Ma

Machine learning based PCB routing [video]

Hacker News

Machine learning based PCB routing [video]

Hey everyone, super excited to share with you what me and my friends have been working on with our startup. Auto Routers for PCB boards have been around since the 60s but you have probably never use one...that’s because they aren’t that good. They produce results that are hard for humans to reasons about and reviewing/debugging them takes longer than doing it yourself. We have a machine learning model thats trained to produce human like designs that are easy to comprehend. That’s a first baby step towards automating the full process. Check out this tutorial: https://www.youtube.com/watch?v=JlNsPRh_x-w It’s in closed beta but we are slowly opening up now and happy to give everyone on HN early access...just fill this form: https://forms.gle/nsZciSbTTRK81PaM8

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide · Missing: https docs, just released, exist
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, open · Missing: agents, macos, agent
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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