate, a unique game playing device with a black and white screen and a crank. Is it any good? It is according to this: Playdate, all it’s cranked up to be (The Verge). You can read more about it, here:playdate – teenage engineering.
Device #2: Rabbit R1
For the second device, there is a chance you’ve heard of the rabbit, “ a pocket companion that moves AI from words to action”. I have one and I like it. Is it good AI? Yes. Is it brilliant design? Definitely.
If you’ve only ever read disparaging things about the device, then read this: Rabbit R1 Explained: What This Tiny AI Gadget Actually Does (CNET) and this:I just spent my first day with the Rabbit R1 — here’s what this AI gadget can do | Tom’s Guide.
Device #3: the EP–1320 medieval.
finally for the third device, you might exclaim, what is that?? Well let the folks from Wallpaper explain: Teenage Engineering EP-1320 Medieval: back to the Middle Ages.
What I love about the 3rd device is a) who knows what kind of market there is for this if any, and b) they don’t care, they made it anyway!
I love how different these devices are from most current handheld devices. The form factor is different, the colors are bold, the inputs are unique. They are small pieces of equipment, but they are not minimalistic pieces of equipment. I love them.
For more of teenage engineering’s great products, go here. For the longest time I wanted to have something from them, but most of their musical devices would be wasted on someone as non-musical as me. (Although if I ever get around to building my own computer, I am getting this:)
This piece, According to Big Data We Won the Vietnam War, should be read by everyone who strongly believes the next new technology (e.g. gen AI) will be able to make decisive predictions to solve big problems (e.g. the Vietnam War). The best computers and minds at the time thought they could win the war with technology. They were wrong then, and they will be wrong again.
If you think newer computers will win this time, reconsider that. If you think we learned our lesson last time, read this.
The last time I wrote about what I find interesting in tech, it was winter. Now it’s anything but, and I have lots of things I’ve been studying in IT. Lots of material on COBOL and mainframes since I am working on mainframe modernization. But there’s stuff regarding Python, cloud, Apple computers and so much more. Let’s see what we have here….
Software: this section is so big I need to break it up! First up, COBOL:
Next, here’ some good stuff on Python:
And lastly here’s some general software links:
Mainframe
Apple…a few good links:
Some helpful cloud pieces:
And finally, here’s a good set of Random links that were too good to pass up:
I was developing a web page for my site berniemichalik.com and I used some Google Fonts to make it look better. When I checked the page on my Mac using my browser, it worked fine. However when I uploaded it to AWS and checked it with my browser, the fonts were not working.
It turned out to be a simple error. The link statement I used looked like this:
<link href=”http://fonts.googleapis.com/css?family=Sedan” rel=”stylesheet” type=”text/css”>
Note the use of “http”. However to access my website, I used “https”. That misalignment caused the font not to work. Once I changed the link to the font to “https” like this:
<link href=”https://fonts.googleapis.com/css?family=Sedan” rel=”stylesheet” type=”text/css”>
It all worked fine.
AI a year ago was mostly talking about AI. AI today is about what to do with the technology.
There are still good things being said about AI. This in depth piece by Navneet Alang here in the Walrus was the best writing on AI that I’ve read in a long time. And this New York Times piece on the new trend of AI slop got me thinking too. But for the most part I’ve stopped reading pieces on what does AI mean, or gossip pieces on OpenAI.
Instead I’ve been focused on what I can do with AI. Most of the links that follow reflect that.
Tutorials/Introductions: for people just getting started with gen AI, I found these links useful: how generative AI works, what is generative AI, how LLMs work, best practices for prompt engineering with openai api, a beginners guide to tokens, a chatGPT cheat sheet, what are generative adversarial networks gans, demystifying tokens: a beginners guide to understanding AI building block, what are tokens and how to count them, how to build an llm rag pipeline with llama 2 pgvector and llamaindex and finally this: azure search openai demo.
Software/Ollama: Ollama is a great tool for experimenting with LLMs. I recommend it to anyone wanting to do more hands on with AI. Here’s where you can get it. This will help you with how to set up and run a local llm with ollama and llama 2. Also this: how to run llms locally on your laptop using ollama. If you want to run it in Docker, read this. Read this if you want to know where Ollama stores it’s models. Read this if you want to customize a model. If you need to uninstall Ollama manually. you want this.
