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Checkpoint 401 – forward auth server in TypeScript / Deno

Hacker News

Checkpoint 401 – forward auth server in TypeScript / Deno

I wrote a forward auth server in TypeScript and Deno. I've written several forward auth servers before but they have always been specifically written for that application. I wanted something more generalised that I could re-use. What is forward auth? Web servers likes Nginx and Caddy and Traefik have a configuration option in which inbound requests are sent to another server before they are allowed. A 200 response from that server means the request is authorised, anything else results in the web server rejecting the request. This is a good thing because it means you can put all your auth code in one place, and that the auth code can focus purely on the job of authing inbound requests. Checkpoint 401 aims to be extremely simple - you define a route.json which contains 3 things, the method, the URL pattern to match against and the filename of a TypeScript function to execute against that request. Checkpoint 401 requires that your URL pattern comply with the URL pattern API here: https://developer.mozilla.org/en-US/docs/Web/API/URLPattern/... Your TypeScript function must return a boolean to pass/fail the auth request. That's all there is to it. It is brand new and completely untested so it's really only for skilled TypeScript developers at the moment - and I suggest that if you're going to use it then first read through the code and satisify yourself that it is good - it's only 500 lines: https://raw.githubusercontent.com/crowdwave/checkpoint401/ma...

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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.
Hacker NewsStrong engagement from HN community · Strong signals: nginx, io · 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: user, new, code · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
20%20% 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
13%13% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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