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GENESIS turns a spark into an evidence-aware build path

GENESIS

An idea-to-evidence workspace for turning bold visions into traceable build and measurement plans.

Expand the idea. Ground the claims. Recompute the physics. Keep the gaps visible.

Status Access Engine Source

Overview · How it works · Deep dive · Maturity · Quality evidence · Interfaces · Architecture · Roadmap · Access

Important

This repository is the public product overview for GENESIS. The full engine source and installable package are currently private. The commands below document the collaborator workflow; cloning this repository alone does not install or run GENESIS.

Executive overview

GENESIS is a large, offline-first engineering research system that connects creative ideation with evidence, requirements, calculations, geometry, and the next real-world test. It is built for a difficult middle ground: more structured than a chat assistant, more creative than a conventional validator, and more honest than a pipeline that labels every generated artifact as finished.

Its central promise is not “the machine is always right.” The promise is that a result should reveal:

  • where its factual claims came from;
  • which values were measured, derived, or deliberately chosen;
  • which model and inputs produced a calculation;
  • which requirements were satisfied, violated, unresolved, or not applicable;
  • which files belong to the same run;
  • and what still has to be built, measured, reviewed, or falsified.

GENESIS at a glance

The figures below were measured from the private engine’s August 2026 main snapshot. They describe scale and reachable software surface—not scientific proof or product certification.

Surface Measured snapshot Why it matters
Python modules 383 Broad engine spanning ideation, research, physics, CAD, simulation, domains, delivery, and tooling.
Reachability 347 wired · 0 islands · 36 infrastructure The measured product graph had no uncalled production-code islands.
First-party Python 153,676 lines A substantial research codebase; size is context, not a quality score.
CLI modes 62 Creation, research, assessment, CAD, evidence, specialist, and diagnostic entry paths.
Local API routes 25 Web-accessible ideation, packages, history, research, assessment, ratification, and invention workflows.
Physics registry 53 validators · 58 recipes Bounded checks selected from declared measurands and units.
CAD library 27 shape families Parametric reference geometry routed through an isolated kernel boundary.
Idea expansion 10 lenses · 12 stretch tactics Deterministic breadth before candidate selection.
Test inventory 471 test files · 6,947 collected tests Large verification surface; collection count alone is not pass evidence.

GENESIS capability system showing ideation, research, physics, CAD, simulation, and delivery around the engine core

Why GENESIS

A promising invention rarely fails because imagination was missing. It fails because assumptions became facts, calculations lost their inputs, or a polished artifact was mistaken for proof.

GENESIS is designed to preserve both sides of the work:

  • creative breadth — open the possibility space before narrowing it;
  • traceable evidence — separate sources, calculations, decisions, and unknowns;
  • bounded engineering checks — apply physics, geometry, and manufacturing checks only where their inputs and models support them;
  • honest delivery — package the next build or measurement step without hiding unresolved gaps.

Dream first · prove honestly · build traceably.

Principle What it means in practice
Expand before filtering A human spark becomes an IdeaField with multiple lenses, alternatives, and stretch tactics.
Integrity is scaffolding Evidence gates protect downstream decisions; they are not a generic gate on creativity.
Abstention is a valid result unknown, unsupported, or not built is preferable to an invented answer.
Reality closes the loop A software result becomes stronger only through external building, measurement, review, and revision.

From spark to measurement

flowchart LR
    U["Human vision<br/>goal + context"] --> I["Ideate<br/>IdeaField"]
    I --> C{"Choose a direction"}
    C --> R["Research<br/>claims + receipts"]
    R --> S["Specify<br/>requirements + decisions"]
    S --> P{"Assess<br/>physics + geometry"}
    P -->|bounded evidence| B["Build path<br/>CAD · DFM · package"]
    P -->|missing or failed| G["Named gap<br/>question · test · measurement"]
    G --> R
    B -. external work .-> M["Prototype<br/>measure · inspect"]
    M -. feedback .-> R
Loading

The final dashed step matters: GENESIS can structure and check a path, but it cannot turn a generated report, a passing test, or a CAD file into physical proof by declaration.

