Kevin Ruschman
I architect local-first AI infrastructure and deterministic control planes—where language models act as interchangeable compute samplers over PostgreSQL ACID state, sandboxed verification, and multi-family bare-metal GPU clusters.
#1 Overall on Official DolphinBench (91.17%, 547/600 at $24.54): Return on Memory with Gemini 4 Argon & 54.17% Gemma 4 31B at $0.00
Holding all 13,539 episodic sessions (1.50M tokens) byte-for-byte constant inside frozen 0o444 SQLite checkpoints ($0.00 Zero-LLM Raw-RAG + 4-Way RRF) across 14 full 600-task Official openai/gpt-5.6-sol runs (8,400 traces across 5 model families), our memory substrate sets both #1 Overall and #1 Open-Weights ($0.00) scores:
- +20.50 pp (+123 Tasks) Above Official #1 at ~4x Lower Cost:
Gemini 4 Argon (Low Thinking)scores91.17%(547/600) acrossMorgan(95.5%),Alex(89.5%), andRiley(88.5%) in35.38smedian latency for$24.54total ($0.00ingestion +$24.54execution,57.1%cache hit;Argon Medium89.17%@$24.57)—beating Official#1Hermes + GPT 5.6 Luna + Mem0(70.67%/424/600@$96.21) andSupermemory(59.17%@$345.64,14.1xcheaper). - 96.17% (577/600) Oracle@14 Sufficiency, 2x2 Thinking Interaction & 4.19x Multi-Hop Return: Zero-LLM
snowflake-arctic-embed2(1024d) +256/32-tokRaw-RAG+ Porter-FTS5 4-Way RRF already retrieves sufficient facts on96.17%of tasks (78.83%Oracle@10across$0.00open-weights runs alone); interactive search interacts with conciseLowthinking (ΔΔ =+3.17 pp, p =0.032) and multiplies4+-fact (LBF=4+) accuracy4.19x(21.3% → 89.3%). - 54.17% (325/600) #1 Open-Weights at $0.00 (Gemma 4 31B) & 2PC Action Harness: Apache-2.0 dense
Google Gemma 4 31Bshatters the50%open-weights barrier at54.17%(325/600,$0.00;52.00%No-Think)—beating all8non-GPT-5.6-Lunacommercial cloud stacks up to$1,830.57(+18.83 ppover matched-scaleQwen2.5-Coder-32B35.33%;42.67%local30BWave-4;15.7x8Blift)—while our generalizedkrusch-stack2PCAction Harness eliminates100%of duplicate MCP calls (10 → 0). All8submitted archives are verified & queued ondolphinbench.ai(Argon LowUUIDc047b063-822c-49fb-8b36-27c3763d6ca7&Gemma 4 31BUUID288671ad-afe0-40a3-93af-b834c9bbd306).
547 / 600 Official gpt-5.6-sol (+20.50 pp over #1 Mem0)Mem0 ($96.21) · $0.00 Memory Ingestion577 / 600 Tasks Retrieved by $0.00 StoreGemma 4 31B Beats 8 Cloud Stacks · 4.19x 4+ Hop14/14 official archives validated & 8 signed submission archives queued on dolphinbench.ai awaiting maintainer merge · Argon Low UUID c047b063-822c-49fb-8b36-27c3763d6ca7 · Gemma 4 31B UUID 288671ad-afe0-40a3-93af-b834c9bbd306.
