class DccOaasCandidate:
def __init__(self, priority_tier, role, avg_salary,
required_certs, training_methods):
self.priority_tier = priority_tier
self.role = role
self.avg_salary = avg_salary
self.required_certs = required_certs
self.training_methods = training_methods
# Initialize the Workforce Array
dcc_oaas_workforce_array = [
DccOaasCandidate(
priority_tier="A",
role="Technician I/II",
avg_salary=70000,
required_certs=["CompTIA Server+", "CompTIA Network+", "CompTIA A+"],
training_methods=["Hands-on hardware labs", "Income Share Agreements"]
),
DccOaasCandidate(
priority_tier="B",
role="Engineer",
avg_salary=110000,
required_certs=["Cisco CCNA", "Cisco CCNP", "VMware VCP-DCV"],
training_methods=["Virtualization clustering", "ATS Resume Optimization"]
),
DccOaasCandidate(
priority_tier="C",
role="Manager / Foreman",
avg_salary=145000,
required_certs=["Uptime Institute CDCTP", "Project Management"],
training_methods=["AI-Powered Behavioral Counseling", "Milestone Tracking"]
)
]
def get_candidates_by_priority(array, tier):
return [c for c in array if c.priority_tier == tier]"Look at the macroeconomic reality of our industry: U.S. utilities have committed to serving 64 gigawatts of new data center capacity, driven by the AI infrastructure buildout. Companies like Microsoft, Google, and Meta are investing hundreds of billions into these expansions. The physical infrastructure is scaling rapidly — but our human workforce capital is not keeping pace.Right now, data center facilities are losing revenue before a client ever walks through the door because their admissions and training processes are not working effectively. Staff turnover affects teams before they reach operational mastery. Traditional continuing education is not delivering the desired results because it is a passive format that does not measure actual human competency or behavioral resilience under pressure.DCC OaaS fundamentally reworks this model. We do not sell content hours; we deliver Outcomes as a Service. We provide an AI-driven training platform built directly on proven, high-conversion operational playbooks. Through Vertex AI and Gemini Advanced, we automate customized learning roadmaps and host live, highly realistic crisis simulations. Trainees navigate real-world structural emergencies and high-pressure operational failures in a sandboxed, risk-free environment — scored on the exact metrics that predict real-world performance.By partnering nationally with AFCOM, we turn your trusted regional chapter networks into an active, high-velocity workforce engine. We reduce the friction of entry-level technical onboarding, systematically advance mid-level facilities engineers, and provide operators with predictive analytics on exactly who is ready to lead a mission-critical facility. Don't wait for a critical uptime failure to reveal a training gap. Let's build a talent pipeline that lasts."
# ============================================================
# K10 DAF Backstop Engine — Stablecoin Specification
# DCC OaaS | Donor Advised Fund Liquidity Controller
# Charter Gift: $5,000,000 USD | In-Kind: ECX Digistructure ($350,000 CapEx)
# ============================================================
from dataclasses import dataclass, field
from typing import Literal
from decimal import Decimal
# --- Constants ---
CHARTER_GIFT_USD = Decimal("5_000_000.00") # DAF charter gift
ECXI_INKIND_CAPEX_CAP = Decimal("350_000.00") # ECX Digistructure hard CapEx limit
K10_PEG_PRICE = Decimal("1.00") # Target stablecoin peg: $1.00 USD
K10_BAND_UPPER = Decimal("1.02") # +2% tolerance band
K10_BAND_LOWER = Decimal("0.98") # -2% tolerance band
RESERVE_RATIO_TARGET = Decimal("0.20") # 20% of DAF held as liquidity reserve
@dataclass
class DonorAdvisedFund:
donor_name: str
gift_type: Literal["cash", "crypto", "securities", "in_kind"]
gift_amount_usd: Decimal
inkind_asset_name: str = ""
inkind_capex_value: Decimal = Decimal("0.00")
recognition_tier: str = field(init=False)
def __post_init__(self):
if self.gift_amount_usd >= CHARTER_GIFT_USD:
self.recognition_tier = "K10 Founding Architect"
elif self.gift_amount_usd >= Decimal("500_000"):
self.recognition_tier = "K10 Cornerstone Donor"
else:
self.recognition_tier = "K10 Supporter"
def validate_inkind(self) -> bool:
"""Enforce ECX Digistructure hard CapEx cap of $350,000."""
