Vix.cpp v1.17.0 – Production-grade web back end examples in modern C++

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Vix.cpp is a modern C++ runtime for building backend services. In v1.17.0, the focus is on complete, real-world examples, not toy demos. What’s included: - HTTP routing & REST APIs - WebSocket runtime with typed messages - Shared HTTP + WebSocket lifecycle in a single process - Middleware pipeline (cache, CORS, rate limiting, auth, body limits) - Static files, compression, headers, security - Clean, production-oriented project layouts Everything runs as a single binary with explicit control over performance, memory, and concurrency. Repo & examples: https://github.com/vixcpp/vix Happy to answer questions about design tradeoffs, performance, or architecture.

Visit product View on Hacker NewsDec 26, 2025

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

84%84% 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: pipe, io · Missing: https docs, excited, just released

70%70% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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