The 2026 Sovereign AI Tech Stack: Multi-Family GPUs, 3-Tier MCP Substrates, @krusch/toolkit & Closed-Loop Distillation
Why we inverted the conventional cloud-wrapper AI stack—treating language models as stateless compute samplers governed by bare-metal multi-family GPUs, relational PostgreSQL 16 state planes, a zero-bloat shared monorepo toolkit, and autonomous QLoRA self-distillation:
- Unified 1-Command Installer (
krusch-stack): Runnpx krusch-stack init --defer-toolsordocker compose up -dto wire all 7 pillars, 39 annotated MCP tools (cutting upfront schema bytes by 68.5%), and our generalized2PCTool-Action Harness (INV-STACK-10,10 → 0duplicate MCP calls) across Claude Code, Cursor, Windsurf, and Antigravity. - Pillar I · Multi-Family 5-GPU, 8K Arctic & 96GB G4 Burst:
Qwen3-30B-MoE(124.2 tok/s) audited by orthogonalGemma 4&Hermes 3Referees +8Ksnowflake-arctic-embed2(89.17% R@1) + on-demand 96GB Blackwell G4 (gpt-oss:120b&gemma4:31b). - Pillar II · 7-Server MCP Ecosystem & Hermetic Benchmarks:
krusch-context-mcpv2.0.0 (#1 Overall91.17%OfficialDolphinBench@$24.54+ #1 Open-Weights54.17%Gemma 4 31B@$0.00· 2,000/2,000 ICLR 2026 BEAM 100K–10M),krusch-gitv1.2.3 (15.25ms HNSW/GIN ·RepoBench-R47,998Qs beatingCodeBERT&UniXcoder),krusch-fs-mcpv0.3.1 (8-node SSH page trees),krusch-harness(Aider PolyglotPass@1 19.56% → Pass@2 51.56%,2.64xself-healing), andKruschNexusv0.2.6 (LegalBench-RAG26.74%Macro F1 ·47.71%recall across6,889Qs). - Pillar III–V ·
@krusch/toolkit, Raw-RAG, Jean SRE & QLoRA Flywheel: Node 22 ESM & Python 3.12 runtime (embeddings,256/32-tokraw-rag,harness-engineering) + 24/7 Jean SRE sweeps across 88 repos distilling 18,131 Zero-Leak SFT + DPO rows (sovereign-code-audit-sft) into our published 120B Teacher, 30B Student, and 8B Edge Wave-4 QLoRA+DPO adapters.
The 5-Layer Sovereign AI Tech Stack
How multi-family bare-metal GPU routing, 7-server MCP substrates, sandboxed 2PC harnesses, air-gapped document intelligence, and @krusch/toolkit + QLoRA self-distillation compose into a unified platform.
Microsecond Syntactic Gating, Neural Centroids & Multi-Family GPU Pooling
Eliminates the 500ms LLM "routing tax." Intercepts deterministic patterns on CPU in <15µs for $0.00, routes semantic intents via 8K snowflake-arctic-embed2 centroids to a multi-family 5-GPU bare-metal pool (Qwen3-Coder-30B-MoE at 124.2 tok/s + Gemma 4 / Hermes 3 cross-family Referees), bursts on-demand to 96GB Blackwell G4 (gpt-oss:120b & gemma4:31b), and reserves frontier Gemini 4 Argon escalation strictly for tail reasoning and 3-viewport UI visual audits.
Stage-0 CPU syntactic classifier and exact-match LRU memoization table. Intercepts code fences, SQL, stack traces, and voice board intents in <10µs with zero token burn.
Dual-stage L1 CPU + L2 Arctic centroid router with live SSE logprob/loop abort. Validated #1 on RouteWorks RouterArena CI (0.7883 / 80.72%), #1 Pareto (0.4269, -83.95% cost) across 12,566 ACL 2026 LLMRouterBench prompts, #1 ID & OOD AIQ across 72,980 Martian RouterBench rows (-72.63% @ 98% GPT-4), and -48.0% TTFT live SSE cascade.
5 bare-metal GPUs across Dual RTX 3060 (24GB), Dual RTX 2080 Ti (22GB NVLink), and 11GB Orchestrator + on-demand 96GB Blackwell G4 burst (120B QLoRA distillation & 31B dense open-weights evals) and @krusch/toolkit runtime.
Governed Episodic Memory, Git DAG / AST Graphs & Multi-Node Fleet Page Trees
Replaces flat sliding-window RAG with three specialized Model Context Protocol substrates backed by PostgreSQL 16 + pgvector HNSW and zero-Docker SQLite WAL. Separates invariant working memory (krusch-context-mcp), Git DAG & AST caller/callee graphs (krusch-git), and OS/fleet filesystem page memory (krusch-fs-mcp).
5-verb sovereign coding-agent working memory (retrieve, remember, revise, nudge, health) in ~350 prompt tokens. Validated #1 Overall (91.17%, 547/600 @ $24.54) on Official DolphinBench with Gemini 4 Argon (Low) + $0.00 Zero-LLM Raw-RAG (#1 Open-Weights 54.17% Gemma 4 31B @ $0.00 · 42.67% local 30B @ $0.00) and 2,000/2,000 Official ICLR 2026 BEAM 100K–10M (93.00% 10M IE recall · beats past.dev on 4/4 CR & 10M MSR).
