Jev Based Code Review

gatapia2•3 minutes ago

I don't understand Jev. Its a generic classifier right? Like the classifiers we were building 15 years ago with random forrests and logistic regressions, but just generic. What's so revolutionary? And how can the accuracy be any better than a custom trained classifier that can be built in a day (an hour using Claude).

I don't understand the hype.

lm2s•about 1 hour ago

I think your engineering process is fundamentally broken if you are generating PRs with 230 files changed so regularly that you need to bolt on more AI. You’re solving the wrong problem.

stuaxo•about 1 hour ago

That really stood out.

Code is there to be read and understood by the human developers who come later.

The git history is a similar record, that's why the commits that make it to main (the squashed PRs) should cover one(ish) thing each and be self contained.

Something covering 230 files should be a mechanical change like running a linter or the AI is moving an API from one signature to another.

If an LLM generated a 230 file change they are also capable of going back and breaking it up.

One thing they are bad at is comments that are succinct since they almost only ever add words.

moffkalast•12 minutes ago

> human developers who come later

Tbf, once the codebase is slopped enough that becomes impossible and only LLM can come later.

realusername•about 1 hour ago

I can confirm that our code quality at work is regressing and we are shipping less product features than before with AI.

ncruces•about 1 hour ago

Welcome to 2026.

My dev branch got broken when I rebased to main after a week of drift.

I had to bisect over around a million commits to the monorepo to find the culprit.

pjc50•about 1 hour ago

This is bananas. I've seen 25 year old software systems that haven't broken the 100k commits barrier. It feels like that ought to be enough for entire product lifecycles. What's going on that isn't simply wheelspinning?

yube01•21 minutes ago

confidence score really feels like real machine learning project which we test to check the confusion matrix

namanbhulawat•about 4 hours ago

Most PRs that are generated by agents today get YOLO merged because its hard for human mind to comprehend when your agent just suddenly shows up with 230 file changes. This is an attempt to reduce the mental burden by classifying each change in a review to P0, P1, P2. Only P0 are shown by default. The priorities are configurable. The diffs are also show using a natural language. The original code is one toggle away.

Foobar8568•16 minutes ago

I yolo vibe develop, but how one end up with 210 files change commits? Unless one shooting a project, there is no way to end up with such scope? I really don't get it.

fny•about 1 hour ago

You can tell it not to do this?

vidarh•41 minutes ago

Something like:

"Plan out the change. Structure the plan so that it can be broken into individual PR's that each covers a single logical part of the change. Aim for less than 500 lines per PR unless the change is trivial and mechanical." (with a lower end model you need to be a lot more precise; with a frontier model you only really need to let it know you care about the PR size)

... in your prompts, coupled with gating PR creation on passing a stringent automated code review that also reviews it on whether or not it adheres to the rules you set regarding complexity of the PR, solves that problem without hiding the complexity.

If you have 230 file changes, that is a process failure unless they're purely obviously committing purely e.g. linter cleanups.

adityamishra241•about 4 hours ago

Nice idea. This could make large agent-generated PRs much easier to review.

manojlds•about 1 hour ago

Slop projects to solve slop

Jev Based Code Review

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