SwiftNet v0.5.0 – Networking Library

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Hello. I’ve been working on a networking library for the past year. It operates at Layer 2, which means it handles custom Ethernet headers, IP headers, and its own protocol data. It’s made for simplicity, experiments and mostly for making my own multiplayer video game in OpenGL. I would really appreciate any tips, reviews or comments. My goal is to really make this library usable, which cannot be done without proper decision making by other people besides me. It has its own thread-safe memory allocator, hashmap and vector. It doesn’t use any libraries besides PCAP for sending packets. Pcap will mostly be used for MacOS compatibility, but will implement either AF_XDP or DPDK on linux for more server-side performance. The performance isn’t the best, but not really bad. Performance metrics are listed in README. It’s not an AI slop :)

Visit product View on Hacker NewsMar 23, 2026

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

80%80% 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 · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, 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

44%44% 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: reviews · Missing: plus, platform, intuitive

40%40% 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

18%18% 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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