Appendable – A Statically Hosted Database

Hacker News

Hey HN, I was inspired by protomaps's ( https://protomaps.com/ ) use of HTTP Range requests and wondered if it would be possible to build an entire database off of this premise, namely querying the database without a backend service. Turns out, I got it to work! I've created a demo here: https://kevmo314.github.io/appendable/ and observe that it queries GitHub Pages, so nothing but static hosting while fetching a fraction of the underlying data (ie proportional to the result set, not the input set). The hope is that for certain datasets, the Appendable can be used to host the database with static file hosting instead of the traditional, more expensive database service routes. The demo right now is pretty bare bones as I was pretty excited to get it working, but I outlined an aspirational roadmap in the linked repository that includes more features. Thanks for checking it out!

Visit product View on Hacker NewsNov 27, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

75%75% 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 · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

37%37% 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

15%15% 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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