AI-Assisted Open Product Development Protocol - Revision history

Marcin: /* Bottom Line */

2026-05-13T19:18:29Z

Bottom Line

← Older revision		Revision as of 19:18, 13 May 2026
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	The hard part is building the validation and collaboration engine that turns many contributors into reliable open-source products.		The hard part is building the validation and collaboration engine that turns many contributors into reliable open-source products.

			=Moving on to a Civilization Compiler=

			See [[Civilization Compiler]].

Marcin: /* Education - Build - and Economics */

2026-05-13T19:13:25Z

Education - Build - and Economics

← Older revision		Revision as of 19:13, 13 May 2026
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	3D printing scale models can be created, productized, and used for teaching. These should reflect the actual build process to be a highly-functional pedagogical tool. See more about the requirements at [[3D Printed Scale Model]].		3D printing scale models can be created, productized, and used for teaching. These should reflect the actual build process to be a highly-functional pedagogical tool. See more about the requirements at [[3D Printed Scale Model]].

	Similarly, the production of scale models can diversify into materials printing for various builds. Around30% of construction cost can be saved with 3D printing - see [[3D Printing Cost Savings]]		Similarly, the production of scale models can diversify into materials printing for various builds - based on recycled feedstocks to clean up the environment. Around30% of construction cost can be saved with 3D printing - see [[3D Printing Cost Savings]].

			The [[Multipurpose Kiln]] can also be designed and built for real materials production - based on local and recycled materials - with a 20kW kiln producing 200 square feet of glass tile worth $1k per day.

			These are potential enterprise models that can be run from a garage - and realize the promise of [[Housing-as-Production]].

	=Bottom Line=		=Bottom Line=

Marcin: /* Why This Can Solve Almost Anything Existing */

2026-05-13T19:10:28Z

Why This Can Solve Almost Anything Existing

← Older revision		Revision as of 19:10, 13 May 2026
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	Large swarms provide:		Large swarms provide:

			*Parallel generation of schemas, compilers, part libraries for all modules - this is a big win in modular design.
	* parallel review		* parallel review
	* rapid prototyping		* rapid prototyping
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	* failure discovery		* failure discovery
	* field feedback		* field feedback

			=Education - Build - and Economics=
			3D printing scale models can be created, productized, and used for teaching. These should reflect the actual build process to be a highly-functional pedagogical tool. See more about the requirements at [[3D Printed Scale Model]].

			Similarly, the production of scale models can diversify into materials printing for various builds. Around30% of construction cost can be saved with 3D printing - see [[3D Printing Cost Savings]]

	=Bottom Line=		=Bottom Line=

Marcin: /* Stage 4: Manufacturing Realism */

2026-05-13T19:01:58Z

Stage 4: Manufacturing Realism

← Older revision		Revision as of 19:01, 13 May 2026
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	\|}		\|}

	This is where ~~experienced builders are~~ essential.		This is where experience is essential. AI can be trained here with all known builds, such as for the home. This information comes from CAD, build pictures, working build docs, and BOMs.

	AI can ~~propose an assembly plan~~, ~~but builders determine whether it is real~~.

	Manufacturing realism checks:		Manufacturing realism checks:

	* Can the part be cut?		*Specific assembly sequence - this has most impact for effective builds. Fortunately, the OSE design language has lots of this already embedded in the design of modules - according to the [[Expertise Embedded Design Principle]]
	* Can it be welded?		* How Can the part be cut?
	* Can it be fixtured?		*How Can it be welded?
	* Can it be reached by tools?		*How Can it be fixtured?
	* Can it be assembled in the proposed order?		*How Can it be reached by tools?
	* Can it be repaired?		*How Can it be assembled in the proposed order?
	* Can it be built without expensive proprietary equipment?		*How Can it be repaired?
	* Can mistakes be detected early?		*How Can it be built without expensive proprietary equipment?
	* Can a new builder follow the procedure?		*How Can mistakes be detected early?
			*How Can a new builder follow the procedure?
			*How the tools used affect buildability or build procedure?
			*How the machines used affect buildability or build procedure?
	'''Gate 4: No design becomes canonical until it has been built, tested, documented, and improved from real fabrication feedback.'''		'''Gate 4: No design becomes canonical until it has been built, tested, documented, and improved from real fabrication feedback.'''

