dd

ddgrey – dynamic and optionally distributed greylisting daemon

Hacker News

ddgrey – dynamic and optionally distributed greylisting daemon

I use - like many email admins - greylisting for first line spam fighting. Existing software is slightly insensitive to server reputation, giving the same delay for new senders, irregardless of server reputation. This is the reason for making ddgrey. It acts as a normal greylisting daemon, being queried over a UNIX domain socket from a MTA, and answering if a mail should be allowed, deferred or denied. It can also get reports from the MTA (currently by reading exim4 log files) about spam classification and suspect activities, and receive reports from spamtraps, spam reporting aliases and ddgrey servers on other hosts (normally on your other MX servers). Using this information, ddgrey can adjust the greylisting delay for a specific sender host from no greylist at all, to a long delay or outright blacklisting. The purpose of this is to allow legitimate email through faster, and to delay more spam based on combined IP reputation before possibly processing in spamassassin. Early beta - I have been using this live on my own servers (exim4, Debian) for half a year, but I would appreciate feedback if the instructions are actually usable and if it works on other system configurations. Written in perl with nonblocking single-thread IO. Only normal perl modules used; SQLite for storage. Download: https://github.com/perericr/ddgrey

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, single · Missing: mac, agents, macos
81%81% 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: exist, existing, io · Missing: https docs, excited, just released
75%75% 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
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% 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
15%15% 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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