Three entry paths

You are exploring as a… Start with… GENESIS helps produce…
Dreamer or inventor a plain-language “what if?” alternative concepts, reframes, trade-offs, and next moves
Builder or engineer a goal, constraints, and known inputs a specification, bounded checks, CAD/DFM paths, and named gaps
Researcher a question, dataset, or disputed claim claims, receipts, uncertainty labels, counterchecks, and a measurement plan

Core capabilities

Area Current role
Idea machine Deterministic offline ideation, multi-lens expansion, history, remixing, and packaged idea fields.
Research pipeline Structured reports, claims, fetch receipts, source provenance, challenge paths, and explicit uncertainty.
Goals and hard requirements Keeps a human goal separate from machine-checkable constraints and blocks candidates with violated or unresolved bound requirements.
Physics and simulation Selects bounded validators from supplied measurands and records assumptions, failures, non-applicability, and missing evidence.
CAD and manufacturing Uses an isolated CAD-kernel bridge, reference geometry, printability/DFM checks, and explicit CAD_NOT_BUILT outcomes.
Realization packages Writes reports, structured data, manifests, next actions, and artifact references into a traceable run package.
Interfaces Offers a CLI, a local web atelier, and a Python API for the same underlying workflows.
Specialist explorations Includes research paths for discovery, simulation, humanoids, aero, and domain-specific engineering pipelines.

Capability deep dive

1. Idea machine and creative memory

The idea machine deliberately runs before the strict engineering filters. A seed is reframed, expanded through domain lenses, transformed with stretch tactics, and written as an IdeaField. Each spark is a proposal—not a disguised fact.

Ten current lenses

mechanics · energy · materials · sensing · biology · software · manufacturing · society · space · planet

Twelve stretch tactics

invert · miniaturize · scale-up · combine · remove-part · democratize · fail-soft · time-machine · biomimic · open-source · stack · scavenge

The memory surface supports:

  • newest-first history loading;
  • deterministic selection by run ID or N-th newest entry;
  • re-expansion of unique historical seeds;
  • focused lens and stretch filters;
  • batch ideation;
  • transfer from an idea package into invent or solve;
  • local idea-note deposits that never promote themselves to verified claims.

Typical idea-package files include IDEA_FIELD.md, IDEA_FIELD.html, sparks.json, sparks.csv, SPARKS.md, MANIFEST.json, and safe suggested next moves.

2. Research, claims, receipts, and delivery gates

GENESIS structures research as a sequence of inspectable artifacts rather than a single fluent answer:

Phase Purpose Typical output
α — report gather claims, sources, and contradictions research report and claim ledger
β — solution map alternatives and trade-offs solution space
γ — specification turn a selected direction into typed requirements and geometry specification
δ — assessment apply relevant physics and applicability checks physics verdict and gaps
φ — divergence explore the wider possibility space divergence map
χ — frontier identify high-value open boundaries frontier map

Every serious factual path is expected to distinguish:

  • the claim itself;
  • the source reference;
  • the actual fetch attempt and returned bytes;
  • extraction or span information;
  • the producer and run identity;
  • confidence and verification state;
  • contradictions, missing coverage, and unresolved dependencies.

Checkpoint V3 binds current fetch-attempt evidence to claims and fails closed when relations are added, removed, reordered, duplicated, orphaned, or borrowed from another claim. A digest detects mutation inside that software contract; it is not a signature, an external trust root, or proof that a source is correct.

For live model-assisted work, generator and verifier must come from different model families. Model agreement is still not source independence.

3. Goals, hard requirements, and candidate selection

A human goal and a machine-checkable requirement are not the same thing:

  • a free-form goal is preserved as declared_unscored;
  • a hard requirement uses a bounded grammar such as payload.mass <= 12 kg;
  • the requirement must bind to an exact measurand in the generated specification;
  • incompatible units, missing measurands, and non-finite values fail closed;
  • only physics-grounded candidates with satisfied or non-applicable bound requirements can enter the eligible selection front.
flowchart LR
    G["Human goal<br/>declared_unscored"] --> R["Request envelope"]
    C["Hard constraints<br/>measurand · operator · value · unit"] --> R
    R --> S["Generated specification"]
    S --> B{"Exact binding?"}
    B -->|yes| P{"Physics grounded?"}
    B -->|missing or ambiguous| U["UNRESOLVED"]
    P -->|yes| E{"Requirement result"}
    P -->|no| U
    E -->|satisfied / not applicable| F["Eligible candidate"]
    E -->|violated| V["Blocked from selection"]
    U --> V
Loading

This closes a software selection seam. It does not prove that the free-form goal was achieved, that the source inputs were true, or that the candidate is safe in reality.

