whetstone_DSL/docs/REQUIREMENTS_OVERVIEW.md

Whetstone DSL: Project Requirements Overview

Vision Statement

Source code should be a structured database of logic, not a text file of syntax.

Whetstone is a Universal Logic Database that stores code as a semantic graph (AST), with traditional programming languages serving as projections of that canonical representation. This enables lossless transpilation, massively parallel development (human and AI agents), and complete separation of intent from implementation.

---

Core Philosophy

The Problem: Text Blindness

Current software engineering couples Intent (what we want to happen) with Implementation (how the machine does it) through language-specific syntax. This creates:

• Lock-in to specific languages and ecosystems
• Loss of semantic information during translation
• Inability to express cross-cutting concerns (ownership, risk, optimization hints)
• Barriers to parallel development by multiple agents

The Solution: Projectional Editing

Move the "Source of Truth" from text files to a Logical Graph Database:

Text-Based	Graph-Based (Whetstone)
Linear	Structural
Ambiguous	Explicit
Syntax-heavy	Meaning-heavy
Language-specific	Universal

---

Architecture: The Three Layers

Layer 1: The Logic Database (Definition)

A schema of pure logical concepts stored in a graph/tree database (JetBrains MPS).

Core Principle: This layer must be a superset of all target languages. It must express concepts like memory ownership (Rust-style) even when targeting languages that don't require them (Python).

Contents:

• AST node concepts (expressions, statements, functions, types)
• Semantic annotations (SemAnno schema)
• Hypergraph relationships (nodes participate in multiple relationships)

Layer 2: Semantic Generators (Bridge)

Generators that translate the Logic Database into idiomatic text for specific runtimes.

Example - SharedMemoryBlock concept:

Generator A (Python):   x = [1, 2, 3]  # Note: Shared usage implied
Generator B (Rust):     let x = Arc::new(Mutex::new(vec![1, 2, 3]));
Generator C (Verilog):  reg [31:0] memory [0:2];

Layer 3: Runtime (Metal)

Standard toolchains (GCC, LLVM, Python interpreter) compile/execute the generated code. Whetstone does not replace compilers; it feeds them optimized, correct input.

---

SemAnno: Universal Semantic Annotation Schema

Six annotation systems provide metadata for agents (human and AI):

1. Complexity & Intelligence ("Rank" System)

Defines who/what is qualified to modify a node.

Annotation	Type	Purpose
@complexity-score	int	Derived from cyclomatic complexity, nesting, mutation
@intelligence-requirement	junior/mid/senior	Agent tier required for modification
@review-lock	bool	Requires second agent verification

2. Dependency & Impact ("Radius" System)

Calculates the "blast radius" of changes.

Annotation	Type	Purpose
@dependency-count	int	Incoming references to this node
@dependency-level	0-100	Normalized importance score
@downstream-impact	list	Affected domains [Network, Security, Billing]
@fragility	low/high	Load-bearing legacy code flag

3. Performance & Resource ("Machinist" System)

Low-level optimization metadata.

Annotation	Type	Purpose
@optimization-priority	low/mid/high	Generator focus for SIMD/unrolling
@latency-critical	bool	Real-time execution requirement
@memory-footprint	estimate	Heap/stack allocation intent
@execution-mode	deterministic/probabilistic	Formal verification hint
@deref-explicit	bool	Every pointer touch must be accounted

4. Architectural Intent ("Paradigm" System)

Defines behavioral contracts.

Annotation	Type	Purpose
@intent	Pure/Stateful/Parallel/Legacy	Primary behavioral contract
@preferred-view	language	Optimal projection for readability
@lifecycle	owner/borrower/transient	Ownership model hints

5. Security & Risk ("Sentinel" System)

Granular risk tracking.

Annotation	Type	Purpose
@risk-type	identifier	BufferOverflow, RaceCondition, SideChannel
@trust-boundary	marker	Secure environment transition point
@provenance	hash	Chain of agent modifications

6. Projective Metadata ("Lens" System)

Controls rendering in projectional environments.

Annotation	Type	Purpose
@view-mask	category	Toggle annotation visibility by type
@cognitive-level	0-100	Detail density filter
@projection-alias	string	Localized renaming without AST change
@hidden-detail	bool	Collapse boilerplate for reviews

7. Language-Specific Idioms ("Polyglot" System)

Preserves language-specific features that don't map directly to other languages.

Annotation	Type	Purpose
@lang_specific	structured	Captures idiom with language, type, raw syntax, and semantic hint

Architecture:

@lang_specific(python, "decorator", "@lru_cache(maxsize=128)", hint="memoization")
@lang_specific(cpp, "template", "template<typename T>", hint="generic")

Projection Behavior:

• In native language: renders as original syntax
• In foreign language: renders as comment with semantic hint

Long-term Vision: These annotations enable future semantic equivalence detection. When the system understands that @lru_cache and a C++ std::map-based cache serve the same purpose ("memoization"), it can generate idiomatic implementations in either direction rather than just commenting.

---

Memory Dereferencing Strategies

Memory management is decoupled from logic as a first-class AST field.

Strategy	Description	Generator Responsibility
@deref(imperative)	Manual control - developer defines time/location	Whetstone Generator / Substrate
@deref(streamed)	Ownership-based - lifetime tracked at declaration (Rust-like)	Whetstone Generator
@deref(batched)	Garbage collected - background process or ref-counting	Whetstone Generator
@deref(content-addressed)	Immutable - identity by hash, location irrelevant	Whetstone Generator

The "Unfilled Node Constraint"

In @deref(imperative) mode, the AST requires an explicit Dereference Time Field. If empty, the editor flags it as a "Missing Intent" error. This makes it impossible to forget a dereference.

