I spent 6 months building a C debugger as a 17-year-old

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Hey HN my name is Thassilo, I'm a student and passionate programmer from Germany. I want to showcase Spray, a small C debugger I've been working on for a few months now. Spray has a very simple and approachable interface. Its feature set is limited at this point, but it's already enough to tackle some basic problems. I stared to work on Spray because I was curious about how debuggers work. I am also trying to design Spray in such a way that it's easy to grasp and has a small mental overhead. I'd love to get your feedback on Spray. Email: d4kd (at) proton (dot) me PS: I'm generally interested compilers and language tool chains, and I'm looking for similar-minded people to work and collaborate with. I have a few similar projects on my GitHub: https://github.com/d4ckard?tab=repositories . If you find Spray interesting, you might enjoy playing around with them too.

Visit product View on Hacker NewsNov 11, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos

69%69% 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.

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

56%56% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, way · 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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