We explore the latest development trend that improves software reliability by verifying precise 93-line blueprints containing core functions, rather than relying on vast amounts of AI-generated code.
Imagine you are trying to 3D print a very complex part. The design blueprint for this part is so intricate that it is difficult for a human to inspect it directly. You asked an AI to draw the blueprint, and it produced over 1,000 lines of code. Would you trust this code 100% and press the print button?
As we enter the era where AI writes software, the question of “how to trust it” has become more important than “how to write good code.” Today, instead of blindly trusting complex AI code, we introduce a cutting-edge technical approach that ensures software safety with just a 93-line precise blueprint.
Why is this important?
Until now, whenever AI wrote code, we tried to find errors by having humans read it line by line. However, when the amount of code exceeds thousands of lines, this task becomes practically impossible. It is easy to accidentally overlook critical bugs. If software is used in fields where errors are fatal, such as 3D architectural structures or precision machine design, it could lead to major accidents. Don’t ReviewAICode.VerifyIt. - YouTube
This technology shifts the paradigm from “an era of manually checking AI-generated code” to “an era of proving it passes set rules (specifications).” This is because even if humans don’t see all the code, as long as there is a mathematically accurate, short blueprint, safety can be guaranteed.
Easy to understand: Recipes and Formal Verification
To understand this technology, let’s look at the concept of CSG (Constructive Solid Geometry). CSG is a way of creating complex 3D shapes by stacking, overlapping, or carving out very simple shapes (cubes, cylinders, etc.) like Lego blocks. Constructive solid geometry - Wikipedia
It is similar to applying multiple layers of filters in a photo editing app. A single filter is simple, but combining several creates a great result. In the 3D world, applying rules to combine, overlap, and carve out basic shapes can create complex 3D objects.
But what if a human writes these “combination rules”? Mistakes could happen. That is why developers recently created a ‘93-line core specification.’ Formally verified 3D mesh intersection - GitHub
This process is called Formal Verification. Think of it like this: when cooking, instead of checking every single one of 100 ingredients after they’ve been added to see if it tastes good, you just perfectly verify the precise recipe, such as “a pinch of salt, two pinches of sugar.” Once the recipe is proven to be mathematically accurate, the remaining complex cooking process just follows that recipe, significantly reducing errors.
Current Status
In recent development sites, complex functions are being implemented in this way. In one project, they successfully completed an automated process to control and verify AI-generated code in about 8 hours using a formal verification library. ShowHN:Formallyverifiedpolygon intersection – Opus… -HNDebrief
In the past, developers had to stay up all night reviewing over 1,000 lines of AI-generated code; now, they have reached a stage where they can gain trust simply by inputting a “cheat sheet” of less than 100 lines into a formal verification tool. However, while this technology is very powerful in engineering fields requiring high precision, it has the limitation of being an “advanced technology” that still requires significant time and cost for creating general web pages or lightweight apps.
Future Outlook
In the future, the AI tools we use will gradually become smarter. Beyond just writing code, they will evolve into AI capable of verifying for themselves whether the code they wrote is mathematically sound. Linear– The system for product development
Instead of manually reviewing code, you might soon judge the safety of software with a single question: “Did the result produced by this AI pass the 93-line formal specification?” The standard of trust is shifting from “human eyes” to “mathematical proof.”
MindTickleBytes’ AI Reporter Perspective
The era of blindly trusting AI-generated results is over. As the complexity of technology increases, this case shows that we must focus on simpler, more powerful essences (specifications). Ultimately, the way to handle smart tools is not to “check more,” but to “define more accurately.”
References
- Using more AI models simultaneously
- Increasing line-by-line manual code reviews
- Formally verifying small, perfect blueprints (specifications)
- Converting simple photos into 3D
- A method of creating complex 3D objects by combining basic shapes or using set differences
- A simple tool for drawing 2D sketches
- To write code faster
- To mathematically guarantee the correctness of code
- To make AI smarter