---

Strategy Toggles

Declarative optimization patterns applicable without manual implementation:

Toggle	Effect
@strategy(TCO)	Convert recursive logic to iterative machine code
@strategy(Memoize)	Inject thread-safe cache for function results
@strategy(Unroll)	Duplicate loop bodies per target-hardware heuristic
@strategy(SSO)	Enable Small String/Buffer Optimization

---

Transpilation Modes

Direct Transpilation ("Wrapper")

Maps nodes 1-to-1. High compatibility, poor performance.

• Python dynamic list → std::vector<std::any> with runtime type-checking

Idiomatic Transpilation ("Intent")

Analyzes usage patterns. High performance, hardware sympathy.

• Python list (append-only, sequential read) → std::vector<T> or Memory Arena

---

The Resolution Slider

Four levels from intent to execution:

Level	Description	Example
1	Pseudocode/Intent	"Find a user in a list"
2	High-Level (JS/Python)	Array.find()
3	Intermediate (Rust/Java)	Type safety + ownership scopes
4	Execution Substrate (C++/C)	B-Trees, SIMD, Branch Hints

---

Multi-Agent Collaboration

The Round-Trip Problem

When a systems engineer optimizes code that a high-level developer later modifies.

Solution: Persistent Metadata Separation

• Logic Field: Algorithmic intent (editable by junior)
• Optimization Field: Systems-level instructions (requires senior)

Conflict Management

When high-level changes break low-level optimizations:

1. Shadowed Metadata: Old optimizations hidden but not deleted
2. Alerting: "Performance Guardrail" warning shown
3. Diff Lens: Visualizes where intent changes invalidated hardware instructions

---

Hypergraph Requirement

Standard ASTs are strictly hierarchical (Parent → Child). Real-world logic is a Hypergraph:

• A Variable is owned by Function A
• But used by Function B
• And monitored by Agent C

Implementation: Smart References (Hyperedges) allow a single logical node to participate in multiple relationships simultaneously.

---

Agent-Native Development

Task Decomposition Model

1. High-context coordinator agent builds semantic graph and queues tasks
2. Small Language Models (8B) handle tightly scoped tasks
3. Human developers work from the same task queue

Projection Optimization

Tasks can be projected in:

• Human-optimized view: Natural language descriptions, readable code
• AI-optimized view: Structured data, explicit constraints, minimal ambiguity

Agent Tiers

Tier	Capabilities
Junior	Basic logic, no memory management, no high-risk annotations
Mid	Standard logic, @deref(streamed) and @deref(batched)
Senior	@deref(imperative), hardware optimization, high-risk refactoring

---

Target Applications

Legacy Modernization

Parse old Fortran/COBOL → Logic Database → Generate modern C++/Rust/Julia

Cross-Platform Logic

Write business logic once → Generate Kotlin (Android), Swift (iOS), Rust (backend)

Agentic Coding

Agents manipulate Graph Nodes directly (no syntax errors from text generation)

Parallel Development

Multiple developers/agents work on same codebase with explicit conflict boundaries

---

Technical Principles

Behavior Over Implementation

A node's primary definition is its Behavioral Contract (e.g., "Sort this collection") rather than implementation (e.g., "Quicksort with pointer increments").

Substrate-Agnostic Core Nodes

Core AST nodes must not assume specific memory models or hardware constraints.

• Bad: LoopNode requires a "Pointer" field
• Good: LoopNode requires an "IteratorIntent" - realization is metadata

The Semantic Boundary

Strict boundary between What (Logic) and How (Execution Strategy). Future capabilities are implemented by adding Strategy Engines to existing nodes, not re-architecting nodes.

Strictest Common Denominator

The Logic Database enforces strict rules by default (explicit ownership), while Generators can "relax" rules for permissive languages or "poly-fill" rules for strict languages.

---

The C Primitives Substrate (Long-term Vision)

All high-level language features ultimately resolve to C-level primitives:

High-Level Feature	C Primitive Resolution
Python @lru_cache	Hash table + function pointer + malloc/free
C++ template<T>	N compiled copies with concrete types
Python decorator	Function pointer manipulation
C++ RAII	Constructor/destructor at scope boundaries
Rust ownership	Compile-time tracking → equivalent to manual C
Garbage collection	Reference counting or mark-sweep algorithms

Architectural Implication:

Level 4: Language Idioms     @py_decl, @cpp_tmpl, @rust_lifetime
            │
            ▼
Level 3: Universal AST       Function, Loop, Variable, Expression
            │
            ▼
Level 2: Semantic Intent     "memoize", "parameterize by type", "scope-bound cleanup"
            │
            ▼
Level 1: C Primitives        pointers, structs, malloc/free, function calls

End State: Languages become pure syntactic projections over C primitives + semantic annotations. This enables true lossless transpilation because the canonical form captures both the intent (semantic annotations) and the implementation (C primitives).

---

Implementation Platform

JetBrains MPS (Meta Programming System)

• Projectional editor (edit AST directly, not text)
• Language workbench for defining DSLs
• Built-in support for generators
• Extensible type system and constraints

---

Success Criteria

1. Lossless Round-Trip: Python → AST → C++ → AST → Python produces semantically equivalent code
2. Parallel Safety: Junior developer cannot accidentally break senior's optimizations
3. Agent Compatibility: Tasks can be completed by SLMs with sufficient annotation context
4. Performance Parity: Generated C++ matches hand-written C++ for common patterns
5. Adoption Path: Existing codebases can be incrementally imported via tree-sitter parsing