Just Fucking Use Cloudflare – A satirical guide to the CF stack

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Just Fucking Use Cloudflare – A satirical guide to the CF stack

Inspired by the recent justfuckingusetailwind.com, I built a similar "love letter" to the Cloudflare ecosystem. I find myself reaching for Workers, R2, D1, and KV for almost every project lately, so I wanted to capture that sentiment in a single, slightly over-the-top page. It’s a simple side project, but the stack is: - Vite + TypeScript - Biome + Ultracite - Deployed (obviously) on Cloudflare First draft in Claude, the copy was drafted with Grok to get that specific tone, then I used Google's AI Studio to accelerate the build with touch up's in Cursor. Link: https://justfuckingusecloudflare.com Repo: https://github.com/mynameistito/justfuckingusecloudflare Curious to hear everyone's thoughts on the CF stack vs alternatives and more traditional deployments.

Visit product View on Hacker NewsDec 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, google · Missing: mac, agents, macos

91%91% 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 · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% 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

34%34% 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

32%32% 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

16%16% 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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