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Dl

Dlg – Zero-cost printf-style debugging for Go

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Dlg – Zero-cost printf-style debugging for Go

Hey HN, I tend to use printf-style debugging as my primary troubleshooting method and only resort to gdb as a last resort. While I like its ease of use printf debugging isn't without its annoyances, namely removing the print statements once you're done. I used to use trace-level logging from proper logging libraries but adding trace calls in every corner quickly gets out of control and results in an overwhelming amount of output. To scratch my own itch I created dlg - a minimal debugging library that disappears completely from production builds. Its API exposes just a single function, Printf [1]. dlg is optimized for performance in debug builds and, most importantly, when compiled without the dlg build tag, all calls are eliminated by the Go linker as if dlg was never imported. For debug builds it adds optional stack trace generation configurable via environment variables or linker flags. GitHub: https://github.com/vvvvv/dlg Any feedback is much appreciated. [1]: Actually two functions - there's also SetOutput.

Visit productView on Hacker NewsJul 27, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
92%92% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
41%41% 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
16%16% 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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