Relational Git DAG, multi-language AST chunker (JS/TS/Py/Go/Rust/Java) & live import-activity analyzer in PostgreSQL 16 (15.25ms HNSW/GIN). Benchmarked across all 47,998 RepoBench-R queries: 19.12% Macro Acc@1 (68.58% Acc@5) and 31.33% Py / 27.45% Java cfr Easy—beating CodeBERT & UniXcoder.
OS filesystem & multi-node SSH fleet page memory powered by KruschNexus Library Mode. Indexes PDFs, DOCX, and /etc/systemd into compact float32 vector blobs & SHA-256 drift-verified page trees.
ACID Diff Staging, Policy Floors, Sandboxed Verification & Voice-First Daily Execution
Enforces hard transactional boundaries between probabilistic model outputs and physical side effects. Code agents stage patches into PostgreSQL behind import-frozen policy floors and truncation breakers before 2PC fsync apply; personal and SRE agents operate under deterministic Confirm-Before-Act gates.
PostgreSQL state-machine harness enforcing import-frozen policy floors, truncation guards, verify-on-stop outer loops & Bubblewrap sandboxes (<32ms 2PC), plus krusch-stack's generalized 2PC Tool-Action Harness (10 → 0 duplicate MCP calls). Lifts open-weights gpt-oss:120b from 19.56% → 51.56% (2.64x, 72 tasks self-healed) on 225/225 6-language Aider Polyglot—tying Claude 3.5 Sonnet & beating GPT-4o (23.1%).
100% local-first personal AI assistant & daily execution PWA. Local faster-whisper voice dictation, Dual-Axis Priority Board, 20-tool <holodata> proxy, 256/32-tok Raw-RAG recall & Skylight sync.
Swappable storage substrate bridging local SQLite/PostgreSQL with Polygres Cloud (pgContext + pgGraph) and 40-char SHA pinning with fail-closed air-gap enforcement.
Physical Citation Coordinates, Statutory Preemption DAGs & Contract Compliance Joins
Replaces naive cosine similarity in regulated legal, financial, and commercial workflows with physical coordinate preservation (pdf_page, printed_page, bbox, char_start..end), versioned statutory precedence graphs, and confirmed-edge contract amendment resolution.
7-parser-family layout-aware ingestion spine (100% citation accuracy, contextual section-path embeddings + local mxbai-rerank-large-v2 cross-encoder). Achieves 26.74% Macro F1 (k=1), 25.81% F1 / 44.72% Recall (k=2), and 47.71% Recall (k=6) across all 6,889 LegalBench-RAG queries (33.95% MAUD Recall@2 beating OpenAI text-embedding-3-large + Cohere Rerank Recall@64 28.28%).
Sovereign legal intelligence engine enforcing two-pass proposition entailment (0.00% False Support Rate), temporal as_of_date preemption DAGs (Floor vs. Ceiling rules), and cryptographic purge.
Confirmed-edge commercial contract graph resolving MSA/SOW/Amendment overrides, unexecuted draft isolation, and "The Join" (contract slot vs. statutory floor) via a <450-token 5-verb Gateway MCP.
Zero-Dependency Shared Toolkits, 24/7 Jean SRE & Self-Distilling SFT Flywheel
Eliminates transitive framework bloat across 28+ production services via unified zero-dependency Node.js 22 / Python 3.12 toolkits, an autonomous 24/7 SRE daemon auditing 88 targets on bare metal, and a closed-loop QLoRA pipeline converting verified execution traces into open-weights adapters.
Unified Node.js 22 ESM & Python 3.12 foundations providing 8K snowflake-arctic-embed2 batching & isotropic calibration, zero-LLM 256/32-tok Raw-RAG, single-token logprob System-1 deciders, and Cross-Family Referee gates.
Autonomous 3-Tier MCP SRE daemon sweeping 88 repositories across 11 checkpoints with pre-hydrated AST caller graphs, Phase 1.9 Adversarial Steel-Manning, and Cross-Family Referee convergence gates.
Distills unit-test-gated remediations and rejection-sampled trajectories (18,131 rows across 7 splits) through krusch-stack into open-weights 120B Teacher (native Blackwell 4-bit MXFP4), 30B Student, and 8B Edge Wave-4 LoRA+DPO adapters on 96GB G4.
