Project summary

Software deployment became automated years ago. Push code, it builds, it ships. Hardware has not had that transition. A finished PCB design still requires weeks of manual work before it becomes a physical object, exporting Gerber files, sourcing components from distributor spreadsheets, emailing factories about tolerances, negotiating minimum order quantities, and iterating on design for manufacturing feedback through days of back and forth.

This is not a design problem. The design tools already exist. This is an orchestration problem. The software bridge between a finished design and a factory floor does not exist.

vibing build is building that bridge.

The system takes a natural language device description and decomposes it into engineering domains. Specialized AI agents generate design artifacts for each domain, KiCad schematics via SKiDL, component matched BOMs against real distributor inventory like LCSC, STEP enclosure files, and skeleton firmware. These artifacts pass through a deterministic constraint validator, not LLM based, that runs DRC/ERC checks, verifies component availability in real time, and flags DFM violations against target factory capabilities. Only after passing validation can the system route an order to manufacturing.

The critical architectural decision is where to draw the line between generative AI and deterministic systems. The agents generate. The validators verify. The human approves. No order is placed without explicit user confirmation. When AI output has physical consequences, a shorted trace, a wrong footprint, a mismatched component, the failure mode is not a bad paragraph. It is a destroyed production batch. Building reliability infrastructure around autonomous engineering systems is the actual hard problem.

The initial scope is deliberately narrow. 2 layer PCBs, ESP32/RP2040 based designs, standard sensors with known good footprints, JLCPCB compatible components only, and 3D printed enclosures. This is not where the system ends. It is where it can be validated reliably enough to trust.

Recent advances in LLM structured reasoning, programmatic EDA tooling, and factory APIs make this category newly possible. Five years ago, the models were not reliable enough, the tooling ecosystem was fragmented, and most manufacturing workflows were inaccessible programmatically. The convergence of these systems creates a narrow window where hardware orchestration can finally become software defined.

Key proof points so far:

• Submitted test quotes through JLCPCB’s API. $7.50 for 5 assembled boards, with response time under 2 seconds

• Surveyed 90+ hardware builders. 90%+ said they would build hardware if the manufacturing barrier did not exist

• EE Agent already generates valid KiCad schematics from structured prompts

• BOM generation pipeline already matches components against LCSC inventory

• A full technical proposal has already been written, including architecture, competitive landscape, and market framing

Full technical proposal:
https://drive.google.com/file/d/1eVHiZ0Ih2Yyjo30MLhoskMl5R6hWLa_e/view?usp=sharing

Relevant context:
Closest competitors like Schematik, Quilter, and Flux each cover only part of the pipeline. None of them manufacture. I am not aware of another system attempting the full flow from natural language intent to validated design to programmatic factory order to manufactured product.

What are this project's goals? How will you achieve them?

Phase 1. Golden Prototype

The first goal is a single end to end proof point. Type a device description, generate a valid design, validate it deterministically, place a real factory order, and hold a working assembled board a few weeks later.

To get there, I will:

• Complete the EE Agent schematic generation pipeline for ESP32 class devices

• Build the deterministic constraint validator against JLCPCB manufacturing specs

• Implement BOM generation with real time LCSC availability checking

• Test the full JLCPCB ordering flow end to end

• Produce the first Golden Prototype

The Golden Prototype is the core proof. If the system can reliably turn a natural language request into a real manufactured device, then the rest of the product becomes a question of scope, reliability, and iteration rather than basic feasibility.

If it fails, the failure will still be useful because it will identify where the real bottleneck actually is.

Phase 2. Validation at Scale

After the first working prototype, I will run repeated end to end cycles across different device types to measure where the pipeline actually breaks.

That phase will include:

• Confidence scoring for every generated design

• Broader component libraries

• Beta users from hardware builder communities

• Systematic failure logging

• Demand batching experiments

The goal here is not just to ship a demo. It is to learn whether this pipeline can be made reliable enough to trust with physical manufacturing.

