Add verifiable 1B LOC fractal code generator with compact proof system

[Copilot]

Implements a deterministic generator + auditor that produces and verifies 1 billion lines of code as an external artifact. The repository contains the system, not the generated code—proving 1B LOC via compact manifests (~25 KB) rather than storing it in Git.

Architecture

Three layers:

• Definition (in Git): Generator/verifier scripts, config constants
• Expansion (runtime): Writes batches to ./out/ (ignored by Git)
• Proof (in Git): JSONL manifests with SHA-256 hashes

LOC formula:

100 batches × 10,000 files/batch × 1,000 lines/file = 1B LOC

Implementation

Generator (tools/generate_fractal_code.py)

• CLI with configurable target LOC, lines/file, files/batch, seed
• Deterministic fractal pattern (parametric Python functions)
• Writes JSONL manifest: run metadata, per-batch hashes, totals
• Dry-run default (requires --apply flag)
• ~4M LOC/sec throughput

Verifier (tools/verify_fractal_manifest.py)

• Re-scans output tree, recounts LOC/files
• Recomputes SHA-256 hashes, compares against manifest
• Exit codes: 0=pass, 1=fail, 2=error

Example:

# Generate 10K LOC test
python tools/generate_fractal_code.py --target-loc 10000 --apply

# Verify
python tools/verify_fractal_manifest.py ./out/fractal_manifest.jsonl
# ✅ VERIFICATION PASSED: All 10 files totaling 10,000 LOC verified

Documentation

• docs/FRACTAL_EXECUTION_STRATEGY.md: Mathematical formulas, usage, determinism guarantees
• proofs/example_100k_manifest.jsonl: Example compact proof (2 lines for 100K LOC)
• Updated .gitignore to exclude /out/, /generated/, artifacts

Testing

• 7 test functions covering LOC math, generation, verification, determinism
• Integration tested at 1K, 5K, 10K, 100K LOC scales
• CodeQL: 0 alerts
• All tests passing

Truthfulness

Repository does not contain 1B LOC. It contains a verifiable system to generate 1B LOC externally with compact cryptographic proofs. Same inputs produce identical outputs (SHA-256 verified).

Original prompt

Implement a verifiable 1B LOC fractal code generation system for PR #20 (or a new PR #21) in the aidoruao/orthogonal-engineering repository, aligned with Yeshua’s standards of truthfulness and GitHub’s practical constraints.

Goals:

• PR introduces a deterministic generator + auditor that can actually generate and verify >= 1,000,000,000 lines of code as an external artifact, without attempting to store 1B LOC directly in Git.
• Provide clear documentation and manifests so the 1B LOC claim is mathematically precise, reproducible, and auditable.
• Keep the repository itself small and healthy by ignoring generated outputs and only tracking source, configs, and compact proofs.

Key Requirements:

1. Definition vs Expansion Architecture

   • Implement a Definition Layer in the repo:
     • Source code that defines:
       • Target LOC (e.g., 1_000_000_000).
       • Lines per file, files per batch, batches.
       • The content pattern (optionally a fractal/recursive pattern).
       • Integrity checks and logging.
   • Implement an Expansion Layer as runtime behavior:
     • When executed, code writes large batches of files to a configured output_root directory (e.g., ./out/), not version-controlled.
   • Implement a Proof Layer recorded in Git:
     • A manifest/JSONL log that contains run metadata, totals, and checksums.

2. Precise 1B LOC Targeting

   • In code, define:
     • TARGET_LOC = 1_000_000_000.
     • LINES_PER_FILE (e.g., 1000).
     • FILES_PER_BATCH (e.g., 10_000).
   • Implement logic to calculate:
     • LOC_PER_FILE = LINES_PER_FILE (minus any headers if needed).
     • LOC_PER_BATCH = FILES_PER_BATCH * LOC_PER_FILE.
     • Number of batches required to meet or exceed TARGET_LOC.
   • Ensure the generator stops when total LOC >= TARGET_LOC, and records final totals.

3. Generator Implementation

   • Add a generator script (use Python unless the repo dictates a different primary language), e.g. tools/generate_fractal_code.py with capabilities:
     • CLI arguments (e.g., via argparse):
       • --target-loc (default 1_000_000_000)
       • --lines-per-file
       • --files-per-batch
       • --output-root (default ./out)
       • --manifest (default ./out/fractal_manifest.jsonl)
       • --seed (optional RNG seed for deterministic variations)
     • For each batch:
       • Create batch_00000, batch_00001, ... under output_root.
       • Generate FILES_PER_BATCH files named like shard_00000.py, shard_00001.py, etc., or similar naming scheme consistent with repo conventions.
       • Each file contains a deterministic pattern of code lines. The pattern may be simple (repeated print lines) or fractal (parametric recursion) but must:
         • Be deterministic given the same inputs.
         • Produce exactly LINES_PER_FILE lines of code per file.
     • Maintain a running count of total LOC and stop when >= TARGET_LOC.

4. Optional Fractal / Recursive Pattern

   • If consistent with the existing repo vision, implement a simple fractal logic:
     • Define a base “shard” template.
     • Optionally vary constants or parameters per depth/batch/file so it is not trivially identical text everywhere (if desired).
   • Keep the fractal logic secondary to the core invariant: total LOC must be correct and auditable.

5. Auditor and Manifest

   • Implement an auditor in the same script or a separate file, e.g. tools/verify_fractal_manifest.py.
   • The generator should produce a JSONL manifest (one JSON object per line) with at least:
     • run_id (timestamp or UUID).
     • generator_version (git commit SHA if available, or script version string).
     • Global totals: target_loc, actual_loc, total_files, total_batches.
     • Per-batch entries:
       • batch_id
       • batch_path
       • files_in_batch
       • loc_in_batch
       • sha256_batch (hash over concatenated file hashes or similar).
     • Optionally per-file entries (could be a separate JSONL if it’s too large):
       • path
       • lines
       • sha256
   • Implement post-run verification:
     • Re-scan the generated tree.
     • Recount total LOC and file counts.
     • Recompute hashes.
     • Verify consistency with the manifest.
     • Exit non-zero with clear error message if any mismatch.

6. .gitignore and Repo Hygiene

   • Add or update .gitignore to ensure generated code is not accidentally tracked, e.g.:
     • /out/
     • /generated/
     • Large artifacts like *.tar, *.tar.gz unless explicitly needed.
   • Confirm existing ignore rules are respected.

7. Documentation

   • Add or update a markdown document, e.g. docs/FRACTAL_EXECUTION_STRATEGY.md (or similar path consistent with repo structure) to explain:
     • What “1B LOC” means precisely in this context (external generated artifact, not in Git).
     • The formulas for target LOC, LOC per file, LOC per...

This pull request was created from Copilot chat.

---

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.