Marcin: /* Stage 3: Physical Reasoning */

2026-05-13T18:58:09Z

Stage 3: Physical Reasoning

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	\|}		\|}

	This is where AI is weakest.		This is where AI is weakest - but humans can guide AI here very cleanly by asking AI about:

	~~The swarm must check~~:

	* forces		* forces

Marcin: /* Stage 1: Concept Generation */

2026-05-13T18:56:33Z

Stage 1: Concept Generation

← Older revision		Revision as of 18:56, 13 May 2026
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	* simplicity		* simplicity
	* repairability		* maintainability / repairability
	* manufacturability		* manufacturability
	* safety		* safety

Marcin: Created page with "=AI-Assisted Open Product Development Protocol= For existing product categories such as inverters, tractors, kilns, machine tools, pumps, and construction equipment, the goal is not pure invention. The goal is open-source equivalence. That means producing a buildable, repairable, documented, validated, modular, low-cost version of a known product class. The development process can be reduced to four stages: # Concept Generation # Detailed Design # Physical Reasoning..."

2026-05-13T18:54:52Z

Created page with "=AI-Assisted Open Product Development Protocol= For existing product categories such as inverters, tractors, kilns, machine tools, pumps, and construction equipment, the goal is not pure invention. The goal is open-source equivalence. That means producing a buildable, repairable, documented, validated, modular, low-cost version of a known product class. The development process can be reduced to four stages: # Concept Generation # Detailed Design # Physical Reasoning..."

New page

=AI-Assisted Open Product Development Protocol=

For existing product categories such as inverters, tractors, kilns, machine tools, pumps, and construction equipment, the goal is not pure invention.

The goal is open-source equivalence.

That means producing a buildable, repairable, documented, validated, modular, low-cost version of a known product class.

The development process can be reduced to four stages:

# Concept Generation
# Detailed Design
# Physical Reasoning
# Manufacturing Realism

Each stage is AI-assisted, deep-generalist guided, and swarm-executed.

=Core Principle=

'''If a product already exists in the world, then the open-source task is not to prove possibility. The task is to formalize, simplify, document, validate, and distribute the design.'''

This changes the problem from invention to disciplined reconstruction.

=Stage 1: Concept Generation=

{| class="wikitable"
! Objective
! Output
|-
| Identify physically plausible design families
| A short list of candidate architectures
|-
| Study existing commercial products
| Reverse-engineered functional breakdown
|-
| Compare known approaches
| Tradeoff matrix
|-
| Select simplest open architecture
| Preferred concept
|}

AI can generate candidate concepts rapidly.

For example:

* inverter: transformer-based, transformerless, grid-tie, off-grid, hybrid
* tractor: hydraulic drive, mechanical transmission, electric drive, skid steer, articulated
* kiln: batch kiln, continuous kiln, electric, gas, biomass, solar-assisted
* CNC machine: moving gantry, fixed gantry, vertical mill, router, plasma table

The deep generalist selects concepts based on:

* simplicity
* repairability
* manufacturability
* safety
* open supply chain
* scalability
* field serviceability
* validation feasibility

'''Gate 1: No concept proceeds unless it is physically plausible, manufacturable with known tools, and comparable to existing proven products.'''

=Stage 2: Detailed Design=

{| class="wikitable"
! Objective
! Output
|-
| Convert concept into explicit design
| CAD, schematics, BOM, calculations
|-
| Define all modules
| Module tree
|-
| Define all interfaces
| Mechanical, electrical, hydraulic, software, thermal interfaces
|-
| Define build procedure
| Step-by-step fabrication and assembly file
|-
| Define test procedure
| Module-level and system-level test plan
|}

AI is strong here.

AI can help produce:

* CAD structure
* wiring diagrams
* hydraulic schematics
* circuit block diagrams
* bills of materials
* calculations
* assembly sequences
* risk checklists
* documentation
* version-controlled design files

But AI must not be trusted blindly.

The deep generalist checks:

* dimensions
* loads
* heat
* current
* voltage
* tolerances
* failure modes
* code issues
* assembly order
* tool accessibility

'''Gate 2: No design proceeds unless every part, interface, material, process, and test is explicit.'''