Phase 3. Architecture Decision

Once there is enough evidence, I will decide whether the right model is:

• Direct factory integration

• A marketplace model

• Or some hybrid between the two

I do not know the answer yet. The first two phases exist to generate enough evidence to make that decision correctly.

How will this funding be used?

Minimum funding: $2,000

This is enough to start the project in a serious way and produce the first useful evidence.

ItemCostClaude/OpenAI API credits for agent development and structured generation$500Initial prototype manufacturing runs through JLCPCB, 5 to 8 boards across iterations$350Components for physical validation and debugging$250Hosting, databases, and infrastructure, domain, compute, storage$200Extended KiCad libraries, footprints, and EDA tooling$150Shipping and import costs for prototype boards and components to Egypt$200Development contingency$350

Total: $2,000

At this level, the goal is not to build a company. It is to answer one question:

Can the pipeline produce a real functional device from a natural language prompt?

Full funding: $8,000

This expands the work from one proof point into a real validation program.

ItemCostSustained LLM/API usage for 6 months$1,00020 to 30 manufacturing test runs across multiple device categories$1,500Expanded component inventory and hardware debugging equipment$800Hosting, infrastructure, databases, and compute runway$600Beta user manufacturing subsidies for early testing$7003D printing and enclosure validation$500Development tooling and services$500Shenzhen factory visit and manufacturing relationship building$1,200Shipping, import, and logistics overhead$500Contingency$700

Total: $8,000

At this level, the goal is to move from “interesting prototype” to “credible system with real empirical evidence.”

Who is on your team? What is your track record on similar projects?

Solo founder. Mohamed Ramadan, 18, Cairo, Egypt.

I have shipped 60+ software projects and built organizations and technical projects that required both execution and persistence. I founded TELQAI, a free Arabic AI education initiative, and co founded Phiga, an international physics competition.

I have also built physical hardware before, including drones, water filtration systems, and IoT devices. I have experience in professional software environments and I have a strong bias toward building real systems rather than just talking about them.

The project description and architecture were written from direct experience with the gap between “finished design” and “manufactured object.”

Selected background:

• 1st Place, Egyptian National AI Olympiad

• Represented Egypt at the Arab AI Olympiad

• NASA Space Apps, Global Honorable Mention

• Software engineering intern at Novomind GmbH

• Youngest intern hired at Paymob

• Published ML and bioinformatics research, ColiFormer

I am not going to university. I am building full time starting July 2026.

portfolio: https://www.genoo.me/

What are the most likely causes and outcomes if this project fails?

The most likely failure modes are practical, not philosophical.

1. Factories reject or partially human review machine originated orders

If the ordering layer cannot be fully automated, the product may still be useful as a design generation tool, but less transformative than intended.

2. AI generated designs fail in manufacturing

Hardware is unforgiving. A bad footprint or wrong component choice can destroy a batch. That is why the validator is deterministic and the user must approve every order.

If the system cannot catch enough errors, the scope may need to remain limited to known reference designs.

3. The first market is too small

The initial wedge may not be the right entry point. If low complexity IoT prototypes are not enough, the entry market may need to shift toward:

• Education

• Small labs

• Small batch commercial hardware

4. The core thesis is wrong

It is possible the real bottleneck is not design to factory orchestration but something else, like compliance or trust.

If that happens, the project still produces useful evidence about where automation is and is not viable.

5. Solo founder bottleneck

If I hit a domain I do not yet know deeply enough, progress could stall.

That is why the initial scope is intentionally narrow, and why I will seek the right collaborators as the system matures.

How much money have you raised in the last 12 months, and from where?

$0 in direct funding for vibing build.

I have received a 500,000 EGP investment offer from the National Bank of Egypt through a previous incubation program for a different AI project, but that funding has not been deployed toward vibing build.

I have also built other projects with sponsorship support in the past, but those funds belong to separate initiatives and are not available here.

This would be the first external funding for vibing build. Development so far has been self funded and constrained by limited personal resources.