Fixing “Media kit reports not enough space on device” error in Disk Utility

I got this error today when trying to partition a Western Digital My Passport 4TB:

Volume erase failed: Media kit reports not enough space on device

Nothing I could do inside Disk Utility worked. Thanks to some kind soul on Reddit, here is how I solved the issue from the command line:

$ diskutil list
$ diskutil unmountDisk force disk2  #replace disk2 with your disk number

and then write zeros to the boot sector:
$ sudo dd if=/dev/zero of=/dev/disk2 bs=1024 count=1024

Attempt to partition it again:
$ diskutil partitionDisk disk2 GPT JHFS+ "My External HD" 0g

Get Back On Track

Sometimes I get off track. This is what I need to do to get back on track:

  1. Turn off social media. Remove apps from phone, turn on the 1Blocker (iPad and iPhone) and WasteNoTime (Mac) rules.
  2. Wash your face.
  3. Drink a full glass of water and eat a healthy snack if you need one.
  4. Get your keys and headphones, put on a podcast, go for a walk around the building. Breathe deeply the whole time. Check the mail when you come back in.
  5. Clean off your desk, clean off the dining table, and empty/load the dish washer.
  6. Turn off the podcast and turn on music (Jazz Vibes, Hundred Days Off, or Tycho). Sit down at the dining table with your notebook and make a list of the most important things that need to get one. Evaluate each item and block out a time on the calendar to knock it out over the next few days.
  7. Pick one thing to start work on immediately. Start working.

Venkatesh Rao on Big Data, Machine Learning, and Blockchains

Venkatesh Rao had a good take on the big data/machine learning/blockchain mania in Breaking Smart a few weeks ago:

Many people, database experts among them, dismiss Big Data as a fad that’s already come and gone, and argue that it was a meaningless term, and that relational databases can do everything NoSQL databases can. That’s not the point! The point of Big Data, pointed out by George Dyson, is that computing undergoes a fundamental phase shift when it crosses the Big Data threshold: when it is cheaper to store data than to decide what to do with it. The point of Big Data technologies is not to perversely use less powerful database paradigms, but to defer decision-making about data — how to model, structure, process, and analyze it — to when (and if) you need to, using the simplest storage technology that will do the job.A organization that chooses to store all its raw data, developing an eidetic corporate historical memory so to speak, creates informational potential and invests in its own future wisdom.

Next, there is machine learning. Here the connection is obvious. The more you have access to massive amounts of stored data, the more you can apply deep learning techniques to it (they really only work at sufficiently massive data scales) to extract more of the possible value represented by the information. I’m not quite sure what a literal Maxwell’s Historian might do with its history of stored molecule velocities, but I can think of plenty of ways to use more practical historical data.

And finally, there are blockchains. Again, database curmudgeons (what is it about these guys??) complain that distributed databases can do everything blockchains can, more cheaply, and that blockchains are just really awful, low-capacity, expensive distributed databases (pro-tip, anytime a curmudgeon makes an “X is just Y” statement, you should assume by default that the(X-Y) differences they are ignoring are the whole point of X). As with Big Data, they are missing the point. The essential feature of blockchains is not that they can poorly and expensively mimic the capabilities of distributed databases, but do so in a near-trustless decentralized way, with strong irreversibility and immutability properties.

Video: How Panobook is Made

Studio Neat put together a cool video showing how the Panobook is made. I preordered three Panobooks and can’t wait for them to arrive.

Studio Neat makes some of my favorite products: Neat Ice Kit, Highball, and the Glif. I love the way they document their work through videos like this, their weekly newsletter, and their podcast, Thoroughly Considered.

 

New Wes Anderson Trailer: Isle of Dogs

Wes Anderson and his team are so good. Their attention to detail is extraordinary. Every single one of the dogs in this animation have a deep level of emotion and personality. I’m looking forward to seeing this in theaters next year.

EYES by Lucas Zanotto

This is a super cool short film documenting a series of art installations by Lucas Zanotto. Simple colors, shapes, and movements can convey so much emotion and character.

My Inbox Clearing Method

Like many, I’m all about that Inbox Zero life. I’m not going to preach here about it. You’ve heard enough of that elsewhere. I’m going to show you how I get it done.

Winning Before Starting

I like to set myself up for success whenever possible. What that looks like here is severely limiting the amount of inbound email I get. Fewer incoming messages means fewer messages to process.

  • I am ruthless about unsubscribing to unwanted emails. I am only subscribed to seven newsletters, all of which I get value out of regularly. I immediately unsubscribe from sales and marketing emails I get after buying stuff online. If I have to give an email address on a website, I add “+promo” to the end of my address and use a rule to automatically send it to the trash.
  • For important day-to-day questions and messages from coworkers, we use Slack.

