Graphlib 3.1 – the C++ 23, header only solution to directional graphs

Hacker News

Graphlib 3.1, the newest version of Graphlib has added a bunch of new features and algorithms we are excited to show. BFS: The BFS algorithm is finally done and implemented DFS: The DFS algorithm is also finished Dijkstra's algorithm is 90% finished! It will be released on version 3.2 NEW ADDITIONS: Reshaped README.md Added more functions BFS was added DFS was added graphlib:: namespace was added You are now able to do more than just integers for node names (check README) weights can now be integers or floats! Heavy performance improvements! If you want, come check out Graphlib!

Visit product View on Hacker NewsAug 25, 2026

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

57%57% 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 · Missing: plus, platform, intuitive

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on Indie Hackers, 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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