Software/RAG: I tried to get started with RAG fusion here and was frustrated. Fortunately my manager recommended a much better and easier way to get working with RAG by using this no-code/low-code tool, Flowise. Here’s a guide to getting started with it.
Meanwhile, if you want more pieces on RAG, go here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, and here. I know: it’s a lot. But I found those all those useful, and yes, each “here” takes you to a different link.
Software/embedding: if you are interested in the above topics, you may want to learn more about vector databases and embeddings. Here are four good links on that: one two, three, four.
Software/models: relatedly, here’s four good links on models (mostly mixtral which I like alot): mixtral, dolphin 25 mixtral 8x7b, dolphin 2 5 mixtral 8x7b uncensored mistral , Mistral 7B Instruct v0.2 GGUF,plus a comparison of models.
Software/OpenAI: while it is great to use Ollama for your LLM work, you may want to do work with a SaaS like OpenAI. I found that when I was doing that, these links came in handy: how OpenAI’s billing works, info on your OpenAI api keys, how to get an OpenAI key, what are tokens and how to count them, more on tokens, and learn OpenAI on Azure.
Software/Sagemaker: here’s some useful links on AWS’s Sagemaker, including pieces on what is amazon sagemaker, a tutorial on it, how to get started with this quick Amazon SageMaker Autopilot, some amazon sagemaker examples , a number of pieces on sagemaker notebooks such as creating a sagemaker notebook, a notebooks comparison, something on distributed training notebook examples and finally this could be helpful: how to deploy llama 2 on aws sagemaker.
Software in general: these didn’t fit any specific software category, but I liked them. There’s something on python and GANs, on autogen, on FLAML, on python vector search tutorial gpt4 and finally how to use ai to build your own website!
Prompt Engineering: if you want some guidance on how best to write prompts as you work with gen AI, I recommend this, this, this, this, this, this, this, and this.
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IT Companies: companies everywhere are investing in AI. Here’s some pieces on what Apple, IBM, Microsoft and…IKEA…are doing:
Apple: Microsoft copilot app is available for the iphone and ipad.
IBM: Here’s pieces on ibm databand with self learning for anomaly detection; IBM and AI and the EI; IBM’s Granite LLM; WatsonX on AWS; installing watsonX; watsonx-code-assistant-4z; IBM Announces Availability of Open Source Mistral AI Model on watsonx; IBM’s criteria for adopting gen AI ;probable root cause accelerating incident remediation with causal AI; Watsonx on Azure; Watsonx and litellm; and conversational ai use cases for enterprises
IKEA: here’s something on the IKEA ai assistant using chatgpt for home design.
Microsoft: from vision to value realization – a closer look at how customers are embracing ai transformation to unlock innovation and deliver business outcomes, plus an OpenAI reference.
Hardware: I tend to think of AI in terms of software, but I found these fun hardware links too. Links such as: how to run chatgpt on raspberry pi; how this maker uses raspberry pi and ai to block noisy neighbors music by hacking nearby bluetooth speakers; raspberry pi smart fridge uses chat gpt4 to keep track of your food. Here’s something on the rabbit r1 ai assistant. Here’s the poem 1 AI poetry clock which is cool.
AI and the arts: AI continues to impact the arts for ways good and bad. For instance, here’s something on how to generate free ai music with suno. Relatedly here’s a piece on gen ai, suno music, the music industry, musicians and copyright. This is agood piece on artists and AI in the Times. Also good: art that can be easily copied by AI is meaningless, says Ai Weiwei. Over at the Washington Post is something on AI image generation. In the battle with AI, here’s how artists can use glaze and nightshade to stop ai from stealing your art. Regarding fakes, here’s a piece on Taylor Swift and ai generated fake images. Speaking of fake, here’s something on AI and the porn industry. There’s also this piece on generative ai and copyright violation.
Finally: I was looking into the original Eliza recently and thought these four links on it were good: one, two, three and four. Then there’s these stories: on AI to help seniors with loneliness, the new york times / openai/ microsoft lawsuit, another AI lawsuit involving air canada’s chatbot. stunt AI (bot develop software in 7minutes instead of 4 weeks) and a really good AI hub: chathub.gg.