These few things cut my email volume by 80%. The remaining 20% is primarily important, valuable, or actionable: Emails from clients, customers, friends, and family, important notifications, and interesting newsletters that I actually read.

Method

  • I primarily process email on my 10.5″ iPad Pro using Spark or Airmail. I switch back and forth between the two every few weeks. Emails I can respond to immediately, I do. Emails that need further action get added to my to-do list. Both have a key feature that is critical to my workflow: The Share Sheet. This allows me to take an email and put it as a to-do item in my favorite task manager with a few taps without switching apps. As soon as an email gets added to my task list, it gets archived. The task includes a link directly to the email so I can get back to it quickly if needed.
  • On my Mac I also use Spark and Airmail, switching to whichever one I’m using on my iPad at the time. Both have widgets that allow me to share the email to my favorite task manager.
  • I use Things 3 as my task manager. Tasks that I share from my email get put into a holding zone (also called the Inbox), which I process and assign a due date and put into the correct bucket twice a day. Things has my definitive task list and I use it as a launch pad for planning my day each morning.
  • Every Monday I set my plan for the week and send it over to my boss. Because I’m not dogmatic about maintaining Inbox Zero every single day, I clear it out on Monday mornings before organizing my task list for the week just in case something in my email needs to go on the list.

That is it. This is consistent for me because it is tied to a concrete weekly deliverable: My weekly check-in with Isaac. In order to give an accurate representation of my priorities and tasks for the week, I must clean out my inbox first. I leave myself no choice in the matter, because if I did, I’m likely to ignore my inbox and let it get out of hand.

Notes: The Future of Intelligence on the Sam Harris Podcast

Here are my notes from The Future of Intelligence, a Conversation with Max Tegmark on the Sam Harris Podcast.

You can listen to it here:

My notes and thoughts:

  • We always focus on the downsides of super intelligent AI. There are, however, upsides. Super intelligence can help solve some of the biggest problems of our time: Safety, medical issues, justice, etc.
  • Containment is both a technical and a moral issue. Much more difficult than currently given credit for. Given ways we have to construct it, we likely can just “unplug” it.
  • Tegmark defines these three stages of life:
    • Life 1.0: Both hardware and software determined by evolution. (Flagella)
    • Life 2.0: Hardware determined by evolution, software can be learned (Humans)
    • Life 3.0: Both hardware and software can be changed at will. (AI machines)
  • Wide vs narrow intelligence: Humans have wide intelligence. Generally good a lot a lot of different tasks and can learn a lot implicitly. Computers have (so far) with narrow intelligence. They can calculate and do programmed tasks much better than us. But will completely fail at needing to account for unwritten constraints when someone says, “take me to the airport as fast as possible.”
  • The moment the top narrow intelligence gets knit together and meets the minimum of general intelligence, it will likely surpass human intelligence.
  • What makes us intelligent is the pattern in which the hardware is arranged. Not the building blocks themselves.
  • The software isn’t aware of the hardware. Our bodies are completely different from when we were young, but we feel like the same person.
  • The question of consciousness is key. A subjective experience depends on it.
  • We probably already have the hardware to get human-level general intelligence. What we are missing is the software. It is unlikely to be the same architecture as the human brain, likely similar. (Planes are much more simple than birds.)
  • AI Safety research needs to go hand-in-hand with AI research. How do we make computers unhackable? How do we contain it in development? How do we ensure system stability?
  • One further issue you are going to need to overcome is having computers answer how a decision was made in an understandable way instead of just dumping a stack trace.
  • Tegmark councils his own kids to go into fields that computers are bad at. Fields where people pay a premium for them to be done by Humans.

Fallacies, Illusions, and Biases (Part 2)

I’m working my way through Rolf Dobelli’s The Art of Thinking Clearly by reading a few sections each morning. Below are my notes on sections 12-23. Read 1-11 here.

