u/heavykenny — reddlx

£248M saved from 1p challenge on Monzo

Mid-year update: The 4p version of the 1p challenge

I know the usual Monzo challenge is 1p a day, but I’ve been doing the 4p version this year. I’m around halfway through now, currently on day 181, saving £7.24 today, with £661.98 in the pot.

The first half felt easy, but the next 6 months are definitely where it gets serious. The daily amount keeps climbing and should finishes at £14.60 on the last day, not sure I’m ready for that though. This has been my way to build discipline without overthinking saving. Anyone else doing the 4p challenge and what’s your plan for the next 6months?

u/heavykenny — 6 days ago

▲ 82 r/monzo

I’ve actually made £461.11 from Monzo interest, so Perks has paid off for me

Was just wondering how much value I get from Monzo Perks, I checked mine properly and it has actually paid off for me.

For £7/month, and 9months on perks, I’ve earned £461.11 in interest and saved £76.18 through Uber One.

That’s before even counting things like Railcard, weekly gregs, and the other smaller benefits.

Obviously it depends on how much money you keep in Monzo and whether you use the perks, but for me it has been worth keeping.

Anyone else actually getting decent value from it?

u/heavykenny — 1 month ago

▲ 2 r/devtools+1 crossposts

Vibe coders: what messy patterns do you look out for in AI-generated code?

I’ve been doing a lot of vibe coding with tools like Cursor, Claude Code, Codex, Lovable, Bolt, and Replit.

One thing I keep noticing is that AI-generated code can work on the surface, but still leave behind messy patterns that become painful later.

Things like:

- useless comments that only explain obvious code
- swallowed errors
- unsafe `as any` casts
- leftover console.logs
- dead code
- duplicate logic
- hardcoded URLs, IDs, or config values
- weird helper/temp variable names
- over-engineered wrapper functions
- validation logic that repeats what TypeScript already knows

I’ve been building a small CLI called aislop to scan for these kinds of patterns and give the codebase a quality score.

How do you currently clean up the mess?

github.com

u/heavykenny — 1 month ago

▲ 3 r/ProductHunters

I built a tool to help vibe coders clean up AI-generated code

Hey everyone, I launched Scan AI Slop on Product Hunt today.

It’s a tool for developers building with AI coding tools like Cursor, Claude Code, Codex, Lovable, Bolt, Replit, and similar tools.

The idea is simple:

AI can help you build fast, but it can also leave behind messy code that looks fine on the surface.

Things like:

unsafe as any casts
swallowed errors
dead code
hardcoded secrets
useless comments
duplicate logic
messy refactors

Scan AI Slop helps you scan your codebase, get a quality score, and spot these issues before they become painful later.

You can try the CLI with:

npx aislop scan

I’d really appreciate your feedback, upvotes, and any ideas on what patterns the tool should catch next.

Product Hunt link: https://www.producthunt.com/posts/scan-ai-slop

u/heavykenny — 1 month ago

▲ 3 r/buildinpublic

Built a CLI that catches the patterns AI agents leave in code. 7 languages, runs in CI. Looking for people to break it.

Hey r/buildinpublic, been heads-down on this for a few months and wanted to share where it's at.

The problem. I kept catching my AI agent (Claude Code, Cursor, Codex, OpenCode, depending on the day) leaving patterns in my code that I'd never have written myself. Narrative comments explaining what the next line does. Hallucinated imports for packages that don't exist. Swallowed exceptions. `as any` casts when the agent gave up on a type. Half-renamed variables from incomplete refactors. Dead code left over from earlier passes. Duplicate functions sitting in three files because the agent didn't know one already existed. 600-line components that started as 80 and grew every time the agent touched them. Files no human would split that way.

The kind of stuff that compiles, passes tests, slowly rots a codebase, and makes the next review impossible. Tests don't measure any of it. The score moves in the wrong direction quietly.

So I built aislop. An open source CLI that scans for these patterns. 40+ rules across 7 languages (TS/JS, Python, Go, Rust, Ruby, PHP, Java). Runs as a CI gate, as a pre-commit hook, or as a hook directly inside Claude Code / Cursor / Codex so the agent sees its own slop on the turn it writes the code.

https://preview.redd.it/ahi53wrs723h1.png?width=1200&format=png&auto=webp&s=6750addcb86d822b884919e23dae515dd76ca43a

Recent milestone. Someone pointed out the tool was noisy on real code. They were right. So I ran it against 70 popular open-source repos (pytest, vue, guava, rubocop, regex, faker, phpunit, and 63 more) and triaged every false positive class. Tightened 9 rules, cut findings by 38%, didn't disable a single rule. Writeup with the numbers if anyone wants the details: v0.9.3 Report

Where I'd love help

Run it on your repo and tell me what's noisy. npx aislop@latest scan . No install, no auth.
Tell me what slop patterns your agent keeps leaving that the tool doesn't catch yet.
If you've thought about AI code quality in your own projects, I'd love feedback on what's worth flagging vs. what's just idiomatic in your language.

Where it's still rough

The rust-non-test-unwrap rule still has too many edge cases. Python local-package detection is heuristic-based. PHPDoc handling could be better. Working on all of it, but extra eyes help a lot.