vslira's blog

     Fun story: I came to Python through data analysis - so I wasn't a "python/programming expert" - and for an unreasonably long time used exclusively csv, parquet (thanks to pandas) and later JSON to persist data from my projects (local projects, of course. Mongo, RDS and Athena were always there). At work this was 100% enough (mostly parquet), but it always felt a little clunky to use JSON for a quick cache, especially when said cache grew larger for longer running processes or longer projects. Opening, parsing then serializing back again becomes slow surprisingly fast.      Then after a few years I decided to start using SQLite. I knew it existed, but always felt like it was a bit of a hassle to define a schema and "get" and "set" functions in all projects just to have a basic persistent k-v functionality. It was OK and had the extra benefit of allowing me to safely check this cache on a different jupyter notebook process when running longer proce

Stripe Press recently released a new edition of Poor Charlie's Almanack, exactly a week after Munger sadly passed away. I had preordered the book and picked it up a few weeks after release. There are many good quips and general good advice in there and I will share the most interesting tidbits to me. First, the "mental model checklist". Charlie Munger is famous for his "mental models" framework and many outlets - books, websites, content writers - push mental models either because they believe in the framework or, more cynically, because people buy it trying to be more like Munger (that is, rich). I don't have a lot to add here, other than that when I read about this "mental models" frameworks, it never came to me the idea of a literal checklist. But that's how Munger explains his framework: a literal checklist. You go one by one, see which ones apply and - crucially important, according to him - when many apply (as in most real life cases) on

As I write this post, the board of directors of OpenAI is under extreme pressure to reinstate Sam Altman as CEO and giving him complete control of the company while they're at it. If they don't do that, it's likely that they'll lose the company's financial backers, compute enablers and a significant portion of their most important employees - the proverbial members of technical staff. Oh, and be sued to high Heaven by Satya  Nadella. Far from accusing Sam of being just talk as the title of this post may imply, but however intelligent he is what he brought for the company certainly wasn't his AI development capabilities, but the external and internal support that the company needed to move forward. Contrary to everything I would like to be true, this skill - convincing people, selling to them - is more important than being able to build what they want. Technically being able to, I mean, because actually forming a team to deliver, and presenting it in a way that c

Look on my works, ye Mighty, and despair! The common interpretation of the poem is that we're supposed to appreciate the irony of the self-styled king of kings' works not being around anymore. Time, like sand, eventually wears everything out. But I always found it funny that the pharaoh is addressing "ye Mighty,". Is it out of respect or irony that Ramesses II addresses his non-peers this way? The pharaoh not-so gently cues them to despair, so I don't suppose it's out of respect. But the irony is also a bit uncalled for: he already drove the point home about who's in charge with his title. Why "ye Mighty," then? Unless it's not supposed to be ironic, but earnest. Maybe Shelley isn't picturing a cocky, hubristic ruler, but a wise one. He was, after all, one of the most successful pharaohs of Egypt, in both war and diplomacy. Maybe the pharaoh is trying to send an important message to those who happen upon his works: "It doesn't m

Note: there's no hidden knowledge contained in this post, it's just a reflection on how reading someone's reaction to an analogous situation changed my outlook Like everyone who works in an information job, I have been worried these last few months about how AI would impact my place in the labor force in the coming decade. Even though I didn't get to the depressed extremes we've been seeing on the Internet, I could not shake off the feeling that what I could offer to a company - no, to the whole economy, which just means "other people" - would get a lot less valuable. Come mid January, I wast taking a week off and had taken to read a few books since I read so little non-technical content on a day to day basis. One of these books was "Founders at work" by author and YC co-founder Jessica Livingston. The book is a series of interviews conducted by Jessica with the founders of some of the most important tech companies - sometimes specific products -

A few weeks ago I was looking for a function that would squeeze variables with large magnitude, but behaved like the identity function close to zero. After a couple of days without finding something that suited me, I gave up. Luckily for me, however, Kaiser Fung blogged a couple days ago about a weird scale used on an article and one of the commenters pointed out that arsinh(x) (that is, the inverse of the sinh(x) function) works like a "pseudo-log" (the poster themselves use this name for a different function described on the same comment), and best of all, fits my need for something close to the identity around zero. Talk about coincidences. Well, this post makes it public to the world that even after a whole Engineering degree and nearing 10 years in Statistics/ML, I had never studied nor used hyperbolic functions before. One day I'll find out why and where I should use sinh and cosh, but for today, adding arsinh(x) to my data visualization toolkit is enough. Click