snicker7

Message queues (e.g. SQS) are inappropriate for tracking long-running tasks/workflows. This is due to the operational requirements such as:

- Checking the status of a task (queued, pending, failed, cancelled, completed) - Cancelling a queued task (or pending task if the execution environment supports it) - Re-prioritizing queued tasks - Searching for tasks based off an attribute (e.g. tag)

You really do need a database for this.

We literally had a major us-east-1 incident on AWS today. Only thing we can do is sit on our butts and wait for it to end so that we can clean up. This happens every few months. I am unimpressed with the the "thousands of engineers" argument.

It may be worthwhile to understand where dynamic typing is helpful since this gets mentioned a lot. Python and other dynamic languages are increasingly reliant on static type checkers.

An interpreter is an optimization barrier relative to native composition.

Imagine being the dev competing game Y and seeing the changelog.

Not looking to apply, but can I DM you when the E train is late again?

The Neopets HTML Guide [1] remains the best beginner’s guide to Web development.

[1] https://www.neopets.com/help/html1.phtml

I believe the problem is the lack of proper dependency indexing at PyPI. The SAT solvers used by poetry or pdm or uv often have to download multiple versions of the same dependencies to find a solution.

Mathematical Methods of Classical Mechanics by Arnold is a good introductory book.

The point is not to spend time frantically fixing code at 3 AM.

Why not dual license with AGPL?

This API is very similar to DynamoDB, which is basically a hash table of B-trees.

My experience is that this architecture can lead to very chatty applications if you have a rich data model (eg a graph).

“Stuck making obsolete parts” sounds great. Guaranteed revenue without needing to stay competitive.

How does the DX compare against AWS step functions? My experience is that it is very difficult to “unit test” step workflows.

It turns out that DBs were invented to solve hard problems with state management. When people moved away from DBs (at least transactional relational DBs) they had to rediscover all the old problems. Tech is cyclical.

OR if the primary key is exported out of the DB, i.e. for constructing URLs.

SICP. Lectures are on YouTube.

What’s interesting about this paper is that, with some very generic mathematical assumptions, only Galilean and special relativity are permissible.

I find managing the AWS API Gateway to be too cumbersome. We use a proxy lambda that handles authentication and has routing logic to other lambdas (or SQS | State Machine).

Why would I prefer a winsorized mean over a median?

$1B? Imagine if even 1% of that went into LibreOffice funding.

YAML was a mistake. Couldn’t count how many issues I and my colleagues encountered due to white space misalignment.

Wouldn’t you get similar performance by just loading the file to ramdisk?

I wonder how this is is actually simpler than an a managed queue, say SQS.

Python is especially bad wrt to accessibility.

Spec, not library. Otherwise, no chance of having many-lang adoption.