  1. “It’ll-get-worse-before-it-gets-better” fallacy: A variant of confirmation bias. If the problem gets worse, the prediction is confirmed. If the situation improves unexpectedly, the customer is happy and the expert attributes it to his prowess. Look for verifiable cause-and-effect evidence instead.
  2. Story bias: We tend to interpret things with meaning, especially things that seem connected. Stories are more interesting than details. Our lives are mostly series of unconnected, unplanned events and experiences. Looking at these ex post facto and making up an overarching narrative is disingenuous. The problem with stories is that they give us a false sense of understanding, which leads us to take bigger risks and urges us to take a stroll on thin ice. Whenever you hear a story, ask: Who is the sender, what are his intentions, and what does this story leave out or gloss over?
  3. Hindsight bias: Possibly a variant on story bias. In retrospect, everything seems clear and inevitable. It makes us think we are better predictors than we actually are, causing us to be arrogant about our knowledge and take too much risk. To combat this, read diaries, listen to oral histories, and read news stories from the time you are looking at. Check out predictions from the time. And keep your own journal with your own predictions about your life, career, and current events. Compare them later to what happened to see how poor of a predictor we all are.
  4. Overconfidence effect: We systematically overestimate and our ability to predict on a massive scale. The difference between what we know and what we think we know is huge. Be aware that you tend to overestimate your knowledge. Be skeptical of predictions, especially from so-called experts. With all plans, favor the pessimistic scenario.
  5. Chauffeur Knowledge: There are two types of knowledge: Real knowledge (deep, nuanced understanding) and Chauffeur knowledge (enough knowledge to put on a show, but understanding to answer questions or make connections). Distinguishing between the two is difficult if you don’t understand the topics yourself. One method is the circle of competence. True experts understand the limits of their competence: The perimeter of what they do and do not know. They are more likely to say “I don’t know.” The chauffeurs are unlikely to do this.
  6. Illusion of Control: Similar to placebo effect. The tendency to believe that we can influence something over which we have absolutely no sway. Sports, gambling, etc. Also: Elevators, cross walks, fake temperature dials. This illusion led prisoners (like Frankel, Solzhenitsyn, etc) to not give up hope in concentration camps. Federal reserve’s federal funds rate is probably a fake dial, too. The world is mostly an uncontrollable system at the level we currently understand it. The things we can influence are very few.
  7. Incentive Super-Response Tendency: People respond to incentives by doing whatever is in their best interest. Extreme examples: Hanoi rats being bred, Dead Sea scrolls being torn apart. Good incentive systems take into account both intent and reward. Poor incentive systems often overlook and even corrupt the underlying aim. “Never ask a barber if you need a haircut.” Try to ascertain what actions are incentivized in any situation.
  8. Regression to Mean: A cousin of the “It’ll-get-worse-before-it-gets-better” and the Illusion of Control fallacies. Extreme performances are often interspersed with less extreme ones. There are natural variations in performance. Students are rarely always high or low performers. They cluster around the mean. Thinking we can influence these high and low performers is an illusion of control.
  9. Outcome Bias: We tend to evaluate decisions based on the result rather than the decision process. This is a variant on the Hindsight Bias. Only in retrospect do signals seem clear. When samples are too small, the results are meaningless. A bad result does not necessary indicate a bad decision and vice versa. Focus on the reasons behind actions: Were they rational and understandable?
  10. Paradox of Choice: A large selection leads to inner paralysis and also poorer decisions. Think about what you want before inspecting existing offers. Write down the criteria and stick to them rigidly. There are never perfect decisions. Learn to love a good choice.
  11. Liking Bias: The more we like someone, the more we are inclined to but from or help that person. We see people as pleasant if (a) they are outwardly attractive, (b) they are similar to you, or (3) they like you. This is why the salesperson copies body language and why multi-level marketing schemes work. Advertising employs likable figures in ads. If you are a salesperson, make people like you. If you are a consumer, judge the product independent of the seller and pretend you don’t like the seller.
  12. Endowment effect: We consider things to be more valuable the moment we own them. If we are selling something, we charge more than we ourselves would spend on it. We are better at holding on to things than getting rid of them. This effect works on auction participants, too, which drives up bidding. And late-stage interview rejections. Don’t cling to things, rather view them as the universe temporarily bestowing them to you.

TK Coleman’s Career Journey on the Isaac Morehouse Podcast

TK Coleman, my coworker on the education team at Praxis, told his career journey story in two parts on the Isaac Morehouse podcast. It is worth a listen:

I’ve heard many parts of this story through working with TK, but I hadn’t heard the entire thing laid out. I immensely respected TK before listening to this, but hearing his early story just added to it further. Here are a few things from these shows that I find admirable:

  • TK’s complete dedication to topics.
  • How he unapologetically structures his life around his top priorities.
  • How humble he is. He knows so much more than he lets on. The last time he stayed with Amanda and me, I assumed that he knew very little about cocktails because he didn’t drink and never hinted at knowing about cocktails when I talked about them. In this show I learned that he was a professional bartender for a while and dove into bartending with the same intensity that he dives into everything else. He is this way about everything. He knows so much, be he never flaunts it. He approaches everything as a learning opportunity and doesn’t let his current knowledge get in the way of learning something new. He told me that one of his pet peeves is that people prefer to talk instead of listen, so he tries his best to avoid that.
  • He isn’t afraid to admit that he was scared and that stopped him from going to Hollywood at first. He always seems confident and fearless, so hearing this makes him seem more real. And even better.

