Simple, consistent, SVG, adjustable stroke-width, and there are 1900 of them (and counting).

By Michael Dearing. Couldn’t find any video and not sure if this is tongue-in-cheek (like the “48 Laws of Power”.) The distortions are:

1. Personal Exceptionalism
   “I am special.”
2. Dichotomous Thinking
   “X is sh*t. Y is genius.”
3. Correct Overgeneralization
   “I see two dots and draw the right line.”
4. Blank-Canvas Thinking
   “Painting by numbers isn’t art. And I want to make art.”
5. Schumpeterianism
   “I am a creative destruction machine.”

TL;DR be a ruthless, inflexible, self-absorbed dick so you can identify, refine, and deliver Value™.

Lovely stuff. Cached here.

Being a long and informative post that leads to “Use /dev/urandom” and features a quote by DJB and a list of computationally secure PRNGs. Cached here.

LaTeX, pdfTeX, XeTeX, LuaTeX and ConTeXt. That’s a lot of TeX! This was most helpful, even though MacTeX solves all my problems.

A still-very-relevant 9-year old article. Pandas has gone from strength to strength since he wrote that.

In terms of expressing your computations, Hadoop is strictly inferior to SQL. There is no computation you can write in Hadoop which you cannot write more easily in either SQL, or with a simple Python script that scans your files.

SQL is a straightforward query language with minimal leakage of abstractions, commonly used by business analysts as well as programmers. Queries in SQL are generally pretty simple. They are also usually very fast - if your database is properly indexed, multi-second queries will be uncommon.

Hadoop does not have any conception of indexing. Hadoop has only full table scans. Hadoop is full of leaky abstractions - at my last job I spent more time fighting with java memory errors, file fragmentation and cluster contention than I spent actually worrying about the mostly straightforward analysis I wanted to perform.

If your data is not structured like a SQL table (e.g., plain text, json blobs, binary blobs), it’s generally speaking straightforward to write a small python or ruby script to process each row of your data. Store it in files, process each file, and move on. Under circumstances where SQL is a poor fit, Hadoop will be less annoying from a programming perspective. But it still provides no advantage over simply writing a Python script to read your data, process it, and dump it to disk.

In addition to being more difficult to code for, Hadoop will also nearly always be slower than the simpler alternatives. SQL queries can be made very fast by the judicious use of indexes - to compute a join, PostgreSQL will simply look at an index (if present) and look up the exact key that is needed. Hadoop requires a full table scan, followed by re-sorting the entire table. The sorting can be made faster by sharding across multiple machines, but on the other hand you are still required to stream data across multiple machines. In the case of processing binary blobs, Hadoop will require repeated trips to the namenode in order to find and process data. A simple python script will require repeated trips to the filesystem.

From over 10 years ago (I’m sorting through my old bookmarks). A single object looks like this:

{
    "ACCTOUNT_NUMBER":"1234567890",
    "CUSTOMER_NAME":"ACME Products and Services, Inc.",
    "ADDRESS":"123 Main Street",
    "CITY":"Albuquerque",
    "STATE":"NM",
    "ZIP":"87101-1234"
}

He tested the usability of a given browser while it loaded between 1 and 1,000,000 such records.

From this test, I am considering the sweet spot to be around 10,000 records at (1.55MB). The maximum number of usable records I would push to a browser would be around 25,000 records (3.87MB). Keep in mind there are numerous factors to keep in mind when determining how many records you should return to your JavaScript application. The purpose of this test was to help identify a general maximum number for conversations around large record sets with JSON.

Would love to see an updated version of the tests.

No macOS love though 😔

The focus of this project is to build a super reliable, durable, and stable network device from tried and tested tech. This is not a project for pushing the limits or testing out flashy new stacks. This affinity for ‘boring’ technology will reflect on most of the choices made here, from the hardware to the way we configure services and daemons.

Sounds lovely. (Cached)

By a single dev. At $42, an absolute steal for all the things you can do with it. Perpetual license, no bullshit subscription model. 😍

It’s run by the fine folk at Loganberry Books, costs a nominal $4 per submission, and has a very admirable 50%+ success rate. Here’s my submission 🤞 Via CK ♥️

Bit pricey but appears to generate lovely layouts. Via Ash Furrow’s photography site.

Learn C and build a basic Lisp #VALUE 😍

, ThriftBooks, and Bookshop are three alternatives to purchasing books on Amazon. Powell’s is withdrawing from the Amazon Marketplace: “The vitality of our neighbors and neighborhoods depends on the ability of local businesses to thrive. We will not participate in undermining that vitality.”