Vi

VisCircuit – A Note-Taking Website for Electronics and Circuits

Hacker News

VisCircuit – A Note-Taking Website for Electronics and Circuits

Hi, everyone. I created a note-taking website for electronics and circuits where you can draw circuit diagrams and write text notes at the same time. I am a Digital IC designer, and I self-study different types of analog and digital circuits a lot. However, I found a problem. Circuits have many different architectures and are hard to memorize due to numerous experiential tips. I want to document what I learn in my note app, but I found there is no method for me to easily draw circuit and block diagrams alongside text notes. This issue has bothered me for a long time, from my master's school to my current working life. I decided to solve it, so I created a note-taking website specifically for electronics and circuits, called VisCircuit. With VisCircuit, you can easily draw circuit diagrams, block diagrams, and write text notes simultaneously. I have already used it for two weeks and have noted down things I find hard to remember, such as SRAM, amplifier circuits, and PCB components of Arduino and Raspberry Pi. I found this tool really useful for memorizing knowledge about electronics and circuits. Currently, I have opened VisCircuit for alpha testing, and I want to let some people use it and give me feedback. Feel free to try it, and I will really appreciate what you think about this project. Leave any suggestions for improvement. Thank you very much.

Visit product View on Hacker NewsJul 17, 2024

Actual performance

120points

27comments

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: notes, open · Missing: mac, agents, macos

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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.

Correct prediction on native model

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