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All about me: https://billmei.net

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billmei.net 6mo ago

AI taught me to be a better human

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www.alltext.nyc 11mo ago

Search all text in New York City

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billmei.net 1y ago

The deception that we crave

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www.historicaltechtree.com 1y ago

Historical Tech Tree

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hdr.ardov.me 1y ago

Snow White – HDR Using CSS

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docs.google.com 1y ago

Save Money on LLMs

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www.high-capacity.com 1y ago

Why Chinese EVs will not take over the world

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billmei.net 1y ago

Daydreams Decide Your Destiny

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billmei.net 1y ago

Why early success online is a trap

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www.theverge.com 1y ago

Final Cut Pro 11 with AI features

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www.lesswrong.com 1y ago

Immortality: A Practical Guide (2015)

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billmei.net 1y ago

Neuroscience reasons for what AI cannot do yet

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billmei.net 1y ago

Most ideas cannot be explained simply

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moretothat.com 1y ago

Metric-Less Success

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billmei.net 1y ago

Common ways startups fail to make what people want (and what to do instead)

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billmei.net 1y ago

BLUF (Bottom Line Up Front) hurts communication and hinders innovation

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jasonfantl.com 1y ago

Collision Avoidance

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balajis.com 1y ago

Founding vs. Inheriting (2021)

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sive.rs 1y ago

How to sync Mac and Linux /home

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billmei.net 1y ago

Trust Your Tests

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billmei.net 1y ago

ChatGPT turns every user into a power user

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www.linguistic-antipatterns.com 2y ago

Linguistic Antipatterns

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billmei.net 2y ago

Psychological tricks rich people use to look generous without spending more

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en.wikipedia.org 2y ago

List of Individual Body Parts

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www.nytimes.com 2y ago

The No-Stats All-Star (2009)

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billmei.net 2y ago

The Real Purpose of History

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figgie.com 2y ago

Figgie: Simulate stock trading with a deck of cards

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developer.android.com 2y ago

IsUserAMonkey

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ralphammer.com 2y ago

Make Me Think (2018)

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cdn.jsdelivr.net 2y ago

Breakout.pdf [pdf]

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I found this to be incredible, an NBA player whose stats are worse than other players on practically every dimension, "yet every team he has ever played on has acquired some magical ability to win". Many tech companies look at individual-level productivity metrics like PRs submitted, sprint velocity, deploy time, etc. But what are some good ways to assess team-level productivity?

Part 1 was significantly easier than Part 2; Part 1 (the hardware part) only took 2 weekends for me, but every assignment on Part 2 took several weekends each. Granted, this was also because I was learning about operating systems and compilers at the same time; if you're already familiar you could considering skipping Part 2 altogether, or otherwise you might have an easier time than I did.

This was an amazing course and is one of the most rewarding computer science courses I've taken! I loved that there was nothing left to "magic" and it was the first time I felt like I understood the "full stack" of the Java-like code I was writing right down to the transistors.

Self-plug for a full-blown minesweeper game I made for the final project: https://github.com/billmei/nand2minesweeper It's a complete game with a tutorial, custom RNG, and unit tests, using their hardware simulator.

Seaflooding 3 years ago

XKCD talked about why flooding Death Valley in this manner would be a bad idea, because unless you remove all the salt and sediments left behind from evaporation it would create a toxic waste pool https://what-if.xkcd.com/152/

The term to google for is "insured cash sweep", but the specifics depend on the particular financial institution you work with. Instead of a single insurance provider, your cash is sharded behind the scenes among many member institutions. Here's one bank I picked at random from Google[0]. Brokerages like Schwab[1] and IBKR[2] also have a private insurer (both use Lloyd's of London) they offer as a service to customers.

[0] https://www.stearnsbank.com/personal/high-balance-deposit

[1] https://www.schwab.com/legal/sipc-account-protection

[2] https://ibkr.info/node/2012

For me, the main reason for Duolingo's drastic decline in quality is not solely due to gamification but due to their learning algorithm no longer being effective.

I was one of the early beta users and joined Duolingo in June 2012 before it was publicly available, and was a huge fan in the early days. At one point I had a streak of 1,000+ consecutive days, and even applied to work there.[0]

Back then, Duolingo used the SRS algorithm[1] and it was very helpful in keeping me on track with my learning.

