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Onda – a cross-platform alternative to DSView for DSLogic analyzers [video]

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Onda – a cross-platform alternative to DSView for DSLogic analyzers [video]

I work on building authentic plug-in recreations of old reverb units. Typically I'm able to use schematics and data sheets, but since I started working with ASICs I had to get a logic analyzer I could afford. The software that comes with my DSLogic, DSView, leaves a lot to be desired so I began working on a cross-platform logic analyzer I could customize over time. Let me know what you think and if you would be interested in trying it out once it is ready. For those of you who use logic analyzers in your work, what are the features and decoders most important to you. Is there anything you would want to see in a software package?

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% 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: video · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
27%27% 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
15%15% 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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