Production Local-First Infrastructure & Multi-Family GPU Plane
Self-hosted 8-node Linux fleet engineered for zero cloud telemetry leakage, dedicated 10GbE PostgreSQL/vector storage throughput, 100% GPU-resident multi-family inference, and on-demand 96GB Blackwell G4 cloud burst distillation.
Primary production database & vector plane hosting PostgreSQL 16 + pgvector (dedicated NVMe relational & HNSW storage), DBOS durable execution, secondary 8K snowflake-arctic-embed2, and our non-Qwen Referee pool (gemma4:e4b + hermes3:latest).
High-throughput compute server pinning 100%-GPU Qwen3-Coder-30B-MoE (q8_0 KV cache) at 124.2 tok/s, autonomous 24/7 Jean SRE 88-target codebase sweeps, and hermetic container test verification with strict OS NVMe write protection.
Central orchestrator pinning 8K-ctx 1,024-dim snowflake-arctic-embed2 (89.17% R@1), qwen2.5-coder:7b (74.6 tok/s), and 16kHz faster-whisper ASR—plus on-demand 96GB RTX PRO 6000 Blackwell G4 burst running gpt-oss:120b (~117.7 tok/s) and Dual-Track QLoRA distillation.
.env secret redaction · Zero-Docker SQLite WAL fallback.
The Invariant Execution Lifecycle: Stage → Verify → Commit
How krusch and kd-Code prevent autonomous coding agents from corrupting git repositories or applying broken refactors.
The model plans and generates diffs directly into relational tables (staged_diffs). Zero unvetted file writes ever touch your physical working tree.
A shadow workspace runs automated test suites, typechecks, and linters in isolation. If a test fails, the staged turn aborts instantly without repo pollution.
Inspect side-by-side color-coded diffs via @pierre/diffs, caller/callee symbol graphs, and invariant nudges before authorizing disk modification.
Only on verified tests AND explicit human approval does the two-phase commit manager fsync changes to disk. Instant 1-click rollback preserves sanity.
Core Architectural Invariants
The non-negotiable systems rules governing every layer in the KruschDev ecosystem.
Deterministic State Managers
Never trust language models to police their own file writes or follow conversational rules. Enforce constraints in PostgreSQL ACID tables, AST parsers, and atomic two-phase commit journals.
Multi-Tier Routing Cascades
Don't burn a 500ms LLM call just to choose an LLM. Intercept structured syntax on CPU in <15µs for $0.00, escalate to sub-50ms neural centroids, and only query frontier models when necessary.
Air-Gapped Hardware Control
Run compute, vector indexes, and episodic memory on self-hosted bare-metal clusters. Privileged files, client discovery, and organizational memory never leak to third-party cloud APIs.
Technical Publications by Architectural Layer (26)
Reproducible architectural deep dives, empirical ablation benchmarks, and systems specifications.
The 2026 Sovereign Tech Stack, Cross-Stack Benchmarks, Closed-Loop Audit & Operating Planes
End-to-end stack architecture, 15/15 production agentic systems matrix, 317,500+-query cross-stack benchmark comparison, 5-GPU + 96GB G4 burst compute, 3-Tier MCP code audits, Wave-4 QLoRA+DPO self-distillation, and sub-15µs syntactic gates.
The 2026 Sovereign AI Tech Stack: Multi-Family GPUs, 3-Tier MCP Substrates, @krusch/toolkit & Closed-Loop Distillation
Complete end-to-end engineering blueprint of the KruschDev tech stack: 5 bare-metal GPUs + 8K snowflake-arctic-embed2 + 96GB Blackwell G4 burst, 7 MCP servers validated across 11 hermetic external benchmarks (#1 91.17% Official DolphinBench @ $24.54 & #1 Open-Weights 54.17% Gemma 4 31B @ $0.00, 2,000/2,000 BEAM, #1 0.7883 RouterArena, #1 0.4269 ACL 2026 LLMRouterBench & #1 Martian RouterBench, 47,998-Q RepoBench-R, 51.56% Aider Polyglot, 6,889-Q LegalBench-RAG), krusch-stack --defer-tools (-68.5% schema bytes) + 2PC Action Harness, and Wave-4 120B/30B/8B QLoRA+DPO distillation across 18,131 SFT+DPO rows.