Whew! That’s a tremendous amount of research I’ve done on AI in the last year. I hope you find some of it useful.
I was futzing around with code the other day. I wrote some html/css/javascript and then I wrote some unrelated prolog code. None of it had any value. The code didn’t solve some important problem. Some might consider it a waste of time.
But it wasn’t a waste. In both cases, I learned skills I didn’t have until I wrote the code. Those skills have value for the next time I do have to solve an important problem. Besides that, I enjoyed myself while coding. I was proud of myself for getting the code to work. That enjoyment and pride have value too.
Futzing around is a form of play, and any form of play is good for us as humans. Remember that the next time you consider taking on seemingly useless activities.
If you are using python packages like xmltodict or yaml to write and read your own XML and yaml files, you probably don’t need to know this. But if you are reading someone else’s files, here is something to be aware of.
This week I had to process an XML files in python. No problem, I thought, I’ll use a python package like xmltodict to translate the XML into a dictionary variable. Then I could edit it and print out a new file with the changes. Sounds easy!
Well, first off, it wasn’t too easy: the nesting was horrendous. However, with some help from VS Code, I was able to power through and get the value I want.
Here’s where I got burned. I wanted to change the text in the XML file, so I had a statement like this to read it
mytext = python_dict["graphml"]["graph"]["node"][nodecount]["graph"]["node"][i]["data"]["y:ShapeNode"]["y:NodeLabel"]["#text"]
and then a simple statement like this to change it to lower text:
python_dict["graphml"]["graph"]["node"][nodecount]["graph"]["node"][i]["data"]["y:ShapeNode"]["y:NodeLabel"]["#text"] = mytext.lower()
Very basic.
Now this particular file is an XML file that has a graphml extension, which allows an editor like YED to read it. YED can read the original file, but it turns out xmltodict writes the file in such a way that the YED editor can no longer see the text. I don’t know why.
I spent hours working on it until I finally gave up. I wrote a much dumber program that read through the graphml file a line at a time and changed it the way I wanted to. No fancy packages involved. Dumb but it worked.
This is the second time this year a package has given me problems. In late January I wrote some code to parse yaml files for a client to extract information for them and to produce a report. Again, there is a package to do that: yaml. Which is….good…except when the yaml it is processing it is poorly written. Which this yaml was.
Again, I spent hours linting the yaml and in some cases having to forgo certain files because they were poorly constructed. What should have been easy — read the yaml file, transform it, write a new yaml file — was instead very difficult.
And that’s often the problem with yaml files and XML and JSON files: they are often handcrafted and inconsistent. They MAY be good enough for whatever tool is ingesting them, but not good enough for the packages you want to use to process them.
I think those packages are great if you are making the input files. But if you are processing someone elses, caveat emptor (caveat programmer?).
Apple released it’s latest iPad (Pro) recently and whenever this happens people debate the value of the iPad in general and ask questions like: is the iPad worth it?
I used to ask myself that question too. After all, between my iPhone and my Macbook, I thought I had all the computing technology I needed. But in the last year I got a new iPad — not even the latest and greatest — and I have to say that the iPad just does certain things better than either one. It’s especially better than my iPhone for:
And what I like about the iPad over my Macbook is a) there is no work apps on it so I don’t get distracted by work b) I can recline with the iPad (I don’t like doing that with the Macbook…it’s just no comfortable).
That’s just a start of my list. I’ll keep updating this list for anyone debating getting an iPad.
If you want to merge PDF files on a Mac, you might be tempted to use a tool like www.ilovepdf.com. Worse still, you might try and do it from Adobe’s Acrobat site and end up signing up to pay $200 or more per year for the privilege!
The good news is if you are on a Mac, you don’t need to do any of that.
Instead, open your PDF files using Preview. Make sure your view shows Thumbnails of the pages in each document. Then drag the thumbnail pages of one document into another. Then save the document you added the thumbnails to and you are done.
