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Domain Digger – Full Toolkit for Next-Level Domain Analysis

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Domain Digger – Full Toolkit for Next-Level Domain Analysis

Domain Digger was born in 2021 out of my frustration with existing DNS lookup tools. I'd often find myself using multiple different tools to get the job done. What job? Understanding the technical underlying architecture of a website or app by digging into the used infrastructure. With Domain Digger I had a couple of goals: 1. A snappy user experience 2. Have all possible tools in one place and be able to quickly jump between them 3. And of course have it all open-source: https://github.com/wotschofsky/domain-digger Features include: * DNS Lookup from selectable location * WHOIS lookup * Certificate history * IP Details * A quick access bookmarklet (does anyone even remember those?) The app is built using Next.js on Vercel and shadcn/ui. Here are a couple of fun or shocking findings I gathered over the years: * GitHub uses a mixture of own IP addresses and Azure IPs depending on the region: https://digger.tools/lookup/github.com/map * Cloudflare uses different IP addresses in Europe, despite using an anycast setup: https://digger.tools/lookup/wsky.dev/map * The majority of startups use Google Workspace - Microsoft 365 is the exception * DNSSEC adoption is ridiculously low - even Google doesn't use it: https://digger.tools/lookup/google.com * Too often, companies opt not to support IPv6, even though it is easily supported by their CDN: https://digger.tools/lookup/www.deviantart.com (compare to https://digger.tools/lookup/d2ade2shja8hmk.cloudfront.net )

Visit productView on Hacker NewsJan 10, 2024
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · 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 · Strong signals: google · 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
11%11% 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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