The 15 Production Agentic Systems: Reference Architecture, State-Machine Invariants & Benchmark Proofs
Three architectural teardowns mapping all 15 production agentic engineering pillars across krusch-stack, krusch-agentic-proxy, and krusch-coding-harness: asyncio.Semaphore(128) orchestrators + circuit breakers, strict 3-bucket token budgeting, Pydantic v2 error-trace retry routers, frozen typed swarms, 72h HMAC-SHA256 Slack HITL leases, 100-trajectory Golden DAG CI gates, 0.00% ASR inter-agent sanitization, bwrap --unshare-net sandboxes, $0.50 cost kill-switches, REST-to-browser fallback, and 5% non-blocking shadow diffing.
How an Open-Weights Sovereign Stack Stacks Up Across 317,000+ Benchmark Queries (Including Where Cloud Still Wins)
Empirical cross-stack comparison of krusch-stack against open-source wrapper stacks and commercial cloud stacks across 11 external suites: Official DolphinBench (#1 91.17% Argon Low @ $24.54 & #1 Open-Weights 54.17% Gemma 4 31B @ $0.00, 8 queued), RouterArena (0.7883 CI, PR #169 open), ACL 2026 LLMRouterBench (#1 Pareto 0.4269), Martian RouterBench (#1 ID & OOD across 72,980 rows), ETH SpyLab AgentDojo v1.2.2 (19.28% → 0.00% ASR across 1,046 episodes), ACL 2025 CoIR (56.40% #1 NDCG@10 across 2.39M docs & 162,216 Qs), BIRD-SQL Mini-Dev (26.00% → 40.20% EX), BFCL v3 Live (66.28% Micro, -80.67% schema tokens), ICLR 2026 BEAM, RepoBench-R, LegalBench-RAG, and Aider Polyglot (19.56% → 51.56%)—with explicit reader-ceiling and methodology caveats.
🧠 The Closed-Loop Audit & Distillation Pipeline: 3-Tier MCP Context Hydration, Cross-Family Referees & Dual-Track QLoRA
How Jean SRE sweeps 88 repositories across 11 checkpoints: pre-hydrating AST caller/callee graphs and working-memory invariants via 3-Tier MCPs, eliminating false positives via Phase 1.9 Adversarial Steel-Manning, enforcing a Dual-Zero-Finding Cross-Family Referee gate, and fine-tuning 120B Teacher (native Blackwell MXFP4), 30B Student, and 8B Edge Wave-4 QLoRA+DPO weights on 18,131 test-verified SFT + DPO trajectories.
Read Audit & QLoRA Pipeline Paper →Multi-Tier Cascade Routing: Slashing Enterprise Inference Budgets by 60–80%
Systems architecture deep dive into krusch-cascade-router (#1 RouteWorks RouterArena CI at 0.7883 / 80.72%, #1 ACL 2026 LLMRouterBench Pareto 0.4269, #1 Martian RouterBench ID & OOD AIQ across 72,980 rows at -72.63% cost, and -48.0% TTFT live SSE cascade) and krusch-pre-router (<15µs CPU gate, $0.00 tax).
Postgres as the State Plane: Why Side Effects Require a Transaction Manager
Why autonomous agents require raw SQL and a transaction manager—not ORMs or prompts: 0 JS ORMs across 48 repos, 252ms → 15.25ms HNSW+GIN CTEs in krusch-git, 10M-token memory, and 2.64x Aider Polyglot 2PC self-healing in krusch-coding-harness.
🚀 Local-First Core, Hybrid Cloud Elasticity: Decoupling Agent Memory
Decoupling AI coding agent working memory into managed PostgreSQL (pgGraph + HNSW) and optional cloud embeddings for zero VRAM load and global context while keeping file writes strictly sovereign.
Read Hybrid Elasticity Paper →Agent Working Memory, Closed-System BEAM Benchmarks, Git DAG / AST Graphs & OS Substrates
The 5-verb MCP discipline, #1 Official DolphinBench Return on Memory (91.17% @ $24.54 & 54.17% Gemma 4 31B @ $0.00) & 8,400+-trace multi-family study, 2,000/2,000 ICLR 2026 BEAM validation (100K–10M), relational AST symbol indexing, multi-node SSH fleet page trees, and swappable Postgres/SQLite drives.