For example, let’s say you have two PDF files: abc.pdf and xyz.pdf. You want all the pages in abc.pdf to be in xyz.pdf. You open them both using Preview, you drag the thumbnails of abc.pdf over to the thumbnail section of xyz.pdf. Then you save xyz.pdf. (You can save abc.pdf as an empty document or quit and have it revert back to how it was.)
If you want to leave abc.pdf and xyz.pdf untouched but merge them into a third document, first copy xyz.pdf and give it a name like abcxyz.pdf. Then open abc and abcxyz.pdf using Preview. Then copy the thumbnails of abc.pdf into abcxyz.pdf and save abcxyz.pdf and quit and do not save abc.pdf. Now you have three files: abc.pdf and xyz.pdf are unchanged and abcxyz.pdf are merged copies of the two of them.
Happy birthday, Gmail! According to the Verve, you are 20 years old! The big two-oh! Sure, you had some growing pains at first. And then there was the whole period when you and your users felt snobbish about their gmail accounts and looked down on people with yahoo accounts. But that’s all water under the bridge. We’re all old now.
Google is notorious for killing off services, but it is inconceivable they’ll ever kill off you, Gmail. I expect you and your users will be around for a long long time. Heck even an old yahoo email account user like me uses Gmail from time to time. There’s no guarantees, of course, but I expect to be revisiting this post in 2034, god willing, and writing about your 30th. Until then…
If you want to make your Apple Watch more useful, you want to change your App View. Here’s how.
On your iPhone, find the Watch app icon and click on it. Look for App View and click on it. From here you can change the view to Grid View. (Grid View looks like the watch in the photo above.) Now click on Arrangement.
Once in Arrangement, hold your finger on an icon of something you use often. Drag your finger tip and the icon to the top left. Keep doing that so all the Watch apps you will use the most are on the top rows. Once you have it the way you like it, exit the Watch app.
If you are stuck as to what to put on top, my top apps are:
I have a few dozen more Watch apps, but those are the ones I use often.
If you want to see what you can have on your Watch, go back to the Watch app on your phone and scroll down to see what apps are installed on your watch and what ones you can install.
Once you rearrange the Watch apps, press in the crown on your Watch. You will now see the Watch apps organized the way you want. I bet you start pressing your crown more to access and use the apps you have installed.
The Apple Watch is great. Squeeze more greatness from it by taking advantage of the Watch apps you have.
Wow. I have not posted any tech links since last September. Needless to say, I’ve been doing alot of reading on the usual topics, from architecture and cloud to hardware and software. I’ve included many of them in the lists below. There’s a special shout out to COBOL of all things. Is there something on DOOM! in here? Of course there is. Let’s take a look….
Architecture: A mixed bag here, with some focus on enterprise architecture.
Cloud: a number of links on cloud object storage, plus more….
COBOL: COBOL is hot these days. Trust me.
Hardware: mostly but not exclusively on the Raspberry Pi….
Mainframe/middleware: still doing mainframe stuff, but I added on some middleware links….
Linux/Windows: mostly Linux but some of the other OS….
Software: another mixed bag of links…
Misc.: For all the things that don’t fit anywhere else….also the most fun links….
Thanks for reading this!
A year ago I wrote: Sorry robots: no one is afraid of YOU any more. Now everyone is freaking out about AI instead. A year later and it’s still true. Despite that, robots are still advancing and moving into our lives, albeit slowly.
Drones are a form of robot in my opinion. The New York Times shows how they are shaping warfare, here. More on that, here.
Most of us know about the dog robots of Boston Dynamics. Looks like others are making them too. Still not anywhere as good as a real dog, but interesting nonetheless.
What do you get when you combine warfare and robot dogs? These here dogs being used by the US Marines.
Someone related, the NYPD has their own robot and you can get the details here.
Not all robots are hardcore. Take the robot Turing for example (shown below). Or the ecovacs, which can mop your floors and more.
What does it all mean? Perhaps this piece on the impact of robots in our lives can shed some light.
Robots are coming: it’s just a matter of time before there are many of them everywhere.
