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ElectroDB – I made a library to simplify complex DynamoDB techniques

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ElectroDB – I made a library to simplify complex DynamoDB techniques

Over the last four years I've been working on a library that handles all the hard bits of working with DynamoDB: modeling/managing your index values, achieving entity isolation with queries, writing dynamodb parameters and filters that isn't hard to read/review. ElectroDB manages your keys and does a lot under the hood to help with the pitfalls of keeping multiple entities in a single table. I'm proud of the library's use of strong type inference typing, it makes using Electro a breeze out of the box. I also made a playground where you can work with the library (and it's typing directly) and it also outputs the parameters it generates in browser: https://electrodb.fun Here is the documentation site if you'd like to learn more: https://electrodb.dev SST includes it as an example in their `create-sst` app, and I support users in a channel on their discord here: https://discord.com/channels/983865673656705025/994984119224... I figured this would be helpful for folks who are are at re:Invent and consider using it in their next project!

Visit productView on Hacker NewsNov 28, 2023
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
76%76% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, single · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users, para · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, 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
46%46% 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: users · Missing: plus, platform, intuitive
29%29% 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
14%14% 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.

Correct prediction on native model

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