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Why are new startups growing so fast?
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Writing code for a computer is hard enough.
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As I am en route to see my first total solar eclipse, I was curious how hard it would be to compute eclipses in Python.
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CIA produced a fantastic book during the peak of World War 2 called Simple Sabotage.
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Long story short: I’m working on a super cool tool called Modal.
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This is is in many respects a successor to a blog post I wrote last year about what I want from software infrastructure, but the ideas morphed in my head into something sort of wider.
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Hi!
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Here’s a theory I have about cloud vendors (AWS, Azure, GCP): Cloud vendors1 will increasingly focus on the lowest layers in the stack: basically leasing capacity in their data centers through an API.
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This isn’t as much of a blog post as an elaboration of a tweet I posted the other day: I think this specialization of data teams into 99 different roles (data scientist, data engineer, analytics engineer, ML engineer etc) is generally a bad thing driven by
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I guess I should really call this a parable.
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Software infrastructure (by which I include everything ending with *aaS, or anything remotely similar to it) is an exciting field, in particular because (despite what the neo-luddites may say) it keeps getting better every year!
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I joined Better in early 2015 because I thought the team was crazy enough to actually change one of the largest industries in the US.
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It’s a popular attitude among developers to rant about our tools and how broken things are.
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I spent a ton of time looking at different software providers, both as a CTO, and as a nerd “advanced” consumer who builds stuff in my spare time.
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We live in a year of about 350,000 amateur epidemiologists and I have no desire to join that “club”.
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Compensation has always been one of the most confusing parts of management to me.
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Hanlon’s razor is a classic aphorism I’m sure you have heard before: Never attribute to malice that which can be adequately explained by stupidity.
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Let’s consider a toy model where you’re hiring for two things and that those are equally valuable.
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I recently finished the excellent book Kochland.
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Just a quick note that my team is always hiring at Better.
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My company has a buffet every Friday, and the lines grow to epic proportions when the food arrives.
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No one asked for this, but I’m something like ~12 years into my career and have had my fair share of mistakes and luck so I thought I’d share some.
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This is a blog post originally featured on the Better engineering blog.
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Anyone who built software for a while knows that estimating how long something is going to take is hard.
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When I started building up a tech team for Better, I made a very conscious decision to pay at the high end to get people.
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A modern tech stack typically involves at least a frontend and backend but relatively quickly also grows to include a data platform.
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It started with a tweet: New years resolution: every plot I make during 2018 will contain uncertainty estimates — Erik Bernhardsson (@bernhardsson) January 7, 2018 Why?
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This is a bit of a rant but I really don’t like software that invents its own query language.
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I get bored reading management books very easily and lately I’ve been reading about a wide range of almost arbitrary topics.
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As some of you may know, one of my side interests is approximate nearest neighbor algorithms.
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Ok, so I have to first preface this whole blog post by a few things: I really struggle with the term microservices.
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I have done roughly 2,000 interviews in my life.
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I’ve been reading up on operations research lately, including queueing theory.
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I started writing this blog in late 2012, partly because I felt like it would help me improve my English and my writing skills, partly because I kept having a lot of random ideas in my head and I wanted to write them down somewhere.
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UPDATE(2018-06-17): There are is a later blog post with newer benchmarks!
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I’m interrupting the regular programming for a quick announcement: we’re looking for data engineers at Better.
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Turns out having a toddler isn’t super compatible with reading.
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I spent a few days during the holidays fixing up a bunch of semi-dormant open source projects and I have a couple of blog posts in the pipeline about various updates.
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I spent six years at a company that went from 50 people to 1500 and one contributing factor leading to my departure was that I went from a “maker” to a person stuck in meetings every day.
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I had an interesting idea a few weeks ago, best explained through an example.
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I’ve been a bit bad at posting things with a regular cadence lately, partly because I’m trying to adjust to having a toddler, partly because the hunt for clicks has caused such a high bar for me that I feel like I have to post something Pulitzer-worthy.
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There are often close relationships between top level business metrics.
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A funny thing about being a foreigner is how you realize people take broken things for granted.
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Just for fun, I generated these graphs of the number of letters in the word for each number.
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Here’s a dumb extremely accurate rule I’m postulating* for software engineering projects: you need at least 3 examples before you solve the right problem.
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I just bought Machine, Platform, Crowd: Harnessing Our Digital Future and discovered that it mentions my blog – in particular the post When machine learning matters.
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There’s about 765 million blog posts about the diversity “memo” that leaked out of Google a couple of weeks ago.
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I just spent a few days in Italy, on the Ligurian coast.
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I’ve written before about the importance of iterating quickly but I didn’t necessarily talk about some concrete things you can do.
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Remember when everyone had a really ugly blog with a blogroll?