if self.gift_type == "in_kind":
if self.inkind_capex_value > ECXI_INKIND_CAPEX_CAP:
raise ValueError(
f"In-kind CapEx ${self.inkind_capex_value:,.2f} exceeds "
f"hard cap of ${ECXI_INKIND_CAPEX_CAP:,.2f} for ECX Digistructure."
)
return True
@dataclass
class K10LiquidityPool:
total_reserve_usd: Decimal = Decimal("0.00")
k10_circulating_supply: Decimal = Decimal("0.00")
k10_market_price: Decimal = K10_PEG_PRICE
def deposit_charter_gift(self, daf: DonorAdvisedFund) -> None:
"""Route DAF charter gift into K10 liquidity reserve."""
daf.validate_inkind()
reserve_allocation = daf.gift_amount_usd * RESERVE_RATIO_TARGET
operational_allocation = daf.gift_amount_usd - reserve_allocation
self.total_reserve_usd += reserve_allocation
print(f"[DAF DEPOSIT] Donor: {daf.donor_name} | Tier: {daf.recognition_tier}")
print(f" Gift: ${daf.gift_amount_usd:,.2f} ({daf.gift_type.upper()})")
print(f" Reserve Allocated: ${reserve_allocation:,.2f}")
print(f" Operational Deployed: ${operational_allocation:,.2f}")
if daf.gift_type == "in_kind":
print(f" In-Kind Asset: {daf.inkind_asset_name} | CapEx: ${daf.inkind_capex_value:,.2f}")
def backstop_peg(self) -> str:
"""
Stablecoin peg maintenance:
- If K10 price > $1.02 → mint new K10 tokens (expand supply)
- If K10 price < $0.98 → buy back K10 from market (contract supply)
- If within band → hold, no action required
"""
if self.k10_market_price > K10_BAND_UPPER:
mint_amount = (self.k10_market_price - K10_PEG_PRICE) * self.k10_circulating_supply
self.k10_circulating_supply += mint_amount
return f"[MINT] K10 price ${self.k10_market_price} > peg. Minting {mint_amount:,.2f} K10 tokens."
elif self.k10_market_price < K10_BAND_LOWER:
buyback_cost = (K10_PEG_PRICE - self.k10_market_price) * self.k10_circulating_supply
if buyback_cost > self.total_reserve_usd:
return "[ALERT] Insufficient reserve for full buyback. Partial backstop initiated."
self.total_reserve_usd -= buyback_cost
self.k10_circulating_supply -= buyback_cost # tokens retired
return f"[BUYBACK] K10 price ${self.k10_market_price} < peg. Spent ${buyback_cost:,.2f} to retire supply."
else:
return f"[STABLE] K10 price ${self.k10_market_price} within peg band. No action."
# --- Simulation: Charter Gift Execution ---
if __name__ == "__main__":
# Founding donor: ECX contributes $5M cash + ECX Digistructure in-kind
founding_donor = DonorAdvisedFund(
donor_name="ECX / TrustSoftware",
gift_type="in_kind",
gift_amount_usd=CHARTER_GIFT_USD,
inkind_asset_name="ECX Digistructure",
inkind_capex_value=Decimal("350_000.00") # exactly at hard cap
)
pool = K10LiquidityPool(
k10_circulating_supply=Decimal("10_000_000"), # 10M K10 tokens at launch
k10_market_price=Decimal("0.97") # simulate slight depeg
)
pool.deposit_charter_gift(founding_donor)
print()
print(pool.backstop_peg()) # triggers buyback to restore $1.00 peg