Dm

Dmark – DMARC report bulk evaluation tool

Hacker News

Dmark – DMARC report bulk evaluation tool

I've had the RUA tag populated in my DNS records from my mail server for a few years now and I have an email filter set up that dumps all of the DMARC reports into an email folder that I never look at. Right now it has over 4,000 of these emails and I thought maybe it was a good idea to make sure that our DMARC posture was good. Rather than evaluate all the reports, I decided to quickly throw together a tool that will allow you to evaluate them and this is what I came up with. It provides four different metrics by which it measures the effectiveness of your policy and it tells you if anything is incorrectly configured and what you should do about it. It is especially focused on evaluating the configuration of Office 365 mail servers, purely out of my own necessity, but it will work with any mail server. The most useful way to use it is to export a folder full of DMARC reports as a PST file and then upload them to the tool. I vibe coded this with Codex in Visual Studio Code and then I had Gemini Pro take a pass at it to clean it up. I used Grok 4.20 as a third voice to ensure that I wasn't getting too pigeon-holed by the perspective of the two LLMs I was using. I have this other tool that I built called RoundTable, which enables you to easily ask multiple LLMs a question and then take their responses to build a prompt that you then get all of the LLMs to respond to. Through this iterative process they converge towards an ideal across all of the various perspectives that the LLMs hold.

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: codex, email, visual · Missing: mac, agents, macos
88%88% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
88%88% 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 · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, 000 · Missing: https docs, excited, just released
41%41% 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 · Missing: plus, platform, intuitive
36%36% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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