AI Performance Bug Finder
Find slow code and performance bottlenecks fast
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Why does that page take four seconds when the database says the query took eleven milliseconds? And why did it only start being slow once you had a few thousand customers?
Performance bugs are not usually slow code. They are correct code doing something more times than anybody intended. The AI Performance Bug Finder reads a function and looks for the shapes that turn fine into unusable once the data grows.
Short answer: The AI Performance Bug Finder is a free AIToolsay tool that reads code and identifies why it is slow. Paste the code and describe the slowness, and it finds repeated queries, nested loops, blocking calls and work done inside loops that belongs outside them.
What is AI Performance Bug Finder?
A performance bug is a mismatch between how often something runs and how expensive it is. Nothing about it is wrong, which is why nothing catches it.
The prompt box asks you to paste the code and the error or behaviour you are seeing. For performance work the behaviour matters as much as the code. "Fast with ten rows, four seconds with two thousand" tells the analysis to look for something that scales with the input rather than a fixed cost.
Setting Issue Type to Performance moves the review from correctness to cost, which is a different reading of the same lines.
Why Use AI Performance Bug Finder?
Profilers tell you where time is spent. That is necessary and it is not always enough, because the expensive line is often innocent. A query taking eleven milliseconds is fine. The same query called four hundred times inside a loop is your four seconds.
Reading code specifically for repetition and cost finds those relationships. It is also the only approach available when the slow environment is production and the fast one is your laptop.
| Pattern | Why it hides | What it looks like at scale |
|---|---|---|
| A query inside a loop | Each query is fast | Time grows directly with row count |
| Nested loops over the same data | Fine with test data | Time grows with the square of the input |
| Work repeated per iteration | Looks tidy inline | Constant cost multiplied by the loop |
| Blocking call in a request path | Fast when the service is healthy | Everything stalls when it is not |
| Loading a whole collection to count it | Correct and readable | Memory and time both climb |
Note Give the numbers you have. Row counts, request rates, how the timing changes as data grows. Performance analysis without scale is guesswork, and the scale usually identifies the pattern before the code is even read.
How Does AI Performance Bug Finder Work?
Prompt box. Paste the code and describe the slowness, including how it changes with the size of the input.
Model selector. The engine is chosen up front, from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax.
Advanced options accordion. Ten controls in total: Language, Issue Type, Analysis Depth and Output as dropdowns, four toggles, a Thoroughness slider and a free text field.
Generate button. Code, description, model and settings pass through the prompt engineering layer written for debugging, which is the instruction set that makes it count operations rather than check correctness.
Output card. The findings appear under the button with a live word count, plus copy, listen, reuse, download and open in full view.
Export row. DOC, TXT and HTML. DOC when the findings are going into a ticket for someone else to act on.
Activity history. Session generations stay listed under the result, which helps when you review the controller, then the service, then the query layer in sequence.
Step-by-Step Guide
- Measure first. Know which request or job is slow before you read any code.
- Collect the code for that path, including anything it calls in a loop.
- Write down how the timing changes with data size, using real numbers.
- Open the AI Performance Bug Finder and paste the code and the numbers.
- Set Issue Type to Performance and Analysis Depth to Deep.
- Generate, then read the findings in order of how they scale, not how bad they sound.
- Fix one thing and measure again. Two fixes at once tell you nothing about which worked.
- Keep the measurement, so the next person can see what normal looks like.
Key Features
Repetition first
Looks for how many times something runs before it looks at how long it takes.
Scaling behaviour
Distinguishes a fixed cost from one that grows with your data, which decides what is worth fixing.
Blocking call detection
Finds network and disk work sitting in a path that should not wait for it.
Prevention advice
Suggests where a query count assertion or a load test would have caught this before release.
