No one wants to read a series of blog posts about the various tools I use on a daily basis and how I feel about them. But maybe someone wants to read one blog post? Love Bandcamp Lets me buy + download music from all my favourite artists and then gets the hell out of my way. Complice I wrote a love-letter to you. 5 years later the love is still going strong.
Extracting the short (mostly 3 paragraphs or less) book reviews I’ve written from a private group chat onto my blog, because fuck Goodreads and Amazon. Never Split the Difference: Negotiating As If Your Life Depended On It by Christopher Voss and Tahl Raz Excessively long given the simplicity of the content. Instead, read: Patrick McKenzie’s post on salary negotiation Getting to Yes, which is a classic and shorter Nitpicks:
In October 2019, I got bronchitis and listened to a bunch of podcasts while I recovered, at the recommendation of my old labmate Brent Komer. Later during COVID, I started listening to podcasts more often to feel less lonely. These are the podcasts I enjoy the most, ordered according to educational value. Farm to Taber Dr. Sarah Taber is a crop scientist with a spicy Twitter account. Her podcast is less spicy, but it still fights preconceptions around agriculture and the incentives driving its current manifestation in America.
Concepts which refuse to be nailed down
Differentiating concrete concepts from fuzzy categories is an essential step to mastering a domain. This took me years in Computer Science, because practitioners often deny categories are ambiguous. Thus, for all the noobs out there, here is a list of unanswerable questions. I’ve wasted hours of my life searching for definite answers to each of them. Whether to taboo these words or embrace their subjectivity is a personal choice. But at the very least, you should know that if someone claims to have an unassailable answer to any of these questions, they’re a gosh-darned liar.
When you're computation-bound, what are your options?
[epistemic status: hastily written, high-level overview lacking practical grounding] Bottlenecks Python isn’t known for it’s speed. It has a purposefully dumb interpreter which gets blown out of the water (in terms of throughput, memory consumption, and start-up speed) by almost all compiled languages. This is usually fine, because you’re just trying to ship a gosh-darned website or quickly whip together a data-cleaning pipeline. Typically, other bottlenecks, such as network speed, memory access times, algorithmic approach, or the ability to scale across multiple machines take precedence.