SCBE-AETHERMOORE is an experimental agentic operating system focused on durable workflows, governed tool use, audit trails, cross-language transformation, and evidence-backed automation. The rule is simple: unusual ideas are allowed, but public claims need commands, artifacts, and boundaries.
The open-source core runs today for free: agent bus (54 governed tools on npm), squad shell (3-slot agentic CLI that auto-routes tasks to the right AI model), and a governed pathfinding harness with SHA-256 receipt chains. Inspect, fork, and run them before spending anything.
AetherMoore also sells three packaged starter lanes: a $29 governance toolkit (templates, a buyer manual, and live pipeline demos you can use in an afternoon), a $99 Workflow Snapshot (send your agent setup, get a clear action plan back), and a $29 Training Vault (fine-tune your own governed model with clean SFT data and a Colab notebook). One-time purchases, no subscription.
Built by Issac Davis: I explore unusual models, implement them, test them, and keep what survives. SAM.gov registered, CAGE-code verified (UEI J4NXHM6N5F59, CAGE 1EXD5). Live demos and Polly chat are public — inspect the system and test it before you spend a dollar.
For buyers: starter tools and manuals. For researchers: the live pipeline and proof trail. For partners: working software with measurable behavior, not a claim without receipts.
These are executable local results and scoped artifacts, not universal guarantees. The standard is: model, command, artifact, commit, and claim boundary.
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npm run benchmark:vector-field-nav -- --skip-ablation --show-comparison
Type any AI prompt or task below. The live governed-chat preflight checks the request before model dispatch. If the API is unreachable, the panel clearly marks browser fallback mode.
Uses the canonical harmonic wall: Hwall(d*,R) = R(φ·d*)2. Same formula, same math, running in your browser.
Source code & demos →The paid products are the packaged starter lane. These are the working tools underneath — free, open-source, and running today.
Agentic PowerShell with three named AI slots. Every task auto-routes: policy/safety → Groq, code & architecture → Cerebras, disk/system ops → Ollama (free/local). Status bar shows which slot is live on every turn. No configuration needed.
git clone https://github.com/issdandavis/SCBE-AETHERMOORE npm install && npm run build node packages/cli/bin/scbe.js shell --squadOpen repo →
Bounded task packets with verification receipts before every dispatch. 54 tools registered. Use as a library in your own project or run the benchmark harness directly from the repo.
npm install scbe-agent-busOpen on npm →
Ensemble-beam: 100% solve / 0.988 efficiency. Multi-lattice baseline: 75%. Oracle: 100%. SHA-256 receipt chain per step. 8-field ablation table. 415 tests green.
npm run benchmark:vector-field-nav -- --skip-ablation --show-comparisonView benchmark code →
Type any AI task into the panel above. The preflight checks it through the 14-layer pipeline and returns ALLOW / QUARANTINE / ESCALATE / DENY with full rationale and a SHA-256 receipt. No login, no account.
Try the demo →Every item below is a primary source you can verify: a formal proposal, a training run, a published book, or an active federal registration. No roadmap slides.
operator_agent_bus is the training and release coordination lane:
bounded agent task packets, local/private routing first, and receipt-backed
verification before product claims move forward.
33266f0f5.
Run: npm run benchmark:vector-field-nav -- --skip-ablation --show-comparison
The starter kit gets you governing AI workflows in one afternoon. The demos run your inputs through the actual 14-layer pipeline. The research stack has every formula, benchmark, and test result — nothing hidden.
Templates, decision records, threshold worksheets, and a manual. One checkout, no subscription. You'll have a governed AI workflow running before dinner.
Run text through the hyperbolic risk calculator, encode with Sacred Tongues, test the Hydra swarm, or chat with Polly — the website assistant that now distinguishes story canon from technical SCBE answers before it responds.
Every claim on this page has a corresponding benchmark, formula, or test result. The research index separates what's proven from what's experimental. Verify before you trust.
AetherMoore teaches the same system in two forms. Buyers can work from the toolkit and manuals directly, or use the novel layer to absorb the same invariants through narrative, dialogue, and repeated pressure-tested scenarios.
The Six Tongues Protocol reads like a 41-chapter isekai epic: Marcus Chen, betrayal, cosmic stakes, the Crystal Archive, and Polly guiding readers through a world that runs on governed intent.
