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Sqlnetnews and Bystand NNTP Software

Hacker News

Sqlnetnews and Bystand NNTP Software

sqlnetnews is a NNTP server software. bystand is a NNTP client software. You can use them with other software too, and not only with both of them together. I wrote these programs because the other one they are large software and cannot be used with SQL, and the other NNTP client software does not have a command-line interface, etc. There is Fossil repositories, such as: http://zzo38computer.org/fossil/bystand.ui/ and also /fossil/sqlnetnews.ui/ Also it is possible to download the ZIP file at /prog/bystand.zip and /prog/sqlnetnews.zip Currently, this server software does not support authentication, but maybe in future it might. Also not implemented is forwarding messages to other servers (and possibly subscribers by email, if you want to use like a mailing list), but that would be a separate program anyways, but using the same database. Using sqlnetnews and bystand with a different database is difficult because SQLite does not currently support use of incremental blob I/O with virtual tables. Also if you have other question, comment, complaint, to please write them too. Including, if some functions do not work on non-Linux systems, to suggest how to correct it so that it does. (Such as, someone told me on Usenet that MSG_MORE does not work with BSD. So, I added a #ifndef block to define MSG_MORE as zero if it is not already defined; hopefully this is correct.) Also, to see if we can have a more comprehensive comparison of different NNTP server software and NNTP client software.

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Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: computer, new, email · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscribers · 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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