src/devcore/adaptive_curriculum.py

"""
Adaptive Curriculum — Vitalis FSI

This is the training heart.

Every cycle: pick a domain, pick a complexity tier, generate a task.
Every output: a real module written to generated/.
Each module is more complex than the last.
Domains rotate so she builds breadth AND depth.

Domains covered:
  - Systems engineering
  - Software architecture  
  - Programming patterns
  - Framework design
  - Security engineering
  - Data engineering
  - Distributed systems
  - Compiler/language design
  - Networking
  - AI/ML systems
  - Operating systems
  - Database internals

Tiers:
  0 - Scaffold (basic structure)
  1 - Implement (working logic)
  2 - Optimize (performance aware)
  3 - Architect (system design)
  4 - Frontier  (novel/research level)
"""
import time
import json
from pathlib import Path


CURRICULUM = {
    "systems": [
        ("scaffold process scheduler",           0),
        ("implement thread pool executor",        1),
        ("optimize context switch handler",       2),
        ("architect microkernel message bus",     3),
        ("design capability-based security kernel",4),
    ],
    "architecture": [
        ("scaffold hexagonal architecture",       0),
        ("implement ports and adapters pattern",  1),
        ("optimize dependency injection container",2),
        ("architect event-driven service mesh",   3),
        ("design self-healing distributed brain", 4),
    ],
    "programming": [
        ("scaffold async task runner",            0),
        ("implement coroutine scheduler",         1),
        ("optimize hot path with caching layer",  2),
        ("architect reactive stream processor",   3),
        ("design zero-cost abstraction engine",   4),
    ],
    "frameworks": [
        ("scaffold plugin system",                0),
        ("implement middleware pipeline",         1),
        ("optimize request routing table",        2),
        ("architect modular runtime loader",      3),
        ("design meta-framework with reflection", 4),
    ],
    "security": [
        ("scaffold auth token validator",         0),
        ("implement zero-trust policy engine",    1),
        ("optimize cryptographic key store",      2),
        ("architect threat detection pipeline",   3),
        ("design post-quantum key exchange",      4),
    ],
    "data": [
        ("scaffold schema registry",              0),
        ("implement columnar storage engine",     1),
        ("optimize query execution planner",      2),
        ("architect streaming data lakehouse",    3),
        ("design adaptive index structure",       4),
    ],
    "distributed": [
        ("scaffold service registry",             0),
        ("implement raft consensus protocol",     1),
        ("optimize gossip dissemination layer",   2),
        ("architect byzantine fault tolerant mesh",3),
        ("design self-organizing peer network",   4),
    ],
    "compilers": [
        ("scaffold lexer tokenizer",              0),
        ("implement recursive descent parser",    1),
        ("optimize ast transformation pass",      2),
        ("architect ir code generator",           3),
        ("design jit compilation pipeline",       4),
    ],
    "networking": [
        ("scaffold tcp connection handler",       0),
        ("implement http/2 frame parser",         1),
        ("optimize packet routing table",         2),
        ("architect software defined network",    3),
        ("design congestion control algorithm",   4),
    ],
    "ml_systems": [
        ("scaffold feature extraction pipeline",  0),
        ("implement gradient descent optimizer",  1),
        ("optimize inference batching layer",     2),
        ("architect online learning system",      3),
        ("design neuromorphic compute graph",     4),
    ],
    "os": [
        ("scaffold virtual memory manager",       0),
        ("implement page replacement policy",     1),
        ("optimize interrupt dispatch table",     2),
        ("architect real-time scheduler",         3),
        ("design exokernel resource allocator",   4),
    ],
    "databases": [
        ("scaffold btree index structure",        0),
        ("implement mvcc transaction engine",     1),
        ("optimize write-ahead log manager",      2),
        ("architect distributed query engine",    3),
        ("design learned index structure",        4),
    ],
}

DOMAINS = list(CURRICULUM.keys())


class AdaptiveCurriculum:
    def __init__(self):
        self._path    = (Path.home() / ".vitalis_workspace"
                         / "curriculum_state.json")
        self._cycle   = 0
        self._history = []
        self._load()

    def _load(self):
        try:
            if self._path.exists():
                state = json.loads(self._path.read_text())
                self._cycle   = state.get("cycle", 0)
                self._history = state.get("history", [])
        except Exception:
            pass

    def _save(self):
        try:
            self._path.parent.mkdir(parents=True, exist_ok=True)
            self._path.write_text(json.dumps({
                "cycle":   self._cycle,
                "history": self._history[-500:],
            }))
        except Exception:
            pass

    def next_task(self) -> dict:
        # Domain rotates every cycle across all 12 domains
        domain_idx = self._cycle % len(DOMAINS)
        domain     = DOMAINS[domain_idx]

        # Tier advances every 12 cycles (one full domain rotation)
        tier = min(self._cycle // len(DOMAINS), 4)

        tasks       = CURRICULUM[domain]
        task_text, _ = tasks[tier]

        entry = {
            "cycle":    self._cycle,
            "domain":   domain,
            "tier":     tier,
            "task":     task_text,
            "timestamp": time.time(),
        }
        self._history.append(entry)
        self._cycle += 1

        if self._cycle % 10 == 0:
            self._save()

        return entry

    def report(self) -> dict:
        current_tier = min(self._cycle // len(DOMAINS), 4)
        tier_names   = ["SCAFFOLD", "IMPLEMENT", "OPTIMIZE",
                        "ARCHITECT", "FRONTIER"]
        return {
            "total_cycles":   self._cycle,
            "current_tier":   tier_names[current_tier],
            "current_domain": DOMAINS[self._cycle % len(DOMAINS)]
                              if self._cycle > 0 else DOMAINS[0],
            "domains_covered": len(DOMAINS),
            "tasks_logged":   len(self._history),
        }