Best Use Cases
- A page that got slower as the account grew, without any code change
- A background job whose runtime doubled after a data import
- Endpoints where the database is fast and the response is not
- Loops that call an external service one item at a time
- Code review before a feature meets real volumes for the first time
| Symptom you can measure | Likely shape | Where to look |
|---|---|---|
| Time grows in line with rows | Work per item | Anything inside the main loop |
| Time grows much faster than rows | Nested iteration | A loop within a loop over the same set |
| Slow but flat regardless of size | A fixed expensive call | Startup work or an external request |
| Fast alone, slow under load | Contention or blocking | Shared resources and synchronous calls |
Advanced Options Guide
| Option | What it controls | When to change it | Suggested start |
|---|---|---|---|
| Language | Auto Detect, Python, JavaScript, TypeScript, Java, C#, C++, Go, PHP or Ruby | Set it. Lazy evaluation, async behaviour and collection costs differ by language | Your language |
| Issue Type | Auto Detect, Syntax Error, Runtime Error, Logic Bug, Performance, Memory Leak, Exception or Crash | Performance is what changes this from a correctness review to a cost review | Performance |
| Analysis Depth | Quick, Standard or Deep | Deep when the slow path crosses several functions or layers | Deep |
| Output | Fix + Explanation, Corrected Code, Root Cause or Step by Step Debug | Root Cause first, so you can judge which finding is worth the change | Root Cause |
| Explain Root Cause | Explains why the cost grows the way it does | Leave on. The scaling explanation is what tells you whether to bother | On |
| Provide Fixed Code | Returns the rewritten version | Turn on once you have chosen which finding to act on | Off first |
| Suggest Prevention | Suggests query count assertions, load tests and limits | Leave on for anything that reached production slowly | On |
| Show Test to Reproduce | Produces a case with enough data to make the slowness visible | Turn on. Reproducing at scale is most of the work | On |
| Thoroughness | Slider from 1 to 100 setting how carefully repetition and cost are traced | Raise it when several layers each add a little | 75 |
| Custom Instructions | Free text up to 1000 characters over the settings | Use it for the volumes and constraints the code cannot show | "Runs per request, 40 requests a second, the list averages 300 items" |
Caution Do not optimise anything you have not measured. A finding is a hypothesis about where time goes, and acting on it without a before and after measurement is how codebases end up with clever code that is no faster than the readable version was.
Example Inputs
Behaviour: the order list endpoint takes 3.8s for accounts
with about 500 orders and 200ms for accounts with 20.
The database log shows 512 short queries per request.
Code pasted: the controller, the serializer it calls per
order, and the customer lookup inside the serializer.
Context: roughly 40 requests a second at peak. No caching.The query count in that description does most of the work. Roughly one query per order plus a few is the signature of a lookup inside a loop, and naming it in the prompt means the analysis can confirm or reject that immediately instead of exploring.
Once you know which call is repeated, the AI Code Optimizer handles the rewrite, and if the remaining time is genuinely inside the database the AI Query Optimizer is the next stop.
What it finds well
- Queries and calls repeated inside loops
- Nested iteration over the same collection
- Fixed work that could happen once instead of per item
- Blocking calls sitting in a latency sensitive path
What it cannot do
- Measure anything. Every finding needs confirming
- Know your indexes, your cache hit rate or your network
- Tell you whether the slowness matters to your users
- ✅ The slow path measured before any code was read
- ✅ Real volumes and timings included in the prompt
- ✅ Everything called inside the loop pasted as well
- ✅ Findings ranked by how they scale, not by how alarming they sound
- ✅ One change made, then measured again
Pro tip Include the query count from your database log. It is the single most diagnostic number available and almost nobody thinks to paste it. One query per row is a repeated lookup, a constant number is a fixed cost, and knowing which before you start saves the whole exploration.
AIToolsay is a free AI tools platform made of dedicated workspaces rather than one chat box with many names on it. Each tool has its own prompt engineering and its own options panel, so a debugging tool asks about issue type and thoroughness instead of tone and audience. Every tool is free to run and no account is needed. You decide which engine answers, choosing from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax. Next to the tools you get an AI directory, an AI models directory, courses, prompts, guides and news, all reachable from the AIToolsay homepage.
Frequently Asked Questions
Is the AI Performance Bug Finder free?
Yes. It is free to use, nothing is installed, and no account is needed to run a review.
Does it replace a profiler?
No. A profiler tells you where time goes. This explains why, and it works when you cannot profile the environment that is actually slow. Use both.
What should I include besides the code?
Real numbers. Row counts, timings at two different sizes, request rate and the query count per request. Those turn a general review into a specific answer.
Why is my code fast locally and slow in production?
Almost always data volume, network latency between services, or concurrency. Say which of those apply, because all three are invisible in the code itself.
Will it rewrite my code?
Turn Provide Fixed Code on once you have decided which finding to act on. Leave it off for the first pass so the diagnosis is not buried under an implementation.
How do I stop performance bugs coming back?
Leave Suggest Prevention on and act on it. A test asserting the query count for an endpoint catches a repeated lookup the day it is introduced rather than the quarter it becomes a problem.
Should I fix everything it finds?
No. Fix what scales with your data first. A fixed cost of fifty milliseconds is rarely worth touching when something else grows with every row you add.
Slow code is usually correct code doing something too many times. Measure first, bring the numbers as well as the lines, and let the AI Performance Bug Finder tell you which of them scales with your data so you can spend the effort where it changes something.
Thanks for reading, and good luck with the profile. If this earns a place in your investigation, join the AIToolsay community, follow along on social media, turn on push notifications for new tools, and subscribe to the newsletter for the occasional round up.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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