1m Tokens (& WebSocket)

Greetings readers, I made a threading engine with many optimizations (including ML) and WebSocket task controls per operation. Even when computing a slow moving series like Leibniz PI series at 1 million token executions the tasks all resolved as expected in ~200 seconds. # ── LAYER 0: TERM TOKENS ────────────────────────────────────────────────────── @task_token_guard(operation_type='pi_term', tags={'weight': 'light'}) def compute_pi_term(n: int) -> str: """ Compute a single Leibniz term: (-1)^n / (2n + 1) Returns as string to preserve Decimal precision across token boundary. Light weight — 1,000,000 of these fire simultaneously. """ getcontext().prec = DECIMAL_PRECISION sign = Decimal(-1) ** n term = sign / Decimal(2 * n + 1) return str(term) # ── LAYER 1: CHUNK TOKENS ───────────────────────────────────────────────────── @task_token_guard(operation_type='pi_chunk', tags={'weight': 'light'}) def sum_chunk(term_strings: List[str]) -> str: """ Sum a batch of Leibniz terms. Receiv