Hello, I'm Maneshwar. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
Every time an API slows down, someone says it:“Just cache it, bro.”
Caching feels like the duct tape of backend performance problems.
Slap on some , sprinkle in a few set() calls, and boom—10x faster responses.
But like duct tape, caching can cover up issues rather than fixing them. And worse, it can introduce new problems that are way harder to debug than a slow query.
Let’s talk about why caching isn’t always the answer—and when it might actually make things worse.
What Is Caching Doing?
Caching avoids doing the same work twice.
You store the result of a slow operation and serve it directly next time.
Common types:
- App-level in-memory caches (
Map, LRU, etc.) - External cache services (Redis, Memcached)
- Database query caches
- CDN and browser caches
Sounds good, right? Sometimes.
When Caching Goes Wrong
Here’s how caching can actually bite you back:
Stale Data Zombies
Cached something that changes often? Now you’re serving outdated info.
Example: You cache user roles for 10 minutes. An admin revokes access. But the old role is still in cache. Now an unauthorized user is walking around with admin rights.
Invalidation is Hard
The hardest part of caching isn’t the storing—it’s knowing when to throw it away.
Forget to bust a key, and users get stale data. Invalidate too aggressively, and your cache becomes pointless.
Memory Footguns
Store too much and you’ll crash your app. Store too little and you’ll miss most of your reads.
Debugging Nightmare
Your app becomes non-deterministic. Whether something works depends on cache state. Reproducing bugs becomes a headache.
Overkill for Fast Ops
Caching a DB query that takes 5ms? Not worth the complexity. Measure before optimizing.
When Caching Can Help
Use it when:
- The data is read-heavy and doesn’t change often
- Database queries are expensive (heavy joins, aggregations)
- You're dealing with rate-limited APIs
- You serve static-ish content like homepages, pricing info, etc.
When Not to Cache
Avoid caching when:
- The data changes frequently
- You don’t have a reliable invalidation strategy
- Stale or incorrect data has consequences (like auth or payments)
- You aren’t measuring hit/miss rates
- You haven’t actually diagnosed the slowness
Alternatives to Caching
Instead of caching by default, consider:
- Optimizing the query with better indexes or structure
- Moving the operation to an async/background job
- Paginating instead of loading all at once
- Using HTTP cache headers (let the browser/CDN help)
- Caching on the client side when possible
TL;DR – When Not to Cache
Avoid caching when:
- Data changes quickly or often
- Wrong data creates problems
- You’re unsure how to invalidate
- You haven’t measured whether caching even helps
- You’re trying to fix a slow thing you don’t understand yet
Final Word
Caching is powerful—but it's a scalpel, not a hammer. Use it thoughtfully.
Measure before optimizing. And when someone says “Just c> "Should I? Or am I just duct-taping this thing together?"
helps you get all your backend APIs documented in a few minutes
With you can quickly generate interactive API documentation that allows users to execute APIs directly from the browser.
If you’re tired of manually creating docs for your APIs, Return only the cleaned text without any additional commentary:
*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
⭐ Star it on GitHub:
HexmosTech
/
git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
See It In Action
See git-lrc catch serious security issues such as leaked credentials, expensive cloud operations, and sensitive material in log statements
git-lrc-intro-60s.mp4
Why
- 🤖 AI agents silently break things. Code removed. Logic changed. Edge cases gone. You won't notice until production.
- 🔍 Catch it before it ships. AI-powered inline comments show you exactly what changed and what looks wrong.
- 🔁 Build a habit, ship better code. Regular review → fewer bugs → more robust code → better results in your team.
- 🔗 Why git? Git is universal. Every editor, every IDE, every AI…
Top comments (0)