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GerbDiff – Visual diff tool for PCB Gerbers

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GerbDiff – Visual diff tool for PCB Gerbers

GerbDiff is a desktop app that provides layer-by-layer diff comparisons of PCB design files (Gerbers). The intended use-case is a production design environment to compare revisions of the same board where a design mistake can cost thousands of dollars. I built this app after personally experiencing the pain of shipping unintended design changes to fab. A quick rundown of the features: - Local-only desktop app. Design IP never leaves the local machine. - In-house developed gerber parser. Needed to be bulletproof for design validation. - Automatic layer detection to make diff generation quick/painless. - Generate and export diff reports (PNG/HTML). Attach to reviews/PRs as design collateral. - Free Github Action so diff reports can be automatically added to PRs without running the tool. If you want a longer read on the product thesis see: https://gerbdiff.com/blog/why-diff/ Would appreciate any/all feedback, especially from EEs on HN.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, visual · Missing: agents, macos, agent
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
25%25% 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
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.

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