Every major beat carries actual SCBE mechanics: 14-layer governance, Sacred Tongue tokenization, drift pressure, hyperbolic distance, and the same four-band public gate the live demos expose in public. You should finish the story with the architecture already in your head.
Most governance systems hand you a dry PDF. This one gives you the technical manual and the memory hook. Feed the chapters to a model and the same safety invariants land through narrative context, dialogue, and repetition instead of raw papers alone.
If you're connecting LLMs to tools, running browser automations, or deploying agent fleets — you need a deterministic control layer, not more prompt engineering.
Use this if you are building tool-using agents with OpenAI, Claude, Hugging Face, LangChain, MCP, or your own local lane and need an explicit gate instead of ad hoc prompt rules.
Use this if you run workflow automations, cross-app connectors, or browser/CLI routines and want a first pilot surface for thresholding, review, and recovery before a larger rollout.
Use this if prompt injection, unsafe tool calls, or retrieval misuse are already on your threat list and you want a proof-backed starter surface rather than a vague “AI policy” document.
Example: put a policy gate in front of unsafe tool calls so drift, intent accumulation, or context mismatch can trigger DENY, QUARANTINE, or ESCALATE before the action runs. The toolkit gives you the decision record, threshold worksheet, and manual path; the demo hub shows the geometry and gate behavior in public.
The demo hub is the validation lane. The toolkit is the package that turns those same surfaces into a manualized first rollout. The research stack exists so you can inspect the math and benchmark history before trusting the product claim.
The manual, delivery path, and support route are all public before checkout. No mystery boxes.
This is a starter pack, not a subscription or a consulting mystery box. Buy once, receive the package, follow the manual.
The toolkit manual and delivery page are public on purpose so you can inspect the operating surface before you commit.
If something arrives broken or incomplete, the support page and contact path are part of the product, not an afterthought.
Fifteen minutes to your first governed decision record. The toolkit is templates and a manual, not a framework you need to study for a week.
If you can do those four steps, the toolkit is working.
Governance is not just a spreadsheet and the novel is not just flavor text. Follow Marcus Chen into Aethermoor and watch the same SCBE mechanics from the demos show up as plot pressure, dialogue, and world rules you can actually remember.
The complete novel is a second training surface for the same architecture: a story of survival, engineering, intent, and governance pressure that teaches the 14-layer stack without forcing readers through a sterile whitepaper first. It is a training artifact disguised as a story on purpose.
"CAW. You are not lost. You are just early." Meet Polivara (Polly), the Fifth Circle Keeper. She is the guide through the Crystal Archive, where system theory becomes physical place.
The SCBE AI Security Training Vault gives you the clean training path we actually validated: a raw synthetic corpus generator, a train-ready SFT lane, projector weights, benchmarks, and the Colab workflow to fine-tune a governed model on a free T4 GPU.
If you want the narrative layer too, pair the vault with The Six Tongues Protocol. The vault gives you the structured lane; the novel gives you the same invariants expressed as memorable scenes, dialogue, and semantic pressure instead of plain documentation.
The vault follows a Cadet → Role School → Squad training doctrine: shared foundation first, role specialization second, squad integration third. See the Training Vault ($29).
Everything you need to train a governed model from scratch.
Video walkthroughs, project updates, and live demos of the governance pipeline.
Short public videos for the SCBE stack, AetherMoore updates, and governed AI workflow experiments.
AetherMoore now has two reader lanes: published books on KDP, plus the direct behind-the-scenes process pack and practical downloads for AI writing, humanization passes, and AI security governance.
Current titles, KDP status, live Amazon links, and notes on which process material lives on this site while manuscripts stay with Amazon/KDP.
Open BookshelfScene packets, voice anchors, dry reads, humanization checks, and serial tension passes for writers using AI without flattening their voice.
Read Writing GuideReference monitors, action contracts, cutoff rules, and receipt trails for people wiring AI to real tools.
Read Security GuideResearch results, architecture discoveries, and training pipeline updates from active development.
scbe shell --squad ships with deterministic
3-slot routing. Vector-field-nav harness: ensemble-beam 100% / 0.988 efficiency,
multi-lattice 75%, oracle 100%. SHA-256 receipt chain per step. 415 tests green.
Commit 33266f0f5.
All 14 layers locked. Harmonic wall finalized as R(φ·d*)2 after four iterations. Toroidal resonant cavity gives 176-bit equivalent security from geometry alone. 6008 tests passing.