Here are some of my takeaways from the two shows:

  • It is okay to stick with a few things and do them seriously for a few years and then decide to move on to something else. Just don’t treat those two years as a half-hearted effort. Go all-in. You don’t need a grand life plan early in your career. When I think that the place I’m currently at in life is a huge deal, remember that there are multiple parts of TK’s story where he made something his life for two years, moved on, and now it barely comes up unless someone asks.
  • Don’t celebrate or call your Mom until the check clears
  • If your startup has a significant tech component, bring on a tech cofounder. Don’t rely on contractors for a core product.
  • Never take money from someone unless you know they can lose it and be okay with it
  • Never take money out of a place of desperation or powerlessness. Walk away.
  • Doing something that you don’t need permission to do is the ultimate expression of power.
  • When you are working for free or cheap, the expectations are low. It is easy to blow people away. When you get brought on full time, now all the things that were impressive before are expected.
  • Leave things in a way that allows you to come back in the future.
  • The best path forward is doing whatever you are doing now fully and with integrity.

 

Fallacies, Illusions, and Biases (Part 1)

I’m working my way through Rolf Dobelli’s The Art of Thinking Clearly by reading a few sections each morning. Here are my notes on the first 11 sections (Confirmation Bias had two sections, which I’ve only noted as one below):

  1. Survivorship bias: You overestimate your probability of success because you only see success stories. You find common threads in success stories and think they are the answer. Both ignore the failures because those stories aren’t told. When you are a survivor you think, “I did it! Everyone else can!” Look for counter examples and failures to overcome it.
  2. Swimmer’s body illusion: Swimmers usually choose swimming because they have good physiques. Swimming doesn’t necessarily cause good physiques. Harvard has a rigorous vetting process and skilled, driven people tend to get in. They’d likely be successful without Harvard. This may actually be a subset of the survivorship bias. (You don’t see ugly models selling makeup or fat swimmers because they don’t tend to last long in the business. Dumb people don’t make it though Harvard’s screening, so won’t bring down their salary numbers after 4 years.)
  3. Clustering illusion: Our brains are pattern and meaning recognizing machines. First regard patterns as pure chance. If there seems to be more, test it statistically.
  4. Social Proof: We are hardwired to copy the reactions of others. In the past it was beneficial for survival. Remember to look for links. Popular does not equal best on objective measures. “If 50M people say something foolish, it is still foolish.”
  5. Sunk Cost Fallacy: Investments of time or money to date don’t matter. Only future benefits or costs count.
  6. Reciprocity: The allure of both positive and negative reciprocity is so strong that it is best to avoid saying yes in the first place if it is something you don’t want.
  7. Confirmation bias: The tendency to interpret new information so it becomes compatible with your existing beliefs. We filter out disconfirming evidence. Look for disconfirming evidence and give it serious consideration. “Murder your darlings.”
  8. Authority bias: When making decisions, think about which authority figures are influencing your reasoning. Challenge them.
  9. Contrast Effect: Things seem cheaper, prettier, healthier, better, etc in contrast to something else. This is how magicians and con men remove your watch: Press hard in one area so you don’t feel the lighter touch of removing your watch. This is also why it is easy to ignore inflation. Compare things in individual cost/benefit calculations, not in contrast to an “original price” or what they are framed against.
  10. Availability bias: We create a picture of the world using the examples that most easily come to mind. This creates an incorrect risk map in our heads. We attach too much likelihood to flashy outcomes. We think dramatically, not quantitatively. We tend to focus on what is in front of us, whether or not it is the most important question. We can overcome it by getting others’ input with different experiences and expertise.

Notes on how React and Angular work

I got this question from a Praxis participant last night: “Hey Chuck quick general question: do frameworks like angular and react compile to JS? How exactly do they work?”

Here is my response:

This took me a little research because I didn’t quite know. Here is what I found: First, React is a library and Angular is a framework. Seems like a small distinction, but it has big consequences. See this link: https://stackoverflow.com/questions/148747/what-is-the-difference-between-a-framework-and-a-library

If you write React in plain javascript, everything should run as-is. If you write your React code in JSX, babel first finds the JSX, parses and generates the corresponding javascript code, then evaluates it. The big-picture of React is that it is kind of like the view layer in MVC, with a few more bells and whistles added. Everything renders to a virtual DOM first, which is significantly faster than the real DOM. Changes are then compared with the real DOM and then the differences are sent to the real DOM.

It looks like you can write Angular code in javascript or Typescript (which then compiles to javascript). Here is a great high-level architecture overview of Angular that explains how it works: https://angular.io/guide/architecture