Li

Lightbase, an Abstract API for P2P Database

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Lightbase, an Abstract API for P2P Database

I developed an abstraction layer for read/write operations with IPFS. I wanted to build a database tool that solves some current database limitations: + Cost: how traditional server-based databases can become costly to run and operate + Development speed: traditional databases can take time to set up (i.e. defining the schema) + Duplicate items: duplicate items or profiles can appear between databases (i.e. a bio description that has to be inputted separately on different websites or platforms). Lightbase aims to solve these issues by being: + Affordable: low-cost by leveraging P2P file sharing (currently on IPFS) + Frictionless: minimal commands (via an abstraction layer) that are inspired by HTTP-commands + Unique (future feature): allow a single row (or item) to be accessible by different applications with respect to relevant permissions/visibility settings I developed the current codebase on GitHub in one afternoon, which is capable of read/write operations on IPFS. I would love to hear everyone's recommendations on what could be improved, and future features of interest. Thanks in advance!

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, code · Missing: mac, agents, macos
79%79% 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: para · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
24%24% 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
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.

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