See the evolution →The Scattered Attention Sphere is 80-90% of a Holographic Quantum Neural Network. Dodecahedral routing in 12D with A5 symmetry gives O(1) path selection. Confirmed novel.
Read the research →Binary substrate + ternary tongue modulation + holographic scatter field. 10^52 unique engraved shapes from a single dodecahedron. Infinite training data pairs.
See the architecture →The harmonic wall is the core of the system — it decides how expensive adversarial behavior becomes. We didn't get it right on the first try. Here's the honest path from broken to canonical.
Problem: Numerical collapse at small distances. The bare exponent d² produced AUC of only 0.054 — essentially random. The formula couldn't distinguish safe from dangerous at close range.
Lesson: Raw exponential without golden ratio scaling has no structure — it's either too flat or too steep.
Problem: Bounded output — maximum amplification was only 6.76×. An attacker could eat the full cost and still operate. The ceiling made attacks expensive but not infeasible.
Lesson: Any bounded function (tanh, sigmoid, etc.) has a ceiling. Security walls need unbounded cost growth.
Problem: Linear scaling meant geometry contributed only 8.5% of the final risk decision. The formula was stable but toothless — it scored, it didn't wall. Attacks at d=3 faced only 6× higher cost.
Lesson: Additive formulas can't create walls. Security needs multiplication, and exponentiation beats addition.
Why this wins: Super-exponential growth via φ² ≈ 2.618 in the exponent. No ceiling — cost grows without bound. The golden ratio creates self-similar spacing (Fibonacci cascade). Tunable base R controls steepness. d* clamped at 10 prevents overflow.
How φ works here: φ² = φ + 1 means adjacent harmonic walls couple through the Fibonacci recurrence. Six tongue walls in orthogonal planes create a toroidal resonant cavity: R(122.99 · d*²) — equivalent to 176-bit cryptographic security from geometry alone.
Patent pending (USPTO #63/961,403). Full formula registry, all 14 layers locked, and cross-layer invariant proofs available in the research index.
See the full formula registryThis is a starter pack for people who wire things themselves. If you want a fully managed enterprise deployment for $29, this isn't it.
The manual is public. The demos are live. The benchmark data is downloadable. Judge the system on evidence, not promises.
Stripe handles payment. The backend can route each paid product to a buyer-only delivery URL, with the public GitHub release kept as the smoke-test fallback. The manual tells you what to do first. Support exists if anything breaks.
The current toolkit, training vault, and writing process pack have buyer routes.
Product ZIPs rebuild locally under the ignored products/packaged/
lane; the process pack redirects after Stripe checkout to a protected ZIP download.
If delivery fails or a file is broken, the public support route is [email protected].
No. This offer is a one-time purchase.
No. The package is paired with a buyer manual so you can work from the usable surface first.
Use the delivery page and support route immediately. The buyer path is part of the product, not an afterthought.
Yes. Polly (bottom-right corner) is our fine-tuned Qwen model trained on the SCBE architecture. Add a free HuggingFace token in her settings panel and ask anything about the system.
Payment processing, employee scheduling, and business management tools that don't charge you $50/month for basic operations. Open source, with a personal AI assistant that grows smarter over time through governed training updates.
Accept payments, send invoices, and track revenue without the processing bloat of enterprise platforms. Connect to Stripe, Ko-fi, or your own gateway. Zero monthly fees from us.
Scheduling, time tracking, basic HR workflows. Built for shops and small teams who don't need (or want to pay for) a full HCM suite. Local-first, your data stays yours.
Every installation gets an AI assistant that learns your business patterns over time. Governed by the same SCBE pipeline above — your data trains YOUR model, not a cloud vendor's. Updates on a schedule you control.
Built by someone who works at Wendy's and knows what it's like when tools cost more than they help. These will be free and open source — forever.
We're releasing the governance decision dataset as a public benchmark. Train your own models to predict risk scores and governance decisions from the same data our pipeline produces. Launching on Kaggle.
The benchmark dataset and Kaggle competition page are in preparation. Try the live demo above to see what the training data looks like.
Try the demo firstToolkit for builders. Training Vault for model trainers. Free tools for small businesses. Or ask Polly first — she knows the system inside out.
Check the governed workflow use case, Toolkit vs Full Repo, manual delivery proof, red-team page, and red-team summary before buying deeper work.
