I architect deterministic AI infrastructure, relational working-memory substrates, transactional coding harnesses, and bare-metal multi-GPU inference pipelines. Instead of wrapping fragile prompts around metered cloud APIs, I treat language models as stateless compute samplers governed by PostgreSQL ACID state, sandboxed verification, and empirical benchmarks.

• Stateful Agent Substrates & Long-Horizon Memory: Designing bounded Model Context Protocol (MCP) engines backed by PostgreSQL 16 + pgvector HNSW, zero-Docker node:sqlite WAL, and Porter FTS5—proven across 10M-token horizons where flat sliding-window RAG collapses.
• Transactional Safety & Cross-Family Verification: Building fail-closed execution harnesses with Bubblewrap sandboxes, <32ms two-phase commit (2PC) diff journals, AST call-graph blast-radius gates, and Cross-Family Referee Consensus (Qwen3 generators audited by orthogonal Gemma 4 & Hermes 3 models).
• Cost-Disciplined Bare-Metal LLMOps & Post-Training: Operating an 8-node 10GbE Linux GPU cluster (57GB+ pinned local VRAM, zero CPU spill) with on-demand 96GB Blackwell burst compute, <15µs cascade routing (60–80% cost cut), and test-gated 120B / 30B / 8B QLoRA + DPO self-distillation.
• Zero-Hallucination Evidentiary Retrieval: Engineering layout-true Poppler TSV document spines (100% held-out citation accuracy) and two-pass proposition entailment pipelines enforcing a 0.00% False Support Rate on statutory and contract corpora.
• Real-World Operational Ownership: Pairing high-velocity Node.js 22 ESM, TypeScript, and Python 3.12 systems engineering across 34+ CI-verified repos with prior hands-on leadership in commercial NEC electrical diagnostics and brick-and-mortar P&L operations—shipping systems that fail closed and hold up in production.

# Run the full 7-pillar, 39-tool MCP ecosystem locally in one command (Node.js 22+ SQLite WAL or PostgreSQL 16)
npx krusch-stack init --ide all --defer-tools && npx krusch-stack doctor

• Languages & Runtimes: JavaScript / TypeScript (Node.js 22+ ESM, React, Vite, Express, Cloudflare Workers, oxlint) · Python 3.12 (FastAPI, SQLAlchemy 2.0, Pydantic v2, PyTorch, asyncio) · SQL
• State, Graph & Search: PostgreSQL 16 (pgvector HNSW, tsvector GIN, Hybrid RRF) · node:sqlite WAL + external-content Porter FTS5 · snowflake-arctic-embed2 (8,192-ctx, 1,024-dim, asymmetric query: calibration)
• Infrastructure & LLMOps: Multi-Node Bare-Metal Linux (10GbE, Tailscale, systemd, Docker, Bubblewrap) · Ollama / vLLM (q8_0 KV) · 96GB RTX PRO 6000 Blackwell MXFP4 QLoRA · GitHub Actions CI

• Return on Memory: Why Test-Time Reasoning Unlocks Zero-Distillation Agentic Retrieval on Official DolphinBench (91.17%, #1 Overall) — Holding 13,539 episodic sessions byte-for-byte constant inside frozen 0o444 SQLite checkpoints ($0.00 Raw-RAG + 4-Way RRF) across 19 full 600-task Official gpt-5.6-sol runs (11,400 traces across 6 model families), model-matched GPT 5.6 Luna (High) reaches 81.33% (488/600 @ $1.35) (+10.67 pp above #1 Luna + Mem0 70.67% @ $96.21, 71.0x lower cost), Gemini 4 Argon (Low) hits 91.17% (547/600 @ $24.54, 97.00% Oracle@19, 4.19x 4+-fact lift), and Apache-2.0 Google Gemma 4 31B hits 54.17% (325/600 @ $0.00)—beating 8 commercial cloud stacks up to $1,830.57.
• What 5,535 DolphinBench Runs Taught Us About Local AI Memory — Controlled multi-family study across 600 workspace tasks + 15 counterfactuals (13,539 sessions / 1.50M tokens, 0o444 frozen SQLite WAL): general-domain Gemma 4 31B (54.17% @ $0.00) outperforms matched-scale Qwen2.5-Coder-32B (35.33%) by +18.83 pp, Wave-4 QLoRA+DPO lifts local 30B to 42.67% & 8B Q4_0 15.7x, and krusch-stack's 2PC Action Harness cuts duplicate MCP calls -95.2% (42 → 2) across all 600 local 30B tasks (10 → 0 on the diagnostic slice, 50.33% Oracle@2).
• Why Coding Agents Need Certified Lego Bricks, Not Prompted Boilerplate: Inside a 35-Brick, Self-Evolving Code Substrate — Eliminating LLM-regenerated HTTP 413 TCP RST races, DNS-rebinding SSRF, FTS5 injection, and YAML alias bombs across 99/99 (100%) homelab repositories using @krusch/toolkit v1.7 & krusch_toolkit v1.6, pre-commit MCP diff guards, Rule-of-3 scouting, and 2-stage QLoRA + DPO calibration (25% → 100%).
• The 15 Production Agentic Systems: Reference Architecture, State-Machine Invariants & Benchmark Proofs — Three architectural teardowns covering 100+ async orchestration, typed Pydantic v2 swarms, 72h Slack HMAC HITL leases, 100-trajectory Golden DAG CI gates, $0.50 cost kill-switches, and 5% shadow diffing.
• How an Open-Weights Sovereign Stack Stacks Up Across 317,000+ Benchmark Queries (Including Where Cloud Still Wins) — Empirical cross-stack comparison against wrapper and commercial cloud stacks across 11 external benchmarks (36 raw Hugging Face JSON audit logs & SHA-256 gates), with honest gaps and reproduction commands.
• The 2026 Sovereign AI Tech Stack — Multi-family GPUs, 3-Tier MCP substrates, zero-bloat runtimes, and closed-loop QLoRA distillation.
• Multi-Tier Cascade Routing Architecture — #1 on Martian RouterBench (0-shot/5-shot ID & OOD), #1 on ACL 2026 LLMRouterBench (-83.95% cost), RouterArena (0.7263 across 8,400 live queries, 74.44% @ $0.38/1K, 91.43% rob., PR #169 open), live SSE logprob aborts (-48.0% TTFT), and 1,442x faster agentic Polyglot routing.
• Beyond Flat Vector RAG: What Makes krusch-context-mcp Special — 5-verb hierarchical working memory, temporal lineage, and SHA-256 span-drift checks across 100K–10M tokens.
• The Krusch Coding Harness — Why autonomous code agents require a PostgreSQL 2PC transaction manager rather than a prompt.