Pandorium & Trial
Pandorium Pandorium
Trial Trial
Pandorium Pandorium
Hey Trial, I’ve been tinkering with a neural net that predicts the next line of code based on context—basically a smart autocomplete that learns your style. It feels like a living sketchpad that could rewrite how we write software. What do you think about testing its precision?
Trial Trial
Sounds interesting. To really judge its usefulness you need a benchmark: a test set of code with the last line omitted, then measure exact match, functional correctness, and maybe how often it suggests something that compiles. Also check overfitting—does it just echo the training data? A good metric is the edit distance between its output and the real line. If you can’t quantify that, it’s just another shiny toy.
Pandorium Pandorium
Sounds like a solid plan—use a clean dataset, hide the last line, run the net, then compare its suggestion to the actual line with an edit‑distance check. Add a compile‑test pass to catch syntax leaks, and keep a holdout set to spot overfitting. If it passes those, we’re not just chasing a pretty demo. What test set are you thinking of?
Trial Trial
Maybe pull a few large open‑source projects from GitHub—React, Django, or even the Linux kernel. Strip out the last line of each file, feed the rest to the model, then see what it outputs. Keep a separate hold‑out set of smaller scripts or utility functions so you can test for overfitting on big code versus small patterns. That’ll give you a clear picture of precision versus novelty.
Pandorium Pandorium
Pulling in React, Django, and kernel code is brutal but perfect—real world, heavy patterns. I’ll trim the last line, run the net, and then stack the results: exact match count, edit distance, and compile pass. For the hold‑out, I’ll mix tiny scripts that hinge on variable naming quirks, so we can see if it’s just regurgitating or actually generalizing. Let’s see if the model can keep up without just echoing the training data. Ready to spin it up?