The original business model was to make money from community translations.[2] I think they soon discovered that this market wasn’t nearly big enough to sustain Duolingo as a going concern, so they pivoted to an advertising-based business model.

However, the software supporting the original “translations” business model is still in place, so the way they teach you new words is prompting you with sentence pairs to translate. For example, they’ll give you an English sentence and ask you to translate it into Spanish. I find this isn’t an effective way to learn a new language because you’re not learning to think in your target language, you’re learning how to match words between your native language and the target language, which is not the same thing.

Sentence matching is unnatural and requires neural pathways that are never used by native speakers; native speakers don’t think in English and then translate into Spanish when speaking. To be able to fluently speak the language, I found that a “call and response” model is the best way to learn. For example, given a question that a real person would ask (“What do you like about your job?”), you have to creatively formulate a natural answer (“I have a lot of intellectual freedom”) without any “guardrails” of a scripted English sentence provided by the app.

“Call and response” more accurately models how people actually speak in real conversations—you take turns talking like a normal human being. It is also more difficult and requires effortful assimilation of concepts instead of just words. To learn, you must make mistakes, so the most effective learning techniques are also the most frustrating. Coming up with novel responses forces you to fully engage in the dialogue, which efficiently helps you to lay down neural pathways that you will use in a real conversation.

Duolingo is optimized for ad revenue, so they can’t give you difficult challenges because you’ll just give up. Instead, they must spoon-feed you 1:1 sentence translations or “fill in the blank” type challenges because this rewards you with a dopamine rush from their slot-machine-like “ding!” sounds and green checkmarks that pop up when you successfully fill in the blanks. Instead of encouraging you to make mistakes, Duolingo will penalize you by forcing you to redo entire lessons (instead of targeting practice towards your weaker words), sweep mistakes under the rug, or making you pay money for gems. While the paid version removes the ads, it doesn't remove the algorithm that serves lessons optimized for ad-interrupted learning.

Duolingo touts their effectiveness study[3] as evidence that its methods are scientifically supported with a claim that “an average of 34 hours of Duolingo are equivalent to a full university semester of language education”. However, this study was conducted in 2012 under Duolingo’s old SRS algorithm before they changed their teaching model to matching-based problem sets, got rid of their call and response questions, and implemented hearts, gems, and lingots that penalize mistakes, so I don't think this evidence applies to the current app anymore.

Unfortunately, due to these changes I now disrecommend Duolingo for folks wanting to learn a new language, even though it was a pivotal and cherished part of my language learning journey back in the day.

[0] https://billmei.net/blog/silicon-valley-job-search#duolingo

[1] https://en.wikipedia.org/wiki/Spaced_repetition

[2] https://www.buzzfeed.com/buzzfeedpress/buzzfeed-expands-inte...

[3] https://s3.amazonaws.com/duolingo-papers/other/vesselinov-gr...

Hi! Author here - thanks for the feedback. I added the following footnote to the article to clarify:

I use the term "hazing" broadly to mean any way of excluding members from joining an organization. For example, a job interview is a form of "hazing".

In response to "How can you trust and be loyal to someone you just met?" I'll provide a personal example: I recently travelled to a different city, and asked a friend of mine for sightseeing recommendations because he used to live there. During the conversation, he mentioned that his ex-girlfriend still lives there, and offered to put me in touch with her. I then asked his ex-girlfriend (who I had only one prior interaction with!) to be my emergency contact while I was on my trip, which she agreed to, because of the shared trust they had established in their prior relationship, and their familiarity with me in our mutually overlapping social circles. This is what I mean that a person's entanglements are more important than their attributes.

Hi! Author here. I'll admit I struggled to find a more precise word than "context". What I mean is that a friendship is not the 1:1 interaction you have with your friend, but that friend's omni-directional interaction with every other friend. Their location within a network is one way of putting it. In other words, their entanglements in your web of mutual commitments, shared history, and collective responsibility.

Vsauce has a fantastic animation of this too: https://www.youtube.com/watch?v=ObiqJzfyACM&t=16m4s

Deal yourself five cards every billion years. When you finally deal yourself a royal flush, buy a lottery ticket. If the ticket wins the lottery, throw a single grain of sand into the Grand Canyon. As soon as the Grand Canyon is completely full of sand, remove one ounce of rock from Mount Everest. By the time Mount Everest is level: take a look at the clock. You'll have to do the whole thing all over again 256 more times before your timer reaches zero.