cocalc VS Phoenix

I have some unit tests for billing and subscription code for my company that started breaking in CI today due to the leap day: https://github.com/sagemathinc/cocalc/commit/8575029c2b76787...

1. I'm integrating ChatGPT extensively into https://CoCalc.com. This integration makes a lot of sense, because cocalc is a platform in which relatively inexperienced students use Jupyter notebooks, linux terminals and Latex. So far, the most popular feature by far is a "Help me fix this" button that appears above stacktraces in Jupyter notebooks.

2. One software engineering challenges is that ChatGPT often outputs code in markdown blocks. I've had to emphasize in prompts that it should explicitly mark the language. I then got inspired to make it possible to evaluate in place the code that appears in these blocks using a Jupyter kernel, and spent a week making that work (so, e.g., if you type a question into the chatgpt box on the landing page at https://cocalc.com, and code appears in the output, often you can just evaluate it right there). There seem to be endless surprises and challenges though. For example, a few minutes ago I realized that sometimes the giant tracebacks one gets when using Python in Jupyter notebooks are so big (even doing simple things with matplotlib) that they end up resulting in too much truncation: https://github.com/sagemathinc/cocalc/issues/6634

3. I'm mostly using GPT-3.5-turbo rather than GPT4, even though I have a GPT4 api key. Aside from costs, GPT4 takes about 4x as long, which often just feels too long for my use case. The average time for a complete response from GPT-3.5 for my application is about 8 seconds, versus over 30s for GPT4.

Github's implementation is really lazy. There are many much better approaches to precisely this problem. E.g., Jupyter notebooks implement one that has matured in the wild over a decade. There's this very flexible markdown-it plugin that implements anther https://github.com/goessner/markdown-it-texmath, and my version of it here https://github.com/sagemathinc/cocalc/blob/master/src/packag... which I rewrote in typescript with a focus on the same semantics as Jupyter has, but for CoCalc, and I've been working on using unifiedjs to provide more general latex for Markdown (not just formulas) here https://github.com/sagemathinc/cocalc/pull/5982 Parsing math is much easier if you use a plugin to an existing markdown parser, rather than trying to do some hack outside of that (which is what Github probably does, and also what Jupyter does).

A standard Phoenix app contains a priv/repo/seeds.exs script file, which populates a database when it is run, so that developers can work with a conveniently prepared environment.

There was one in the Phoenix Framework (Elixir) about issuing certificates with an invalid end date: https://github.com/phoenixframework/phoenix/issues/5737

Interestingly, Azure had this bug some years ago too leading to an outage. https://azure.microsoft.com/en-us/blog/summary-of-windows-az...

Official website: https://www.phoenixframework.org/

Since you mention Rails, have you seen https://www.phoenixframework.org/

Thus, we set out to build a desktop application using a LiveView from the Phoenix Framework in Elixir. For the uninitiated, a LiveView is a process that receives events, updates its state, and renders updates to a page as diffs. The LiveView programming model is declarative: instead of saying “once event X happens, change Y on the page”, events in LiveView are regular messages which may cause changes to its state.

It seems though like Phoenix is similar like Blazor Server (using web socket), but Phoenix is: SEO friendly (first render is plain html) Light weight, scales well and concurrency is first class Easy to develop (runs a local server so you see live updates) Compiled With auth out of the box https://www.phoenixframework.org/

Sorry to hear this. Phoenix v1.7 changed how it structures files in disk and that broke quite some of the getting started material. However, the guides are always kept up to date, so you can give it a try: https://hexdocs.pm/phoenix/overview.html

You can also see the resources on this page listed by year: https://github.com/phoenixframework/phoenix/blob/main/guides... - the recent launched ones are most likely up to date.

Yes! I love Elixir :) [Phoenix LiveView](https://www.phoenixframework.org/) is really amazing. I feel so fast working in it. I got hooked after watching Chris McCord's ['Build a real-time Twitter clone in 15 minutes'](https://www.youtube.com/watch?v=MZvmYaFkNJI&embeds_referring...), and things have improved a lot since then.

I still work on a lot of Java projects. As of JDK 17 Java has most of "ML the good parts" and has the same scalable, reliable and high-performance threading Java is famous for. JAX-RS provides a Sinatra style framework that makes it easy to write JSON API back ends. JDK 21 is just about to come out as a long term supported version and it will be even better.

I do my side projects in Python with aiohttp and think it is a lot of fun even though people tell me it is suicide (I guess if you block the thread you are in trouble)

I think "Next.js" really wants a node.js backend which has the big advantage that you can share code with the front end and back end. It's basically single-threaded but I know people who are happy with it.

The system I'd most like to try is

https://www.phoenixframework.org/

which is just great if you want to do stuff with websockets that is more interactive than what most people are doing.