pytudes VS lighthouse

Peter Norvig's work is great to learn from https://github.com/norvig/pytudes

I've been a programmer for 25 years. A realization that has crept up on me in the last 5 is that not everyone thinks that functions should be short: there are two cultures, with substantial numbers of excellent programmers belonging to both. My question is: how do we maintain harmonious, happy, and productive teams when people can disagree strongly about this issue?

The short-functions camp holds that functions should be short, tend toward the declarative, and use abstraction/implementation-hiding to increase readability (i.e. separable subsections of the function body should often be broken out into well-named helper functions). As an example, look at Peter Norvig's beautiful https://github.com/norvig/pytudes. For a long time I thought that this was how all "good programmers" thought code should be written. Personally, I spent over a decade writing in a dynamic and untyped language, and the only way that I and my colleagues could make that stuff reliable was to write code adhering to the tenets of the short-function camp.

The long-functions camp is, admittedly, alien to me, but I'll try to play devil's advocate and describe it as I think its advocates would. It holds that lots of helper functions are artificial, and actually make it _harder_ to read and understand the code. They say that they like "having lots of context", i.e. seeing all the implementation in one long procedural flow, even though the local variables fall into non-interacting subsets that don't need to be in the same scope. They hold that helper functions destroy the linear flow of the logic, and that they should typically not be created unless there are multiple call sites.

The short-function camp also claims an advantage regarding testability.

Obviously languages play a major role in this debate: e.g. as mentioned above, untyped dynamic languages encourage short functions, and languages where static compilation makes strong guarantees regarding semantics at least make the long-function position more defensible. Expression-oriented and FP-influenced languages encourage short functions. But it's not obvious, e.g. Rust could go both ways based on the criteria just mentioned.

Anyway, more qualified people could and have written at much greater length about the topic. The questions I propose for discussion include

- Is it "just a matter of taste", or is this actually a more serious matter where there is often an objective reason for discouraging the practices of one or other camp?

- How can members of the different camps get along harmoniously in the same team and the same codebase?

I have the same impression. Reading the code, he uses global variables [1], obscure variable (k, bw, fw, x) and module names ("pal.py" instead of "palindromes.py"), doesn’t respect conventions about naming in general (uppercase arguments [2], which even the GitHub syntax highlighter is confused about). This feels like code you write for yourself to play with Python and don’t plan to read later.

Some parts of the code feel like what I would expect from a junior dev who started learning the language a couple weeks ago.

[1]: https://github.com/norvig/pytudes/blob/952675ffc70f3632e70a7...

[2]: https://github.com/norvig/pytudes/blob/952675ffc70f3632e70a7...

Prolog is excellent for bikeshedding, in fact that might be its strongest axis. It starts with everything you get in a normal language such as naming things, indentation, functional purity vs side effects, where to break code into different files and builds on that with having your names try to make sense in declarative, relational, logical and imperative contexts, having your predicates (functions) usable in all modes - and then performant in all modes - having your code be deterministic, and then deterministic in all modes. Being 50 years old there are five decades of learning "idiomatic Prolog" ideas to choose from, and five decades of footguns pointing at your two feet; it has tabling, label(l)ing, SLD and SLG resolution to choose from. Built in constraint solvers are excellent at tempting you into thinking your problem will be well solved by the constraint solvers (it won't be, you idiot, why did you think that was a constraint problem?), two different kinds of arithmetic - one which works but is bad and one which mostly works on integers but clashes with the Prolog solver - and enough metaprogramming that you can build castles in the sky which are very hard to debug instead of real castles. But wait, there's more! Declarative context grammars let you add the fun of left-recursive parsing problems to all your tasks, while attributed variables allow the Prolog engine to break your code behind the scenes in new and interesting ways, plenty of special syntax not to be sneezed at (-->; [_|[]] {}\[]>>() \X^+() =.. #<==> atchoo (bless you)), a delightful deep-rooted schism between text as linked lists of character codes or text as linked lists of character atoms, and always the ISO-Standard-Sword of Damocles hanging over your head as you look at the vast array of slightly-incompatible implementations with no widely accepted CPython-like-dominant-default.

