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Clean Simple DNS Lookups

Hacker News

Clean Simple DNS Lookups

Hey HN, Last weekend I vibe-coded a cool website that lets you do easy DNS record lookups. I know you can just use dig or nslookup, but oftentimes I'm too lazy to remember the syntax, and there are less technical users who need to manage DNS entries but aren't comfortable with the command line. We debug customer DNS issues often at ImprovMX, and we typically link to tools like mxtoolbox.com to point out DNS record issues. But those tools seem quite bloated and from the 2000s. I wanted something super clean & simple, and there were a few features I thought were ergonomically needed but lacking: - no confusing dropdowns or syntax for DNS lookup, just put in your domain or subdomain - click-to-copy for all values - header-links so we can provide URLs that will direct another user to an exact domain and which record we want to reference This was SUPER FUN to vibe code! The frontend was pretty much one-shotted with lovable. It's amazing how good AI is when working on a clean slate with all the latest popular frameworks (react, tailwind, shadcn, etc.). And I spent the next few hours making small tweaks with cursor. The backend is a dead simple python flask server. Both are hosted on render.com <3 I love how simple and value-oriented render.com is. It's always the provider that gives me the least headache when I want to just launch and forget something. Give it a try and let me know what you think!

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5points
3comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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: cursor, user, lovable · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000 · Missing: https docs, excited, just released
32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.

Correct prediction on native model

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