4. Physics and constraint engine

The current registry contains 53 numerical validators, 58 automatic selection recipes, and 2 deliberately manual-only validators. Recipes are selected from the measurands actually present in a specification; missing inputs stay missing.

Domain Representative checks
Structures torsion, fatigue, buckling, cantilever bending, plate bending, fracture, creep, bearing life, shear and bolted joints
Dynamics and robotics resonance, beam natural frequency, arm reach, ZMP balance, actuator sizing, swing and gait dynamics
Thermal and energy overtemperature, thermal mismatch, vacuum radiation balance, battery endurance, current budget
Fluids and pressure pressure vessels, hydraulic force and flow, contact pressure
Manufacturing bridge span, FDM clearance, pins, modeled threads, walls, embossing, layer adhesion
Aero and space rotor hover, endurance, ISRU oxygen, life-support budgets
Compute and communications compute, inference, and bus budgets
Cryptographic formulas selected key, nonce, collision, and signature-fault checks when their explicit measurands are present

Each run distinguishes numeric success, numeric failure, missing dimensional evidence, and non-applicability. The overall vocabulary includes physics_verified, physics_failed, and no_physics_indicated.

The physics engine is strongest when a narrow case supplies explicit values, units, geometry, material assumptions, and acceptance limits. It is weakest when prose is expected to substitute for those inputs. Passing one selected model never validates the entire product.

5. CAD, geometry, DFM, and manufacturing evidence

The supported CAD path isolates CadQuery/OCCT from the main numerical environment. Canonical requests cross a subprocess boundary so CAD dependencies do not silently destabilize NumPy, SciPy, or the core runtime.

flowchart LR
    SPEC["Specification<br/>typed geometry"] --> IR["Canonical CAD IR<br/>units · references · features"]
    IR --> BRIDGE["Subprocess bridge<br/>JSON request"]
    BRIDGE --> OCCT["Isolated CadQuery / OCCT"]
    OCCT --> ART["BREP · AP242 STEP · STL"]
    ART --> VERIFY["Re-import · geometry receipts · DFM"]
    VERIFY -->|bounded pass| PKG["Golden package"]
    VERIFY -->|missing kernel or failed gate| GAP["CAD_NOT_BUILT / named gap"]
Loading

Twenty-seven registered shape families

anchor_plate · bearing_block · clamp · cone_adapter · enclosure_box · extrusion_profile · flange · gripper_finger · gusset_bracket · heat_sink · hinge_leaf · l_bracket · lever_arm · lid · motor_mount · mounting_panel · plate · pressure_vessel · pulley · shaft · shaft_coupler · spacer · spur_gear · standoff · t_bracket · tube · wheel

The industrialization path is intentionally staged:

Stage Current state Meaning
C1 — CAD IR v1 Verified locally canonical model, units, references, and feature invariants
C2 — OCCT round-trip Verified locally BREP, STEP, and STL from one model with fresh re-import checks
C3 — NEMA 17 golden specification Verified locally deterministic, provenance-carrying reference input
C4 — golden-part package Verified locally atomic ten-file package binding specification, geometry, BOM, DFM, evidence, and manifest
C5 — AP242/XDE and PMI Partial semantic dimensions and material exist inside one kernel; independent PMI verification is missing
C6 — one product-wide CAD IR Open realization, drawings, assemblies, BOM, and manufacturing still need full unification
C7 — text-to-CAD proposals Open generated geometry must remain an untrusted proposal until deterministic gates accept it

GENESIS therefore supports bounded reference CAD and evidence-aware packaging. It does not claim arbitrary text-to-production CAD, certified GD&T, toolpath approval, or factory release.

6. Simulation and scientific discovery

Simulation paths include internal proxy models and optional cross-checks with external engines such as MuJoCo, PyBullet, CalculiX, and Modelica when available. Promotion across the narrow simulation reality boundary requires case- and run-bound receipts for calibration, boundary conditions, solver execution, independent validation, and—where relevant—a finite converged mesh series.

The discovery surface supports data-driven ODE candidates, symbolic identity research, high-precision numerical checks, interval reasoning, and counterexample-oriented verification. Fits, finite sample grids, and computer-algebra output remain candidates until their domains and proof obligations are satisfied.