Somewhere hiding in there is a language with enough flexibility and metaprogramming to let your meat brain stretch as far as you want, enough cyborg attachments to augment you beyond plain human, enough spells and rituals to conjour tentacled seamonsters with excellent logic ability from the cold Atlantic deeps to intimidate your problem into submission.

Which you, dear programmer, can learn to wield up to the advanced level of a toddler in a machine shop in a mere couple of handfuls of long years! Expertise may take a few lifetimes longer - in the meantime have you noticed your code isn't pure, doesn't work in all modes, isn't performant in several modes, isn't using the preferred idiom style, is non-deterministic, can't be used to generate as well as test, falls into a left-recursive endless search after the first result, isn't compatible with other Prolog Systems, and your predicates are poorly named and you use the builtin database which is temptingly convenient but absolutely verboten? Plenty for you to be getting on with, back to the drawing boar...bikeshed with you.

And, cut! No, don't cut; OK, green cuts but not red cuts and I hope you aren't colourblind. Next up, coroutines, freeze, PEngines, and the second 90%.

Visit https://www.metalevel.at/prolog and marvel as a master deftly disecting problems, in the same way you marvel at Peter Norvig's Pytudes https://github.com/norvig/pytudes , and sob as the wonders turn to clay in your ordinary hands. Luckily it has a squeaky little brute force searcher, dutifully headbutting every wall as it explores all the corners of your problem on its eventual way to an answer, which you can always rely on. And with that it's almost like any other high level mostly-interpreted dynamic programming / scripting language.

Google Core Vitals now represent the most important metrics to focus on when it comes to technical SEO. Google Core Vitals are a set of standardized metrics that Google uses to evaluate the user experience offered by a web page and assign it a technical SEO grade. Several tools exist to measure and report technical SEO performance, but the most reliable is Google Lighthouse.

- referred to https://github.com/GoogleChrome/lighthouse/issues/6512 but didnt see network.disable error - added staticdistdir as per https://github.com/GoogleChrome/lighthouse-ci/blob/main/docs/configuration.md,

There has been discussion about adding a visual indicator to make the mode more obvious, but it has not made it into Chrome devtools!

I haven't tried it, but you might be able to control throttling by using lighthouse to get performance data: https://github.com/GoogleChrome/lighthouse/blob/main/docs/throttling.md

When we implemented the MVP of the fintech app, we had a quite complicated form. At that time, I was still young and inexperienced. And eventually, we realized that our project was slowing down. We had to spend additional hours figuring out the reason. We had many unnecessary re-renders because we ignored basic rules related to props in React. I wanted to do everything possible to avoid such situations in the future. So, I added to the project linters like this and an additional starting configuration to package.json to run why-did-you-render. In short, this plugin issues a warning if something is re-rendered unnecessarily and suggests how to avoid it. Also, we included running Lighthouse in headless mode. Some people say that premature optimizations are bad, but for me, it's a principle: do it right from the start.

You can run a Lighthouse audit in two ways: either on the command line or in a browser. If you want to run audits on the command line, you will first need to install the Lighthouse command:

i wanted to run the lighthouse(lh) docker container on multiple EC2 instances in different regions. I'm thinking of triggering the EC2 instance with the lambda function once the lh container has audited the URL the results will be sent "SUB rest API" which will further update the UI

There's a link to what they mean by mobile network there (https://github.com/GoogleChrome/lighthouse/blob/main/docs/throttling.md) and it says:

Here's the non-psuedo code equivalent that can leverage CompressionStream rather than using a browserified-gzip-library: https://github.com/GoogleChrome/lighthouse/blob/437eb4d757c5...

We use it in Lighthouse for the exact same purpose, a URL #hash full of state. We found that modern browsers support 10mb of data in the hash. https://github.com/GoogleChrome/lighthouse/pull/12509#discus...

Lighthouse: This is an open-source tool developed by Google that allows you to audit the performance of your website. You can use Lighthouse to get a detailed report on your website's Core Web Vitals, as well as other performance metrics. You can run Lighthouse in Chrome DevTools or as a command-line tool.