You’ve likely heard of Advent, but have you heard of Advent of Code? Well let the maker of the site, Advent of Code 2023, explain what it is:
Hi! I’m Eric Wastl. I make Advent of Code. I hope you like it! I also made Vanilla JS, PHP Sadness, and lots of other things. You can find me on Twitter, Mastodon, and GitHub. Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as interview prep, company training, university coursework, practice problems, a speed contest, or to challenge each other. You don’t need a computer science background to participate – just a little programming knowledge and some problem solving skills will get you pretty far. Nor do you need a fancy computer; every problem has a solution that completes in at most 15 seconds on ten-year-old hardware.
It seems like just the thing for coders of all kinds, from amateurs to professional devs. Check it out. And if you want to get involved from day 1 in 2024, make a note on your calendar (assuming Eric still does it.)
I am a big fan of usable old computers. After you read this, you will be too.
Currently I have an old Lenovo M57p ThinkCentre M series that was made around 2007 that still works fine and is running Ubuntu 20.04 (the latest version is currently 22.04, so this is very current). Not only that, but it runs well. It never crashes, and I can download new software on it and it runs without a problem.
Here are some the benefits of having such a computer:
As for the version of Linux, I tend to stay with Ubuntu. There’s lots of great Linux distros out there, but I like this one. Plus most times when I come across online Linux documentation, I will find it has explicit references to Ubuntu.
Now you can buy an old machine like this online from Amazon or eBay, but if I can do this on a 15 year old computer, you likely can ask around and get one for free. A free computer that can do all this? The only thing that should be stopping you is how to get started. For that, you will need these Ubuntu install instructions and a USB drive.
Good luck!
P.S. The software neofetch gave the output above. To install it, read this: How do I check my PC specs on Ubuntu 20.04.3 LTS?
If you are reading this, chances are you cannot write to your USB drive on your Mac.
To force a USB drive to be both read and writable, I did the following (note, I had a Kingston drive, so my Mac identified it as KINGSTON and I went with that. If you buy a USB drive that is not from Kingston, you may see something different):
diskutil list
/dev/disk2 (external, physical):
#: TYPE NAME SIZE IDENTIFIER
0: FDisk_partition_scheme *62.0 GB disk2
1: Windows_NTFS KINGSTON 62.0 GB disk2s1
sudo mkdir /Volumes/KINGSTONsudo mount -w -t ExFAT /dev/disk2s1 /Volumes/KINGSTONYou should now be able to write to your drive as well as read it.
IF you are a fan of using Chrome to cast one of your tabs to a TV, you may be surprised to find that the Cast option is missing. Worse, if you look in places like Chromecast Help on how to Cast a Chrome tab on your TV, you may not find that all that helpful.
Fear not. The Cast option is still there, just hidden. As before, go to the top right of your browser where the three dots are and click on them. Then click on Save and Share… and look for Cast…
Now you can Cast as you did before.
The easiest way to install Java on your Mac is by using homebrew. Honestly, if you don’t have homebrew on your Mac, I highly recommend you do that. Plus it’s easy to do. All you need is to enter the following:
$ /bin/bash -c “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)”
Now that you have homebrew installed, you can install Java by entering:
$ brew install java
That should install the latest version of it. If you want to install an older version, you can do something like this:
$ brew install java11
If you’ve done this a few times, you may have a few different version of Java installed liked me, and if you enter the following command it may look something like this:
% ls /usr/local/opt | grep openjdk
openjdk
openjdk@11
openjdk@18
openjdk@19
openjdk@20
openjdk@21
%
As you can see, I have a few different versions installed. However, if I do this:
% java --version
openjdk 11.0.20.1 2023-08-24
OpenJDK Runtime Environment Homebrew (build 11.0.20.1+0)
OpenJDK 64-Bit Server VM Homebrew (build 11.0.20.1+0, mixed mode)
%
It shows the OS thinks I have JDK version 11 running.
Why is that? Well, it turns out if I enter this:
% ls /Library/Java/JavaVirtualMachines/
jdk1.8.0_261.jdk openjdk-11.jdk
%
I can see I have two JDKs installed there. MacOS will go with the latest JDK there when you ask what version is installed, in this case openjdk-11.
