An awesome C library for Windows

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https://github.com/apakian/AshodsLib/blob/master/summary.txt Hi there, It looks like there are not that many available options for a do-it-all C library, that is easily configured, used and compiled. I'm releasing my personal toolkit, which I use to develop windows based programs in pure C without any dependency on DLL's or external libraries. There is only 2 files, aa.c and aa.h , simply use this to have full access to everything from data structures. a massive list of networking functions, database connectivity ( direct - without drivers required ) , a full 32 bit surface engine, video capture, audio input/output, etc etc. Have a look at the summary file for a basic idea of what it comes with. I'm just starting to put together examples and documentation, so I'd like to get a feel , for how well this might be received. Would love feedback ash

Visit product View on Hacker NewsJun 15, 2016

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

62%62% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% 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

46%46% 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: personal, video · Missing: mobile apps, ios, entrepreneurs

37%37% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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