This makes simulation and discovery useful for falsification and experiment design. It does not make them universally calibrated predictors or theorem provers.

7. Humanoids, AETHON, aero, and specialist pipelines

Surface What exists What remains outside the claim
Humanoids kinematics, structure, energy, report, lineage, and optional simulation paths validated actuators, sensors, contact dynamics, fall safety, hardware trials
AETHON fail-closed digital research model and readiness contracts complete evidence, CAD/DFM, manufacture, certification, a real robot
Aero exploratory models and aero-report verified geometry, CFD mesh independence, wind-tunnel/flight data, certification
Architecture and engineering deterministic first-stone system, load, tolerance, and test-plan structures signed professional engineering evidence
Electrical and manufacturing budget, safety, process, and DFM structures EDA/HIL proof, machine/toolpath approval, shop-floor release
Regulatory and software risk, release, failure-state, and bounded code-gate structures legal advice, authority approval, production certification
Design and economics ergonomics, interaction, cost, and market assumptions validated user studies, supplier quotes, market forecasts

These domain pipelines are first-stone mappers: they help expose the structure and missing evidence of a problem. Their existence does not imply expert completeness.

8. Realization packages and artifact integrity

Depending on the path, GENESIS can package:

  • human-readable reports and next actions;
  • structured claims, specifications, constraints, and assessment results;
  • BOM, DFM, printability, and geometry references;
  • STEP, BREP, STL, DXF, OpenSCAD, or build123d artifacts where supported;
  • run manifests, digests, sizes, and producer identities;
  • explicit missing-evidence documents;
  • preregistration, measurement-plan, countercheck, and physical-evidence envelopes.

Sensitive writers use bounded names, staging directories, exact inventories, no-replace publication, and fail-closed checks for symlinks, hardlinks, non-regular files, stale bindings, and post-write mutation. These controls protect artifact identity and publication behavior; they do not prove the scientific truth of artifact contents.

9. Offline-first and optional live intelligence

The deterministic offline path is the baseline. It makes demos, package shapes, gates, and many engineering paths reproducible without cloud access.

Live LLM or source-backed paths are opt-in, budgeted, and inherently less reproducible. External model output must pass the same typed claims, evidence, requirement, physics, and delivery boundaries. Missing live dependencies should produce an explicit unavailable or partial state—not a fabricated offline success.

Illustrative collaborator workflow

The following examples assume access to the private engine checkout and an activated Python environment:

The public overview is English. Some current engine-facing CLI and web copy remains German-first, while command names, schemas, and the evidence vocabulary are language-neutral.

# 1. Expand a spark offline
python -m gen --mode ideate --max-sparks 12 \
  "A quiet, repairable harvesting robot for narrow greenhouse rows"

# 2. Carry the latest idea package into the invention path
python -m gen --mode invent --ideate-package latest \
  "A modular greenhouse harvesting robot"

# 3. Ask for a bounded engineering assessment
python -m gen --mode assess \
  "A wall mount for a 12 kg monitor using a VESA 100 pattern"

# 4. Review existing run packages instead of regenerating them
python -m gen --mode packages

Typical local artifacts are organized by run rather than presented as an untraceable final answer:

out/
├── ideate/<run_id>/
│   ├── IDEA_FIELD.md
│   ├── sparks.json
│   └── MANIFEST.json
├── invent/<run_id>/
│   ├── SUMMARY.json
│   ├── NEXT.md
│   └── MANIFEST.json
└── PACKAGES.md

A manifest describes an expected artifact inventory. It does not by itself prove completeness, correctness, novelty, feasibility, or physical realizability.

Evidence model

GENESIS keeps factual origin explicit instead of flattening every number into the same kind of “answer.”

Origin Meaning
GROUNDED Bound to a source, observation, or measurement receipt.
DERIVED Recomputed from declared inputs and a visible method.
DECISION Intentionally chosen by a human or template, with rationale.
Named gap Required information is missing, conflicting, or not honestly inferable.

Claims retain states such as verified, unverified, refuted, and unsupported. Physics assessment retains distinct outcomes such as physics_verified, physics_failed, and no_physics_indicated.

Those labels describe a bounded contract. For example, physics_verified means the selected model passed for the supplied inputs; it does not mean the whole invention is scientifically proven, safe, certified, or ready to manufacture.