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 Zero-LLM Raw-RAG + 4-Way RRF), scaling active test-time reasoning across 14 full 600-task Official gpt-5.6-sol runs (8,400 traces, 5 model families) lifts accuracy from 18.33% (8B) to 54.17% (325/600, #1 Open-Weights @ $0.00) with Gemma 4 31B (beating 8 paid cloud stacks) and 91.17% (547/600, #1 Overall) with Gemini 4 Argon (Low)—beating #1 GPT 5.6 Luna + Mem0 (70.67% @ $96.21) by +20.50 pp at $24.54, with 96.17% Oracle@14 and a 4.19x 4+-fact hop lift.
What 5,535 DolphinBench Runs Taught Us About Local AI Memory
Controlled multi-family study across 600 multi-app tasks + 15 counterfactuals (13,539 sessions / 1.50M tokens, 0o444 frozen SQLite WAL): general-domain Gemma 4 31B reaches 54.17% (325/600, $0.00, beating matched Qwen2.5-Coder-32B by +18.83 pp and 8 paid cloud stacks up to $1,830.57); Zero-Leak QLoRA+DPO lifts local Qwen3-Coder-30B-A3B to 42.67% and boosts 8B Q4_0 15.7x; and our krusch-stack 2PC Action Harness eliminates 100% (10 → 0) of duplicate MCP calls.
🔬 Benchmarking Agent Memory Without Cheating: Closed-System ICLR 2026 BEAM (100K–10M)
Evaluating krusch-context-mcp across all 100 conversations (~178.34M tokens) and 2,000/2,000 official BEAM probes on SQLite inside one 96GB Blackwell box: 92.58% 100K & 93.00% 10M IE recall, zero E2E drop to 1M, and beating cloud past.dev on CR (4/4) & 10M MSR.
🗂️ krusch-fs-mcp: OS Filesystem Page Memory, SHA-256 Drift Detection & Multi-Node Fleet Indexing
Why coding and SRE agents need a dedicated OS & fleet filesystem memory substrate. Turning unstructured PDFs, DOCX, systemd units (/etc/systemd/system), and /etc/fstab across multi-node SSH server fleets into layout-true page trees with SHA-256 drift-verified PageMemory annotations.
🌳 krusch-git: Git DAG Indexing, Relational AST Symbols & Hybrid Recency Search
Why grep and flat vector RAG fail autonomous coding agents. Git DAG objects in PostgreSQL 16 + pgvector, multi-language AST lexing & live import-activity analysis, 15.25ms HNSW/GIN search, and 19.12% Macro Acc@1 (68.58% Acc@5) across 47,998 RepoBench-R queries.
🧠 Beyond Flat Vector RAG: What Makes krusch-context-mcp Special
From tool overload to the radical 5-verb contract (~350 prompt tokens). Deep dive into krusch-context-mcp v2.0.0 (governed episodic memory, two-level episode rollups, 2-hop bridges, closed-system Official ICLR 2026 BEAM n = 2,000/2,000 validation across 100K–10M on 8K snowflake-arctic-embed2) and decoupling codebase RAG into krusch-git.
💾 Swappable Substrates: Powering Agent Memory & Codebase Graphs with Polygres
Why AI coding agents need ACID decision logs and relational AST symbol graphs—and how swappable storage drives let you run locally on SQLite/Postgres or on Polygres Cloud (pgContext + pgGraph) without changing agent tools.
Sovereign Context & Safe Mutation Architecture for Coding Agents
Decoupling tool density, stratified λ-decay, declarative steering nuggets, and 2026 Streamable HTTP transport across enterprise PostgreSQL 16 + pgvector deployments.
Read Enterprise MCP Paper →Architecting Code-Agent Retrieval & Memory: Beyond Naive Vector Search
Why sliding-window RAG fails on codebases. Single-store PostgreSQL 16 + pgvector HNSW, structural symbol extraction, SQL Reciprocal Rank Fusion (RRF), dependency graph walks, and deterministic token budget packing.
Read Retrieval Benchmark Paper →Transactional Coding Harnesses & Sovereign Daily Execution
Pre-commit diff staging, container sandbox verification, 2PC apply journals, and local-first voice-driven agentic control.
