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Professional tools gain value through relationships.
Photoshop stores more than pixels. It stores a graph of transformations. DaVinci Resolve stores more than video. It stores a node graph of color and effects. AutoCAD stores more than drawings. It stores geometry and the procedures to build it.
When you export from these tools, you lose the architecture. You get the result, but you lose the process.
Developers build temporal depth through long sessions. Every commit and pull request adds relationships to their workflow. Microsoft uses this logic by connecting VS Code, GitHub, and Azure.
There is another way to build depth: inheritance.
You work briefly. You produce an artifact. You use that artifact as a base for a new task. You fork it and repeat. This chain of inheritance creates depth. This is how people use AI prompts.
Many think a prompt is source code. They think a library of prompts is a container. They want Git for prompts.
This analogy fails.
Source code stays stable. A Python script works across different versions. A prompt is not stable. A prompt for GPT-4 fails or changes on Claude or Llama.
The prompt is not the source code. The prompt is the compiler call. It depends entirely on the model. If the model updates, the prompt changes. A "diff" between prompt versions is useless if the model logic changes.
The real value lies underneath the prompt. The algorithm is stable.
Vibe coding proves this. In vibe coding, you describe an intention. The AI generates the prompt and the result. If a machine generates the prompt, the prompt was never the source code. It was just input.
The hierarchy looks like this:
- Human intention
- Algorithm (the stable layer)
- Prompt (the model wrapper)
- Artifact (the result)
You must store the algorithm. The prompt is temporary. The artifact is disposable.
We lack a tool to extract the algorithm from a prompt. We have tools that turn prompts into artifacts. We have reverse prompt engineering. But we do not have a way to turn a prompt into a portable logic graph.
The real product is not Git for prompts. The product is a decompiler for prompts.
It would extract the model-independent algorithm from a prompt. It would build a graph of logic. It would make that logic portable across different models.
In this model:
- GitHub stores a graph of algorithms.
- Cursor transfers the algorithm to a new model.
- Inheritance happens between algorithms, not text.
A fork remains useful even when the model changes. This is because you are forking the logic, not the words.
Current AI tools focus on the surface. They focus on text, syntax, and pixels. No one focuses on the logic beneath the surface.
The niche is a container that stores algorithms. This is a massive, uncaptured opportunity in both software engineering and AI.
Source: https://dev.to/oleg_kholin_551a551b/pinterestcomdervish75-instagramcomoleqxolin-1ab6
Optional learning community: https://t.me/GyaanSetuAi