YouTube: TEDxToronto: Ryan Henson Creighton & Cassandra Creighton:
Make sure to check out Sissy’s Magical Ponycorn Adventure and have some fun.
YouTube: TEDxToronto: Ryan Henson Creighton & Cassandra Creighton:
Make sure to check out Sissy’s Magical Ponycorn Adventure and have some fun.
A video and a post from some friends of mine:
Jon Moore: Video: “Every discussion about REST Ever”
Michael Bevilacqua-Linn: “Unicorns and Strong Typing”.
The team at Mozilla posted a great piece documenting their idea and design process for what may be a new feature set in Firefox: “Save For Later”.
Absolutely one of the best posts on what I consider to be the responsibilities of my work and what I aspire to practice, John Allspaw has written required reading for anyone looking to become a Senior Engineer or anyone who wants to grow into a better one: “On Being A Senior Engineer” (via rc3.org).
I’m reading “Systemantics” by John Gall and find myself at times laughing to myself and at times wretching in scenarios I recognize all too well in my work. His humor, which is straight out of “The Hitchhiker’s Guide to Galaxy”, helps with the pain. This might end up next to “The Mythical Man-Month” on my bookshelf and as a book I recommend to any coder looking to to progress from simply writing code, to solving problems.
Related:
laetus in praesens: Why Systems Fail and Problems Sprout Anew: Commentary on the principles of ‘Systemantics’
Both of these are worth listening to or watching:
NPR.org hosted a Third-Party debate featuring Gary Johnson, of the Libertarian Party, and Jill Stein, of the Green Party. Link includes a transcript.
And Bill O’Reilly and Jon Stewart recently had a fun, and thought provoking debate in The Air-Conditioned Auditorium that is notable for the frankness of discussion and the fact that there was a real back-and-forth between them.
YouTube.com: Doctor Who’s Who’s Who 1986 – EDITED!:
A fun watch for the Doctor Who off-season.
According to CNBC an unknown program triggered 4% of trading activity last week:
The program placed orders in 25-millisecond bursts involving about 500 stocks, according to Nanex, a market data firm. The algorithm never executed a single trade, and it abruptly ended at about 10:30 a.m. Friday.
Someone testing in production perhaps?
I recently read about no starch press’s “Super Scratch Programming Adventure!” from a post at boingboing and had to purchase a copy. The book looks as terrific in person as it does in the discussion at boingboing. My daughter was happy to see the book when it came in the mail. I’m looking forward to starting to read it with her and try some of its projects. Check it out.
This was a fascinating post I tripped upon at LiveJournal that I wanted to share. celandine13: Errors vs. Bugs and the End of Stupidity:
In fact, wrong notes always have a cause. An immediate physical cause. Just before you play a wrong note, your fingers were in a position that made that wrong note inevitable. Fixing wrong notes isn’t about “practicing harder” but about trying to unkink those systematically error-causing fingerings and hand motions. That’s where the “schizophrenia” comes in: pretending you can move your fingers with your mind is a kind of mindfulness meditation that can make it easier to unlearn the calcified patterns of movement that cause mistakes.
Remembering that experience, I realized that we really tend to think about mistakes wrong, in the context of music performance but also in the context of academic performance.
A common mental model for performance is what I’ll call the “error model.” In the error model, a person’s performance of a musical piece (or performance on a test) is a perfect performance plus some random error. You can literally think of each note, or each answer, as x + c*epsilon_i, where x is the correct note/answer, and epsilon_i is a random variable, iid Gaussian or something. Better performers have a lower error rate c. Improvement is a matter of lowering your error rate. This, or something like it, is the model that underlies school grades and test scores. Your grade is based on the percent you get correct. Your performance is defined by a single continuous parameter, your accuracy.
But we could also consider the “bug model” of errors. A person taking a test or playing a piece of music is executing a program, a deterministic procedure. If your program has a bug, then you’ll get a whole class of problems wrong, consistently. Bugs, unlike error rates, can’t be quantified along a single axis as less or more severe. A bug gets everything that it affects wrong.