The value of a professional tool is determined not by how long one works in it — but by the density of relationships that accumulate inside the container. Photoshop stores not pixels — it stores a graph of transformations applied to the original. DaVinci Resolve stores not video — it stores a node graph of relationships between clips, color decisions, and effects. AutoCAD stores not a drawing — it stores geometry plus the procedures for constructing it in AutoLISP. Exporting from any of these tools destroys not the artifact — it destroys the architecture of relationships. You get the result without the procedure that produced it.
Temporal depth — the condition for accumulating relationships — manifests in different ways. A developer sits on a single task for hours and days: every commit, every PR, every issue adds new relationships to the container. Microsoft understood this and covered the entire cycle — VS Code, GitHub, Copilot, Azure — with a single container where relationships are closed. A video editor works on post-production for weeks: Blackmagic builds DaVinci Resolve on the same logic. A musician works intensively on a single track: Ableton with Max for Live embeds a programming language inside the container.
But there is another mode of accumulation — not a long session, but inheritance between short sessions. You work intensively, but briefly. You produce an artifact. You take it as a foundation — fork it for a new task. You work intensively again. Fork again. Temporal depth is created not by the length of a single session but by the chain of inheritance between sessions. Each artifact carries within it the accumulated knowledge of previous iterations. This is the exact model of working with AI prompts.
The first approximation of the niche looks like this: a prompt is source code, the model’s response is the compiled artifact, a library of prompts with inheritance is a container with growing connectedness. Git for prompts — versioning, forking, diff, collaboration. The analogy is elegant. And it breaks at the foundation.
Source code is separated from the compiler. sort(arr) works in Python 3.8 and in 3.12 — the syntax is stable across versions. A prompt is not separated from the model. A prompt written for GPT-4 produces a different result on Claude, a different result on Llama, a different result on the next version of the same model. The prompt is the compiler call — it does not exist independently of the execution environment. Git for prompts breaks precisely here: a diff between prompt versions is meaningless if the model has been updated. The role behaves differently. The context is interpreted differently. Constraints are followed differently. The inference logic — differently. Everything is bound to the model.
But underneath the prompt lies an algorithm. And the algorithm is stable.
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A prompt decomposes into two layers. The first — model-dependent: formulations, style, trigger tokens, syntactic patterns specific to a given model. The second — model-independent: role, inference logic, constraint structure, reasoning chain. GPT-4 and Claude use different formulations — but the logic of decomposing a task into subtasks is the same. Python and Rust have different syntax — but the sorting algorithm is the same. The value is not in the prompt. The value is in the model-independent layer.
Vibe coding proves this thesis from the opposite direction. In vibe coding, a person does not write a prompt at all — they describe an intention, and the AI generates the prompt and the artifact on its own. If a prompt is generated automatically — it was never source code. It was compiler input. The source code was always the algorithm underneath it. The hierarchy: human intention → algorithm as the stable layer → prompt as the model-dependent wrapper → artifact as the result. The algorithm is what must be stored. The prompt is generated. The artifact is discarded.
From this follows a structural argument through symmetry with an adjacent domain. Decompilers exist: IDA Pro and Ghidra take machine code and reconstruct source code. This works. But a tool that takes source code and extracts from it a clean algorithm — in the form of a portable logic graph, a flowchart of relationships — does not exist. Code visualization exists; algorithm extraction does not. Apply the same matrix to the world of prompts: prompt → artifact exists, that is any LLM; artifact → prompt is emerging as reverse prompt engineering; prompt → algorithm exists nowhere.
The product is not Git for prompts. The product is IDA Pro for prompts: a decompiler that extracts the model-independent algorithm from a prompt, builds a graph of its logic, and makes it portable across models and tasks. Copilot in this architecture identifies the algorithm inside the prompt — the way IDA finds functions in a binary. GitHub stores the graph of algorithms and the inheritance relationships between them. Cursor transfers the algorithm into a new prompt for a different model or task — the way a patch is applied to a new binary.
Inheritance happens not between prompts — but between algorithms. A fork does not lose meaning when the model changes because what is forked is not the text of the prompt but the logic underneath it. Temporal depth is created by the accumulation of an algorithm graph: each session adds new nodes and relationships, each fork inherits the stable layer and specializes it for a new context.
The niche is defined by double absence. A tool for decomposing a prompt down to its algorithm does not exist in any current AI product. An analogous tool for extracting an algorithm from code — also does not exist. This is not coincidence. This is a structural void formed because all existing tools work with the surface: with the text of the prompt, with the syntax of the code, with the pixels of the artifact. No one works with the logic beneath the surface as the primary object of storage and inheritance.
A container that stores algorithms rather than prompts — is an uncaptured niche. And uncaptured symmetrically: in the world of code and in the world of AI simultaneously.
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