I built Deals.sh just using Claude and Cursor

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Hi HN, I built this project mostly using Claude and Cursor. It's fully hosted on Cloudflare pages. Deal submissions and approvals are handled through Github issues and actions with custom Cloudflare workers doing the intermediate work. I started this a while back but ended up abandoning it because of time constraints and my lack of frontend skills. With Claude and Cursor's help I finally got it to where it is now. I think the search feature turned out pretty nice. But there's still lots of things to improve. Mainly deal categories, tags and recommendations. One feature I love to add is helping users find the best times to buy a product or service based on historical deal data. Honestly, the whole process felt like a fun project disguised as a mini system design interview and I learnt a lot from it. Working with Claude/Chatgpt sometimes feels like magic and other times like training a very enthusiastic puppy. I can share the files if anyone is interested, though I'll need to clean them up first to make them less embarrassing. There are still some pending deals that I need to approve. Feel free to bookmark the site and check back later. Thanks for reading!

Visit product View on Hacker NewsNov 29, 2024

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

97%97% 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: started · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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 · Strong signals: host, users · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, training · Missing: mrr, revenue, profit

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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