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Stillwind – High Resolution Electronic Component Search

Stillwind – High Resolution Electronic Component Search

by hannesfur·Jun 11, 2026·6 points·0 comments

AI Analysis

MidShip It

AI wrapper on parametric search when DigiKey and Octopart already exist.

Strengths
  • LLM extraction into 1k schemas covering 130k properties is ambitious
  • Verification model checks results before displaying to users
Weaknesses
  • Parametric component search already solved by DigiKey, Mouser, Octopart
  • Version 0.0.1-beta with no benchmarks showing AI actually improves results
Category
Target Audience

Hardware engineers, PCB designers, procurement teams

Similar To

DigiKey · Mouser · Octopart

Post Description

We’ve spent the last couple of months building Stillwind Search, a search engine for electronic components that helps users find parts that fit even the most complex set of specifications.

After talking to the people that actually build PCBs we found out that finding the exact part you are looking for, is consuming enormous amounts of times, is very tedious and then often doesn’t yield the best results. So we tried to cut down this search time by just requiring a (broad) description of the specifications and we find the correct part in minutes, not hours.

This is possible through our own database of parts and their properties. We used LLMs to extract every parameter about a part into >1k schemas, collectively covering more than 130k properties. This depth of properties could no longer be visualized, so the database is queried interactively by an AI agent (Sonnet 4.6) to find the needle in the haystack of parts. Before results are shown, we use another model to verify the data (that’s why it might take a moment before the first results appear).

We currently have almost all microcontrollers, sensors, and other advanced ICs on the market, as well as a wide selection of passives and generic parts. We are working on adding more parts and are more than happy to take suggestions.

I know that folks on HN like technical details on how this works, so let me give a short overview: Frontend: SvelteKit + Cloudflare Workers + Hyperdrive Backend: PostgreSQL 18 (with io_uring) database, with extensions on NVMe drives hosted on a beefy server.

We appreciate all feedback and are happy to answer any questions :)

Btw: We are already working on a way that you can search combinations of parts, finding the optimal combination of parts.

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