Evidence promotion ladder

stateDiagram-v2
    [*] --> PROPOSAL
    PROPOSAL --> GROUNDED: source or measurement bound
    PROPOSAL --> GAP: evidence unavailable
    GROUNDED --> DERIVED: explicit calculation
    GROUNDED --> REFUTED: contradiction or failed check
    DERIVED --> SOFTWARE_VERIFIED: bounded contract passes
    SOFTWARE_VERIFIED --> PREPARED: build and measurement plan complete
    PREPARED --> FABRICATED: identified real artifact
    FABRICATED --> MEASURED: bound raw observations
    MEASURED --> COUNTERCHECKED: independent comparison
    COUNTERCHECKED --> CORROBORATED: all preregistered criteria pass
    GAP --> PROPOSAL: new evidence
    REFUTED --> PROPOSAL: revised hypothesis
Loading

The upper half of this ladder can be exercised in software. The lower half requires external people, instruments, artifacts, and independent review. GENESIS must not skip from SOFTWARE_VERIFIED to a real-world claim.

Maturity snapshot

Snapshot: August 2026. The labels below apply only to the stated software contract.

Area Maturity Evidence boundary
Idea expansion and packaging 🟢 VERIFIED The deterministic software path is wired and tested; idea quality and novelty remain human judgments.
Evidence ledger 🟢 VERIFIED The bounded append-only/revision contract is tested; stored provenance does not prove a source's content.
Goals and hard requirements 🟢 VERIFIED Exactly bindable constraints participate in selection; a free-form goal remains declared but unscored.
Research synthesis 🟡 PARTIAL Fail-closed evidence paths exist, but independent scientific verification is not complete.
Physics and simulation 🟡 PARTIAL Selected modeled cases can be checked; coverage, calibration, boundary conditions, and real measurements remain case-specific.
CAD and manufacturing 🟡 PARTIAL / PREPARED Reference software contracts and receipt checks exist; independent PMI review, fabrication, and physical measurement are still required.
Humanoid and aero paths 🟡 PARTIAL These are exploratory digital research paths, not validated machines or certified aircraft.
Overall scientific or industrial readiness 🔴 NOT VERIFIED External replication, hardware validation, safety approval, certification, and production release remain open.

Caution

A process that runs successfully is not automatically a true result. A green test proves only the behavior covered by that test. It does not substitute for scientific evidence or real-world validation.

Measured engineering evidence

This is the most honest answer to “how good is GENESIS?”: judge each surface by the strongest test that can falsify its specific claim.

August 2026 software snapshot

Evidence Observed result Interpretation
Reachability analysis 383 modules · 347 wired · 0 script-only · 0 islands · 36 infrastructure The measured first-party product graph had no unreachable production modules.
Test discovery 6,947 collected tests across 471 test files Broad automated surface; collection is not the same as execution.
Isolated CAD/OCCT suite 378 passed · 0 failed · 0 errors · 0 skipped Strong evidence for the named CAD kernel and golden-reference software contracts.
Kernel-disabled exact shards 6,324 passed · 292 declared skips · 0 failures/errors across 6,616 exact IDs, plus one separate passing negative regression Strong process evidence for that dated source snapshot; skips remain explicit environmental gaps.
Golden-specification tests 40 passed Focused evidence for the deterministic NEMA 17 golden specification.
Post-fix regression slice 106 passed · 11 expected kernel skips Focused guard against the repaired CAD and package regressions.
Distribution checks wheel and sdist inspected, installed, dependency-checked, resources loaded outside the checkout, negative CLI case passed Evidence that the dated package could be built and used outside its source tree.
Local acceptance gate Maestro 1/1 passed, zero failures, errors, or skips before the referenced product commit One executed browser acceptance contract, not a full UI certification.
Cloud CI Not an active proof surface; workflows were removed on 7 August 2026 after prior account/spending failures stopped jobs before repository steps No current green-cloud claim is made. Verification is local and explicitly scoped.

Where GENESIS is strongest

  • deterministic offline idea expansion and artifact packaging;
  • fail-closed evidence, mutation, and publication contracts;
  • explicit unit, requirement, and specification binding;
  • bounded physics selection with honest missing-input behavior;
  • isolated reference CAD with round-trip and package verification;
  • negative tests that require failed, missing, stale, or inapplicable evidence to remain visible.

