Items in Feb, 2020
As discussed previously, the biggest hole in Datasette’s feature set at the moment involves writing to the database.[... 604 words]
I’ve really come to appreciate that performance isn’t just some property of a tool independent from its functionality or its feature set. Performance — in particular, being notably fast — is a feature in and of its own right, which fundamentally alters how a tool is used and perceived.
Why Google invested in providing Google Fonts for free. Fascinating comment from former Google Fonts team member Raph Levien. In short: text rendered as PNGs hurt Google Search, fonts were a delay in the transition from Flash, Google Docs needed them to better compete with Office and anything that helps create better ads is easy to find funding for. # 23rd February 2020, 2:13 pm
So next time someone is giving you feedback about something you made, think to yourself that to win means getting two or three insights, ideas, or suggestions that you are excited about, and that you couldn’t think up on your own.
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pup. This is a great idea: a command-line tool for parsing HTML on stdin using CSS selectors. It’s like jq but for HTML. Supports a sensible collection of selectors and has a number of output options for the selected nodes, including plain text and JSON. It also works as a simple pretty-printer for HTML. # 14th February 2020, 4:25 pm
A group of software engineers gathered around a whiteboard are a joint cognitive system. The scrawls on the board are spatial cues for building a shared model of a complex system.
I’ve been making a lot of progress on Datasette Cloud this week. As an application that provides private hosted Datasette instances (initially targeted at data journalists and newsrooms) the majority of the code I’ve written deals with permissions: allowing people to form teams, invite team members, promote and demote team administrators and suchlike.[... 885 words]
Deep learning isn’t hard anymore. This article does a great job of explaining how transfer learning is unlocking a new wave of innovation around deep learning. Previously if you wanted to train a model you needed vast amounts if data and thousands of dollars of compute time. Thanks to transfer learning you can now take an existing model (such as GPT2) and train something useful on top of it that’s specific to a new domain in just minutes it hours, with only a few hundred or a few thousand new labeled samples. # 7th February 2020, 8:47 am