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Describe a circuit in English, get a schematic and explanation

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Describe a circuit in English, get a schematic and explanation

Hi HN! I built a small tool that lets you describe simple circuits in plain English and get a schematic. You can then ask follow up questions like "whats the voltage drop across R1?" or "How would you calculate the Thevenin equivalent resistance?" Emphasis on simple—right now it handles very basic circuits (think: 1 voltage source, a few resistors, maybe a cap). I built it because I am a mechanical engineer by training and sometimes forget basic EE. LLM's are making it so quick and easy to understand codebases, I thought it would be cool to try and build the same for circuits. It's a prototype for now, but I think it could eventually be really useful for autodidacts, students, and engineers looking to brush up their circuit theory. Would love feedback on what works, what doesn't, and where you'd want it to go next. Link: https://circuit-tutor.xyz/

Visit productView on Hacker NewsMar 21, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: code, plain · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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