=Stage 3: Physical Reasoning=

{| class="wikitable"
! Objective
! Output
|-
| Determine whether the design can actually work
| Verified calculation set
|-
| Identify hidden failure modes
| Failure mode list
|-
| Check operating envelopes
| Load, thermal, electrical, hydraulic, mechanical limits
|-
| Compare to real products
| Benchmark table
|}

This is where AI is weakest.

The swarm must check:

* forces
* torques
* stresses
* current ratings
* heat dissipation
* duty cycle
* vibration
* fatigue
* overload behavior
* grounding
* insulation
* fire risk
* runaway conditions
* human misuse
* environmental exposure

For an inverter, this includes:

* switching losses
* thermal management
* DC bus safety
* short-circuit response
* EMI
* grounding
* isolation
* surge handling
* battery compatibility
* grid-code implications

For a tractor, this includes:

* axle loads
* traction
* hydraulic pressure
* chain tension
* braking
* rollover risk
* fatigue
* steering failure
* operator safety

For a kiln, this includes:

* heat balance
* insulation
* refractory life
* thermal shock
* burner control
* exhaust
* fire safety
* material throughput

'''Gate 3: No prototype proceeds unless the design survives first-principles checks, benchmark comparison, and failure-mode review.'''

=Stage 4: Manufacturing Realism=

{| class="wikitable"
! Objective
! Output
|-
| Determine whether ordinary builders can make it
| Buildable fabrication plan
|-
| Reduce tooling burden
| Tool list and process simplification
|-
| Validate assembly sequence
| Shop-tested build procedure
|-
| Capture tacit knowledge
| Photos, videos, jigs, fixtures, lessons learned
|}

This is where experienced builders are essential.

AI can propose an assembly plan, but builders determine whether it is real.

Manufacturing realism checks:

* Can the part be cut?
* Can it be welded?
* Can it be fixtured?
* Can it be reached by tools?
* Can it be assembled in the proposed order?
* Can it be repaired?
* Can it be built without expensive proprietary equipment?
* Can mistakes be detected early?
* Can a new builder follow the procedure?

'''Gate 4: No design becomes canonical until it has been built, tested, documented, and improved from real fabrication feedback.'''

=Swarm Development Method=

{| class="wikitable"
! Swarm Role
! Function
|-
| Deep Generalist Integrator
| Holds the whole-system map and resolves architecture conflicts
|-
| AI Operators
| Generate drafts, calculations, comparisons, documentation, and checklists
|-
| Domain Reviewers
| Check specific subsystems such as power electronics, hydraulics, structures, or controls
|-
| Fabricators
| Test buildability and expose tacit manufacturing problems
|-
| Test Crew
| Performs module tests, destructive tests, and field trials
|-
| Documentation Crew
| Converts every finding into build files, diagrams, photos, videos, and lessons
|-
| Standards Crew
| Maps relevant safety codes, certification pathways, and compliance requirements
|}

=The Repeatable Pattern=

For any existing product class:

# Choose a proven commercial reference class.
# Decompose it into functions.
# Generate open architecture options.
# Select the simplest viable architecture.
# Produce explicit CAD, schematics, BOM, and build procedure.
# Run physical reasoning checks.
# Run failure mode review.
# Build module prototypes.
# Test modules independently.
# Integrate into a complete prototype.
# Field test.
# Document failures.
# Redesign.
# Repeat until reliable.
# Publish canonical open-source release.

=Why This Can Solve Almost Anything Existing=

This method works because most civilizational artifacts are not mysteries.

They already exist.

The missing piece is not possibility.

The missing piece is an open, validated, modular, teachable, economically viable design pathway.

AI reduces the cost of:

* research
* drafting
* comparison
* calculation
* documentation
* iteration
* translation
* training material generation

Deep generalists provide:

* coherence
* judgment
* physical grounding
* integration
* prioritization
* validation discipline

Large swarms provide:

* parallel review
* rapid prototyping
* distributed testing
* documentation labor
* failure discovery
* field feedback

=Bottom Line=

'''The correct development strategy is: existing product class → open functional decomposition → AI-generated design candidates → deep-generalist architecture selection → swarm review → prototype → test → canonical release.'''

This makes open-source equivalents of tractors, inverters, kilns, CNC machines, pumps, and other civilization-critical artifacts a systematic process rather than an heroic invention process.

The hard part is no longer whether the artifact can exist.

The hard part is building the validation and collaboration engine that turns many contributors into reliable open-source products.