An AI code generator you can check

An AI code generator is close to solved on the generating and not on the checking; pointing generated code at a live market turns correctness from a matter of judgement into something the data contradicts within seconds when it is wrong.

Generating code is close to solved. Knowing whether the generated code is correct is not, and that gap is where most of the disappointment with these tools lives.

Plausible code is the problem, not broken code

Code that fails to compile is caught immediately and costs nothing. Code that runs, looks reasonable and is subtly wrong is the expensive kind, and a model producing it has no way to notice. Nothing in the loop ever tested it against reality.

A live source is a test you cannot fake

Connect the generated code to a real market and correctness stops being a matter of opinion. A price that does not match the exchange is visibly wrong. A rate calculated with the wrong divisor produces an absurd number rather than a plausible one. The feedback is immediate and it does not depend on anyone reviewing the code.

What that looks like in practice

Twenty three public endpoints, no key required, covering markets that move constantly. Generated code here reads them at request time, so a mistake shows up on screen within seconds instead of surviving into something somebody trusts.

What this does not fix

It does not make generated code good, and it does not remove the need to read it. It removes one specific failure, the confident wrong answer that nothing in the process was able to challenge. That is a narrower claim than most tools in this category make.

Why review does not catch the expensive mistakes

Reading code tells you whether it looks right. The mistakes that cost money look right, which is exactly why they survive: an off-by-one in a fee calculation, a rate applied per day when it accrues per hour, a divisor from the wrong market. Every one of those reads perfectly. None of them survives contact with a real number, which is why a live source is a better reviewer than a careful person.

What to do with generated code you cannot check

Reduce it until you can. If a function's output cannot be compared against anything, either split it until part of it can, or accept that you are trusting it and mark that clearly. The failure mode worth avoiding is code that nobody can verify sitting quietly in a path that matters, being trusted by default because it has not visibly broken yet.

Live, right now, on this page

Market	Price	Funding	24h volume
BTC	$82,719.50	0.0013%	$505,480,281
ETH	$2,480.35	0.0004%	$288,913,580
SOL	$105.57	0.0013%	$76,884,752
HYPE	$80.79	0.0013%	$11,899,750

Read from a live market during render. If any figure here were wrong, it would be obvious rather than plausible. Read at 2026-10-14 13:11 UTC; accurate as of that time and not afterwards.

Describe something and watch it get built.

Open the builder, or start from a working app and change it.

Common questions

Is AI-generated code reliable?

Generating it is close to solved, and knowing whether it is correct is not. Code that fails to compile is caught at once. Code that runs, looks reasonable and is subtly wrong is the expensive kind, and nothing in the loop tests it against reality.

Why does code review miss the expensive mistakes?

Because those mistakes look right: an off-by-one in a fee calculation, a rate applied per day when it accrues per hour, a divisor from the wrong market. Every one reads perfectly, and none survives contact with a real number.

What should I do with generated code I cannot check?

Reduce it until you can. Split a function until part of its output can be compared against something, or accept that you are trusting it and mark that clearly, rather than leaving unverifiable code in a path that matters.