If I want the OS to use a different version like openjdk 21, I can enter this symbolic link (all one line):
sudo ln -sfn /usr/local/opt/openjdk@21/libexec/openjdk.jdk /Library/Java/JavaVirtualMachines/openjdk-21.jdk
Then when I check on things, I see the following:
% java --version
openjdk 21 2023-09-19
OpenJDK Runtime Environment Homebrew (build 21)
OpenJDK 64-Bit Server VM Homebrew (build 21, mixed mode, sharing)
% ls /Library/Java/JavaVirtualMachines/
jdk1.8.0_261.jdk openjdk-11.jdk openjdk-21.jdk
%
Now the system thinks openJDK 21 is running.
If I want to reverse this and go back to openjdk 11, I can use this unlink command and see this:
% sudo unlink /Library/Java/JavaVirtualMachines/openjdk-21.jdk
% java --version
openjdk 11.0.20.1 2023-08-24
OpenJDK Runtime Environment Homebrew (build 11.0.20.1+0)
OpenJDK 64-Bit Server VM Homebrew (build 11.0.20.1+0, mixed mode)
% ls /library/Java/JavaVirtualMachines
jdk1.8.0_261.jdk openjdk-11.jdk
berniemichalik@Bernies-MacBook-Air-4 ~ %
Normally I would recommend going with the latest and greatest version of Java on your Mac. However, you may have a situation where you have some Java code that only runs on older versions of Java. This is one way to deal with that.
For more on this, here are some good links I found:
While there are still prophets and pundits arguing doom and gloom regarding AI, most people and organizations have moved past them and have been adopting the technology widely. Some times that has been good, some times not. To get a sample of how it’s going, here’s a few dozen pieces on AI worth a look:
(Photo: link to image in the NVIDIA story. By Philip Cheung for The New York Times)
If you were to ask me, on this day, what has changed with regards to computers and IT and IBM in the last 40 years, I would say it’s this:
In 40 years the devices have gotten smaller, the networks have gotten bigger, and the software has gotten smarter. Plus it’s all so much cheaper. If I had to sum it up, I’d say that sums up all the changes that have happened in the last 40 years. And we are just getting started.
You might think: no one is going to hack my Instagram account. And you might be right. But here’s the thing: if someone does hack your account, you have next to no chance of getting someone at Instagram to restore it. Rather than make it easy for hackers to take over your account, spam your friends and delete years of photos, you should use 2FA. To do so, read this article: How to Turn on Two-Factor Authentication on Instagram.
While you can use SMS, I recommend using an authenticator app. That article explains how you can do it either way. Authenticator apps are more secure than SMS and are the way to go these days. For more on that, see PCMag.
If you are using IBM Cloud technology, I recommend you consider setting up MFA for your login account. MFA makes your access more secure, and it’s easy to do. To see how easy it is, go here: IBMid – Verifying your identity and configuring MFA. It’s a well laid out description about how to do it.
You can use either a verification app or email to get a verification code. I recommend an app. While email works, it can take several minutes to get the code, while with an app you get a code instantly. As for apps, I use IBM’s verify app, but you can use Google’s and likely Microsoft’s. They all work fine. Just go to your favorite app store and download one. (Make sure it comes from IBM or Google or Microsoft, not from some developer with a lookalike app.)
Multi-Factor authentication is great. There is only one downside: you lose your phone. The way to deal with that is to have a backup. To set that up, either read this if you use Microsoft’s authenticator: Back up and recover account credentials in the Authenticator app from Microsoft Support or this if you use something else for authentication: Make Sure You Have a Backup for Two-Factor Authentication.
Yes, this is the stuff I read for fun. Not on the beach, but at least in a comfy chair out in the hot sunny weather. 🙂
Architecture links: mostly my IT architecture reading was AWS related this summer, but not all of it.
Cloud links: a mixed bag of things, all good.
Ops links: I’ve been consulting with clients on operations work, among other things, so here’s pieces on AIOps, DevOps and more that I thought were good:
Software links: mostly dashboard related, since I was working on…dashboards.
Finally: here’s a mix bag of things, quantum and otherwise, that I enjoyed.
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While my last few posts on IT have been work related, most of these are on hardware and software and tend to be more hobby and fun related.
Hardware links:
Software links:
Hope something there was useful! As always, thanks for reading!
P.S. Before I forget… here’s a piece on how a hacker brought Doom to a payment terminal. Love it!
Smart People I Know 
