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Dwarfreflect – Extract Go function parameter names at runtime

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Dwarfreflect – Extract Go function parameter names at runtime

While working on openai-agents-go, I wanted users to define tools by passing in a plain Go function and have the agent figure out the inputs automatically. But I ran into a gap: Go's reflect gives you parameter types and positions, but not the actual names you wrote. So I built dwarfreflect: it parses the DWARF debug info embedded in Go binaries (unless stripped) to recover real function parameter names at runtime. This made it easy to: - Bind incoming JSON/map data to actual parameter names - Build a clean API without boilerplate Try it out here: https://github.com/matteo-grella/dwarfreflect Happy to hear thoughts, ideas, use cases, or bug reports.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
75%75% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
45%45% 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
45%45% 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: users, para · Missing: mobile apps, ios, personal
29%29% 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
17%17% 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.

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