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In-memory graph "database" with NetworkX and openCypher

Hacker News

In-memory graph "database" with NetworkX and openCypher

Cypher is a super useful language for querying graph structures, but sometimes it's overkill to load a tiny graph into Neo4j or memgraph. We wrote this tool to act as an abstraction layer so you can query in-memory graph data -- or, using [Grand]( https://github.com/aplbrain/grand ), rewrite Cypher queries to run on SQLite dbs or even other graph databases that don't support Cypher out of the box. Hoping it'll be helpful to those in the network theory, graph ML, and data science communities!

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Actual performance

2points
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
74%74% 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 · Strong signals: tiny, using, open · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
19%19% 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
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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