Where GENESIS remains partial

  • independent scientific replication;
  • calibration and empirical validation across arbitrary physics cases;
  • general text-to-production CAD and complete assembly semantics;
  • real manufacturing, metrology, fit, lifetime, and safety evidence;
  • validated humanoid hardware, aero vehicles, and industrial deployments;
  • external PKI, independent ledger, certification, or regulatory approval.

Large test counts, module counts, hashes, manifests, and successful processes are useful evidence of software discipline. None of them alone prove novelty, scientific truth, safety, manufacturability, or commercial value.

Flagship proof: NEMA 17 reference bracket

GENESIS currently concentrates its real-world proof strategy on one narrow reference claim instead of claiming universal engineering readiness.

The target is a uniquely identified bracket built from one exact golden package, measured with declared uncertainty, fitted to one previously inspected Nanotec ST4118 motor, and independently counterchecked on a preregistered critical subset. The claim deliberately excludes universal NEMA 17 compatibility, strength, lifetime, batch production, certification, and safety approval.

flowchart LR
    S["Source bytes<br/>authenticated receipts"] --> D["Design basis<br/>golden package digest"]
    D --> A{"Fabrication<br/>authorized?"}
    A -->|no| P["PREPARED / PARTIAL"]
    A -->|yes| F["As-built bracket<br/>identity + traveler"]
    F --> M["Motor incoming inspection"]
    M --> R["Primary measurements<br/>raw data + uncertainty"]
    R --> T["Fit test"]
    T --> C["Independent countercheck"]
    C --> V{"All preregistered<br/>criteria pass?"}
    V -->|yes| O["DIMENSIONALLY CORROBORATED"]
    V -->|fail or overlap| I["FAIL / INCONCLUSIVE"]
Loading

Current flagship state

Layer State
Canonical CAD IR, OCCT round-trip, golden specification, and atomic ten-file package Locally verified software contracts
Immutable preregistration package and standalone verifier Executable
Physical-evidence v1 for source bytes and registration receipts Executable
Optional Ed25519/DSSE receipt trust with pinned policy and local replay ledger Executable, operator-provisioned trust anchor required
Real owner source bytes and authenticated production receipts Missing
Fabrication authorization, as-built artifact, motor inspection, measurements, uncertainty evaluation, fit test, independent countercheck Not yet completed
Overall flagship verdict PREPARED / PARTIAL · release_candidate=false

This flagship is valuable precisely because it can end in PASS, FAIL, or INCONCLUSIVE. A complete negative package is still evidence; it simply cannot be promoted as success.

Architecture

flowchart TB
    subgraph surfaces[Interfaces]
      CLI[CLI]
      WEB[Local web atelier]
      API[Python API]
    end

    subgraph create[Create and choose]
      IDEA[Idea machine]
      INV[Inventor and candidate selection]
    end

    subgraph truth[Evidence and decisions]
      RES[Research and source receipts]
      LED[Claims and evidence ledger]
      REQ[Goals and hard requirements]
    end

    subgraph assess[Bounded assessment]
      PHY[Physics validators]
      SIM[Simulation cross-checks]
      CAD[CAD · geometry · DFM]
    end

    subgraph deliver[Delivery]
      PKG[Run package]
      GAP[Named gaps and measurement plan]
    end

    surfaces --> IDEA --> INV
    INV --> RES --> LED
    INV --> REQ
    LED --> PHY
    REQ --> PHY
    PHY --> SIM
    PHY --> CAD
    CAD --> PKG
    SIM --> PKG
    LED --> GAP
    PHY --> GAP
Loading

The core engine is Python 3.11+. Optional stacks—web, CAD, simulation, databases, and live model adapters—are kept behind explicit boundaries so that the deterministic offline path remains the safest starting point.

Interfaces

The engine exposes the same underlying contracts through a command line, a local web atelier, and a Python API. The examples in this section require collaborator access to the private engine checkout.

