IdeaWell

Project ideas from Hacker News discussions.

Can AI design circuit boards yet?

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

1. AI excels at schematic/BOM tasks and verification, but still struggles with PCB layout/placement
- “I have found that AI is great for sorting out your libraries, drawing and helping double check footprints managing your BOM.” — dubbie99
- “Sourcing parts has been a huge motivation… Having the ability to formally verify that an alternative part fulfills the original design constraints is huge.” — iopapa
- “PCB layout has basically been terrible every time I've tried it.” — rpcope1

2. Enthusiasm for democratizing hardware design and speeding up prototyping
- “It feels like magic to be able to go from idea to a physical thing in such a short amount of time.” — alex7o
- “Love the democratization of access.” — maxdo
- “I do believe we are less than a year from prompt to full assembly including enclosure.” — boznz

3. Concerns about hallucinations, over‑engineering, and the need for human oversight
- “While AI has been helpful in talking through component choices… it’s consistently been a spectacular fail each time in actually designing the board.” — cmiles8
- “LLMs like to fail spectacularly at real life measurement and not tell you about it as well.” — sitzkrieg
- “The design that was ultimately landed on was pretty excessive with lots of total overkill…” — WarmWash

🚀 Project Ideas

[SubstiMate: AI Component Substitutor]

Summary

  • Scans component datasheets (PDF/HTML) to extract key electrical, mechanical, and thermal parameters.
  • Matches those parameters against a user‑provided design constraint set (footprint, voltage/current ratings, tolerance, etc.) to flag suitable drop‑in replacements.
  • Integrates with distributor APIs (Digi‑Key, LCSC, Mouser) to show real‑time stock, pricing, and lead‑time for each substitute.

Details

Key Value
Target Audience Hardware engineers, hobbyists, and procurement teams needing fast, verified part alternatives
Core Feature LLM‑driven datasheet parsing + constraint‑checking engine that outputs a ranked list of pin‑compatible, spec‑matched substitutes
Tech Stack Python (LangChain, pypdf/pdfplumber), FAISS vector store for param search, FastAPI backend, React frontend, optional Docker
Difficulty Medium
Monetization Revenue-ready: Subscription tier ($9/mo for unlimited lookups, free tier 10 lookups/mo)

Notes

  • HN users repeatedly lament the pain of sourcing parts and verifying substitutes (iopapa: “Having the ability to formally verify that an alternative part fulfills the original design constraints is huge.”).
  • Combines LLMs’ strength in reading technical docs with a deterministic constraint solver to avoid hallucinated specs.
  • Could spark discussion on best practices for representing component constraints and integrating with existing BOM tools.

[PlaceOpt: Constraint‑Driven PCB Placement Assistant]

Summary

  • Uses a constraint‑solver backend (akin to atopile’s solver) to receive a netlist and component list, then proposes optimal placements that satisfy clearance, thermal, RF, and mechanical constraints.
  • LLM interacts with the user to refine placement goals (e.g., “keep analog away from noisy digital”, “minimize trace length for high‑speed pairs”) and explains trade‑offs.
  • Generates a placement file (KiCad/Eagle) that can be imported for final routing.

Details

Key Value
Target Audience PCB designers frustrated with manual placement; mid‑complexity board developers
Core Feature LLM‑guided, solver‑based placement optimizer that outputs constraint‑satisfying component coordinates
Tech Stack Rust (or Python) for Z3/OR‑Tools constraint solver, LangChain agent for LLM interaction, KiCad plugin (Python), WebSocket UI
Difficulty High
Monetization Hobby (open‑source core, optional paid cloud solver API)

Notes

  • Commenters note placement is the hard part (nickff: “the difficult task is in placing components… routing is the easiest part”).
  • PlaceOpt turns vague design intent into concrete constraints, letting the solver do the heavy lifting while the LLM explains why a placement works.
  • Would invite HN debate on blending LLMs with formal solvers for physical design.

[DataSheet2SPICE: Automated Datasheet Model Extractor]

Summary

  • Upload a component datasheet (PDF) and the service extracts SPICE subcircuit models, parasitics, package dimensions, and pin‑out info.
  • Outputs ready‑to‑use .lib/.subckt files and KiCad footprint symbols, with confidence scores for each extracted parameter.
  • Includes a validation step that runs a simple simulation (e.g., DC sweep) to sanity‑check the model against typical values from the datasheet.

Details

Key Value
Target Audience Analog/RF designers, simulation‑heavy engineers, educators needing quick model creation
Core Feature LLM‑enhanced PDF parsing + rule‑based extraction to produce SPICE models and footprints automatically
Tech Stack Python (pdfminer.six, nougat LLM for layout understanding), SymPy for equation extraction, Ngspice for validation, FastAPI, Svelte frontend
Difficulty Medium
Monetization Revenue-ready: Pay‑per‑extract ($0.05 per successful model) with free tier 5 extracts/mo

Notes

  • Several users lament that LLMs still struggle to read datasheets and extract simulation models (amelius: “Finally it would be great if LLMs could extract simulation models from datasheets!”).
  • Provides a concrete, verifiable output that solves a real bottleneck in analog design workflow.
  • Likely to generate practical utility and discussion on improving LLM grounding in technical documents.

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