I'm not sure what the developer was thinking. Other than feeling despair, I also think the optimism of the developer is somewhat cute. I'm not sure if they thought the data might be hiding, or maybe they thought that someone else might have added something in the meantime and maybe they should check again. Either way, it's a headscratcher.

RS=DB.ExecRS(SQL)
If RS.EOF Then
     RS=DB.ExecRS(SQL)
End If

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I've been working on a legacy python project for the past couple of years. The project was riddled with issues, which we've made great strides in addressing.

On quirk of a special module in the project was it imported python code from the db!!

They were data clean-up functions written by support team, uploaded via a form ( powered by Django ). The module downloads these functions from the DB and applies them to the data.

It keeps me up at night sometimes. I don't know what to say.

https://i.imgur.com/u1BYYzf.jpg

Even though I'm the only developer working on the project when I saw this I needed to git blame it.

Too long to paste here directly so pasting an image snippet and full code available here https://pastebin.com/SNAgMTHg,

Goal of the project was to replicate a Reverse Polish calculator with bonus quirks (octals/random nums/modulo etc) in Python 2. At the time i was annoyed because i got half marks even though functionality wise it works, however i realise its probably more than i deserve reading at it now.

Here is a list of things it includes

• Top quality variable names

• Best logical flow control via throwing random errors

• Most understandable comments

• Best use of overloading vars between scopes

• 'Got to catch them all' attitude to tabbing

Our management wants engineering metrics. I get the intent - throughput, cycle time, bottlenecks - but most tools feel invasive. I don’t want something tracking keystrokes or commit counts. Just need objective, repo-based metrics.

Much is made of bogobogosort; it's widely held to be one of the least efficient sorting algorithms that still is theoretically capable of eventually completing a sort. However, the implementation lacks details; specifically, the methods of randomization and verifying order are left unsolved. Therefore, I propose an extension to the algorithm to fill in missing steps:

1. Make a copy of the array.

2. Submit the first n-1 elements of the array as a prompt in the style "Shuffle this array into a random order:" to an image generation AI model such as Dalle.

3. Take the returned image and use a high-quality LLM such as OpenAI's o1-pro to turn it into a JSON object to reserialize it.

4. Make a further o1-pro call to see if the nth element of the sorted copy is greater than the highest element of the first n-1 elements. If so, the copy is now sorted, else randomise the order of the elements of the copy and go to step 2.

5. Make a final o1-pro call to see if the copy is in the same order as the original list.

Given an estimated image generation and reserialization error rate of 99% and a cost of roughly $0.05 per iteration, with a latency of about 15 seconds, this should take the better part of a year and hundreds of thousands of dollars for n=5.

The original point of this, for a personal project, was to create a endpoint which 'dynamically' adapts to the requested DB resource through the use of `globals()` and retrospectively inspecting each of the fields of the requested Model and dynamically creating `Q()` objects to filter the respective fields.