CLI quick tour

# Expand one idea deterministically
python -m gen --mode ideate --max-sparks 12 "A repairable greenhouse robot"

# Expand and carry the result into candidate invention
python -m gen --mode dream-loop "A modular greenhouse harvesting robot"

# Keep a human goal separate from exact hard requirements
python -m gen --mode invent --goal "quiet and locally repairable" --constraint "payload.mass <= 12 kg" "A greenhouse robot"

# Build a specification and ask for bounded assessment
python -m gen --mode spec "A modular sensor mount"
python -m gen --mode assess "A wall mount for a 12 kg VESA 100 monitor"

# Review existing packages before recomputing work
python -m gen --mode packages

Complete 62-mode surface

Group Modes Purpose
Create and remember ideate, dream-loop, packages, lenses, history, deposit, stats, ideas, dream, invent, solve Expand, select, replay, remix, and package ideas.
Research and specification report, solution, spec, capstone, eval, protocol, council, feynman, campaign, research, discover-ode, goldset Research phases, evaluation, source work, structured specification, and discovery.
Assess and build assess, print, bundle, cad-golden, nema17-preregister, nema17-physical-evidence, realize, breakthrough, horizon-full, section, topology, structural, chip Physics, printability, CAD, evidence, realization, and specialist build paths.
Frontier and diagnostics divergence, frontier, surface, well-probe, sources, caps, multi-physics, sim-crosscheck, training Capability inventory, external-source surfaces, frontier work, and crosschecks.
Professional first-stone maps fach, architekt, ingenieur, physiker, techniker, elektriker, fertigungs, regulatorik, software, designer, wirtschaft Domain-specific structures and explicit seam gaps.
Humanoid and aero research humanoid, aethon, humanoid-research, humanoid-chat, humanoid-report, aero-report Digital robotics and aerospace research surfaces.

Important cross-mode flags include:

Flag Role
--demo deterministic offline fixture path
--live and --live-budget explicit live-model opt-in and budget
--from-ideate expand sparks before candidate invention
--ideate-package latest and --spark select an existing package or one spark
--from-history, --history-run-id, --history-n deterministic history selection
--remix-history re-expand unique historical seeds
--lens, --stretch, --max-sparks control breadth and creative focus
--goal and repeated --constraint preserve intent and machine-checkable hard requirements separately
--deliver and --format select deliverable or structured export format

The installed version remains the authority for exact flags:

python -m gen --help

Local web atelier

python -m pip install -e ".[web]"
python -m gen.web --port 8080
# Open http://127.0.0.1:8080

The web UI is a loopback development surface, not a hardened public service. It exposes goal and constraint fields for invention paths, shows requirement and evidence states, and provides local access to ideas, packages, history, research, assessment, ratification, and evaluation.

Current 25 API routes
Method Route Role
GET /api/status compact runtime status
GET /api/report/demo · /api/spec/demo deterministic report and specification demos
GET /api/capstone · /api/assess · /api/printability · /api/eval bounded assessment and evaluation surfaces
GET /api/ratification list local ratification items
POST /api/ratification/check submit a bounded ratification decision
GET /api/clarify/demo clarification example
POST /api/clarify/answer submit a clarification answer
POST /api/research/assess assess a research relation
GET /api/history newest history entries
GET /api/history/run/{run_id} · /api/history/nth/{n} deterministic history selection
GET /api/lenses · /api/packages lens and package catalogs
POST /api/stats · /api/deposit local idea-memory operations
POST /api/ideate · /api/dream-loop idea expansion and connected invention
POST /api/invent · /api/solve candidate invention and problem solving
GET /api/invent/eval deterministic invention evaluation
POST /api/ask bounded ask surface

The public overview is English. Some current engine-facing CLI and web output remains German-first; command names, schemas, paths, and evidence vocabulary are language-neutral.

Python API

from gen.idea_machine import expand_idea, format_idea_field
from gen.pipelines.idea_specification import build_specification_from_idea
from gen.pipeline import assess_specification

field = expand_idea(
    "A quiet, repairable greenhouse robot",
    max_sparks=12,
)
print(format_idea_field(field))

spec = build_specification_from_idea(
    "A wall mount using a VESA 100 pattern for a monitor",
    run_id="public-overview",
)
assessment = assess_specification(spec)
print(assessment.overall)

Additional APIs cover runners for the α/β/γ phases, evidence and ledger stores, CAD bridges, shape builders, simulation adapters, export formats, and specialist pipelines. Optional subsystems are imported lazily where practical so the core does not require every heavy dependency.