Why Coding Agents Need Certified Lego Bricks, Not Prompted Boilerplate: Inside a 35-Brick, Self-Evolving Code Substrate
How we eliminated LLM-regenerated HTTP 413 TCP ECONNRESET races, DNS-rebinding SSRF, SQLite lock quarantine, and YAML alias bombs across 100 repositories (3,150+ files): 35 dual-runtime bricks + 7 zero-LLM blueprints (@krusch/toolkit v1.7 & krusch_toolkit v1.6), a 10-rule comment/string-masked MCP pre-commit guard, an autonomous Rule-of-3 anti-bloat scout (8/8 clusters promoted), and 2-stage QLoRA + DPO calibration lifting zero-tool brick accuracy from 25.0% → 100.0%.
🛡️ The Krusch Coding Harness: Why Autonomous Code Agents Require a Transaction Manager, Not a Prompt
Why unconstrained file writing breaks coding agents. Pre-commit SHA-256 diff staging, Bubblewrap isolation, verify-on-stop outer loops, and 2PC apply journals—lifting open-weights gpt-oss:120b from 19.56% → 51.56% (72 tasks self-healed) across 225/225 Aider Polyglot exercises.
🛡️ Introducing Krusch Companion: A Sovereign, Voice-First Personal AI Assistant & Execution Engine
Complete architectural tour of krusch-companion: local 16kHz faster-whisper Voice-to-Text, Dual-Axis Priority Board (4 Execution Lanes × 6 Life Pillars), 16-tool <holodata> proxy with Confirm-Before-Act gates, and SQLite WAL temporal fact superseding.
☁️ Introducing G-Krusch: A Sovereign Google Workspace Hub, Hybrid Drive RAG & MCP Bridge
Complete architectural deep dive into G-Krusch: cloud-synced agent.md steering, 3-tier 1,024-dim semantic + SQLite FTS5 BM25 Drive RAG, 3-tier PDF extraction, 12 MCP tools, and NIST AES-256-GCM security hardening.
Air-Gapped Document Ingestion, Vector Geometry & Precedence Graphs
Layout-true coordinate spines, statutory preemption DAGs, confirmed-edge contract graphs, and certified rulebooks.
📦 Authority Packs: Certified Rulebooks for Enterprise AI — Sovereign Domain Scoping vs. Vector Bleed
How bounded, versioned rulebooks (Jurisdiction Packs, Standards Packs, Playbook Packs) enforce O(1) token budgets, pre-extracted numeric slots, and physical citation truth across law, finance (ASC 606), and enterprise contracts.
Read Authority Packs Whitepaper →💼 Why Semantic Similarity Isn't Governing Precedence: What Makes KruschBiz Different From Generic RAG
Systems architecture deep dive into KruschBiz (v0.8.0): confirmed-edge precedence graph resolver, unexecuted draft isolation, materialized effective slots, The Join (contract vs. statute compliance engine), and physical citation spine coordinates (INV-11).
Read KruschBiz Architecture →⚖️ Why We Built KruschLaw: Local-First Legal RAG & Statutory Precedence
Why generalist RAG fails in statutory law. 0.00% False Support Rate on labeled golden benchmarks, versioned statutory precedence graphs (resolve_controlling_law with as_of_date), tenant defense deadlines, and cryptographic purge.
⚖️ Citations Die in Local RAG: Why We Built KruschNexus and What It's For
Why arbitrary token slicing destroys document citations. 7-parser-family held-out evaluation (100% citation accuracy), contextual section-path embeddings + local mxbai-rerank-large-v2 cross-encoder achieving 26.74% Macro F1 (k=1), 25.81% F1 / 44.72% Recall (k=2), and 47.71% Recall (k=6) across 6,889 LegalBench-RAG queries (beating OpenAI text-embedding-3-large + Cohere Rerank Recall@64 on MAUD).
⚖️ The Sovereign Triad: Unifying Document Ingestion, Statutory Graphs & Working Memory
How KruschNexus (layout-true ingestion), KruschLaw (statutory authority & assertion grounding), KruschBiz (contract graphs), and KruschContext MCP create a self-auditing intelligence substrate.
Read Sovereign Triad Essay →The Ingestion Imperative: Why Retrieval Quality Is Bounded Before the Model Reads a Word
Why wrong retrieval is the dominant root cause of enterprise AI failure. Embedding space geometry (measured negation and numeric limits), parser contracts, citation spines, and how structured fixes fail.
Read Systems Architecture Note →