Engine stack

Layer Current choice
Language Python 3.11 or newer
Numerical and symbolic core NumPy, SciPy, SymPy, mpmath
Typed contracts Pydantic
Web FastAPI and Uvicorn, optional
Constraint solving Z3, optional
CAD isolated CadQuery/OCCT; build123d uses a separate environment
Simulation internal models; PyBullet and other engine crosschecks are optional
Ledger in-memory default; PostgreSQL optional
Receipt trust optional Cryptography-based Ed25519/DSSE verification
Drawing and visual exports ezdxf and Pillow, optional
Testing pytest, pytest-asyncio, Hypothesis, Ruff

Roadmap

The roadmap is evidence-first: deepen one demonstrable proof before adding more impressive-looking surface area.

Priority Workstream Next falsifiable exit
F1 NEMA 17 flagship real authenticated source and registration receipts, fabrication authorization, identified part, metrology, fit, and independent countercheck
F2 Research truth require concrete fetch-attempt binding and source independence across every public research consumer
F3 Case-bound physics and simulation bind model validity, boundary conditions, convergence, calibration, measurement, and independent comparison to one selected case
F4 CAD and manufacturing C5–C7 independent AP242/PMI review, one shared product CAD IR, then bounded untrusted text-to-CAD proposals
F5 Professional pipelines and UI consume upstream evidence fully and make error, abstention, and unresolved-requirement paths visible to operators
OPS Reproducible engineering local acceptance per commit, clean package builds, and restored cloud execution only when it can run real repository steps

For the flagship, the next proof steps are deliberately external and concrete:

  1. authenticate source and registration receipts;
  2. perform an independent AP242/PMI review;
  3. authorize fabrication through an explicit role-bound gate;
  4. manufacture and inspect the part;
  5. bind measurements, uncertainty, and deviations back to the run;
  6. revise the design from observed evidence.

Parallel work continues on research provenance, case-bound physics, operator-visible failure paths, and evidence consumption across specialist pipelines.

What GENESIS does not claim

  • It is not a scientific authority, certification body, or fabrication approval system.
  • It does not make arbitrary generated CAD production-ready.
  • It does not treat model agreement, finite numerical output, or a passing software test as universal truth.
  • It does not hide missing dependencies or failed checks behind a success label.
  • It does not make live LLM runs deterministic or reproducible.
  • Its humanoid, aero, and simulation paths are research tooling—not validated physical products.

Human domain review remains mandatory before any safety-critical, regulated, medical, structural, aerospace, or manufacturing decision.

Frequently asked questions

Is GENESIS just a chat assistant with CAD tools?

No. The engine uses typed specifications, claim and evidence records, requirement envelopes, numerical validators, artifact manifests, package writers, and optional CAD/simulation processes. Live models can assist selected paths, but the deterministic offline contracts remain independently usable.

Can it run without cloud models?

Yes. Ideation, demos, package generation, many gates, physics checks, history, local web paths, and reference engineering flows are designed to run offline. Live model and live-source work is explicit opt-in.

Does it generate real CAD?

For supported structured geometry and an available isolated kernel, GENESIS can produce and re-import real BREP, AP242 STEP, and STL artifacts. Arbitrary prose does not automatically become correct, complete, or production-approved CAD.

Does “physics verified” mean the invention is proven?

No. It means the selected bounded validator passed for the supplied, specification-bound inputs. Other loads, models, assumptions, materials, tolerances, failure modes, and real measurements may still be missing.

Is it ready for industrial or safety-critical deployment?

No. The overall research and industrial readiness verdict remains NOT VERIFIED. The strongest current claims are bounded software contracts and a prepared physical-evidence path.

What is GENESIS best at today?

Turning an early idea into a structured, inspectable research and engineering trail: alternatives, claims, source attempts, requirements, calculations, reference geometry, artifact packages, and a clear list of what must happen next in reality.

Access and collaboration

This public repository intentionally contains the English product overview and its visual assets. It does not contain the private engine, a public wheel, or a supported public installation path.

The private engine’s project metadata currently declares an MIT license. This overview repository is not a substitute for the engine source distribution or its license file.

For questions, research collaboration, or access discussions, open an issue or contact Oz4462.


Generative Engine for Networked Ideation, Synthesis & Specification

Dream first · expand boldly · prove what you ship · keep honest gaps visible.

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GENESIS V2 — public product overview. Anti-hallucination idea-to-verified-spec engine narrative. Full source is private.

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